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2009 Eurostat Regional Yearbook 09
2009 Eurostat Regional Yearbook 09
2009 Eurostat Regional Yearbook 09
2009 Eurostat Regional Yearbook 09
2009 Eurostat Regional Yearbook 09
2009 Eurostat Regional Yearbook 09
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2009 Eurostat Regional Yearbook 09
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2009 Eurostat Regional Yearbook 09
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2009 Eurostat Regional Yearbook 09
2009 Eurostat Regional Yearbook 09
2009 Eurostat Regional Yearbook 09
2009 Eurostat Regional Yearbook 09
2009 Eurostat Regional Yearbook 09
2009 Eurostat Regional Yearbook 09
2009 Eurostat Regional Yearbook 09
2009 Eurostat Regional Yearbook 09
2009 Eurostat Regional Yearbook 09
2009 Eurostat Regional Yearbook 09
2009 Eurostat Regional Yearbook 09
2009 Eurostat Regional Yearbook 09
2009 Eurostat Regional Yearbook 09
2009 Eurostat Regional Yearbook 09
2009 Eurostat Regional Yearbook 09
2009 Eurostat Regional Yearbook 09
2009 Eurostat Regional Yearbook 09
2009 Eurostat Regional Yearbook 09
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2009 Eurostat Regional Yearbook 09
2009 Eurostat Regional Yearbook 09
2009 Eurostat Regional Yearbook 09
2009 Eurostat Regional Yearbook 09
2009 Eurostat Regional Yearbook 09
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2009 Eurostat Regional Yearbook 09
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2009 Eurostat Regional Yearbook 09
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2009 Eurostat Regional Yearbook 09
2009 Eurostat Regional Yearbook 09
2009 Eurostat Regional Yearbook 09
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2009 Eurostat Regional Yearbook 09
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2009 Eurostat Regional Yearbook 09
2009 Eurostat Regional Yearbook 09
2009 Eurostat Regional Yearbook 09
2009 Eurostat Regional Yearbook 09
2009 Eurostat Regional Yearbook 09
2009 Eurostat Regional Yearbook 09
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2009 Eurostat Regional Yearbook 09
2009 Eurostat Regional Yearbook 09
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2009 Eurostat Regional Yearbook 09
2009 Eurostat Regional Yearbook 09
2009 Eurostat Regional Yearbook 09
2009 Eurostat Regional Yearbook 09
2009 Eurostat Regional Yearbook 09
2009 Eurostat Regional Yearbook 09
2009 Eurostat Regional Yearbook 09
2009 Eurostat Regional Yearbook 09
2009 Eurostat Regional Yearbook 09
2009 Eurostat Regional Yearbook 09
2009 Eurostat Regional Yearbook 09
2009 Eurostat Regional Yearbook 09
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2009 Eurostat Regional Yearbook 09

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  • 1. ISSN 1830-9674 Statistical books Eurostat regional yearbook 2009
  • 2. 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. Introduction
  • 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. 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. Population
  • 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. Population 1 Map 1.1: Population density, by NUTS 2 regions, 2007 Inhabitants per km2 Eurostat regional yearbook 2009 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. 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. 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. 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. 1 Population Map 1.4: Net migration, by NUTS 2 regions, average 2003–07 Per 1 000 inhabitants 20 Eurostat regional yearbook 2009
  • 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. 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. 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. European cities
  • 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. European cities 2 Map 2.1: Boundaries of cities participating in the Urban Audit data collection Eurostat regional yearbook 2009 27
  • 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. 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. 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. 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. 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. 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. Labour market
  • 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. 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. 3 Labour market Map 3.2: Unemployment rate, by NUTS 2 regions, 2007 Percentage 38 Eurostat regional yearbook 2009
  • 36. Labour market 3 The lowest levels of unemployment were recorded public, Poland and Slovakia, tend to spend more in all regions in the Netherlands and Austria, time at work, on average, than other European citi­ the northern parts of Italy and Belgium and the zens, while employed persons living in the Nordic southern parts of the United Kingdom. There are countries and in the United Kingdom tend to spend still big differences in regional unemployment less time at work. In 2007 the average number of rates, ranging in 2007 from 2.1 % in Zeeland hours usually spent at work varied from 30.1 hours (Netherlands) to 25.2 % in Réunion (France). per week in Groningen and Overijssel (both Nether­ lands) to 45.7 hours in Notio Aigaio (Greece), which Long­term unemployment, which is the worse is 1.5 times more than in the two Dutch regions. case of unemployment, also fell in 2007. The share of long­term unemployment, i.e. the share of per­ It is obvious that the share of part­time workers sons looking for a job for more than one year as has a significant influence in lowering the average a percentage of all unemployed, stood at 43 %, a hours spent at work. Unfortunately no breakdown decrease of 2.8 percentage points compared with of average hours worked into part­time workers 2006. This decrease was seen in most EU regions, and full­time workers is available at regional level. but two regions recorded a significant increase All regions in the Netherlands record a remark­ of more than 10 percentage points in one year, ably low average compared with other regions. Brabant Wallon (Belgium) and Corse (France). The highest value in the Netherlands was found In all EFTA regions, unemployment was below in Flevoland with an average of 31.6 hours per 5 %. In the candidate countries, the rate ranged week, which is still 2.4 hours less than in Mar­ from 3.1 % in Kastamonu to 18 % in Mardin tinique (France), the region with the lowest val­ (both in Turkey). ue of all regions in the EU­27, not counting the Netherlands. This leads us to conclude that the Lastly, a brief word on the cohesion of labour mar­ Netherlands is a special case regarding the aver­ kets. In 2007, the dispersion of employment and age time spent at work and the reasons for this unemployment rates, which measures regional will be analysed more in detail later. differences of employment and unemployment levels, decreased from 45.6 to 44.1 for unemploy­ Differences in the usual weekly hours of work are ment, and from 11.4 to 11.1 for employment. This not as great among regions in the same country as means that, overall, the rise in employment and they are between different EU regions. In fact, the the fall in unemployment were not achieved at average time spent at work in one region depends the cost of letting some regions lag behind, con­ less on the region itself than to which country it belongs. Nevertheless, some countries, such as tinuing the five­year trend. Belgium, Germany and France, record regional differences in the time spent at work. Regional work patterns Two regions recorded significantly higher usual Hours usually worked are the hours most com­ number of hours spent at work than the rest of the monly or typically worked in a short period of country: Praha (Czech Republic) and Inner Lon­ time, e.g. during a week. For each employed per­ don (United Kingdom), both capital regions. In son, this indicator shows the number of hours the capital region of Greece, the precise opposite spent working, including regular overtime work was found, with the capital recording a significant­ and excluding regular absences. ly lower average than in other Greek regions. Working time patterns are influenced by several Significantly lower averages compared with the rest factors, such as different historical and cultural of their respective countries were also observed in backgrounds, female participation in regional la­ Ciudad Autónoma de Ceuta and Ciudad Autóno­ bour markets, specialisation in a specific industry ma de Melilla in Spain, Åland in Finland and in the and the share of part­time workers. French overseas departments, Guadeloupe, Marti­ nique, Guyane and Réunion. All these regions are (4) This statement can be confirmed in a regression. Map 3.3 shows the different usual weekly hours of islands or regions that are not contiguous to other Some 95 % of the work in a person’s main job. The map reveals two country regions (Guyane (France) and the two regional variability in time spent at work can be clear facts: the average number of usual weekly Spanish autonomous cities). This geographic separ­ explained with (a) the share of part­time hours of work varies considerably among the ation enhanced the marked differences in time workers, (b) the share of EU­27 and regional differences are larger between patterns, while in contiguous regions the average employees, (c) the share of employed persons per countries than within countries (4). time spent at work tended to be more similar. economic sector and (d) a country dummy variable. Employed persons living in Greece and in east Now let’s look at the factors causing these dif­ The country effect is very significant in this European countries, e.g. Bulgaria, the Czech Re­ ferences to usual weekly hours spent at work at regression. Eurostat regional yearbook 2009 39
  • 37. 3 Labour market Map 3.3: Average number of usual weekly hours of work in main job, by NUTS 2 regions, 2007 Hours 40 Eurostat regional yearbook 2009
  • 38. Labour market 3 regional level. Most differences in the regional gion is the share of part­time workers, and this working time can be explained by two other re­ is quite evident in the Dutch regions. In 2007, gional labour market indicators: the percentage the share of employed men working part time of part­time workers and the percentage of em­ was 23.6 % and the share of women working ployees (which means all persons employed, not part time was an impressive 75 % in the Neth­ including self­employed or family workers). The erlands. Having almost a quarter of men and share of part­time workers in overall employment three quarters of women working part time is responsible for lowering the average weekly substantially lowers the average of usual weekly hours of work, and the share of employees also hours at work. seems to have a significant influence on the aver­ age time that an employed person spends in his or Working part time is more a country­level char­ her job, since self­employed and family workers acteristic, as shown in Map 3.4, which shows tend to spend more time in their jobs (5). scant regional differences within each country. (5) It has, however, to be The map also shows well­defined patterns of the noted that the statistical measurement of weekly share of part­time workers. These patterns are so working hours of self­ Part-time jobs: lowering well defined that the EU­27 regions can be divided employed and family workers is quite difficult the average working time into four distinct groups of part­time workers: and hence less reliable than other statistics. The main factor explaining the low average of • Group 1: the Dutch regions, with a share of usual weekly hours of work in main job in a re­ 46.8 % of part­time workers; Table 3.1: Average number of usual weekly hours of work in main job, by NUTS 2 regions, 2007 Average number of usual weekly hours of work in main job Country Regional minimum Regional maximum EU-27 38.0 30.1 Groningen 45.7 Notio Aigaio BE 37.1 35.8 Prov. Limburg (B) 38.7 Prov. West-vlaanderen BG 41.6 40.5 Severozapaden 42.4 Severoiztochen CZ 41.7 40.4 Moravskoslezsko 43.3 Praha DK 39.5 : : : : DE 35.5 34.1 Bremen 37.4 Thüringen EE 39.5 - - - - IE 36.4 36.1 Border, Midland and Western 36.5 Southern and Eastern EL 42.5 41.4 Attiki 45.7 Notio Aigaio ES 39.3 37.3 Ciudad Autónoma de Ceuta 40.7 Galicia FR 38.0 34.0 Martinique 39.6 Basse-Normandie IT 38.4 37.2 Calabria 39.1 Piemonte Cy 40.2 - - - - Lv 40.7 - - - - LT 38.8 - - - - LU 36.7 - - - - HU 40.2 39.8 Dél-Dunántúl 40.6 Közép-Magyarország MT 39.0 - - - - NL 30.8 30.1 Groningen 31.6 Flevoland AT 38.9 38.2 vorarlberg 39.7 Kärnten PL 41.0 37.9 Podkarpackie 41.9 Podlaskie PT 39.0 37.2 Centro (P) 40.1 Alentejo RO 40.5 39.1 Sud — Muntenia 41.4 Bucureşti — Ilfov SI 40.3 - - - - SK 41.1 40.1 východné Slovensko 41.7 Západné Slovensko FI 37.5 36.0 Åland 37.8 Länsi-Suomi SE 36.4 36.2 västsverige 36.7 Övre Norrland UK 36.9 35.3 North yorkshire 39.5 Inner London Notes: NUTS level 2 employment data not available for DK - = not applicable (EE, IE, Cy, Lv, LT, LU, MT and SI comprise only one or two NUTS level 2 regions) Eurostat regional yearbook 2009 41
  • 39. 3 Labour market Map 3.4: Share of employees in overall employment, by NUTS 2 regions, 2007 Percentage 42 Eurostat regional yearbook 2009
  • 40. Labour market 3 Map 3.5: Share of part-time workers in overall employment, by NUTS 2 regions, 2007 Percentage Eurostat regional yearbook 2009 43
  • 41. 3 Labour market • Group 2: regions in the Nordic EU­27 coun­ broken down into three categories: employees tries, plus Belgium, Germany, Austria and the (which comprises all personnel with a contract of United Kingdom, which together have an av­ employment), self­employed and family workers. erage share of 25 %; The number of hours a person spends at work per • Group 3: regions in Ireland, Spain, France, Italy, week seems to be related to his or her working Luxembourg, Malta and Portugal, with an aver­ status, since employees tend to spend less time age share of 14.2 %; working per week compared to family workers or self­employed persons. Map 3.5 shows the re­ • Group 4: the rest of the EU­27 regions, mainly gional distribution of the share of employees in from the new Member States, with an average overall employment. share of part­time workers of 7.2 %. The share of employees in total employment tends Over the past five years, the EU­27 has recorded to be lower compared with other EU regions in an increase of 1.6 percentage points in the share of almost every region of Greece, Italy, Poland and part­time workers. This increase was recorded in Romania and in the north­western part of Spain most regions in Group 1 (1.9 percentage points), and in the northern part of Portugal. The share of Group 2 (2.2 percentage points) and Group 3 (2.6 employees in overall employment at regional level percentage points), as defined above. The opposite varies from a minimum of 45.8 % in Pelopon­ trend was recorded in most Group 4 regions, with nisos (Greece) to a maximum of 96.1 % recorded a decrease in the share of part­time workers of 0.7 in Bucureşti — Ilfov (Romania). percentage points over the last five years. Apart from some exceptions, like in Romania or Turkish regions recorded a relatively low share in Spain, the share of employees tends to be more of part­time workers in 2007 as compared with or less even within countries, showing that, as the EU regions, with 8.8 % of employed persons with the share of part­time workers, the level of working part time. employees depends mostly on the country. Nev­ ertheless, there are some region­specific differ­ ences that could be linked to the type of activity Employees spend less time at work predominant in these regions. Employed persons are classified according to their Employee status is closely related to the type of working status. Regional labour market data are sector in which a person is employed. For in­ Figure 3.1: Share of employees in overall employment versus share of employed persons in the agriculture sector, by NUTS 2 regions, 2007 Percentage 60 50 40 30 20 10 0 40 50 60 70 80 90 100 44 Eurostat regional yearbook 2009
  • 42. Labour market 3 stance, the share of family workers and self­ While part­time work appears to be influenced employed in agriculture tends to be higher than more at national level, the average time a person in other sectors. Agriculture has the lowest share spends at work, the share of employees and the of employees of all sectors. Based on this, we can distribution of employment among sectors is in­ conclude that rural regions tend to have a lower fluenced more at regional level. share of employees, which also tends to lead to a higher average in usual weekly hours of work. Conclusion There is a significant negative correlation between the share of employees and the share of employed The results presented in this chapter show that 2007 was a year of strong performance regard­ persons in agriculture, as shown in Figure 3.1. ing both employment and unemployment, and Each point in Figure 3.1 represents one NUTS 2 re­ disparities in regional labour markets have nar­ gion where data was available for 2007. The points rowed. Nonetheless, the Lisbon employment roughly align on a downward straight line. That targets seem unlikely to be achieved. The reces­ means that regions with higher levels of employ­ sion currently faced by Europe and the rest of the ment in agriculture are more likely to have lower world will make the Lisbon employment targets shares of employees and, consequently, higher even more difficult to achieve, since labour mar­ averages of weekly time spent at work. At country kets are expected to deteriorate. level, the effect of employment in the agriculture The number of hours per week that people usually sector is maybe not so significant in explaining dif­ spend at work was also analysed in this chapter. If ferences in the average hours spent at work, since we look at working time patterns at regional level, the share of persons working in the agricultural the differences are clearly greater between countries sector is not very high in most countries. But at re­ than between regions within the same country, but gional level, especially in rural areas, this is an im­ there are also some regional variations. The average portant factor to consider in order to have a better time a person living in a specific region spends at understanding of different regional time patterns. work depends on many factors, such as female par­ To sum up, we can conclude that the average usu­ ticipation in the labour market, the share of part­ al time spent at work in a specific region varies time workers, the share of employees and the pre­ significantly throughout the EU­27, which is ex­ dominant sector of activity. All these factors dictate plained not only by the share of part­time work­ how much free time people have on average. ers, the most influential factor, but also by the Although it seems like an odd paradox, the aver­ share of employees, who tend to spend less time age time people spend at work does not equate to at work. The share of employees depends itself on strong labour market or economic performance. the predominant sector in each region. In fact, it is precisely the reverse. Eurostat regional yearbook 2009 45
  • 43. 3 Labour market Methodological notes The source of regional labour market information down to NUTS level 2 is the European Union labour force survey (LFS). This is a quarterly household sample survey conducted in the Member States of European Union. The LFS target population is made up of all members of private households aged 15 or over. The survey follows the definitions and recommendations of the International Labour Organisation (ILO). To achieve further harmonisation, the Member States also adhere to common principles in drafting questionnaires. All regional results presented here concern NUTS 2 regions and all regional figures are annual aver- ages of the quarterly surveys. For further information on regional labour market statistics, see the metadata on the Eurostat web- site (http://ec.europa.eu/eurostat). Definitions Population covers persons aged 15 and over, living in private households (persons living in collec- tive households, i.e. residential homes, boarding houses, hospitals, religious institutions and work- ers’ hostels, are not included). This comprises all persons living in the households surveyed dur- ing the reference week. This definition also includes persons absent from the households for short periods (but having retained a link with the private household) owing to studies, holidays, illness, business trips, etc. Persons on obligatory military service are not included. Employed persons are persons aged 15 years and over (16 and over in Spain, Sweden and the United Kingdom (1995–2001); 15–74 years in Denmark, Estonia, Finland, Hungary, Latvia, Norway and Sweden (from 2001 onwards); and 16–74 years in Iceland) who worked during the reference week, even for just one hour, for pay, profit or family gain, or who did not work but had a job or busi- ness from which they were temporarily absent because of, for example, illness, holidays, industrial dispute, education or training. Unemployed persons are persons aged 15–74 years (in Norway, Spain and Sweden (1995–2000), the United Kingdom and Iceland 16–74 years) who were without work during the reference week, were currently available for work and were either actively seeking work in the past four weeks or had already found a job to start within the next three months. Employment rate represents employed persons as a percentage of the population. Unemployment rate represents unemployed persons as a percentage of the economically active population. The unemployment rate can be broken down further by age and gender. The youth unemployment rate covers persons aged 15–24 years. Long-term unemployment share represents the long-term unemployed (12 months or longer) as a percentage of the total unemployed persons. Dispersion of employment (unemployment) rates is the coefficient of variation of regional em- ployment (unemployment) rates in a country, weighted by the absolute population (active popula- tion) of each region. Usual weekly hours of work in main job are the hours most commonly or typically worked in a short period of time, e.g. during a week, in a person’s main job. Employees are all personnel with a contract of employment with a local entity or enterprise. ‘Other personnel’ include active proprietors, family helpers, the self-employed, trainees without a contract of employment and voluntary workers. Part-time employees are considered to be those who, in accordance with a contract with the em- ployer, did not perform a full day’s work or did not complete a full week’s work within the local entity. 46 Eurostat regional yearbook 2009
  • 44. Labour market 3 Self-employed persons are defined as persons who work in their own business, professional prac- tice or farm for the purpose of earning a profit, and who do not employ any other person. Family workers are persons who help another member of the family to run an agricultural holding or other business, provided they are not considered as employees. Eurostat regional yearbook 2009 47
  • 45. Gross domestic product
  • 46. 4 Gross domestic product What is regional gross domestic vided data (for reference years 2004–06) in line with the European system of accounts (ESA 95) product? transmission programme. It ranges from 25 % of The economic development of a region is, as a rule, the EU­27 average (5 800 PPS) per inhabitant in expressed in terms of its gross domestic product North­East (Romania) to 336 % (79 400 PPS) in (GDP). This indicator is also frequently used as a the UK capital region of Inner London. The fac­ basis for comparisons between regions. But what tor between the two ends of the distribution is exactly does it mean? And how can comparability therefore 13.6:1. Luxembourg at 267 % (63 100 be established between regions of different sizes PPS) and Bruxelles/Brussel at 233 % (55 100 PPS) and with different currencies? are in positions 2 and 3, followed by Hamburg at 200 % (47 200 PPS) and Groningen (Netherlands) Regions of different sizes achieve different levels at 174 % (41 000 PPS) in positions 4 and 5. of regional GDP. However, a real comparison can be made only by comparing the regional GDP The regions with the highest per inhabitant GDP with the population of the region in question. are in southern Germany, the south of the UK, This is where the distinction between place of northern Italy and Belgium, Luxembourg, the work and place of residence becomes significant: Netherlands, Austria, Ireland and Scandinavia. GDP measures the economic output achieved The capital regions of Madrid, Paris and Praha within national or regional boundaries, regard­ also fall into this category. The economically less of whether this was attributable to resident or weaker regions are concentrated at the southern non­resident employed persons. The use of GDP and western periphery of the Union and in east­ per inhabitant is therefore only straightforward if ern Germany, the new Member States, Croatia all employed persons involved in generating GDP and the former Yugoslav Republic of Macedonia. are also residents of the region in question. Praha (Czech Republic), the region with the In areas with a high proportion of commuters, re­ highest GDP per inhabitant in the new Member gional GDP per inhabitant can be extremely high, States, has 162 % of the EU­27 average of 38 400 particularly in economic centres such as London PPS and is thus in 12th place, whilst Bratislavský or Wien, Hamburg, Praha or Luxembourg, and kraj (Slovakia) at 149 % (35 100 PPS) is in 19th relatively low in the surrounding regions, even if place among the 275 NUTS 2 regions of the coun­ households’ primary income in these regions is tries examined here (EU­27 plus Croatia and the very high. Regional GDP per inhabitant should former Yugoslav Republic of Macedonia). How­ therefore not be equated with regional primary ever, these two regions must be regarded as ex­ income. ceptions among the regions in the new Member States which joined in 2004, since the next richest Regional GDP is calculated in the currency of the regions in the new Member States are far behind: country in question. In order to make GDP com­ Közép­Magyarország (Hungary) at 106 % (24 900 parable between countries, it is converted into euros, using the official average exchange rate for PPS) in position 101, Zahodna Slovenija (Slovenia) the given calendar year. However, exchange rates at 105 % (24 900 PPS) in position 103 and Cyprus do not reflect all the differences in price levels at 90 % (21 300 PPS) in position 161. With the between countries. To compensate for this, GDP exception of three other regions (Mazowieckie is converted using conversion factors, known as in Poland, Malta and Bucureşti — Ilfov in Ro­ purchasing power parities (PPPs), to an artifi­ mania), all the other regions of the new Member cial common currency, called purchasing power States, Croatia and the former Yugoslav Republic standard (PPS). This makes it possible to com­ of Macedonia have a per inhabitant GDP in PPS pare the purchasing power of different national of less than 75 % of the EU­27 average. currencies (see methodological notes at the end If we classify the 275 regions considered here by of the chapter). their per inhabitant GDP (in PPS), the follow­ ing picture emerges: in 2006, GDP in 72 regions Regional GDP in 2006 was less than 75 % of the EU­27 average. These 72 regions are home to 25.2 % of the population Map 4.1 gives an overview of the regional distri­ (EU­27, Croatia and the former Yugoslav Repub­ bution of per inhabitant GDP (as a percentage of lic of Macedonia), of which three quarters are in the EU­27 average of 23 600 PPS) for the European the new Member States, Croatia and the former Union, Croatia and the former Yugoslav Republic Yugoslav Republic of Macedonia and one quarter of Macedonia, which has, for the first time, pro­ are in EU­15 countries. 50 Eurostat regional yearbook 2009
  • 47. Gross domestic product 4 Map 4.1: GDP per inhabitant, in PPS, by NUTS 2 regions, 2006 In percentage of EU-27 = 100 Eurostat regional yearbook 2009 51
  • 48. 4 Gross domestic product At the upper end of the spectrum, 41 regions mania and Poland. This group also includes two have a per inhabitant GDP of more than 125 % out of the three Croatian regions and the former of the EU­27 average; these regions are home to Yugoslav Republic of Macedonia. On the other 20.1 % of the population. The regions with per in­ hand, all the Czech regions now have GDP of habitant GDP of between 75 % and 125 % of the more than 50 % of the EU­27 average. EU­27 average are home to 54.7 %, a clear ma­ jority of the population of the 29 countries con­ sidered here. Some 11.5 % of the population live Major regional differences even in regions whose per inhabitant GDP is less than within the countries themselves 50 % of the EU­27 average; all these regions are in new Member States, Croatia and the former There are also substantial regional differences Yugoslav Republic of Macedonia. even within the countries themselves, as Figure 4.1 shows. In 2006, the highest per inhabitant GDP was more than twice the lowest in 13 of the Average GDP over the three-year 22 countries examined here with several NUTS 2 period 2004–06 regions. This group includes six of the eight new Member States plus Croatia but only seven of the Map 4.2 gives an overview of the average per 14 EU­15 Member States. inhabitant GDP (in PPS) for the years 2004–06. Three­year averages are particularly important The largest regional differences are in the United because they are used for the decision as to which Kingdom, where there is a factor of 4.3 between regions receive support from the Structural Funds the highest and lowest values, and in France and of the European Union. Romania, with a factor of 3.5 and 3.4 respectively. The lowest values are in Slovenia, with a factor of The map shows a concentration of less developed 1.5, and in Ireland and Sweden, with a factor of 1.6 regions, i.e. with per inhabitant GDP of less than in each case. Moderate regional disparities in per 75 % of the 2004–06 average for the EU­27 (22 600 inhabitant GDP (i.e. factors of less than 2 between PPS), in southern Italy, Greece and Portugal and the highest and lowest values) are found only in in the new Member States, Croatia and the former EU­15 Member States, plus Slovenia and Croatia. Yugoslav Republic of Macedonia. In Spain, only Extremadura is still under the 75 % level, and in In all the new Member States, Croatia and a number France only the four overseas departments. All the of EU­15 Member States, a substantial proportion regions of eastern Germany are now above the 75 % of economic activity is concentrated in the capital level. Overall, as an average for the period 2004–06, regions. Consequently, in 19 of the 22 countries GDP in 72 regions was less than 75 % of the EU­27 included here in which there are several NUTS average; these regions were home to 25.3 % of the 2 regions, the capital regions are also the regions population of the 29 countries considered here. with the highest per inhabitant GDP. For example, Map 4.1 clearly shows the prominent position of Map 4.2 also shows the particularly prosperous the regions around Bruxelles/Brussel, Sofia, Praha, regions of the EU, where GDP is greater than Athens, Madrid, Paris, Lisboa as well as Budapest, 125 % of the EU­27 average. There are 43 of these Bratislava, London, Warszawa and Zagreb. regions, home to 21.7 % of the population of the EU­27 plus Croatia and the former Yugoslav Re­ A comparison of the extreme values between 2001 public of Macedonia. Contrary to a common mis­ and 2006, however, shows that trends in the EU­ conception, these regions are by no means all in 15 have been very different from those in the new the geographical centre of the Union, but include Member States. Whilst the gap between the re­ examples such as Etelä­Suomi (Finland), South­ gional extreme values in the new Member States ern and Eastern (Ireland), Madrid (Spain) and At­ and Croatia is clearly increasing in some cases, it tiki (Greece). However, it is true that many capital is falling in one out of every two EU­15 countries. cities are among the richest regions, in particular London, Dublin, Bruxelles/Brussel, Paris, Ma­ Dynamic catch-up process drid, Wien, Stockholm, Praha and Bratislava. in the new Member States The new Member States show certain differences in terms of regions with less than 50 % and with Map 4.3 shows the extent to which per inhabitant between 50 % and 75 % of the EU­27 average. GDP changed between 2001 and 2006 compared Some 33 regions with 12 % of the population have with the EU­27 average (expressed in percent­ less than 50 %; most of these are in Bulgaria, Ro­ age points of the EU­27 average). Economically 52 Eurostat regional yearbook 2009
  • 49. Gross domestic product 4 Map 4.2: GDP per inhabitant, in PPS, by NUTS 2 regions, average 2004–06 In percentage of EU-27 = 100 Eurostat regional yearbook 2009 53
  • 50. 4 Gross domestic product dynamic regions, whose per inhabitant GDP in­ Among the EU­15 Member States, strong growth creased by more than 2 percentage points com­ can be seen in Greece, Spain, Ireland and parts of pared with the EU average, are shown in green. the United Kingdom, Finland and Sweden in par­ Less dynamic regions (those with a fall of more ticular. On the other hand, a trend which started than 2 percentage points in per inhabitant GDP several years ago is continuing: sustained weak compared with the EU­27 average) are shown in growth in certain EU­15 countries. Particularly orange and red. The range is from +33 percentage badly hit have been Italy, Belgium and France, points for Bratislavský kraj (Slovakia) to ­23 per­ where no region achieved the average growth of centage points for Emilia­Romagna in Italy. the EU­27 during the five­year period 2001–06; half the regions in Germany and Portugal also The map shows that economic dynamism is fell back compared to the EU average. well above average in the western, eastern and northern peripheral areas of the EU, not only Of the new Member States and Croatia, where all in EU­15 countries but also in the new Member of the capital regions are very dynamic, the Baltic States and Croatia. States, Romania, the Czech Republic, Slovakia, Figure 4.1: GDP per inhabitant, in PPS, by NUTS 2 regions, 2006 In percentage of the EU-27 average (EU-27 = 100) Région de Bruxelles-Capitale/ BE Hainaut Brussels Hoofdstedelijk Gewest Severo- BG zapaden Yugozapaden CZ Střední Morava Praha DK Sjælland Hovedstaden DE Brandenburg-Nordost Hamburg EE IE Border, Midland and Western Southern and Eastern EL Dytiki Ellada Attiki ES Extremadura Madrid FR Guyane Île de France IT Campania Provincia Autonoma Bolzano/Bozen CY LV LT LU HU Észak-Alföld Közép-Magyarország MT NL Flevoland Groningen AT Burgenland (A) Wien PL Lubelskie Mazowieckie PT Norte Lisboa RO Nord- Bucureşti — Ilfov Est SI Vzhodna Slovenija Zahodna Slovenija SK Východné Bratislavský kraj Slovensko FI Itä-Suomi Åland SE Östra Mellansverige Stockholm UK West Wales and Inner London Središnja i The Valleys HR Istočna Sjeverozapadna Hrvatska Hrvatska MK 0 50 100 150 200 250 300 350 400 National average Capital region 54 Eurostat regional yearbook 2009
  • 51. Gross domestic product 4 Map 4.3: Change of GDP per inhabitant, in PPS, by NUTS 2 regions, 2006 as compared with 2001 In percentage points of the average EU-27 Eurostat regional yearbook 2009 55
  • 52. 4 Gross domestic product Croatia and most regions of Poland have experi­ Different trends even within enced above­average growth. the countries themselves Closer analysis of the most dynamic regions shows that 42 of them have growth of more than 7 A more detailed analysis of trends within the percentage points above the EU average; of these, countries between 2001 and 2006 shows that the 21 are in the new Member States or Croatia. economic development of regions within a coun­ try can be almost as divergent as between regions The fastest­growing regions are scattered relative­ in different countries. ly widely across the 29 countries examined here. It is striking, however, that the capital regions The largest differences were in the Netherlands, continue to have an above­average rate of growth Slovakia and the United Kingdom, where there was not only in the EU­15 countries but also in the a difference of some 30 percentage points relative new Member States and in Croatia. The non­ to the EU­27 average for the per inhabitant GDP capital region with the strongest growth in the of the fastest­ and slowest­growing regions. The new Member States was Vest (Romania), where countries with the smallest differences between per inhabitant GDP (in PPS) increased by 15.3 regions were Ireland and Slovenia, with regional percentage points between 2001 and 2006, from ranges of 0.2 and 4 percentage points respectively, 29.4 % to 44.7 % of the EU­27 average. and Croatia and Poland, where the values were around 6 and 9 percentage points respectively. A clear concentration in certain Member States is, however, apparent at the lower end of the dis­ In both new Member States and EU­15 countries, tribution curve: of the 35 regions which fell by this significantly diverging regional development more than 7 percentage points compared to the was the result mainly of dynamic growth in capi­ EU­27 average, 20 are in Italy, six in France and tal regions. However, as the values for Poland and three in the UK. Croatia in particular show, the data available do Closer examination of the new Member States and not confirm the assumption that such regional Croatia yields the pleasing result that only four re­ growth disparities are a typical feature of new gions fell compared to the EU­27 average between Member States or accession countries. 2001 and 2006: Dél­Dunántúl in Hungary (­1.1 The data also show that the least economically dy­ percentage points), Malta (–1.0), Severozapaden in namic regions in seven countries attained levels Bulgaria (­0.7) and Kypros/Kıbrıs (­0.6). of growth above the EU­27 average. It is pleasing The catch­up process in the new Member States to note that, with the exception of Ireland, all of and Croatia was of the order of 1.5 percentage these were in five new Member States or Croatia. points compared with the EU average per year between 2001 and 2006 and was therefore con­ Convergence makes progress siderably faster than in the 1990s. Per inhabitant GDP (in PPS) in these 13 countries thus rose from This section addresses the question of the extent 46.0 % of the EU­27 average in 2001 to 53.7 % in to which convergence between the regions of the 2006. It is feared, however, that the financial cri­ EU­27, Croatia and the former Yugoslav Republic sis which started in mid­2008 may mean that this of Macedonia made progress over the five­year rate of growth cannot be maintained throughout period 2001–06. Regional convergence of per in­ the first decade of the new century. habitant GDP (in PPS) can be assessed in various Table 4.1: Proportions of resident population in economically stronger and weaker regions Percentage of population of EU-27, Croatia and the former Yugoslav Republic of Macedonia 2001 2006 resident in regions with a GDP per inhabitant of > 125 % of EU-27 = 100 23.0 20.1 > 110–125 % of EU-27 = 100 16.0 16.5 > 90–110 % of EU-27 = 100 22.7 24.9 > 75–90 % of EU-27 = 100 9.8 13.3 less than 75 % of EU-27 = 100 28.5 25.2 less than 50 % of EU-27 = 100 15.3 11.5 56 Eurostat regional yearbook 2009
  • 53. Gross domestic product 4 ways on the basis of indicators supplied to Euro­ poorer regions benefited only marginally during stat by the national statistical institutes. the first half of the decade from increased con­ vergence in the EU. A simple approach is to measure the gap between the highest and the lowest values. By this meth­ However, a more detailed analysis shows that od, the gap closed from a factor of 16.0 in 2001 to many regions with a GDP of less than 75 % of the 13.6 in 2006. The main reason for this clear con­ EU­27 average have made considerable progress. vergence was the faster economic growth in Bul­ The population living in regions with a GDP of garia and Romania. However, as this approach less than 50 % of the average fell between 2001 looks at only the extreme values, it is clear that and 2006 by almost a quarter, from 15.3 % to the majority of shifts between regions are not 11.5 %, or 17 million people. taken into account. Moreover, examination of the 20 economically Another, much more precise, assessment of con­ weakest regions, where 7.5 % of the population vergence consists of classifying the regions accord­ live, shows that this group has progressed as well: ing to their per inhabitant GDP in PPS. In this way, per inhabitant GDP in these regions rose be­ the proportion of the population of the countries tween 2001 and 2006 from 28.2 % to 33.2 % of the being considered (the EU­27 plus Croatia and the EU­27 average, as a result in particular of the strong former Yugoslav Republic of Macedonia) living in catch­up process in Bulgaria and Romania. richer or poorer regions, and how this proportion has changed, can be ascertained. Conclusion Table 4.1 shows that economic convergence between the regions over the five­year period In 2006, the highest and lowest values of per in­ 2001–06 did indeed make clear progress. The habitant GDP (in PPS) for the 275 NUTS 2 re­ proportion of the population living in regions gions in 29 countries (EU­27 plus Croatia and the where per inhabitant GDP is less than 75 % of former Yugoslav Republic of Macedonia) exam­ the EU­27 average fell from 28.5 % to 25.2 %. At ined here differed by a factor of 13.6:1, a figure the same time, the proportion of the population which is still very high but decreasing over the living in regions where this value is greater than medium term. Within the individual countries 125 % fell from 23.0 % to 20.1 %. These shifts the differences are as much as a factor of 4.3; re­ at the top and bottom ends of the distribution gional differences in new Member States tend to meant that the proportion of the population in be greater than in the EU­15. the mid­range (per inhabitant GDP of 75–125 %) increased significantly from 48.5 % to 54.7 %, In 2006, per inhabitant GDP (in PPS) in 72 re­ i.e. by more than 35 million persons. gions was less than 75 % of the EU­27 average. Some 25.2 % of the population live in these 72 Map 4.4 shows, however, that despite the clear regions, three quarters of them in new Member progress made towards convergence overall a com­ States, Croatia and the former Yugoslav Republic parison between the three­year periods 1999–2001 of Macedonia and one quarter in EU­15 countries. and 2004–06 shows that just five regions managed If consideration is broadened to include the three­ to exceed the 75 % threshold. These were one re­ year average for 2004–06, an important period gion each in Greece, Spain, Poland, Romania and for EU structural policy, very similar values are the UK. These regions are home to almost 16 mil­ found: 72 regions with 25.3 % of the population lion people, or around 3.2 % of the population of achieved less than 75 % of the EU­27 average. the 29 countries considered here. At the same time, however, per inhabitant GDP in four regions fell If the trends over the five­year period 2001–06 are again below the 75 % threshold in two Italian, one considered, dynamic growth can be seen in certain French and one Greek region, with a total popula­ EU­15 countries, particularly in Greece, Spain, tion of more than 5 million people, or about 1.1 % Ireland and certain regions of the UK, Finland of the population of the 29 countries considered and Sweden. However, this must be seen against here. If both developments are juxtaposed it is rather disappointing growth in most regions of found that, as a result of economic development Belgium, Germany, France, Italy and Portugal. between 1999 and 2006, the population living in In the new Member States plus Croatia, sig­ regions with a GDP of more than 75 % of the aver­ nificantly above­average growth can be seen age grew by around 10.6 million people. primarily in the Baltic countries, Romania, the These results close to the 75 % threshold, which Czech Republic, Slovakia, Croatia and most re­ is important for regional policy, suggest that gions of Poland. Eurostat regional yearbook 2009 57
  • 54. 4 Gross domestic product Map 4.4: Regions whose GDP per inhabitant, in PPS, moved upwards or downwards over the 75 % threshold of the average EU-27, by NUTS 2 regions, average 2004–06 compared with average 1999–2001 58 Eurostat regional yearbook 2009
  • 55. Gross domestic product 4 The catch­up process which has started in the Malta and Poland. All the new Member States new Member States and Croatia has accelerated and Croatia, considered together, caught up by significantly compared to the 1990s and contin­ around 7.7 percentage points to reach 53.7 % of ued until 2006 with an annual rate of around 1.5 the EU­27 average between 2001 and 2006. It is percentage points compared to the EU­27 aver­ feared, however, that the financial crisis which age. However, not all the regions of the new Mem­ started in mid­2008 may mean that this rate of ber States are yet able to benefit from this to the growth will not be maintained throughout the same extent. This is particularly true of Hungary, first decade of the new century. Methodological notes Purchasing power parities and international volume comparisons The differences in GDP values between countries, even after conversion by means of exchange rates to a common currency, cannot be attributed solely to differing volumes of goods and services. The ‘level of prices’ component is also a major contributory factor. Exchange rates are determined by many factors related to demand and supply in the currency markets, such as international trade, inflation forecasts and interest rate differentials. Conversions using exchange rates are therefore of only limited relevance for international comparisons. To obtain a more precise comparison, it is essential to use special conversion rates which eliminate the effect of price-level differences be- tween countries. Purchasing power parities (PPPs) are conversion factors of this kind which convert economic indicators from national currencies into an artificial common currency, called purchasing power standard (PPS). PPPs are therefore used to convert GDP and other economic aggregates (e.g. consumption expenditure on certain product groups) of various countries into comparable vol- umes of expenditure, expressed in purchasing power standards. With the introduction of the euro, prices can now, for the first time, be compared directly between countries in the euro area. However, the euro has different purchasing power in the different coun- tries of the euro area, depending on the national price level. PPPs must therefore also continue to be used to calculate pure volume aggregates in PPS for the Member States within the euro area. In their simplest form, PPPs are a set of price ratios between the prices in national currency of the same good or service in different countries (e.g. a loaf of bread costs EUR 2.25 in France, EUR 1.98 in Germany, GBP 1.40 in the United Kingdom). A basket of comparable goods and services is used for price surveys. These are selected so as to represent the whole range of goods and services, taking account of the consumption structures in the various countries. The simple price ratios at product level are aggregated to PPPs for product groups, then for overall consumption and finally for GDP. In order to have a reference value for the calculation of the PPPs, one country is usually chosen and used as the reference country, and set to 1. For the European Union the selection of a single country as a base seemed inappropriate. Therefore, PPS is the artificial common reference currency unit used in the European Union to express the volume of economic aggregates for the purpose of spatial comparisons in real terms. Unfortunately, for reasons of cost, it will not be possible in the foreseeable future to calculate re- gional conversion factors. If such regional PPPs were available, the GDP in PPS for numerous periph- eral or rural regions of the EU would be higher than that calculated using national PPPs. The regions may be ranked differently when calculating in PPS instead of euros. For example, in 2006 the Swedish region of Östra Mellansverige had a per inhabitant GDP of EUR 29 600, putting it ahead of Madrid at EUR 29 100. However, in PPS, Madrid at 32 100 PPS per inhabitant is ahead of Östra Mellansverige, at 24 600 PPS per inhabitant. In terms of distribution, the use of PPS rather than the euro has a levelling effect, as countries with a very high per inhabitant GDP also generally have relatively high price levels. The range of per inhabitant GDP in NUTS 2 regions in the EU-27 plus Croatia and the former yugoslav Republic of Macedonia thus falls from 86 500 in euro to 73 600 in PPS. GDP per inhabitant in PPS is the key variable for determining the eligibility of NUTS 2 regions under the European Union’s structural policy. Eurostat regional yearbook 2009 59
  • 56. Household accounts
  • 57. 5 Household accounts Introduction: measuring wealth The primary distribution of income shows the income of private households generated directly One of the primary aims of regional statistics is from market transactions, i.e. the purchase and to measure the wealth of regions. This is of par­ sale of factors of production and goods. These in­ ticular relevance as a basis for policy measures clude in particular the compensation of employ­ which aim to provide support for less well­off ees, i.e. income from the sale of labour as a factor regions. of production. Private households can also receive income on assets, particularly interest, dividends The indicator most frequently used to measure and rents. Then there is also income from oper­ the wealth of a region is regional gross domes­ ating surplus and self­employment. Interest and tic product (GDP). GDP is usually expressed in rents payable are recorded as negative items for purchasing power standards (PPS) per inhabitant households in the initial distribution stage. The to make the data comparable between regions of balance of all these transactions is known as the differing size and purchasing power. primary income of private households. GDP is the total value of goods and services pro­ Primary income is the point of departure for the duced in a region by the persons employed in that secondary distribution of income, which means the region, minus the necessary inputs. However, state redistribution mechanism. All social benefits owing to a multitude of interregional linkages and transfers other than in kind (monetary trans­ and state interventions, the GDP generated in a fers) are now added to primary income. From their given region does not tally with the income actu­ income, households have to pay taxes on income ally available to the inhabitants of the region. and wealth, pay their social contributions and ef­ One drawback of regional GDP per inhabitant as fect transfers. The balance remaining after these an indicator of wealth is that a ‘place­of­work’ fig­ transactions have been carried out is called the ure (the GDP produced in the region) is divided disposable income of private households. by a ‘place­of­residence’ figure (the population For an analysis of household income, a decision living in the region). This inconsistency is of rele­ must first be made about the unit in which data vance wherever there are net commuter flows — are to be expressed if comparisons between re­ i.e. more or fewer people working in a region than gions are to be meaningful. living in it. The most obvious example is the In­ ner London region of the UK, which has by far For the purposes of making comparisons between the highest GDP per inhabitant in the EU. Yet regions, regional GDP is generally expressed in this by no means translates into a correspond­ PPS so that meaningful volume comparisons can ingly high income level for the inhabitants of the be made. The same process should therefore be ap­ same region, as thousands of commuters travel to plied to the income parameters of private house­ London every day to work there but live in the holds. These are therefore converted with specific neighbouring regions. Hamburg, Wien, Luxem­ purchasing power standards for final consump­ bourg, Praha and Bratislava are other examples tion expenditure called PPCSs (purchasing power of this phenomenon. consumption standards). Apart from commuter flows, other factors can also cause the regional distribution of actual Results for 2006 income not to correspond to the distribution of GDP. These include, for example, income from Primary income rent, interest or dividends received by the resi­ dents of a certain region, but paid by residents of Map 5.1 gives an overview of primary income in other regions. the NUTS 2 regions of the 23 countries exam­ ined here. Centres of wealth are clearly evident in This being the case, a more accurate picture of a southern England, Paris, northern Italy, Austria, region’s economic situation can be obtained only Madrid and north­east Spain, Flanders, the west­ by adding the figures for net income accruing to ern Netherlands, Stockholm, Nordrhein­West­ private households. falen, Hessen, Baden­Württemberg and Bayern. Also, there is a clear north–south divide in Italy Private household income and a west–east divide in Germany, whereas in France wealth distribution is relatively uniform In market economies with state redistribution between regions. The United Kingdom, too, has mechanisms, a distinction is made between two a north–south divide, although less marked than stages of income distribution. the divides in Italy and Germany. 62 Eurostat regional yearbook 2009
  • 58. Household accounts 5 Map 5.1: Primary income of private households per inhabitant (in PPCS), by NUTS 2 regions, 2006 Eurostat regional yearbook 2009 63
  • 59. 5 Household accounts In the new Member States, it is mainly the capi­ nent position with the highest disposable income tal regions that have relatively high income levels, for the country in question. particularly Bratislava and Praha, where income Of the 10 regions with the highest disposable in­ levels are close to the EU­27 average. Közép­Mag­ come per inhabitant, five are in the United King­ yarország (Budapest), Mazowieckie (Warszawa) dom, four in Germany, and one in France. The and București — Ilfov also have relatively high region with the highest disposable income in income levels. The primary income of private the new Member States is Bratislavský kraj with households is over half the EU average in all the 12 309 PPCS per inhabitant, followed by Praha other Czech regions, in two other Hungarian re­ with 12 241 PPCS. gions, and in Slovenia and Lithuania, while in all the other regions of the new Member States it is A clear concentration of regions is also evident below that level. when the ranking is extended to the top 30 re­ gions: this group contains 11 German and nine The regional values range from 3 197 PPCS per UK regions, along with four regions in Austria, inhabitant in north­east Romania to 35 116 PPCS three in Italy and one each in Belgium, France in the UK region of Inner London. The 10 regions and Spain. with the highest income per inhabitant include five regions in the UK, three in Germany and one The tail end of the distribution is very similar to each in France and Belgium. This clear concen­ the ranking for primary income. The bottom 30 tration of regions with the highest incomes in the include 13 Polish and seven Romanian regions, United Kingdom and Germany is also evident four in Hungary, two in Slovakia and one in when the ranking is extended to the top 30 re­ Greece, plus the three Baltic States. gions: this group contains 11 German and seven The regional values range from 3 610 PPCS per UK regions, along with three each in Italy and inhabitant in north­east Romania to 25 403 PPCS Austria, two in Belgium and one each in France, in the UK region of Inner London. State activity the Netherlands, Spain and Sweden. and other transfers significantly reduce the dif­ It is no surprise that the 30 regions at the tail end ference between the highest and lowest regional of the ranking are all located in the new Mem­ values in the 23 countries dealt with here from a ber States; the list contains 15 of the 16 Polish factor of around 11.0 to 7.0. regions, seven of the eight Romanian regions, In contrast to primary income, there is a signifi­ four of the seven Hungarian regions and two of cant trend in disposable income towards a nar­ the four Slovakian regions, together with Estonia rowing of the range in regional values: between and Latvia. 2001 and 2006 the difference between the highest In 2006, the highest and lowest primary incomes and lowest values fell from a factor of 8.5 to 7.0. in the EU regions differed by a factor of 11.0. Five It can thus be concluded overall that measurable years earlier, in 2001, this factor had been 10.4. regional convergence between 2001 and 2006 oc­ There was therefore a slight increase in the gap curred only with regard to the disposable income between the opposite ends of this distribution affected by state intervention; this was not the over the period 2001–06. case with regard to the primary income generated from market transactions. Disposable income The regional spread in disposable income within A comparison of primary income with disposable the individual countries is naturally much lower income (Map 5.2) shows the levelling influence of than for the EU as a whole, but varies consider­ state intervention. This particularly increases the ably from one country to another. Figure 5.1 gives relative income level in some regions of Italy and an overview of the range of disposable income per Spain, in the west of the United Kingdom and in inhabitant between the regions with the highest parts of eastern Germany and Greece. Similar ef­ and the lowest value for each country. It can be fects can be observed in the new Member States, seen that, with a factor of over 2, the regional dis­ particularly in Hungary, Romania, Slovakia and parities are greatest in Romania and Greece. This Poland. However, the levelling out of private in­ means that the disposable income per inhabitant come levels in the new Member States is generally in the region of București — Ilfov is more than less pronounced than in the EU­15. twice as high as in north­east Romania. With In spite of state redistribution and other trans­ factors of around 1.8, Slovakia, the United King­ fers, most capital regions maintain their promi­ dom, Hungary and Italy also have wide regional 64 Eurostat regional yearbook 2009
  • 60. Household accounts 5 Map 5.2: Disposable income of private households per inhabitant (in PPCS), by NUTS 2 regions, 2006 In percentage of EU-27 = 100 Eurostat regional yearbook 2009 65
  • 61. 5 Household accounts variations. For Spain, Poland and Germany the NUTS 2 region also have the highest income val­ highest value is about two thirds higher than the ues. This group includes four of the six largest respective lowest value. The regional concen­ new Member States. tration is in general higher in the new Member The economic dominance of the capital regions is States than in the EU­15. also evident when their income values are com­ Of the new Member States, Slovenia, with 11 %, pared with the national averages. In four coun­ has the smallest spread between the highest and tries (the Czech Republic, Romania, Slovakia and lowest values and thus comes very close to Austria, the United Kingdom), the capital cities exceed the which has the lowest regional income disparities. national values by more than a third. Only in Bel­ Ireland, Finland, Sweden and the Netherlands gium and Germany are the values lower than the also have only moderate regional disparities, with national average. the highest values ranging between 10 % and 28 % To assess the economic situation in individual re­ greater than the lowest values. gions, it is important to know not just the levels Figure 5.1 additionally shows that the capital cit­ of primary and disposable income but also their ies of 13 of the 18 countries with more than one relationship to each other. Map 5.3 shows this Figure 5.1: Disposable income of private households per inhabitant (in PPCS), by NUTS 2 regions, 2006 BE Hainaut Vlaams-Brabant CZ Severozápad Praha DK DE Mecklenburg-Vorpommern Hamburg EE IE Border, Midland and Western Southern and Eastern EL Ionia Nisia Attiki ES Extremadura País Vasco FR Nord — Pas-de-Calais Île de France IT Campania Prov. Autonoma Bolzano/Bozen LV LT HU Eszak-Alföld Közép-Magyarország NL Groningen Utrecht AT Kärnten Wien PL Podkarpackie Mazowieckie PT Norte Lisboa RO Nord-Est Bucureşti-Ilfov SI Vzhodna Slovenija Zahodna Slovenija SK Východné Slovensko Bratislavský kraj FI Itä-Suomi Åland SE Övre Norrland Stockholm UK West Midlands Inner London 0 2 500 5 000 7 500 10 000 12 500 15 000 17 500 20 000 22 500 25 000 27 500 30 000 National average Capital region Notes: DK: data only available at national level FR: without overseas departments 66 Eurostat regional yearbook 2009
  • 62. Household accounts 5 Map 5.3: Disposable income of private households as % of primary income, by NUTS 2 regions, 2006 Eurostat regional yearbook 2009 67
  • 63. 5 Household accounts quotient, which gives an idea of the effects of state from people temporarily working in other re­ activity and of other transfer payments. On aver­ gions) can play a role in some cases. age, disposable income in the EU­27 amounts to 87.2 % of primary income. In 2001 this figure had been 87.0 %, so over this five­year period the scale Dynamic development of state intervention and other transfers hardly on the edges of the Union changed. In general the EU­15 Member States have somewhat lower values than the new Mem­ The focus finally turns to an overview of medi­ ber States. um­term trends in the regions compared with the EU­27 average. Map 5.4 uses a five­year compari­ On closer inspection, substantial differences son to show how disposable income per inhab­ can be seen between the regions of the Mem­ itant (in PPCS) in the NUTS 2 regions changed ber States. Disposable income in the capital cit­ between 2001 and 2006 compared to the average ies and other prosperous regions of the EU­15 for the EU­27. is generally less than 80 % of primary income. Correspondingly higher percentages can be ob­ It shows, first of all, powerful dynamic processes served in the less affluent areas, in particular on in action on the edges of the Union, particularly the southern and south­western peripheries of in Spain and Ireland, the Czech Republic, Slo­ the EU, in the west of the United Kingdom and vakia, Hungary and the Baltic States. in eastern Germany. On the other hand, below­average trends in in­ This is because in regions with relatively high come are apparent in Belgium, Germany, France income levels a larger proportion of primary and especially Italy, where even regions with only income is transferred to the state in the form average levels of income were affected. of taxes. At the same time, state social benefits The changes range from +16.4 percentage points amount to less than in regions with relatively for Bucureşti — Ilfov (Romania) to ­14.4 percent­ low income levels. age points in Liguria (Italy). The regional redistribution of wealth is generally Despite overall clear evidence of a catching­up less significant in the new Member States than in process in the new Member States, the same posi­ the EU­15. For the capital regions the values are tive trend is not found everywhere. In seven of between 80 % and 90 % and are almost without Poland’s 16 regions incomes increased by only up exception at the bottom end of the ranking with­ to 1.5 percentage points compared with the EU in each country. This shows that incomes in these average. The figures for Romania, on the other regions require much less support through social hand, are very encouraging. With an increase of benefits than elsewhere. The difference between 16.4 percentage points, the București — Ilfov re­ the capital region and the rest of the country is gion achieved the highest relative improvement particularly large in Romania and Slovakia, at of all regions, with even the Nord­Est region (the around 15 percentage points. region with the lowest income in the whole EU) In the 23 EU Member States examined here, there catching up by 4.8 percentage points on average is a total of 30 regions in which disposable income income growth in the EU. The structural problem exceeds primary income. This applies in particu­ nevertheless remains that in all the new Member lar to 12 of the 16 regions in Poland and four of States the wealth gap between the capital city the eight regions in Romania. In the EU­15, the and the less prosperous parts of the country has most noticeable instances are six regions of east­ widened further. ern Germany, three regions in Portugal and two On the whole, the trend between 2001 and 2006 in the United Kingdom. resulted in a slight flattening of the upper edge of When interpreting these results, however, it the regional income distribution band, caused in should be borne in mind that it is not just mon­ particular by substantial relative falls in regions etary social benefits from the state which may with high levels of income. At the same time, all cause disposable income to exceed primary in­ of the 10 regions at the tail end of the ranking come. Other transfer payments (e.g. transfers have caught up considerably on the EU average. 68 Eurostat regional yearbook 2009
  • 64. Household accounts 5 Map 5.4: Development of disposable income of private households per inhabitant, by NUTS 2 regions Change between 2001 and 2006 in percentage points of the average EU-27 in PPCS Eurostat regional yearbook 2009 69
  • 65. 5 Household accounts Conclusion gions are income values more than three quarters of the EU average. The regional distribution of disposable house­ An analysis over the five­year period 2001–06 hold income differs from that of regional GDP in shows that incomes in many regions of the new a large number of NUTS 2 regions, in particular Member States are catching up only very slowly. because unlike regional GDP the figures for the This applies in particular to certain regions of income of private households are not affected by Poland. In Romania, on the other hand, a strong commuter flows. In some cases, other transfer catching­up process has taken hold — a develop­ payments and flows of other types of income re­ ment which, happily, extends beyond the capital ceived by private households from outside their region of București — Ilfov. region also play a substantial role. In addition, state intervention in the form of monetary social For disposable income there is a measurable trend transfers and the levying of direct taxes tends to towards a narrowing of the spread in regional level out the disparities between regions. values: between 2001 and 2006 the difference be­ tween the highest and lowest values fell from a Taken together, state intervention and other influ­ factor of 8.5 to 7.0, while for primary income the ences bring the spread of disposable income be­ differences between regions increased from a fac­ tween the most prosperous and the economically tor of 10.4 to 11.0. weakest regions to a factor of about 7.0, whereas the two extreme values of primary income per With regard to the availability of data concerning inhabitant differ by a factor of 11.0. The flatten­ income it may be said that the comprehensiveness of the data and the length of the time series have ing out of regional income distribution desired by gradually improved. Once a complete data set is most countries is therefore being achieved. available, data on the income of private house­ The income level of private households in the new holds could be taken into account alongside GDP Member States continues to be far below that in statistics when decisions are taken on regional the EU­15; in only a small number of capital re­ policy measures. 70 Eurostat regional yearbook 2009
  • 66. Household accounts 5 Methodological notes Eurostat has had regional data on the income categories of private households for a number of years. The data are collected for the purposes of the regional accounts at NUTS level 2. There are still no data available at NUTS 2 level for the following regions: Bulgaria, Départements d’Outre-Mer (France), Cyprus, Luxembourg and Malta; for Denmark only national data are available. The text in this chapter therefore relates to only 23 Member States, or 254 NUTS 2 regions. Three of these 23 Member States consist of only one NUTS 2 region, namely Estonia, Latvia and Lithuania. Since the beginning of 2008 Denmark has consisted of five NUTS 2 regions, but is shown here only as a single NUTS 1 region, as no data are yet available for the newly defined NUTS 2 regions. Because of the limited availability of data, the EU-27 values for the regional household accounts had to be estimated. For this purpose it was assumed that the share of the missing Member States in household income (in PPCS) for EU-27 was the same as for GDP (in PPS). For the reference year 2005 this share was 1.0 %. Data that reached Eurostat after 28 April 2009 are not taken into account in this chapter of the yearbook. Eurostat regional yearbook 2009 71
  • 67. Structural business statistics
  • 68. 6 Structural business statistics Introduction ferent activities within the business economy. While some activities are distributed relatively What effects do the European Union’s economic evenly across most regions, many others exhibit and regional policies have on the business struc­ a considerable variation in the level of regional ture of the regions? What sectors are growing, specialisation, often with a few regions having a what sectors are contracting and what regions are particularly high degree of specialisation. likely to be most affected? A detailed analysis of the structure of the European economy can only The share of a particular activity within the busi­ be made at regional level. Regional structural ness economy gives an idea of which regions are business statistics (SBS) provide data with a de­ the most or least specialised in that activity, re­ tailed activity breakdown that can be used for gardless of whether the region or the activity con­ this kind of analysis. The first part of this chap­ sidered is large or small. There are various reasons ter looks at regional specialisation and business for relative specialisation. Depending on the type concentration within the EU’s business economy. of activity, these can include availability of natu­ The second part analyses the activity of the busi­ ral resources, availability of skilled employees, ness services sector in detail. culture and tradition, cost levels, infrastructure, legislation, climatic and topographic conditions and proximity to markets. Regional specialisation and business concentration Figure 6.1 shows that, on an aggregate activity level (NACE sections), the widest spread in the relative There are significant disparities between Euro­ importance of an activity in each region’s non­ pean regions in terms of the importance of dif­ financial business economy (NACE sections C to Figure 6.1: Degree of regional specialisation by activity (NACE sections), EU-27 and Norway, by NUTS 2 regions, 2006 Share of non-financial business economy employment, in percentage Distributive trades Dytiki Ellada (GR23) (G 50–52) Západné Manufacturing Slovensko (D 15–37) (SK02) Real estate, renting and business activities Inner London (UKI1) (K 70–74) Construction Andalucía (ES61) (F 45) Transport, storage and communication Åland (FI20) (I 60–64) Hotels and restaurants Ionia Nisia (GR22) (H 55) Electricity, gas and water supply Sud-Vest Oltenia (RO41) (E 40–41) Mining and quarrying Agder og Rogaland (NO04) (C 10–14) 0 5 10 15 20 25 30 35 40 45 50 55 60 65 Notes: Excluding BG, SI, DK (no data by NUTS 2 regions), MT, North Eastern Scotland (UKM5) and Highlands and Islands (UKM6) (data not available) CY excluding Research and development (K 73) 74 Eurostat regional yearbook 2009
  • 69. Structural business statistics 6 I and K) workforce was in manufacturing (NACE quarrying accounted for less than 0.2 % of people section D). Manufacturing accounted for only employed in a quarter of all regions, and between 3.1 % of people employed in Ciudad Autónoma de 0.2 % and 0.5 % in half of the regions. However, Melilla (Spain) and under 10 % in a further 13 re­ this sector accounted for over 5 % in six regions gions, including the capital regions of both Spain and as much as a 10th of the total non­financial and the United Kingdom. The distribution of the business economy workforce in Śląskie (Poland) remaining regions was relatively symmetrical, and Agder og Rogaland (Norway). from 10 % to almost half of the workforce in two Table 6.1 shows which region was the most special­ Czech and two Slovak regions: Střední Morava ised in 2006 on a more detailed activity level (all (Czech Republic) and Východné Slovensko (Slo­ NACE divisions within each NACE section) and, vakia) — both 48.0 % — and Severovýchod (Czech as a comparison, the median and average share Republic) and Stredné Slovensko (Slovakia) — of the non­financial business economy workforce both 48.8 %. Západné Slovensko (Slovakia) was among all regions within the EU­27 and Norway. the only region where the share of employment Manufacturing activities which involve the pri­ in manufacturing exceeded half the non­financial mary processing stages of agricultural, fishing or business economy workforce (57.8 %). In contrast, forestry products are particularly concentrated in the spread of employment was much narrower in areas close to the source of the raw material. The distributive trades (NACE section G), which was regions most specialised in food and beverages the activity displaying the highest median employ­ manufacturing (NACE 15) were all located in ment, present in all regions and serving more local rural areas in or close to agricultural production clients. Shares ranged from less than 17 % in Åland centres: Bretagne (the most specialised of all the and Länsi­Suomi (Finland) to just over 40 % in regions) and Pays de la Loire in France, Lubelskie, Anatoliki Makedonia, Thraki, Kriti and Kentriki Podlaskie and Warmińsko­mazurskie in the east­ Makedonia (Greece), and almost 45 % in Dytiki ern part of Poland, Dél­Alföld in Hungary, and Ellada (Greece). La Rioja in Spain. Heavily forested Nordic and On the other hand, transport, storage and com­ Baltic regions were the regions most specialised munication (NACE section I) and mining and in the manufacture of wood and wood products quarrying (NACE section C) are two activities (NACE 20) and in the related manufacturing of with a similar relative size in most regions, but pulp, paper and paper products (NACE 21). Itä­ where there are a few strong outlier regions that Suomi (Finland) was the most specialised region are highly specialised. Transport, storage and in wood and wood products and Norra Mellans­ communication accounted for not more than 7.1 % verige (Sweden) in pulp and paper. in a quarter of the regions and less than 10.1 % in Regions traditionally associated with tourism, in three quarters of them. These narrow ranges are particular in Spain, Greece and Portugal, were mainly due to the fact that road transport and post the most specialised in hotels and restaurants and telecommunications account for a large share (NACE 55). Hotels and restaurants accounted for of employment in this sector and that these activi­ more than 20 % of the workforce in the Greek is­ ties tend to be of relatively equal importance across land regions of Ionia Nisia and Notio Aigaio, the most regions. There were only three regions, for Spanish Illes Balears, the Algarve in the south of example, where the share of employment in trans­ Portugal, Provincia Autonoma Bolzano/Bozen in port, storage and communication exceeded 20 %. the north­east of Italy on the border with Aus­ The highest specialisation of the Finnish island re­ tria and the region of Cornwall and Isles of Scilly gion of Åland, where almost half of the workforce (United Kingdom). (47.9 %) was employed in this sector, is due almost exclusively to the importance of water transport. Greek regions were the most specialised in dis­ Åland was far ahead of Köln in Germany (31.3 %), tributive trades (NACE G 50–52), with the excep­ where post and telecommunications was particu­ tion of motor trades (NACE 50), where the Italian larly important, and Bratislavský kraj (23.8 %), the region of Molise had the highest specialisation. capital region of Slovakia, owing to the impor­ Construction activities (NACE 45) accounted for tance of road and other land transport. Natural the highest shares of the workforce in Spanish re­ endowments play an important role in the activi­ gions. Transport services are also influenced by lo­ ties of mining and quarrying. Many regions record cation, with water transport (NACE 61) naturally little or no such activity, with only a very few of being important for coastal regions and islands, them being highly specialised on account of de­ while air transport (NACE 62) is also important posits of metallic ores, coal, oil or gas. Mining and for many island regions (especially those with a Eurostat regional yearbook 2009 75
  • 70. 6 Structural business statistics Table 6.1: Most specialised region by activity (NACE sections and divisions), EU-27 and Norway, 2006 Share of total non-financial business economy employment of the region and the median and average share of all regions, in percentage All regions Most specialised region Activity (NACE) Median Average Share of the Name (NUTS 2 region) share (%) share (%) region (%) Mining and quarrying (C 10–14) 0.3 0.6 Agder og Rogaland (NO04) 10.4 Coal, lignite and peat (10) 0.0 0.2 Śląskie (PL22) c Crude petroleum and natural gas (11) 0.0 0.1 Agder og Rogaland (NO04) 10.0 Uranium and thorium ores (12) 0.0 0.0 Severovýchod (CZ05) c Metal ores (13) 0.0 0.0 Övre Norrland (SE33) c Other mining and quarrying (14) 0.2 0.2 Alentejo (PT18) c Manufacturing (D 15–37) 25.0 26.2 Západné Slovensko (SK02) 56.9 Food and beverages (15) 3.6 3.8 Bretagne (FR52) 11.1 Tobacco products (16) 0.0 0.1 Trier (DEB2) c Textiles (17) 0.4 0.7 Prov. West-vlaanderen (BE25) 5.6 Wearing apparel; fur (18) 0.3 0.9 Dytiki Makedonia (GR13) 11.5 Leather and leather products (19) 0.1 0.4 Marche (ITE3) 7.7 Wood and wood products (20) 0.8 1.2 Itä-Suomi (FI13) 5.8 Pulp, paper and paper products (21) 0.5 0.6 Norra Mellansverige (SE31) 4.7 Publishing and printing (22) 1.1 1.2 Inner London (UKI1) 4.2 Fuel processing (23) 0.0 0.1 Cumbria (UKD1) c Chemicals and chemical products (24) 1.0 1.3 Rheinhessen-Pfalz (DEB3) 11.6 Rubber and plastic products (25) 1.2 1.4 Auvergne (FR72) 7.8 Other non-metallic mineral products (26) 1.1 1.3 Prov. Namur (BE35) 5.3 Basic metals (27) 0.5 1.0 Norra Mellansverige (SE31) 9.6 Fabricated metal products (28) 2.7 3.0 Arnsberg (DEA5) 8.7 Machinery and equipment (29) 2.2 2.7 Unterfranken (DE26) 12.2 Office machinery and computers (30) 0.0 0.1 Southern and Eastern (IE02) 1.4 Electrical machinery and apparatus (31) 0.9 1.3 Západné Slovensko (SK02) 9.8 Radio, Tv and communication equipment (32) 0.3 0.6 Pohjois-Suomi (FI1A) 6.1 Medical, precision and optical equipment (33) 0.6 0.7 Border, Midland and Western (IE01) 5.9 Motor vehicles and (semi)-trailers (34) 0.8 1.7 Braunschweig (DE91) c Other transport equipment (35) 0.5 0.8 Agder og Rogaland (NO04) 6.3 Furniture and other manufacturing (36) 1.1 1.4 Warmińsko-mazurskie (PL62) 8.0 Recycling (37) 0.1 0.1 Brandenburg — Nordost (DE41) 0.7 Electricity, gas and water supply (E 40–41) 1.0 1.3 Sud-vest Oltenia (RO41) 5.5 Electricity, gas and hot water supply (40) 0.8 1.0 Martinique (FR92) 4.8 Water supply (41) 0.2 0.3 východné Slovensko (SK04) 1.9 Construction (F 45) 10.4 10.9 Andalucía (ES61) 28.6 Distributive trades (G 50–52) 26.2 26.1 Dytiki Ellada (GR23) 44.8 Motor trades (50) 3.5 3.7 Molise (ITF2) 9.3 Wholesale trade (51) 7.2 7.4 Kentriki Makedonia (GR12) 15.1 Retail trade and repair (52) 14.8 14.9 Dytiki Ellada (GR23) 27.1 Hotels and restaurants (H 55) 7.2 8.1 Ionia Nisia (GR22) 33.8 Transport, storage and communication (I 60–64) 8.4 8.9 Åland (FI20) 47.9 Land transport and pipelines (60) 4.5 4.6 Bratislavský kraj (SK01) 15.8 Water transport (61) 0.1 0.4 Åland (FI20) 38.7 Air transport (62) 0.0 0.2 Outer London (UKI2) 3.9 Supporting transport activities (63) 1.7 1.9 Bremen (DE50) 11.1 Post and telecommunications (64) 1.8 2.0 Köln (DEA2) 24.4 Real estate, renting, business activities (K 70–74) 16.9 18.1 Inner London (UKI1) 49.1 Real estate activities (70) 2.0 2.0 Latvija (Lv00) 5.6 Renting (71) 0.4 0.5 Hamburg (DE60) 1.7 Computer activities (72) 1.4 1.7 Berkshire, Buckinghamshire and Oxfordshire (UKJ1) 8.0 Research and development (73) 0.2 0.0 voreio Aigaio (GR41) 4.8 Other business activities (74) 12.7 13.6 Inner London (UKI1) 38.3 Notes: Excluding BG, SI, DK (no data by NUTS 2 regions), MT, North Eastern Scotland (UKM5) and Highlands and Islands (UKM6) (data not available) Cy excluding Research and development (K 73) c = confidential data 76 Eurostat regional yearbook 2009
  • 71. Structural business statistics 6 developed tourism industry), and for regions with this dominance is due to the concentration in large or close to major cities. The small island region of metropolitan regions where the large airports are Åland (Finland) is a centre for the ferry services situated: chief among them the regions of Paris, between Sweden and Finland and other Baltic Sea Outer London, Köln, Amsterdam and Madrid. traffic. Åland was very highly specialised in water Leather and leather products manufacturing, on transport, which accounted for almost 40 % of the other hand, is a small activity in Europe, heav­ people employed in 2006 — over 10 times more ily concentrated in Italy, Portugal and Romania: than the next most specialised regions, Hamburg five of the 10 regions with the largest workforces in Germany and Agder og Rogaland in Norway. were situated in Italy, three in Romania and one Outer London was the region most specialised in each in Portugal and Spain. The region with the lar­ air transport, followed by Noord­Holland (Dutch gest workforce was Norte in Portugal, with 43 000 region of Amsterdam), the French island of Corse, people employed. This region alone accounted for Köln in Germany and the Illes Balears in Spain. more than 8 % of the total leather manufacturing workforce in the EU­27 and Norway. As with air transport, specialisation in real estate, renting and business activities (NACE 70–74) may In contrast to the more specialised types of min­ be based on access to a critical mass of clients (en­ ing and quarrying, other mining and quarrying terprises or households) or to a knowledge base (NACE 14) was among the activities in which the (external researchers and qualified staff). Within 10 largest regions were least dominant, account­ the countries themselves, the capital region or ing for only 17 % of total sectoral employment. other large metropolitan regions were normally This is due to the widespread availability and local among the most specialised in the business ser­ sourcing of many construction materials, such as vices sectors: computer services (NACE 72) and sand and stone, which dominate this type of min­ other business activities (NACE 74). A detailed ing in most regions. Of all the activities (NACE analysis of the business services sector is included divisions), only retail trade (NACE 52), food and in the last part of this chapter. Latvia was most beverages manufacturing (NACE 15) and motor specialised in real estate (NACE 70) in 2006, ahead trades (NACE 50) had a lower concentration in of Algarve (Portugal) and Inner London (United 2006, but, in contrast to other mining and quar­ Kingdom), while Hamburg was most specialised rying, these are all major activities in terms of in renting, ahead of the French overseas depart­ employment in the EU. ments of Guadeloupe and Martinique. Post and telecommunications (NACE 64) and While an analysis of specialisation shows the motor vehicles manufacturing (NACE 34) are relative importance of different activities in the examples of major activities that were relatively regions, regardless of the size of the region or the highly concentrated in a few regions. activity, an analysis of concentration looks at the Map 6.1 gives an indication of how concentrated dominance of certain regions within an activity, or diversified the regional business economy was or activities, within a region. In most activities, in 2006, measured as the share of the five larg­ there are many examples of regions that are high­ est activities (NACE divisions) in the total non­ ly ranked in terms of both specialisation and con­ financial business economy workforce. The level centration. Figure 6.2 shows the extent to which of concentration tends to be highest in regions employment in certain activities was concentrat­ where trade and services dominate the business ed in a limited number of regions in 2006. Four of economy, as industrial activities are more frag­ the five mining and quarrying activities topped mented. By this measure, the most concentrated the rankings based on the share of total employ­ regions were generally in countries tradition­ ment in the EU­27 and Norway, as accounted for ally associated with tourism (in particular Spain, by the 10 regions with the largest workforces. The Greece and Portugal), underlining the impor­ most concentrated was the mining of uranium tance of construction, trade, and hotels and res­ and thorium ores (NACE 12), with people em­ taurants in tourism­oriented regions. ployed in only seven of the 262 regions (for which However, high concentrations were also recorded data are available) in 2006. in several densely populated areas, such as the Air transport (NACE 62) and leather and leather south­east of the United Kingdom, most parts products manufacturing (NACE 19) were also of the Netherlands and also the capital region in highly concentrated in the 10 largest regions, which most countries (at least relative to the national together accounted for 62 % and 53 % of total em­ average). The situation was similar in most coun­ ployment respectively. In the case of air transport, tries — the capital region was usually among the Eurostat regional yearbook 2009 77
  • 72. 6 Structural business statistics Figure 6.2: Most concentrated activities (NACE divisions), EU-27 and Norway, by NUTS 2 regions, 2006 Share of regions in total sectoral employment, in percentage Uranium and thorium ores (12) Metal ores (13) Coal, lignite and peat (10) Crude petroleum and natural gas (11) Air transport (62) Leather and leather products (19) Post and telecommunications (64) Textiles (17) Water transport (61) Wearing apparel; fur (18) Tobacco prodcuts (16) Office machinery and computers (30) Fuel processing (23) Computer activities (72) Research and development (73) Radio, TV and communication equipment (32) Motor vehicles and (semi)-trailers (34) Basic metals (27) Chemicals and chemical products (24) Supporting transport activities (63) Medical, precision and optical instruments (33) Real estate activities (70) Other business activities (74) Publishing and printing (22) Machinery and equipment (29) Construction (45) Fabricated metal products (28) Other transport equipment (35) Furniture and other manufacturing (36) Electronic machinery and apparatus (31) Renting (71) Other non-metallic mineral products (26) Wood and wood products (20) Electricity, gas and hot water supply (40) Wholesale trade (51) Recycling (37) Land transport and pipelines (60) Hotels and restaurants (H55) Pulp, paper and paper products (21) Water supply (41) Rubber and plastic products (25) Other mining and quarrying (14) Retail trade and repair (52) Food and beverages (15) Motor trades (50) 0 10 20 30 40 50 60 70 80 90 100 Regions ranked: 1−10 11−20 21−50 51−262 Notes: Excluding BG, SI, DK (no data by NUTS 2 regions), MT, North Eastern Scotland (UKM5) and Highlands and Islands (UKM6) (data not available) CY excluding Research and development (K 73) 78 Eurostat regional yearbook 2009
  • 73. Structural business statistics 6 Map 6.1: Regional business concentration, by NUTS 2 regions, 2006 Share of five largest activities (NACE divisions) in total non-financial business economy employment in percentage Eurostat regional yearbook 2009 79
  • 74. 6 Structural business statistics regions with the highest business concentration activities (NACE division 70) are among the top and was often top of the list. five activities in Inner London (and not construc­ tion), whereas in all other regions shown the top In contrast, the lowest business concentrations five activities in terms of employment were retail were recorded mainly in regions with a relatively trade, construction, hotels and restaurants, other small services sector and a large manufacturing business activities and wholesale trade. In fact, sector in eastern Europe (in particular in Slova­ looking at all regions for which data are avail­ kia, the Czech Republic, Hungary, Romania and able, retail trade is among the five largest activities Bulgaria), although low shares were also recorded (NACE divisions) in every region, other business in Sweden (except the capital region) and Finland activities is among the five largest in more than (except the island region of Åland). The five lar­ 90 % of the regions, construction and wholesale gest activities accounted for less than 40 % of to­ trade in more than 80 % of the regions, and hotels tal employment in Západné Slovensko (Slovakia), and restaurants in more than 60 % of the regions. Severovýchod (the Czech Republic), Vest (Ro­ mania) and Stredné Slovensko (Slovakia). Specialisation in business services Figure 6.3 provides a more detailed analysis of the most specialised regions. Among the top 10 The services sector is an important and growing regions, Inner London stands apart as the only area of the EU economy which in recent years has large metropolitan region with a fundamentally attracted increasing political and economic inter­ different business profile. Here, other business est. In 2006, real estate, renting and business ac­ activities dominate, accounting for 38 % of total tivities (NACE section K) made up a third of this employment, which is much higher than in all sector in terms of employment, and was second by the other regions shown. In addition, real estate only 7 percentage points to distributive trades. Figure 6.3: Most specialised regions, EU-27 and Norway, by NUTS 2 regions, 2006 Share of five largest activities (NACE divisions) in non-financial business economy employment of the region, in percentage Melilla (ES64) 12.6 25.0 24.2 9.6 9.5 19.1 Ionia Nisia (GR22) 33.8 22.4 9.8 6.6 8.0 19.5 Notio Aigaio (GR42) 29.9 24.6 10.1 6.1 8.5 20.8 Algarve (PT15) 23.1 17.7 19.9 9.4 7.4 22.4 Ceuta (ES63) 10.0 23.4 26.1 6.5 10.4 23.6 Kriti (GR43) 19.2 26.1 12.4 7.0 11.3 24.0 Canarias (ES70) 18.2 14.5 22.6 10.2 8.7 25.7 Inner London (UKI1) 13.0 12.7 38.3 4.8 4.9 26.2 Illes Balears (ES53) 23.9 11.6 22.0 10.1 6.1 26.3 7.5 14.2 13.5 24.7 7.3 32.9 5.01 Comunidad de Madrid (ES30) 0 20 40 60 80 100 Hotels and restaurants Retail trade Construction Other business activities Wholesale trade Other divisions in top five Other divisions (not in top five) Notes: Excluding BG, SI, DK (no data by NUTS 2 regions), MT, North Eastern Scotland (UKM5) and Highlands and Islands (UKM6) (data not available) CY excluding Research and development (K 73) 80 Eurostat regional yearbook 2009
  • 75. Structural business statistics 6 The importance of this sector, measured as the range of areas, in almost all economic activities. share in the total workforce of the non­financial It is quite common for enterprises to outsource business economy, has been seen to increase in their requirements for hardware and software to recent years. The structure of employment in this specialist providers. The possibility to trade such sector is shown in Figure 6.4. as services across borders has been increased by improved telecommunications, notably growing It can be observed that three quarters of the work­ access to broadband Internet. Those two divisions force in 2006 was divided between other business together (NACE 72 and 74) make up the business services (NACE 74), which include many highly services sector. specialised knowledge­intensive activities such as legal, accounting and management services, ar­ All the divisions within the section of real estate, chitectural and engineering activities, advertising, renting and business activities noted positive and the supply of personnel and placement services growth rates in employment in 2006 (see Figure provided by labour recruitment agencies. Security 6.5). Besides research and development (NACE and industrial cleaning services are also included, 73), all rates were significant. The growth rate for as are secretarial, translation, packaging and other computer activities reached 3.3 % and for other professional business services. A significant share, business activities 7.3 % — and it exceeded the av­ of just over 10 %, was taken up by computer ac­ erage growth rate for the whole section. The busi­ tivities (NACE 72), which cover consultancy for ness services sector was quite clearly one of the hardware and software, data processing, database most dynamic sectors in the non­financial busi­ activities and the maintenance and repair of office ness economy in terms of employment growth. and information technology machinery. This sec­ One of the prime reasons for the rapid growth of tor is at the forefront of the information society, this sector could be the outsourcing phenome­ with enterprises that support clients in a broad non. Business services can be produced either in­ Figure 6.4: Structure of employment in real estate, renting and business activities (NACE section K) by divisions, EU-27 and Norway, 2006 Real estate activities (K 70) 11.0 % Renting (K 71) 2.4 % Computer activities (K 72) 10.6 % Research and development (K 73) 1.6 % Other business activities (K 74) 74.4 % Notes: Excluding MT, North Eastern Scotland (UKM5) and Highlands and Islands (UKM6) (data not available) CY excluding Research and development (K 73) Eurostat regional yearbook 2009 81
  • 76. 6 Structural business statistics Map 6.2: Persons employed in business services (NACE divisions K 72 and K 74), by NUTS 2 regions, 2006 Share in non-financial business economy employment of the region, in percentage 82 Eurostat regional yearbook 2009
  • 77. Structural business statistics 6 ternally by an enterprise itself or they can be pur­ regions with very high specialisation in business chased. Many enterprises have outsourced some services in Germany, in a belt from the region of of the services activities they previously produced Oberbayern in the south­east to Hannover. in­house in a bid to procure these services on a Figure 6.6 shows the difference in the degree of competitive market and thus to reduce costs and specialisation in business services across coun­ increase flexibility. Business services enterprises enable their clients to focus on their core business tries and between the regions with the highest activities and lessen their need to employ their and lowest values in each country. The graph own personnel in ancillary or support functions. also clearly illustrates the dominance of the capi­ tal region, which is the most specialised in all Map 6.2 shows how specialised different regions countries except the Netherlands. There are just were in business services, from which a clear pat­ as large differences in specialisation within these tern of high concentration in large metropolitan countries as there are between them. areas emerges. The capital region is the most spe­ cialised region in all countries except the Neth­ Business services in the most specialised country, erlands, where Noord­Holland (which includes the Netherlands, account on average for 28.5 % of Amsterdam) was just behind Utrecht. Of the top people employed; around four times more than in 20 regions with shares exceeding 25 %, six were the least specialised country, Cyprus. The same British, five Dutch and three German. Luxem­ factor also differentiates between the most and bourg (23 %) and the Netherlands were particu­ least specialised region in the four countries with larly specialised in these activities, which account the largest regional disparities. Interestingly, these for a minimum of 17 % of people employed in all include two of the countries with the lowest aver­ Dutch regions. In the United Kingdom, there is a age specialisation, Slovakia and Romania, and also high degree of specialisation in the regions around one of the most specialised countries, the United London and other metropolitan areas such as Kingdom. The greatest difference between the most Greater Manchester and West Midlands. There is and the least specialised region within one country also a relatively high share of people employed in (4.3 times) was observed in Spain. At the other end business services in South Western Scotland, part­ of the scale are the Netherlands and Ireland, with ly stemming from the location of many call centres a factor lower than 2 differentiating between the in the region. There was also a significant cluster of regions with the highest and lowest values. Figure 6.5: Growth rates of employment in real estate, renting and business activities (NACE section K) by divisions, EU-27 and Norway, 2005–06 Percentage Real estate, renting, 6.6 business activities (K) Real estate activities (K 70) 7.8 Renting (K 71) 5.5 Computer activities (K 72) 3.3 Research 0.1 and development (K 73) Other business activities (K 74) 7.2 0 1 2 3 4 5 6 7 8 Notes: Excluding MT, North Eastern Scotland (UKM5) and Highlands and Islands (UKM6) (data not available) CY excluding Research and development (K 73) Eurostat regional yearbook 2009 83
  • 78. 6 Structural business statistics Employment growth in business Characteristics of the top 30 most services specialised regions in business Employment in business services in the EU­27 grew services by an impressive 40 % between 1999 and 2006. Map Figure 6.7 provides information on the top 30 most 6.3 shows the growth rate of employment in 2006 specialised regions in business services. The most in business services. In total, 18 out of the group of 34 regions with the highest growth rate exceeding specialised of all regions is Inner London (United 20 % were French and the next six were Dutch. The Kingdom), where just under 650 000 people — or two Irish regions were also included in this group. over 40 % of the total non­financial business econ­ Only one region from the countries that joined the omy workforce — are employed in these activities. EU in 2004 or 2007 is in this top list, namely the Only one region from the countries that joined the Romanian Sud — Muntenia in 33rd place. EU in 2004 or 2007 is in the top 30: the capital re­ gion of the Czech Republic in 26th place. About one in every six regions recorded negative employment growth rates, but in only 10 cases The number of people employed also grew con­ did the decrease reach 10 %. Half of these were siderably in many of the top­ranked regions in Greek regions and two of them Belgian. 2006, with by far the highest growth rate, higher Figure 6.6: Specialisation in business services (NACE divisions K 72 and K 74), EU-27 and Norway, by NUTS 2 regions, 2006 Share of non-financial business economy employment, in percentage NL Zeeland Utrecht LU UK Cumbria Inner London BE Prov. Région de Bruxelles-Capitale / Luxembourg (B) Brussels Hoofdstedelijk Gewest FR Corse Île de France DE Oberfranken Berlin DK SE Småland med öarna Stockholm IE Border, Midland Southern and Eastern and Western PT Centro (P) Lisboa Provincia Autonoma IT Bolzano/Bozen Lazio NO Nord-Norge Oslo og Akershus ES Ciudad Autónoma Comunidad de Madrid de Ceuta HU Észak-Magyarország Közép-Magyarország FI Åland Etelä-Suomi AT Burgenland (A) Wien CZ Severovýchod Praha EL Sterea Ellada Attiki EE SI PL Lubelskie Mazowieckie Západné SK Slovensko Bratislavský kraj RO Nord-Est Bucureşti — Ilfov LV BG LT CY 0 5 10 15 20 25 30 35 40 45 50 National average Notes: BG, SI, DK (no data at NUTS 2 level), North Eastern Scotland (UKM5) and Highlands and Islands (UKM6) (data not available) CY excluding Research and development (K 73) 84 Eurostat regional yearbook 2009
  • 79. Structural business statistics 6 Map 6.3: Growth rates of employment in business services (NACE divisions K 72 and K 74), by NUTS 2 regions, 2005–06 Eurostat regional yearbook 2009 85
  • 80. 6 Structural business statistics Figure 6.7: Most specialised regions in business services (NACE divisions K 72 and K 74), EU-27 and Norway, by NUTS 2 regions, 2006 Share of non-financial business economy employment of the region and the region's share of total business services employment, in percentage Inner London (UKI1) 43.2 2.86 Utrecht (NL31) 34.6 0.64 Région de Bruxelles-Capitale/ Brussels Hoofdstedelijk 33.6 0.56 Gewest (BE10) Noord-Holland (NL32) 32.7 1.36 Berkshire, Buckinghamshire 31.7 and Oxfordshire (UKJ1) 1.13 31.3 Berlin (DE30) 0.91 30.7 Groningen (NL11) 0.20 30.3 Zuid-Holland (NL33) 1.41 29.9 Île de France (FR10) 5.06 29.3 Prov. Vlaams-Brabant (BE24) 0.35 28.7 Comunidad de Madrid (ES30) 3.69 28.5 Lisboa (PT17) 1.32 28.5 Flevoland (NL23) 0.12 Surrey, East and 28.2 West Sussex (UKJ2) 0.94 28.1 Darmstadt (DE71) 1.44 27.8 Stockholm (SE11) 0.86 27.7 Hamburg (DE60) 0.68 27.3 Outer London (UKI2) 1.40 26.3 Noord-Brabant (NL41) 0.97 Hampshire and 25.9 Isle of Wight (UKJ3) 0.64 Bedfordshire and 25.8 Hertfordshire (UKH2) 0.62 25.4 Gelderland (NL22) 0.64 25.4 Limburg (NL) (NL42) 0.36 24.7 Düsseldorf (DEA1) 1.45 24.6 Cheshire (UKD2) 0.39 24.6 Praha (CZ01) 0.66 24.5 Oslo og Akershus (NO01) 0.42 24.4 Overijssel (NL21) 0.37 24.4 Wien (AT13) 0.56 24.0 Lazio (ITE4) 1.39 0 10 20 30 40 50 Region's share of total business Share of non-financial business economy services employment (%) employment of the region (%) Notes: Excluding BG, SI, DK (no data by NUTS 2 regions), MT, North Eastern Scotland (UKM5) and Highlands and Islands (UKM6) (data not available) CY excluding Research and development (K 73) 86 Eurostat regional yearbook 2009
  • 81. Structural business statistics 6 than 30 %, in the Dutch regions of Limburg and each activity the number of workplaces, number Groningen. Strong growth of over 20 % was also of people employed, wage costs and investments recorded in Noord­Brabant, Flevoland, Noord­ made. This chapter has shown how some of these Holland and Overijssel (Netherlands), and also data can be used to analyse different regional busi­ in Prov. Vlaams­Brabant (Belgium). Regions with ness characteristics: the focus, diversity and spe­ already high concentrations in business services cialisation of the regional business economies and were aiming for even greater specialisation. Only the nature and characteristics of regional business four regions from the top 30, three British and the services activities. The analysis in this chapter has capital region of France, recorded reductions in the generally confirmed the positive expectations for number of people employed in business ser vices, the business services sector, reinforcing the belief but none of them dropped by more than 6 %. that this area will remain one of the key drivers of competitiveness and job creation within the EU economy in the coming years. Conclusion Globalisation, international market liberalisation Regional structural business statistics offer users and further technological gains are likely to lead wanting to know more about the structure and to further integration among Europe’s regions development of the regional business economy a (and beyond), bringing buyers and sellers of these detailed, harmonised data source, describing for services closer together. Methodological notes Regional structural business statistics (SBS) are collected within the framework of a Council and Parliament regulation, in accordance with the definitions and breakdowns specified in the Com- mission regulations implementing it. The data cover all the EU Member States and Norway. Data for Bulgaria are only provided at national level as, at the time of writing, data are only available for pre-accession regional breakdowns. Data at NUTS 2 level in the 2006 classification were also unavailable for Denmark and Slovenia. These and other SBS data sets are available on Eurostat’s website (www.ec.europa.eu/eurostat) on the tag ‘Statistics’, under the theme ‘Industry, trade and services’/‘Structural business statistics’. Selected publications, data and background information are available in this section of the Eurostat website dedicated to European business — see the special topic ‘Regional structural business statistics’. Most data series are continuously updated and revised where necessary. This chapter reflects the data situation in March 2009. Structural business statistics are presented by sectors of activity according to the NACE Rev. 1.1 clas- sification, with a breakdown to two digits (NACE divisions). The data presented here are restricted to the non-financial business economy. The non-financial business economy includes sections C (Mining and quarrying), D (Manufacturing), E (Electricity, gas and water supply), F (Construction), G (Wholesale and retail trade), H (Hotels and restaurants), I (Transport, storage and communica- tion) and K (Real estate, renting and business activities). It excludes agricultural, forestry and fishing activities and public administration and other non-market services (such as education and health, which are currently not covered by the SBS), including financial services (NACE section J). The observation unit for regional SBS data is the local unit, which is an enterprise or part of an en- terprise situated in a geographically identified place. Local units are classified into sectors (by NACE) according to their main activity. At national level, the statistical unit is the enterprise. An enterprise can consist of several local units. It is possible for the principal activity of a local unit to differ from that of the enterprise to which it belongs. Hence, national and regional structural business statistics are not entirely comparable. It should be noted that in some countries the activity code assigned is based on the principal activity of the enterprise in question. Regional data are available at NUTS 2 level for a limited set of variables: the number of local units, wages and salaries, the number of people employed and investments in tangible goods. The latter variable is collected on an optional basis, except for industry (NACE sections C to E), which has more limited availability of data than for the other variables. Structural business statistics define number of persons employed as the total number of people who work (paid or unpaid) in the observation unit, plus people who work outside the unit who belong to it and are paid by it. It includes working proprietors, unpaid family workers, part-time workers and seasonal workers. Eurostat regional yearbook 2009 87
  • 82. Information society
  • 83. 7 Information society Introduction Policies within the European Union at national and European level have recognised the im­ During recent decades information and commu­ portance of bridging the digital divide to give nication technologies (ICTs) have penetrated all citizens equal access to information and com­ areas of economic and social life. ICTs have ac­ munication technologies. The Riga ministerial (6) http://ec.europa.eu/ counted for a significant increase in productivity declaration on e­inclusion of November 2006 (6) information_society/ events/ict_riga_2006/doc/ of the economy and growth of GDP. As a driver calls for an inclusive information society and declaration_riga.pdf for social modernisation they are transforming sets the framework for a comprehensive e­inclu­ our societies in a profound and unprecedented sion policy addressing different aspects of the way. The introduction of the Internet and the digital divide, such as age, accessibility, geogra­ Word Wide Web has led the development of the phy, digital literacy and competences, cultural information society. With access to the Internet it diversity and inclusive online public services. is very easy to obtain information on almost all European statistics play the role of benchmark­ topics. Search engines provide easy, fast access to ing the development of the European informa­ websites and information sources on the World tion society towards these political goals. The Wide Web. Many activities such as communicat­ key benchmarking indicators are defined in the ing and selling or buying goods and services can European Commission’s i2010 benchmarking (7) http://ec.europa.eu/ be performed online. These developments have framework (7), which followed on from the i2010 information_society/ eeurope/i2010/ created new dimensions of economic, social or strategy ‘A European information society for benchmarking/index_ political participation for individuals or groups of growth and employment’ (8). The i2010 strategy en.htm individuals. As these activities are not bound to promotes the positive contribution that ICTs can (8) http://eur­lex.europa.eu/ any specific geographic place, they have the poten­ make to the economy, society and quality of life. LexUriServ/LexUriServ. do?uri=CELEX:52005 tial of bridging large distances. In principle, the Statistics for the European Union and EFTA DC0229:EN:NOT geographic place from where these activities are countries on the access to and use of ICTs in performed does not matter any more as long as households/by individuals and in enterprises there is a connection to the Internet. Nowadays, it have been collected annually by Eurostat since is possible to keep up contacts with family mem­ 2003. Regional statistics for households and indi­ bers or friends via social networking sites, share viduals have been available since 2006. holiday pictures on the web or have a video call with a friend via the Internet. Electronic shopping sites offer the possibility of buying or selling items Access to information via the Internet. ICTs support working from home and communication technologies or from other places outside the enterprise, mak­ ing for greater flexibility in work organisation, Access to information and communication tech­ from which both the enterprise and the employee nologies is at the heart of the digital divide and can benefit. The ubiquitous presence of ICTs car­ geographic location is one aspect of that divide. ries the potential for completely new ways of par­ Regional statistical data on access to the Internet ticipating in the economy and society. within households and the availability of broad­ band for going online exist at European level. In As a basic condition, the participation of citizens contrast to supply­side statistics, the Eurostat fig­ and businesses in the information society depends ures show the actual uptake of ICTs by the popu­ on access to ICTs, i.e. the presence of electronic lation. On average, 60 % of households in Europe devices, such as computers, and connections to with members aged 16–74 years had access to the Internet. The term ‘digital divide’ has been in­ the Internet at home and almost half (49 %) of troduced to distinguish between those who have households accessed the Internet via broadband access to the Internet and are able to make use of in 2008. These figures have grown rapidly in re­ new services offered on the World Wide Web and cent years, with an annual growth rate of 10 % those who are excluded from these services. The for Internet access and 26 % for broadband ac­ term explicitly includes access to ICTs as well as cess between 2006 and 2008. While access to the the related skills needed to participate in the in­ Internet makes it possible to participate in the in­ formation society. The digital divide can be clas­ formation society, broadband connections enable sified according to criteria that describe the dif­ Internet users to fully exploit the potential of the ference in participation according to gender, age, Internet. Many of the advanced Internet services, education, income, social groups or geographic such as social networking sites, uploading and location. This chapter puts emphasis on the geo­ downloading of media content (video and audio graphic aspects of the digital divide. files) or the use of online maps and satellite im­ 90 Eurostat regional yearbook 2009
  • 84. Information society 7 Map 7.1: Internet access and broadband connections in households, by NUTS 2 regions, 2008 Share of households with Internet access and broadband connection Eurostat regional yearbook 2009 91
  • 85. 7 Information society ages, require de facto a broadband connection. Union have lower Internet penetration rates than Websites are getting richer in content, which in­ regions in the centre. creases the demand for traffic volumes constant­ Lastly, households in urban regions tend to have ly, even for less advanced services such as e­mail higher Internet access rates than households in communication. rural regions. At EU­27 level, 65 % of house­ The regional differences in Internet and broad­ holds in densely populated areas have access to band access are still quite large. They range from the Internet, while only 51 % of households in 90 % in Noord­Holland (Netherlands) to 17 % in thinly populated areas have an Internet connec­ Severozapaden (Bulgaria) for access to the Inter­ tion. Depending on the structure and size of the net and from 79 % in Groningen and Noord­Hol­ regions within the country, this pattern is vis­ land (both Netherlands) to 12 % in Severozapaden ible for some regions on Map 7.1. In general, re­ (Bulgaria) for broadband access. The six leading gions with big cities, e.g. Lisbon (PT17), Madrid regions in terms of Internet access are located in (ES30) and Barcelona (ES51), Rome (ITE4) and the Netherlands, whereas the six regions with the Milan (ITC4), Vienna (AT13), Budapest (HU1), lowest share of households with Internet access Prague (CZ01) or Berlin (DE3), show up as is­ are located in Bulgaria and Greece. lands in the surrounding regions owing to high­ Map 7.1 shows the share of households with In­ er levels of Internet access. The visibility of the ternet access and broadband connections in effect is stronger if the region only includes the Europe. A closer look at the map reveals three area of the respective conurbation. Exceptions different patterns of digital divide. Firstly, there to this rule are Brussels (BE10) and London is a north–south gradient. Although the highest (UKI1), where neighbouring regions have equal shares of Internet access are associated with re­ or higher Internet access rates. gions in the Netherlands, the regions in the Scan­ Broadband connection rates show similar pat­ dinavian countries show very high Internet pene­ terns to Internet access, with an average lag be­ tration rates, while regions in southern Europe tween Internet access and broadband connec­ have lower penetration rates. tions of 12 % for the EU­27 in 2008, compared to The second pattern is in a latitudinal direction. 19 % in 2006. The lag has lessened during the last Regions in the west and east of the European two years. Most of the Dutch regions have levels Figure 7.1: Development of Internet access and broadband connections in households 2006–08 Ratio between increase of connected households between 2006 and 2008 and not-connected households in 2006 50 45 40 35 30 25 20 15 10 5 0 EU-27 SE FR IE DE AT UK LU DK LT FI EE SK HU MT SI CZ BE CY NL LV ES PL PT EL IT BG RO IS NO Internet access Broadband connection 92 Eurostat regional yearbook 2009
  • 86. Information society 7 of Internet access and broadband connections for Use of the Internet households above 70 %, whereas the difference between Internet access and broadband connec­ and Internet activities tion rates for all regions in Germany, Slovakia The share of households with Internet access or and Croatia, for most regions in Italy, and for Ire­ broadband connections shows the potential for land, Luxembourg and Romania at national level private use of the Internet from home. Map 7.2 is well above the EU­27 average. The regions in provides an overview of the geographic distribu­ these countries would profit considerably from increased broadband access. tion of regions according to actual use of the In­ ternet in 2008. Regular users of the Internet are Figure 7.1 illustrates the growth rates of Internet defined as those persons who use the Internet at access and broadband connections between 2006 least once a week, regardless of the place of In­ and 2008 at national level. The calculation meth­ ternet usage. The spatial pattern which has been od considers the levels that had been reached in described for Internet access is again visible for 2006, taking into account the fact that efforts regular Internet use. In regions in Scandinavia, have to be higher when reaching saturation (9). the Netherlands, the United Kingdom and Lux­ (9) For example, an increase The increases in Internet access and broadband embourg, more than three quarters of the pop­ of 10 percentage points connection are set against the remaining poten­ at a penetration level of ulation use the Internet at least once a week. A 20 % would exploit 10 out tial from the levels achieved in 2006 to full satura­ of 80 % (100 % ­20 %) of higher share of persons living in densely popu­ tion. When considering Internet access, Slovakia, the remaining potential France, Austria, Luxembourg, Sweden and the lated areas regularly uses the Internet compared whereas the same increase at a penetration level of Netherlands developed most strongly within the to the share of regular Internet users living in 80 % would exploit 10 out of 20 % (100 % ­20 %) of EU­27, whereas Cyprus, Slovenia, Bulgaria and thinly populated areas. As with Map 7.1, there is the remaining potential. Greece show the lowest growth rates. Consider­ a latitudinal gradient in the share of regular In­ ing the development of broadband connections, ternet users. Regions in the east and west of the Sweden, France, Ireland, Germany, Austria, the EU­27 have lower shares of regular Internet users. United Kingdom and Luxembourg performed Almost all regions in Portugal, Italy, Greece, Bul­ most strongly within the EU­27 while Greece (10), garia and Romania as well as the Member State (10) However, Greece has the strongest annual growth Italy, Bulgaria and Romania are among the weak­ Cyprus had a share of regular Internet users be­ rates, starting from a est performers. low 40 % in 2008. quite low level. Figure 7.2: Internet activities in the EU-27, 2006–08 Percentage of individuals using the Internet in the last three months for the following activities Online course (*) Sell goods and services Job search or job application Download software Listen to web radio or television Read online newspapers or magazines Health information search Interaction with public authorities Internet banking Travel and accommodation services Information on goods and services E-mail communication 0 10 20 30 40 50 60 70 80 90 2006 2008 (*) 2007–08 Eurostat regional yearbook 2009 93
  • 87. 7 Information society Map 7.2: Regular use of the internet by NUTS 2 regions, 2008 Percentage of persons who accessed the Internet, on average, at least once a week 94 Eurostat regional yearbook 2009
  • 88. Information society 7 Map 7.3: E-commerce by private persons, by NUTS 2 regions, 2008 Percentage of persons who ordered goods or services, over the Internet, for private use, in the last year Eurostat regional yearbook 2009 95
  • 89. 7 Information society The most popular activities on the Internet are reply of having no need could just as well reveal communication via e­mail and looking for in­ a lack of information as regards the possibilities formation on goods and services (see Figure 7.2). offered by the Internet. In addition to the reasons More than 80 % of Internet users had used the already mentioned, one fourth of non­users con­ Internet within the last three months for these ac­ firm that equipment costs, e.g. buying a computer tivities. Internet users are those persons who have for accessing the Internet, were too high and 21 % used the Internet within the last three months. stated that connection costs were too expensive. Obtaining services related to travel and accom­ Almost one fourth (24 %) report a lack of required modation, Internet banking, interacting with skills for accessing the Internet, whereas only 5 % public authorities, searching for health­related of non­users have security concerns. information and reading online newspapers or It is the explicit objective of European regional magazines are activities engaged in by more than policies to facilitate affordable access to the In­ 40 % of Internet users. The biggest rise from 2006 ternet, including access to the network, terminal to 2008 is accounted for by e­mail communica­ equipment, contents and services, especially in tion, health information searches, Internet bank­ remote and rural areas of the European Union. ing and listening to web radio or web TV. The EU is aiming to achieve broadband cover­ The regional differences regarding e­commerce age for at least 90 % of the population by 2010. activity by persons are illustrated on Map 7.3. The This target describes the supply side, while Euro­ geographic patterns already described are again stat figures from the Community ICT­use survey visible on the map. All regions in Norway have a provide information on the take­up of ICTs in share of more than 55 % of the population buying the regions, which may lag behind the potentially goods or services online, while the EU­27 aver­ reachable population figures. age is 32 % of the target population. Almost all re­ In recent years, the share of non­users of the Inter­ gions in the eastern and southern Member States net has dropped at EU­27 level from 43 % of the tar­ of the EU­27 show a share of 25 % or less of the get population in 2005 to 33 % in 2008. The share total target population. Except for Spain, the var­ of non­users fell in both densely and thinly popu­ iety between the regions in those Member States is lated areas between 2005 and 2008. However, the quite low, ranging within a maximum difference decrease in thinly populated areas is lagging be­ of one class. All regions in Finland, Sweden, Den­ hind the development in densely populated areas, mark, the United Kingdom and the Netherlands thereby widening inequality between the regions. as well as the Member State Luxembourg have a The region with the lowest share of non­users share of e­shoppers higher than 45 % of the total in 2008 was Flevoland (Netherlands), with 7 %, target population, whereas in almost all regions and the region with the highest share was Sud — in Bulgaria and Romania the share is below 5 %. Muntenia (Romania), with 69 % (see Figure 7.3). The Member States with the highest differences Non-users of the Internet between shares of non­users in their regions are Bulgaria and Greece, with more than 25 percent­ E­inclusion relates to the participation of all in­ age points of difference. Denmark, Poland, Fin­ dividuals and communities in all aspects of the land and Sweden are the countries with less than (11) http://ec.europa.eu/ information society (11). The respective policies of 10 percentage points of difference between their information_society/ events/ict_riga_2006/doc/ the European Union aim to reduce gaps in and regions (12). The highest shares of Internet non­ declaration_riga.pdf promote the use of information and communica­ users are reported by Cyprus, Portugal, Greece, tion technologies to overcome digital exclusion and Bulgaria and Romania, with more than half of (12) Although these figures give an impression thus improve economic performance, employment the total target population. of the issue, they are opportunities, quality of life, social participation heavily influenced by and cohesion. At EU­27 level, one third of the popu­ Map 7.4 shows the distribution of regions accord­ the delimitation of the ing to the share of persons who have never used regions and the number lation aged 16–74 years do not use the Internet. of regions in a country. With an increasing the Internet as a deviation from the EU­27 average. number of regions, The Community survey on ICT use in households Regions in green have fewer non­users than the the size of the regions diminishes and the asks for the reasons for not using the Internet. EU­27 average, while the regions in yellow and or­ probability of higher variations increases. In 2008, 38 % of non­users said that they had no ange are above the EU­27 average. The geographic Moreover, statistics need to use the Internet. According to this figure, distribution shows similar patterns to those de­ at regional level are not available for nine it seems that there is a deliberate choice not to go scribed before. All regions in the Scandinavian Member States, which limits comparability online. However, only 14 % of non­users explicitly countries, Norway, Finland, Sweden, Denmark within the EU­27. state that they do not want to use the Internet. The and Iceland, as well as the Netherlands and Lux­ 96 Eurostat regional yearbook 2009
  • 90. Information society 7 embourg, are at least 15 % below the EU­27 aver­ viduals are collected annually at level 1 of NUTS age, while most of the regions in Bulgaria, Greece, on a compulsory basis. Some Member States ad­ Portugal, Romania, southern Italy and Cyprus are ditionally provide information at NUTS 2 level. more than 15 % above the EU­27 average. Regions The available statistics illustrate that there are in the east and west of the EU­27 tend to exceed the considerable differences regarding access and use EU­27 average of non­users of the Internet. Urban of information and communication technologies regions with higher population density tend to be between the regions of the EU­27. Within the last below the EU­27 average. In the map, this tendency few years, all Member States have increased ac­ is visible for, for example, Athens, Lisboa, Madrid, cess to and use of ICTs. However, densely popu­ Paris, Wien, Budapest, Praha or Berlin. lated areas seem to profit more from the current development than thinly populated areas. In or­ Conclusion der to overcome this problem, the European Un­ ion has shaped explicit policy targets to achieve Statistics on use of information and communi­ an inclusive information society, including the cation technologies in households and by indi­ geographic dimension of the digital divide. The Figure 7.3: Non usage of Internet, by NUTS 2 regions, 2008 In percentage of the population aged between 16 and 74 years EU-27 Flevoland Sud — Muntenia RO Bucureşti — Ilfov Sud — Muntenia BG Yugozapaden Severozapaden EL Attiki Kentriki Ellada PT Lisboa Região Autónoma dos Açores CY HR Središnja i Istočna (Panonska) Hrvatska Sjeverozapadna Hrvatska IT Provincia Autonoma Bolzano/Bozen Campania MT PL Region Centralny Region Wschodni LT SI ES Comunidad de Madrid Extremadura HU Közép-Magyarország Alföld és észak LV CZ Praha Severovýchod IE BE Prov. Brabant Wallon Prov. Hainaut FR Île de France Bassin Parisien EE AT Wien Burgenland (A) SK Bratislavský kraj Západné Slovensko DE Berlin Sachsen UK LU FI Etelä-Suomi Itä-Suomi DK Hovedstaden Nordjylland NL Flevoland Zeeland SE Östra Norra Sverige Sverige Vest- NO landet Agder og Rogaland IS 0 10 20 30 40 50 60 70 80 National average Notes: EE, IE, CY, LV, LT, LU, MT, SI, UK, IS (national level); DE, EL, FR, HU, PL, SE (by NUTS 1 regions); FI (FI20 combined with FI19) Eurostat regional yearbook 2009 97
  • 91. 7 Information society Map 7.4: Non-usage of the Internet, by NUTS 2 regions, 2008 Deviation of the share of persons who never have used the Internet from the EU-27 average 98 Eurostat regional yearbook 2009
  • 92. Information society 7 policies are benchmarked according to the i2010 of population accessing and using the Internet benchmarking framework. than thinly populated areas. In order to achieve the policy goals of inclusive participation in the The maps in this chapter reveal specific spatial information society, it will be necessary to keep patterns that are visible for all indicators present­ up existing efforts to provide affordable access to ed. Despite the fact that the levels of Internet ac­ the Internet via broadband and to educate per­ cess are highest for households in Dutch regions, sons with the necessary skills to enable them to there is a clear north–south gradient, with high­ access and exploit the richness of the Internet. The er values of Internet access and use in northern European Council announced on 20 March 2009 Member States. The second pattern is a latitudinal further support for projects in the field of broad­ one. Regions in the west and east of the European band Internet as part of the European economic (13) http://europa.eu/rapid/ Union tend to have lower shares of Internet access recovery plan to tackle the global economic and pressReleasesAction.do?re and use than regions in the centre. Finally, urban financial crisis (13) and has set the goal of achiev­ ference=DOC/09/1&form at=HTML&aged=0&langu or densely populated regions reveal a higher share ing 100 % coverage of the population by 2013. age=EN&guiLanguage=en Methodological notes European statistical data on use of information and communication technologies have been avail- able since 2003. Harmonised data have been published since 2006 based on Regulation (EC) No 808/2004 of 21 April 2004 concerning Community statistics on the information society. The regula- tion describes two modules or areas of statistical data production: statistics on the use of ICT in en- terprises and statistics on ICT use in households and by individuals. Annual Commission regulations define the set of indicators for which data are collected by the EU Member States. Regional data on a limited list of indicators have been available at NUTS 1 level since 2006 as a voluntary contribution by the Member States and since 2008 on a mandatory basis. Some Member States provide regional data at NUTS 2 level on a voluntary basis. The data collection for each module is divided into a core part, i.e. access to ICT, and general use of ICT. Questions on access to ICT are addressed to the household, while questions on the use of ICT are answered by individuals within the household. Following the principles of the i2010 benchmarking framework, the model questionnaire includes an annual topic of special focus, i.e. e-government (2006), e-skills (2007), advanced services (2008), e-commerce (2009) and security (2010). The survey covers individuals aged 16–74 years and households with at least one member within this age range. The reference period is the first three months of the calendar year. The presentation of statistics on ICT use is restricted to a number of core indicators for which re- gional data is available. These regional indicators are ‘access to the Internet at home by household’, ‘access to the Internet via broadband by household’, ‘regular Internet users’, ‘persons who have never used the Internet’ and ‘e-commerce by individuals’. The term ‘access’ does not refer to ‘connectivity’, i.e. whether connections can be provided in the household’s area or street, but to whether anyone in the household was able to use the Internet at home. The term broadband connection refers to the speed of data transfer for uploading and download- ing data. Broadband requires a data transfer speed of at least 144 kbit/s. The technologies most widely used for broadband access to the Internet are digital subscriber line (DSL) or cable modem. Internet users are persons who have used the Internet within the last three months. Regular Internet users have used the Internet at least once a week within the reference period of three months. For the purpose of the households module, e-commerce via the Internet is defined as placing orders for goods or services via the Internet. Purchases of financial investments, for example shares, confirmed reservations for accommodation and travel, participation in lotteries and betting and obtaining payable information services from the Internet or purchases via online auctions, are in- cluded in the definition. Orders via manually typed e-mails are excluded. Delivery or payment via electronic means is not a requirement for an e-commerce transaction. Eurostat regional yearbook 2009 99
  • 93. Science, technology and innovation
  • 94. 8 Science, technology and innovation Introduction 2010, most countries have specified their own tar­ gets in national reform programmes. The national The Lisbon European Council (2000) and the Bar­ targets range from 0.75 % in the case of Malta to celona European Council (2002) both highlight­ 4 % for Finland and Sweden, and — if met — they ed the important role of research and develop­ will bring the average R & D performance in the ment (R & D) and innovation in the EU. Against EU to around 2.6 % by 2010. this background, the 2005 initiative ‘Working On the map, the largest cluster of regions with together for growth and jobs’ relaunched the a relatively high R & D intensity, i.e. above 2 %, Lisbon strategy. ‘Knowledge and innovation can be found in southern Germany, spreading for growth’ thus became one of the three main out to Austria and through Switzerland into areas for action in the new Lisbon partnership France all the way to the Pyrenees. It is also clear for growth and jobs, which put science, technol­ from the map that regions containing capital ogy and innovation at the heart of EU national cities tend to be relatively R & D intensive. The and regional policies. regions containing the capitals Sofia, Bucureşti, The concept of a European research area (ERA), Budapest, Warszawa, Wien, Madrid and Roma introduced in 2000 as the contribution by re­ are the most R & D intensive regions in their re­ search policy to the broader Lisbon strategy, has spective countries. This fact is further illustrated also been a highly successful tool for moving re­ by the region that surrounds Praha, and to some search higher up on the political agenda. Eight extent by the region containing Paris, which is years of developing ERA have transformed it the second most R & D intensive of the French from a theoretical concept to a practical policy regions. However, when ranking the German re­ approach for improving the efficiency and effec­ gions, Berlin comes only sixth, even though its tiveness of fragmented research efforts and sys­ R & D intensity is well above 3 %. tems in Europe, increasing the attractiveness of Europe to researchers and research investment, Regions with a lower R & D intensity are found and raising the coherence and synergies between mainly in the southern and eastern parts of the research policy and other EU policies in order to EU. It is also here that we find many of the re­ implement the renewed Lisbon strategy. gions with the fastest­growing R & D intensities. Of the 30 regions that have recorded an annual This chapter presents statistical data and indica­ average growth rate of over 10 % since 2000, six tors based on a number of data sources available are Greek, two are Czech, two are Spanish, one at Eurostat, which provide statistical information is Portuguese and one is Romanian. Estonia, in order to compare the evolution and composi­ Malta and Slovenia are also among these fast­ tion of science, technology and innovation (STI) growing regions. in European regions and their position relative to other regions. The domains covered are: research R & D personnel is the other basic R & D in­ and development (R & D); patents; high technol­ put indicator (besides R & D expenditure) that ogy; and human resources in science and tech­ measures the human resources going directly nology (HRST). into R & D activities. R & D personnel comprise three categories: researchers, technicians and More regional indicators for science, technology other support staff. Of these, researchers are the and innovation are available on the Eurostat web­ most important in terms of R & D activities. They page under ‘Science and technology’. are professionals engaged in the conception or creation of new knowledge, products, processes, Research and development methods and systems, and in the management of the projects concerned. Increasing investment in R & D is one of the key Map 8.2 shows the regional pattern of distribu­ objectives of the Lisbon strategy. A substantial tion of researchers (expressed as a percentage of increase in investment in R & D is important as a total employment) across Europe. In 15 European means of providing a significant boost to the in­ regions over 1.8 % of all persons employed are re­ dustrial competitiveness of the European Union. searchers. Trøndelag (Norway) is the leading re­ Some 20 of the regions shown in Map 8.1 have gion, with a share of 3.16 %, which is more than an R & D intensity above the 3 % target speci­ three times higher than the EU­27 average. This fied in the Lisbon strategy for the EU as a whole. group also comprises one other Norwegian re­ Although this target remains the EU objective for gion, four German regions, three Finnish regions 102 Eurostat regional yearbook 2009
  • 95. Science, technology and innovation 8 Map 8.1: Total R & D expenditure as a percentage of GDP, all sectors, by NUTS 2 regions, 2006 Eurostat regional yearbook 2009 103
  • 96. 8 Science, technology and innovation Map 8.2: Researchers as a percentage of persons employed, all sectors, by NUTS 2 regions, 2006 104 Eurostat regional yearbook 2009
  • 97. Science, technology and innovation 8 and one region each from the Czech Republic, presence of the head offices of companies and gov­ Austria, Slovakia, Belgium, Iceland and France. ernment institutions. However, another factor is Sweden, for which only data at the country level that capitals are often big cities that naturally con­ is available, also has more than 1.8 % researchers tain large groups of higher education facilities, and in total employment. In a further 48 regions, the thus a large number of highly educated people. This concentration of researchers is above the EU­27 makes these and the nearby regions safe places for average (0.9 %) and, once again, most of these re­ new companies to open up businesses, thanks to gions (18) are in Germany. the supply of highly skilled human resources that are already present in the region. At the same time, The number of researchers as a percentage share of highly skilled people can be attracted to larger cit­ all persons employed in the foremost region of nine ies as they are also more likely to find a skilled job countries is below the EU­27 average (0.9 %): these that meets their requirements in a region where countries are Bulgaria, Cyprus, Latvia, Lithuania, there are many companies. Malta, the Netherlands, Slovenia, Croatia and Turkey. The regions with the lowest concentration This urban concentration of human resources of researchers are in Bulgaria (Severozapaden, employed in science and technology can be seen with 0.08 %), Romania (Sud­Est, with 0.13 %), in Map 8.3, by looking at the capital regions and the Netherlands (Friesland, with 0.13 %) and the also at two of the three large regional clusters Czech Republic (Severozápad, with 0.15 %). with shares of HRSTO exceeding 30 %. This par­ ticular cluster stretches from the Italian region Regional disparities exist not only between coun­ Lazio in the south up through Switzerland to the tries but also between regions of the same country. south­western parts of Germany. In the main, the The largest difference between the leading region regions in this cluster are very densely populated, and the bottom region is observed in the Czech as are the regions in the second distinct cluster Republic (2.88 percentage points between Praha which contains the regions of the Benelux coun­ and Severozápad). Austria, Germany, Finland, tries. The third cluster is in the Scandinavian Slovakia and Norway also present disparities of countries, where the regions — apart from the more than 2 percentage points. At the other end capital regions — are very sparsely populated. In of the scale, the smallest gap is in Ireland, with Scandinavia we also find the regions with the sec­ 0.03 percentage points, followed by the Nether­ ond, third and fourth­highest share of HRSTO; lands with 0.73 percentage points. they are Stockholm in Sweden (48 %), Oslo og Akershus in Norway (48 %) and Hovedstaden in Human resources in science Denmark (44 %) respectively. The highest share, however, is found in Praha (Czech Republic), and technology where 52 % of the labour force are HRSTO. It is interesting to note that, two years previously, the Without sufficient amounts of human resources top three regions were the same and that their there can be no growth. As science and technol­ shares have since increased. The share for Praha ogy have been recognised as key fields for Euro­ has increased the most, up from 47 % of HRSTO pean development, it is therefore of considerable two years ago. Stockholm and Oslo og Akershus importance for policymakers at a regional level have each increased their shares by 2 percentage (as well as at EU and national levels) to analyse points during the past two years. the stock of highly qualified people. One way to measure the concentration of highly qualified people in the regions is by looking at High-tech industries and the human resources in science and technology knowledge-intensive services (HRST). HRST defines those who have completed The statistics on high­tech industries and knowl­ a tertiary level of education and/or are employed edge­intensive services include employment data in a science and technology occupation where a by sectors of economic activity. Based on the tertiary level of education is normally required. ratio of R & D expenditure to GDP (R & D inten­ HRSTO is a sub­group of HRST denoting those sity), sectors can be classified into more specific employed in a science and technology occupation. subsectors so as to analyse employment in sci­ As Map 8.3 shows, there is an urban concentration ence and technology. Two subsectors that are of of HRSTO in particular around the capital regions. great importance to science and technology are In such regions there is often a high concentration the high­tech manufacturing and medium high­ of highly qualified jobs, for example owing to the tech manufacturing sectors, even though they Eurostat regional yearbook 2009 105
  • 98. 8 Science, technology and innovation Map 8.3: Human resources in science and technology by virtue of occupation (HRSTO), by NUTS 2 regions, 2007 Percentage of active population 106 Eurostat regional yearbook 2009
  • 99. Science, technology and innovation 8 Map 8.4: Employment in high- and medium high-tech manufacturing, by NUTS 2 regions, 2007 Percentage of total employment Eurostat regional yearbook 2009 107
  • 100. 8 Science, technology and innovation accounted for only 1.1 % and 5.6 % respectively ing from Franche­Comté (France) in the west to of EU employment in 2007. High­tech manu­ Észak­Magyarország (Hungary) in the east. Stutt­ facturing includes, for example, manufacture of gart and Braunschweig (both Germany) are the computers, televisions and medical instruments, only regions with more than one in five employed while medium high­tech manufacturing includes, persons working in these subsectors; both regions for example, manufacture of chemicals, machin­ have a share of 22 %. In fact, the seven leading re­ ery and transport equipment. gions are all German (in addition to Stuttgart and Braunschweig, they include Karlsruhe, Tübingen, Map 8.4 shows employment in the two subsectors Rheinhessen­Pfalz, Unterfranken and Freiburg). — high­tech and medium high­tech manufactur­ ing — as a percentage of total employment. Em­ Furthermore, Map 8.4 shows a cluster of four Ital­ ployment in these two subsectors is very high in ian regions (Piemonte, Emilia­Romagna, Lom­ the central European regions, in a band stretch­ bardia and Veneto) with relatively high shares Table 8.1: 25 leading regions in employment in knowledge-intensive services and high-tech knowledge- intensive services, 2007 Knowledge-intensive services (KIS) High-tech knowledge-intensive services (High-tech KIS) Total Total % of total % of total number number employment employment (1 000s) (1 000s) Berkshire, Buckinghamshire and Inner London (UK) 59.7 785 101 8.9 Oxfordshire (UK) Stockholm (SE) 55.8 564 84 8.3 Stockholm (SE) Oslo og Akershus (NO) 54.1 317 43 7.4 Oslo og Akershus (NO) Hovedstaden (DK) 51.7 451 44 7.0 Praha (CZ) Åland (FI) 49.9 7 204 6.7 Comunidad de Madrid (ES) Zürich (CH) 49.7 365 52 6.6 Bedfordshire and Hertfordshire (UK) Berlin (DE) 49.5 738 56 6.4 Hovedstaden (DK) Noord-Holland (NL) 49.1 674 21 6.4 Bratislavský kraj (SK) Utrecht (NL) 48.0 299 33 6.2 Auvergne (FR) Övre Norrland (SE) 47.9 119 29 6.2 Prov. vlaams Brabant (BE) Surrey, East and West Sussex (UK) 47.9 614 77 6.2 Közép-Magyarország (HU) Sydsverige (SE) 47.4 306 135 6.1 Lazio (IT) Östra Mellansverige (SE) 47.3 347 56 6.1 Hampshire and Isle of Wight (UK) Région de Bruxelles-Capitale/ 47.2 180 133 6.1 Outer London (UK) Brussels Hoofdstedelijk Gewest (BE) Mellersta Norrland (SE) 47.2 85 11 6.0 Flevoland (NL) Outer London (UK) 47.2 1 037 36 5.9 Utrecht (NL) Nord-Norge (NO) 47.0 109 76 5.8 Inner London (UK) Groningen (NL) 46.8 132 103 5.8 Darmstadt (DE) Berkshire, Buckinghamshire and 46.5 529 297 5.7 Île de France (FR) Oxfordshire (UK) Prov. Brabant Wallon (BE) 46.1 71 74 5.7 Etelä-Suomi (FI) Gloucestershire, Wiltshire and Bristol/ 46.1 529 70 5.6 Karlsruhe (DE) Bath area (UK) Gloucestershire, Wiltshire and Bristol/ västsverige (SE) 45.8 420 62 5.4 Bath area (UK) Région lémanique (CH) 45.5 330 110 5.4 Oberbayern (DE) Île de France (FR) 45.5 2 356 79 5.3 Berlin (DE) Trøndelag (NO) 45.4 99 8 5.2 Prov. Brabant Wallon (BE) 108 Eurostat regional yearbook 2009
  • 101. Science, technology and innovation 8 of employment in high­ and medium high­tech One interesting feature here is that three of the manufacturing. In the other parts of Europe only five regions with the highest shares of employ­ three regions have more than 10 % of their em­ ment in high­tech KIS in 2007 were also among ployment in high­ or medium high­tech manu­ the five highest in 2002, when Stockholm (Swe­ facturing; they are Vest (Romania), Bursa (Tur­ den) was the leading region, followed by Berk­ key) and Herefordshire, Worcestershire and shire, Buckinghamshire and Oxfordshire (United Warwickshire (United Kingdom). Kingdom). Bratislavský kraj (Slovakia) followed Another subsector of interest is knowledge­in­ in third place and Île­de­France (Paris) in fourth tensive services (KIS). KIS can be further split — which was somewhat surprising compared to into different categories, of which high­tech its 19th position in 2007. Oslo og Akershus was in knowledge­intensive services (high­tech KIS) is a fifth place in 2002. subsector of special interest when analysing em­ ployment in science and technology. Examples of Patents services in high­tech KIS include computer and related activities, and research and development. Indicators based on patent statistics are widely KIS, on the other hand, is broader and, in addi­ used in order to assess the inventive and innova­ tion to high­tech KIS, also includes water and air tive performance of a country or a region. The transport, financial intermediation, education current emphasis on innovation as a source of in­ and health and social work, for example. dustrial competitiveness has raised awareness of patents. Patents are used to protect R & D results, Table 8.1 shows the 25 leading regions in KIS but they are just as significant as a source of tech­ and in high­tech KIS. As KIS generally attracts nical information, which may avoid reinventing highly educated persons, there is a similar pat­ and redeveloping ideas because of a lack of in­ tern to that seen in Map 8.3 for human resources in science and technology (HRST), namely that formation. Patent statistics at regional level are urban regions, especially capital regions, often confined to applications to the European Patent exhibit high shares of employment in KIS and Office (EPO). The data are regionalised by linking high shares of HRST. postcodes or city names to the nomenclature of territorial units for statistics (NUTS). Looking at Table 8.1, the four leading regions were all capital regions, with Inner London (United Map 8.5 illustrates the regional patenting ac­ Kingdom) showing the highest percentage of tivity in the EU. In most European countries, KIS (59.7 %). By far the majority of the leading national patenting is concentrated in certain regions are urban, or within commuting distance regions. Regions that are active in patenting of an urban region. The one exception is Åland, are often situated close together, i.e. they form an autonomous province of Finland consisting of economic clusters. This is the case, for example, islands. As shipping is an important part of this in the southern part of Germany, the south­east region’s economy, it is one of the major reasons of France and the north­west of Italy. The most behind the high share of KIS in Åland. active patenting regions (with 100 to 300 ap­ plications and more than 300 applications per Another feature that stands out is the fact that six million inhabitants) are situated in the Nordic of Sweden’s eight regions are represented among countries and in the centre of the EU­27. the 25 regions with the highest shares of KIS. This can be explained in part by the fact that Swe­ Patent activity varies not only across countries den has a large public sector, which includes the but also across regions. In 2004, Île­de­France education and healthcare sectors. Looking at the (France) was the foremost EU region in terms of right­hand side of the table, which shows the 25 total number of patent applications (3 297), while leading regions in high­tech KIS, only one Swed­ Noord­Brabant (Netherlands) was in the lead for ish region remains. This region, the Swedish capi­ patent applications per million inhabitants (761). tal region Stockholm, had 8 % of its employment In Germany large disparities were observed be­ in high­tech KIS, which is the second­highest tween the leading region of Stuttgart in the south share after Berkshire, Buckinghamshire and Ox­ and the lowest­performing region of Sachsen­ fordshire (United Kingdom), with 9 %. Further Anhalt in the east. Regional discrepancies are examination shows that 13 of the 25 regions with even wider in the Netherlands, between Noord­ the highest percentage of employment in high­ Brabant and Friesland. Regional disparities, how­ tech KIS were capital regions (including both In­ ever, are much lower in countries with comparable ner London and Outer London). national averages, such as Finland and Sweden. Eurostat regional yearbook 2009 109
  • 102. 8 Science, technology and innovation Map 8.5: Patent applications to the EPO per million inhabitants, by NUTS 2 regions, 2004 110 Eurostat regional yearbook 2009
  • 103. Science, technology and innovation 8 Conclusion Relevant and meaningful indicators on science, With the aid of the relevant statistics and indica­ technology and innovation are of paramount im­ tors, this chapter has demonstrated the progress portance for informing policymakers about where made in recent years on research and develop­ European regions stand on the path towards more ment activities in European regions. Wide use knowledge and growth. This information is also is also made of statistics on high­tech industries necessary in order to gain a better picture of how and knowledge­intensive services, patents and regions are evolving, compared between them­ human resources in science and technology in selves both at European level and worldwide. order to complete this regional picture. Methodological notes The data in the maps and tables in this chapter are, wherever possible, by NUTS 2 regions. Data are extracted from the ‘Science, technology and innovation’ domain and, more specifically, from the sub-domains ‘Research and development’, ‘Human resources in science and technology’, ‘High- technology industries and knowledge-intensive services’ and ‘Patents’. Statistics on research and development are collected by Eurostat under the legal requirements of Commission Regulation (EC) No 753/2004, which determines the data set, breakdowns, frequen- cy and transmission delays. The methodology for national R & D statistics is further laid down in the Frascati manual: proposed standard practice for surveys on research and experimental development (OECD 2002), which is also used by many non-European countries. The statistics on Human resources in science and technology (HRST) are compiled annually, based on microdata extracted from the EU labour force survey (EU LFS). The basic methodology for these statistics is laid down in the Canberra manual, which lists all the HRST concepts. The data on High-technology industries and knowledge-intensive services are compiled an- nually, based on data collected from a number of official sources (EU LFS, structural business statistics, etc.). The high-technology employment aggregates are defined in terms of R & D in- tensity, calculated as the ratio of R & D expenditure on the relevant economic activity to its value added, and based on the Statistical Classification of Economic Activities in the European Com- munity (NACE). Recently, the NACE was revised from Rev. 1.1 to Rev. 2, which led to changes in the high-technology and knowledge-intensive sectors. However, the statistics in this chapter are still based on NACE Rev. 1.1. Finally, the data on Patent applications to the EPO are compiled on the basis of microdata re- ceived from the European Patent Office (EPO). The patent data reported include the patent applica- tions filed at the EPO during the reference year, classified by the inventor’s region of residence and in accordance with the international patents classification of applications. Patent data are regional- ised using procedures linking postcodes and/or place names to NUTS 2 regions. Patent statistics published by Eurostat are almost exclusively based on the European Patent Office (EPO) Worldwide Statistical Patent Database, Patstat, developed by the EPO in 2005, using its patent data collection and its knowledge of patent data. The data are largely taken from the EPO’s master bibliographic database, DocDB, which is also known as the EPO Patent Information Resource. It in- cludes bibliographic details on patents filed at 73 patent offices worldwide and contains more than 50 million documents. It covers a large number of fields included in patent documents, such as ap- plication details (claimed priorities, application and publication), technology categories, inventors and applicants, title and abstract, patent citations and non-patent literature text. Eurostat regional yearbook 2009 111
  • 104. Education
  • 105. 9 Education Introduction of individuals participating in education irrespec­ tive of the level in which they are enrolled. In 2007 Education, vocational training and lifelong learn­ roughly 21 % of the total European population (the ing play a vital role in the economic and social 27 EU Member States and the candidate and EFTA strategy of the European Union. The relaunched countries) was enrolled in education. It means that Lisbon process, implemented by the ‘Education one person in five is involved in formal education. and training 2010’ programme, cannot be com­ This indicator is influenced by the age distribution pleted without efficient use of resources, quality of the population: ‘old’ populations have relatively improvements in education and training systems low enrolment rates and, conversely, if the age dis­ and implementation of a coherent lifelong learn­ tribution of the population under consideration is ing strategy at national level. Securing education younger the figures are higher. and lifelong learning opportunities in every re­ Some of the regions with the highest percentages gion and for every inhabitant, wherever they live, of students in education are around capital cities in is one of the cornerstones of the national strat­ eastern Europe such as Praha, Bucureşti, Bratislava egies to achieve this goal. Eurostat’s regional sta­ and Lubjiana. These cities represent the focal point tistics on enrolment in education, educational of their region in terms of education. Some coun­ attainment and participation in lifelong learning tries such as Belgium, Sweden, Norway, Iceland make it possible to measure progress at regional and Lithuania display figures that are higher than level and monitor regions lagging behind. anywhere else, whereas in Denmark, in the north Comparable regional data on enrolment in edu­ of Italy and in some regions of Spain, Greece and cation from 1998 onwards are available from Germany the rates are relatively low, below 18 %. Eurostat’s website, while data on educational Furthermore, the differences within the countries attainment levels and participation in lifelong are at times small, as in Poland and France, while in learning are available for the period since 1999. other countries there are noticeable dissimilarities, The Eurostat website contains regional informa­ as in Italy (northern regions compared to southern tion on the total number of enrolments by level regions), Spain (north­west regions compared to of education and sex, and by age and sex plus in­ the others), Germany (eastern area compared to the dicators relating enrolments in education to the western regions) and Greece (where the southern total population. Data on enrolments in educa­ area has lower rates than the rest of the country). tion are generally available for the 15 ‘old’ Mem­ ber States for the period since 1998 and for the 12 Participation of 4-year-olds ‘new’ Member States plus Norway since 2000 or 2001. Information on the educational attainment in education of the population and on participation in lifelong Learning begins at birth. The period from birth learning is available for all the Member States and to entry into primary education is a critical form­ also for Norway. ative stage for the growth and development of children. The learning outcomes, knowledge and Students’ participation in education skills of primary education are stronger when ap­ propriate learning and development occur in the In its broad sense, education refers to any act or years preceding regular schooling. experience that has a formative effect on the mind, The purpose of pre­primary education is to prepare character, or physical ability of an individual. In its children physically, emotionally, socially and men­ technical sense, education is the process by which tally to enter primary school, giving them the abil­ society, through schools, colleges, universities and ity and the skills to enter the first level of the edu­ other institutions, deliberately transmits its cultur­ cational system. This preparation is considered the al heritage and its accumulated knowledge, values foundation for further educational development. and skills from one generation to another. In December 2008, the European Commission This chapter gives evidence of the educational en­ proposed a new benchmark, whereby 90 % of rolment of the regional populations as well as their 4­year­olds should participate in pre­primary educational attainment levels and their participa­ education by 2020. The aim of this proposal is to tion in lifelong learning, reflecting how education underpin progress towards the 2002 Barcelona touches persons throughout life in all regions. Summit conclusion of increasing participation Map 9.1 shows the number of students in all levels in pre­primary education to 90 % of all children of education as a percentage of the total population between 3 years of age and the beginning of com­ at regional level. This indicator reveals the number pulsory education. 114 Eurostat regional yearbook 2009
  • 106. Education 9 Map 9.1: Students in all levels of education, as a percentage of total population, by NUTS 2 regions, 2007 ISCED levels 0–6 Eurostat regional yearbook 2009 115
  • 107. 9 Education The EU­27 rate of participation is already approach­ Map 9.3 shows the percentage of students en­ ing the target (88.5 % in 2007), but this overall high rolled in upper secondary education (ISCED 3 level of participation masks significant variations level) and post­secondary non­tertiary education between the figures for individual countries. (ISCED level 4) as a percentage of the population aged 15–24 years old in the region. When the EU­27 Member States and the candi­ date and EFTA countries are taken into account, The task of general upper secondary education approximately 73 % (in 2007) of the European is to provide extensive all­round learning and 4­year­olds were enrolled in pre­primary and to continue the teaching and educational task of primary education. basic education. The objective is often to offer suf­ ficient skills and knowledge with a view to fur­ The indicator shown here examines the partici­ ther study. It would normally give access to uni­ pation in early childhood education at regional versity­level programmes. In contrast, vocational level (NUTS 2) by measuring the percentage of streams often provide training for specific labour 4­year­olds who are in either pre­primary or pri­ market occupations. mary education. By far the majority of them at­ tend pre­primary schooling (which in many cases Students generally start upper secondary educa­ is also non­compulsory). A 4­year­old child can tion at the age of 15 to 17, at the end of full­time be enrolled either in pre­primary or in primary compulsory education, and finish it three or four school. Data highlight that most of them attend years later. The starting/finishing ages and the pre­primary school. Ireland and the United King­ age range depend on the national educational dom are the only countries where the proportion programmes. However, students can normally of 4­year­olds in primary education is relevant. attend upper secondary education programmes relatively close to where they have grown up. For At the age of 4 most children in the European this indicator a broad age group has been defined Union are therefore in pre­primary education to cover the relatively wide spread in ages, de­ (80 %), which is generally available from at least pending on the country. 3 to 4 years of age in the EU Member States. Only 5 % of 4­year­olds are enrolled in primary educa­ Post­secondary non­tertiary education pro­ tion, of which 89 % are in the United Kingdom grammes (ISCED level 4) lie between the upper and 11 % in Ireland. secondary and tertiary levels of education from an international point of view, even though they might Enrolment in pre­primary education is almost clearly be considered upper secondary or tertiary always voluntary. Nevertheless, many countries programmes in a national context. Although their have participation rates of 100 % or close to this. content may not be significantly more advanced Map 9.2 shows that in some countries, such as than upper secondary programmes, they serve to Denmark, France, Iceland, Italy, Malta, the Neth­ expand the knowledge of participants who have al­ erlands and Spain, and in regions such as Vlaams ready gained an upper secondary qualification. Gewest (Belgium), the participation of 4­year­ In 2007 more than 38 % of the population aged olds in education is nearly 100 %. In contrast, in 15–24 years in the EU­27 was enrolled in upper Croatia, Ireland, Macedonia, Switzerland, Turkey secondary and post­secondary education. and most of Poland and Finland less than 50 % of the 4­year­olds are enrolled in education. No sig­ The highest rates are found in Belgium, Finland, nificant regional differences within the countries Iceland, the Praha region, some regions of Sweden can be noted except for England, Germany and (Mellersta Norrland and Norra Mellansverige), Portugal, where there are some slight differences Valle d’Aosta Basilicata and Friuli­Venezia Giulia (Italy), Közép­Magyarország and Dél­Alföld in levels of participation between the regions. (Hungary) and the Salzburg region (Austria). Taking a wider look at the map, the Nordic coun­ Students in upper secondary tries (Norway, Sweden, Denmark, Finland and education and post-secondary Iceland) show a common pattern with high per­ non-tertiary education centages. Many parts of Europe (such as France, Germany, Switzerland, the Netherlands, Poland, At the age of 16 young people are faced with the Slovakia, Slovenia, Croatia, Romania, Bulgar­ choice of whether to remain in education, go into ia and Greece) have low rates of participation, vocational training or seek employment. Over the whereas Italy, Austria, the Czech Republic and last decade young people have become more likely Hungary show high rates. The United Kingdom to continue with their education at this age. is split in two parts — England (high rates) and 116 Eurostat regional yearbook 2009
  • 108. Education 9 Map 9.2: Participation rates of 4-year-olds in education, by NUTS 2 regions, 2007 At pre-primary and primary education (ISCED levels 0 and 1). Percentage Eurostat regional yearbook 2009 117
  • 109. 9 Education Map 9.3: Students at upper secondary and post-secondary non-tertiary education, as a percentage of the population aged 15 to 24, by NUTS 2 regions, 2007 ISCED levels 3 and 4 118 Eurostat regional yearbook 2009
  • 110. Education 9 the rest (lower rates). In contrast, the Iberian pe­ (Hungary, Budapest region), Dytiki Ellada (Greece) ninsula (Spain and Portugal), Turkey, Lithuania, and Mazowieckie, including the capital Warszawa Malta, Cyprus, Macedonia and some regions in (Poland), the figures are more than 100 %, signify­ Greece have very low participation rates. ing a large student population among the younger cohorts. Many of these regions are around capital cities where big universities are located. Students in tertiary education Relatively few regions have tertiary­level student Tertiary education refers to levels of education that populations below 30 % of the 20–24­year­old age are provided by universities, vocational universi­ group and those that do are spread out among many ties, institutes of technology and other institutions Member States. Many of them have features which that award academic degrees or professional certi­ easily explain the low percentages, such as being in fications. Access to tertiary­level educational pro­ the rural parts of a country or being islands. Most grammes typically requires successful completion of these regions have little, if any, tertiary­educa­ of an upper secondary level and/or a post­second­ tion infrastructure, and the students have to move ary non­tertiary level programme. away in order to obtain higher education. The levels of education can be largely theoretical­ ly based and intended to provide sufficient quali­ Tertiary educational attainment fications for gaining entry into advanced research programmes and professions with high skills re­ The proportion of the population aged 25–64 years quirements (ISCED level 5A) or more practical, who have successfully completed university or uni­ technical and employment­oriented (ISCED level versity­like (tertiary­level) education is shown in 5B), or can lead to an advanced research qualifi­ Map 9.5. The pattern in this map is similar to the cation (ISCED level 6, PhD­like studies). pattern in Map 9.4. In most countries the highest proportions of tertiary­level attainment are found Map 9.4 shows the number of students in tertiary in the same regions as the students in tertiary edu­ education (ISCED levels 5 and 6) as a percentage of cation, i.e. where the tertiary education institutions the population aged 20–24 years old in the region. as well as the largest enterprises and institutions The student population is related to the population and their providers are located. The demographic in the relevant age group in order to see the relative profile of a region also has some influence on the size of the student population at regional level. educational attainment levels, as younger genera­ This indicator is based on data on where the stu­ tions tend to have higher educational attainment dents are studying, not on where they come from levels than older generations. In 2007 only 23 re­ or live. Regions with universities and other terti­ gions in the EU had a proportion of persons with ary education institutions, often big cities, there­ higher education above 35 %; these included large fore tend to have high percentages of students, as cities such as Bruxelles/Brussel, London, Paris, students often travel or move to them for higher Helsinki, Stockholm, Madrid and Amsterdam; education. This is in contrast to younger pupils Oslo (Norway), Geneva and Zurich (Switzerland) and students in lower levels of education, who also fell into this category. In EU Member States usually attend a school close to where they live. such as Ireland, Sweden, Finland, the Netherlands, Therefore, the first thing which this indicator Belgium and Germany educational attainment lev­ shows is an uneven distribution of higher educa­ els are generally high across the whole country. The tion institutions across regions (and not uneven regions with the lowest percentages of people with participation in higher education by region). tertiary education are largely concentrated in the rural parts of 10 EU countries, with a significant In 2007, 58 % of the population aged 20–24 years contrast with their larger cities: this is this case in in the European Union was in tertiary education. Portugal, as well as Romania, Croatia and Turkey, Some countries, such as Malta, Cyprus and Lux­ and to a lesser extent Bulgaria, the Czech Republic, embourg, have relatively low rates because many Greece, Italy, Hungary, Poland and Slovakia and students at tertiary level go abroad to study and includes islands such as Sardegna and Sicilia (Italy), hence are not included in the statistics of their home Açores and Madeira (Portugal) and Malta. countries but in the countries where they study. In the regions with the highest percentages, students Lifelong learning in tertiary education outnumber the population of 20–24­year­olds. In regions such as Praha, Wien, Continuous refreshing of the skills of the labour Région de Bruxelles­Capitale/Brussels Hoofd­ force via lifelong learning has repeatedly been stedelijk Gewest, Brabant Wallon (south of Brus­ underlined in EU policies following up the Lis­ sels), Bratislava, Bucureşti, Közép­Magyarország bon objectives. This is reflected in the ‘Education Eurostat regional yearbook 2009 119
  • 111. 9 Education Map 9.4: Students in tertiary education, as a percentage of the population aged 20 to 24 years old, by NUTS 2 regions, 2007 ISCED levels 5 and 6 120 Eurostat regional yearbook 2009
  • 112. Education 9 Map 9.5: Educational attainment level, by NUTS 2 regions, 2007 Percentage of the population aged 25–64 having completed tertiary education Eurostat regional yearbook 2009 121
  • 113. 9 Education Map 9.6: Lifelong learning, by NUTS 2 regions, 2007 Percentage of the adult population aged 24 to 64 participating in education and training during the four weeks preceding the survey 122 Eurostat regional yearbook 2009
  • 114. Education 9 and training 2010’ programme as well as in the are usually also the regions with the highest levels European employment strategy, which empha­ of educational attainment (see previous section) sises the need for comprehensive lifelong learn­ and the regions where the supply of education ing strategies to ensure the continual adaptability and training activities is wider and continuing and employability of workers. Adult learning can vocational training activities are most frequent be measured via the labour force survey through (e.g. in large enterprises). On the other hand, EU specific questions on participation in education Member States on the fringes of the continent, or training activities during the four weeks pre­ such as Greece, Hungary, Malta, Poland, Portu­ ceding the survey. The data concern the age group gal, Romania and Slovakia, and also Croatia and 25–64 years for all education or vocational train­ Turkey generally have low participation rates in ing, whether or not relevant to current or future education and training for the age group 25–64. employment. As Map 9.6 shows, participation in education and training is largely nationally pro­ Conclusion filed. In fact, this is the education indicator show­ ing the smallest regional variation compared with The examples given above are intended merely to the others discussed earlier in this chapter. The highlight a few of the many possible ways of ana­ participation is high in every region of Denmark, lysing education and lifelong learning in the re­ the Netherlands, Slovenia, Finland, Sweden and gions of the EU and do not constitute a detailed the United Kingdom and also in Iceland, Norway analysis. We hope, however, that they will encour­ and Switzerland. Within countries, the highest age readers to probe deeper into all the data on rates of participation in education and training education freely available on the Eurostat website are often found around the largest cities, which and to make many further interesting discoveries. Methodological notes The maps are presented at NUTS 2 level, except for the educational enrolment indicators for Ger- many and the United Kingdom, where data are available at NUTS 1 level only. In Croatia, Switzerland and Turkey no data on enrolments by age are available at regional level. Hence only national figures have been shown for these countries. As the structure of education systems varies widely from one country to another, a framework for assembling, compiling and presenting both national and international education statistics and in- dicators is a prerequisite for international comparability. The International Standard Classification of Education (ISCED) provides the classification basis for collecting data on education. ISCED-97, the current version of the classification introduced in 1997, is built to classify each educational pro- gramme by field of education and by level. ISCED-97 presents standard concepts, definitions and classifications. A full description of it is available on the Unesco Institute of Statistics website (http://www.uis.unesco.org/ev.php?ID=3813_201&ID2=DO_ TOPIC). Qualitative information about school systems in the EU Member States is organised and dissemi- nated by Eurydice (www.eurydice.org) and covers, for example, age of compulsory school attend- ance and numerous issues relating to the organisation of school life in the Member States (decision- making, curricula, school hours, etc.). The statistics on enrolments in education include enrolments in all regular education programmes and all adult education with content similar to regular education programmes or leading to qualifi- cations similar to the corresponding regular programmes. Apprenticeship programmes are includ- ed except those which are entirely work-based and which are not supervised by any formal educa- tion authority. The data source used for Maps 9.1 to 9.4 are two specific Eurostat tables which form part of the so-called UOE (UIS-Unesco, OECD and Eurostat) data collection on education systems. Information about the UOE data collection can be found at http://circa.europa.eu/Public/irc/dsis/ edtcs/library?l=/public/unesco_collection&vm=detailed&sb=Title. The statistics on educational attainment and participation in lifelong learning are based on the EU labour force survey (LFS), which is a quarterly sample survey. The indicators refer to the annual aver- age of quarterly 2007 data. The educational attainment level reported is based on ISCED-97. Partici- pation in education and training (lifelong learning) includes participation in all kinds of education and training activities during the four weeks prior to the survey. Eurostat regional yearbook 2009 123
  • 115. Tourism
  • 116. 10 Tourism Introduction lection of statistical information in the field of tourism. This includes data both on accommoda­ Tourism is an important and fast­evolving eco­ tion capacity and its utilisation and on the travel nomic factor in the European Union, occupying behaviour of the population. The travel behaviour large numbers of small and medium­sized busi­ data are, however, only available at national level. nesses. Its contribution to growth and employ­ In contrast, the data collected on accommodation ment varies widely across the EU regions. Par­ capacity and its utilisation are also available by ticularly in rural regions, usually peripheral to region. The regionalised data are outlined below. the economic centres of their countries, tourism is often one of the main sources of income for the It is important to point out that the statistical def­ population and a prominent factor in creating inition of tourism is broader than the common, and securing an adequate level of employment. everyday definition. It encompasses not only pri­ vate travel but also business travel. This is primar­ Tourism is a typical cross­cutting industry. Ser vices ily because it views tourism from an economic to tourists involve a variety of economic branches: perspective. Private travellers and business trav­ hotels and other accommodation, gastronomy ellers have broadly similar consumption patterns. (restaurants, cafes, etc.), the various transport op­ They both make significant demands on trans­ erators and also a wide range of cultural and recre­ port, accommodation and restaurant services. To ational facilities (theatres, museums, leisure parks, the providers of these services, it is of secondary swimming pools, etc.). In many tourism­oriented interest whether their customers are private tour­ regions the retail sector also benefits considerably ists or on business. Tourism promotion depart­ from the demand created by tourists in addition to ments, on the other hand, are keen to combine that of the resident population. the two aspects by emphasising the attractiveness Eurostat has been collecting data on the develop­ of conference locations as tourist destinations in ment and structure of tourism since 1995, pur­ their own right, and they give particular promi­ suant to Council Directive 95/57/EC on the col­ nence to this in their marketing activities. Figure 10.1: Top 20 EU-27 tourist regions, number of bedplaces by type of accommodation, by NUTS 2 regions, 2007 ES — Cataluña FR — Provence-Alpes- Côte d'Azur FR — Languedoc-Roussillon FR — Aquitaine FR — Rhône-Alpes IT — Veneto FR — Bretagne IT — Emilia-Romagna FR — Pays de la Loire ES — Andalucía IT — Toscana FR — Île de France ES — Illes Balears UK — West Wales and The Valleys IT — Lombardia FR — Poitou-Charentes HU — Közép-Magyarorszàg FR — Midi-Pyrénées IT — Lazio AT — Tirol 0 100 000 200 000 300 000 400 000 500 000 600 000 700 000 Hotels Campsites 126 Eurostat regional yearbook 2009
  • 117. Tourism 10 Accommodation capacity density) for the countries of Europe. This link with the number of inhabitants shows the rela­ Figure 10.1 shows the 20 NUTS 2 regions of the tive importance of tourism capacity per head of EU with the highest accommodation capacities, population. This indicator is therefore affected measured by the number of bedplaces in hotels not only by the number of available beds (bed­ and similar establishments and on campsites. places) but also by the population figure. It can be Numbers of pitches on campsites are multiplied seen that the highest bed densities are to be found by four to make them comparable with hotel ac­ primarily in coastal regions and on islands, but commodation capacity. This gives a theoretical also in most Alpine regions and in Luxembourg, number of bedplaces, assuming that four people together with its two neighbouring regions to the occupy the average pitch. east (Trier in Germany) and west (the Province of The ranking of the 20 regions with the largest ac­ Luxembourg in Belgium). commodation capacities reveals the dominance of three main tourist destinations in Europe, namely Overnight stays France, Italy and Spain. Nine of the 20 regions on this list are in France, five are in Italy and three are The central indicator for accommodation services in Spain. The United Kingdom, Hungary and Aus­ is the number of overnight stays in establishments. tria complete the list of the top regions for accom­ This figure reflects both the length of stay and the modation capacity, with one region each (West number of visitors. Furthermore, expenditure by Wales and The Valleys, Közép­Magyarország and tourists during their stay at their destination cor­ Tirol). It is clear that the strong position of the relates closely with the number of overnight stays. French regions on this list reflects a very heavy Figure 10.2 shows the 20 regions in Europe with the preponderance of campsite accommodation. highest numbers of overnight stays, broken down Map 10.1 shows the number of bedplaces in ho­ by domestic and foreign visitors. The dominance tels and on campsites per 1 000 inhabitants (bed in European tourism of Italy, Spain and France is Figure 10.2: Top 20 EU-27 tourist regions, number of nights spent in hotels and campsites, by NUTS 2 regions, 2007 Breakdown by residents and non-residents millions FR — Île de France ES — Cataluña ES — Illes Balears ES — Andalucía ES — Canarias IT — Veneto IT — Emilia-Romagna FR — Provence-Alpes Côte d'Azur IT — Toscana ES — Comunidad Valenciana AT — Tirol IT — Lazio IT — Lombardia FR — Rhône-Alpes FR — Languedoc-Roussillon DE — Oberbayern IT — Provincia autonoma Bolzano/Bozen FR — Aquitaine IT — Campania ES — Comunidad de Madrid 0 10 20 30 40 50 60 70 80 Residents Non-residents Eurostat regional yearbook 2009 127
  • 118. 10 Tourism Map 10.1: Number of bedplaces in hotels and campsites per 1 000 inhabitants, by NUTS 2 regions, 2007 128 Eurostat regional yearbook 2009
  • 119. Tourism 10 Map 10.2: Nights spent in hotels and campsites, by NUTS 2 regions, 2007 Eurostat regional yearbook 2009 129
  • 120. 10 Tourism even more pronounced for overnight stays than for average length of stay. This, however, depends on accommodation capacities; these three countries the character of the region. For example, urban re­ accounting for 18 of the 20 regions. At 68.7 million gions frequently tend to have very large numbers overnight stays, the Île­de­France region contain­ of visitors, but these visitors tend to stay for only ing the French capital Paris is well in the lead, fol­ a few days and nights. A big share of visitors to lowed by the four Spanish regions of Cataluña (56.4 these regions are often there on business. But even million), Illes Balears (50.9 million), Andalucía in the case of private tourists there is a trend to­ (48.6 million) and Canarias (48.5 million). Tirol in wards shorter stays. In contrast, stays are generally Austria, at 30.4 million overnight stays, and Ober­ substantially longer in the typical holiday regions bayern in Germany (23.4 million) with the Bavar­ visited chiefly for recreational purposes. To that ian metropolitan area of München are the only extent, an overview of average lengths of stay can regions on the list of 20 that are not in one of the also indicate the touristic nature of a region. three leading tourism countries mentioned before. Map 10.3 shows the NUTS 2 regions in Europe Map 10.2 gives an overview of numbers of over­ according to the average length of stay of visitors. night stays in the regions of Europe. Here, too, it is Once again, it can be seen that the holiday areas clear that the focus of European tourism is in the in the European Union with the greatest average Mediterranean. The Alpine regions also occupy length of visitor stays are very often maritime re­ a strong position. In addition to the abovemen­ gions. They either have extensive coastlines or are tioned five countries (Italy, Spain, France, Austria islands and therefore encircled by the sea. Of the and Germany) represented in the top 20 regions, 22 NUTS 2 regions where the average length of Croatia, the Netherlands, Portugal, Greece, Cy­ stay of visitors is five nights or more, only one is prus, the United Kingdom and the Czech Repub­ completely landlocked, namely the Italian Provin­ lic also have NUTS 2 regions with more than 10 cia Autonoma Bolzano/Bozen. The remaining 21 million overnight stays. are either island regions or have long coastlines. Average length of stay Tourism intensity The number of overnight stays in a region is based Another important indicator of the touristic na­ not only on the number of visitors but also on their ture of a region is tourism intensity. This serves Figure 10.3: Evolution of nights spent in hotels and campsites 2000–07 in the EU-27 Million nights EU-27 2 000 1 950 1 900 1 850 1 800 1 750 1 700 1 650 1 600 2000 2001 2002 2003 2004 2005 2006 2007 Evolution of nights spent Footnote: EE 2000, 2001; IE 2001; CY 2000, 2002; MT (only hotels) 130 Eurostat regional yearbook 2009
  • 121. Tourism 10 Map 10.3: Average length of stay in hotels and campsites, by NUTS 2 regions, 2007 Days Eurostat regional yearbook 2009 131
  • 122. 10 Tourism Map 10.4: Nights spent in hotels and campsites per 1 000 inhabitants, by NUTS 2 regions, 2007 132 Eurostat regional yearbook 2009
  • 123. Tourism 10 as an indicator of the relative importance of tour­ Tourism development ism for a region. Tourism intensity is calculated by comparing the number of overnight stays in Tourism in the European Union increased overall a region with the size of the resident population. from 2000 to 2007. Two particular phases stand It is generally a better guide to the economic out. The years 2000 and 2001 were both record weight of tourism for a region than the absolute years, each recording 1.75 billion overnight stays number of overnight stays. The huge importance in hotels and on campsites, thanks to the favour­ of tourism to many of Europe’s coastal regions able economic climate at the time and to special and, even more so, to its islands, as well as to events such as the Holy Year in Italy and the Han­ most of the Alpine regions of Austria and Italy, nover World EXPO. Tourism declined in 2002 and is evident here too. 2003, due in part to the economic slowdown but certainly also due to the 9/11 attacks. The number Of the 25 regions in Europe with a tourism in­ of overnight stays decreased to 1.73 billion in tensity of more than 10 000 overnight stays per 2003 but then increased markedly from 2004 to 1 000 inhabitants, 10 are island regions, seven are 2007. In 2007 the number of overnight stays in Alpine regions and six are coastal regions. The the EU Member States’ hotels and campsites was Spanish region of Illes Balears shows the highest just below the 2 billion mark, at 1.94 billion. tourism intensity, at 50 178 overnight stays per 1 000 inhabitants, followed by the Greek region The biggest beneficiaries were the three Baltic of Notio Aigaio (48 168), the Italian Provincia States and Poland, all of which recorded double­ Autonoma Bolzano/Bozen (47 438), the Austrian digit growth in overnight stays. Bulgaria, Tirol (43 527), the Portuguese Algarve (39 132), Greece, Romania, Spain, Finland, Portugal, the the Greek Ionia Nisia (33 304) and the Austrian United Kingdom and Hungary also recorded region of Salzburg (30 487). growth figures above the EU average of 2.8 %. Figure 10.4: Nights spent in hotels and campsites, EU-27, average annual change rate 2003–07 Percentage Average annual change rate EU-27 BE BG CZ DK DE EE IE GR ES FR IT CY LV LT LU HU MT NL AT PL PT RO SI SK FI SE UK -5 0 5 10 15 20 25 Eurostat regional yearbook 2009 133
  • 124. 10 Tourism Map 10.5: Nights spent in hotels and campsites, by NUTS 2 regions, average annual change rate 2003–07 134 Eurostat regional yearbook 2009
  • 125. Tourism 10 Only Luxembourg, Slovakia and Cyprus record­ narias and the Portuguese Região Autónoma da ed declines in the number of overnight stays be­ Madeira. Foreign visitors also account for more tween 2003 and 2007. than 90 % of overnight stays in Luxembourg and Praha, the Croatian region of Jadranska Hrvatska Map 10.5 illustrates the trend in overnight stays and the Austrian region of Tirol. over the period 2003–07. It shows that the main beneficiaries of the upswing in tourism over this period were the regions in the new EU Member Conclusion States of the Baltic States, Poland and Bulgaria. Most regions in these countries achieved growth Analysis of the structure and development of tour­ rates of over 10 %. Equally strong growth in over­ ism in Europe’s regions confirms the compensa­ night stays was recorded in the regions of Ro­ tory role which this sector of the economy plays mania, Portugal and Spain. in many countries. It is particularly significant in those regions that are at a distance from and often peripheral to the economic centres of their coun­ Inbound tourism try. Here, tourism services are often an important Inbound tourism, i.e. visits from abroad, is of factor in creating and securing employment and particular interest to most analyses of tourism in are one of the main sources of income for the a given region. The statistically important factor population. This applies especially to Europe’s here is the usual place of residence of the visitors, island states and island regions, to many coastal not their nationality. Foreign visitors, particu­ regions, particularly in southern Europe, and to larly those from distant countries, usually spend the whole Alpine region. The particularly dynamic more per day than domestic visitors during their growth in tourism in most of the new central and stays and thus carry greater weight as a demand east European Member States is a significant fac­ factor for the local economy. Their expenditure tor in helping their economies to catch up more also contributes to the balance of payments of the rapidly with those of the old Member States. country visited. They may therefore help to offset According to the World Tourism Organisation, foreign trade deficits. Europe is the most frequently visited region on Map 10.6 shows overnight stays by foreign visi­ earth. Five of the top 10 countries for visitors tors as percentages of total overnight stays in the worldwide are European Union Member States. various regions. The values differ very widely The wealth of its cultures, the variety of its land­ from region to region, from less than 5 % to well scapes and the exceptional quality of its tourist over 90 %. Europe’s island regions, or at least infrastructure are some of the probable reasons those in the south, show particularly high figures for this prominent position. The accession of for foreign visitors as a percentage of total over­ the new Member States has hugely enriched the night stays. This is true not only for the island European Union’s tourism potential by enhanc­ states of Malta and Cyprus but also for the Greek ing its cultural diversity and providing interesting island regions, the Spanish Illes Balears and Ca­ new destinations for many citizens to discover. Eurostat regional yearbook 2009 135
  • 126. 10 Tourism Map 10.6: Share of non-resident nights spent in hotels and campsites, by NUTS 2 regions, 2007 136 Eurostat regional yearbook 2009
  • 127. Tourism 10 Methodological notes Harmonised statistical data on tourism have been collected since 1996 in the Member States of the European Union on the basis of Council Directive 95/57/EC of 23 November 1995 on the col- lection of statistical information in the field of tourism. The programme covers both the supply side, i.e. data on available accommodation capacity (establishments, rooms, bedplaces) and its utilisation (number of visitor arrivals and overnight stays), and the demand side, i.e. the travel behaviour of the population. Results by region below Member State level are available only for the supply side, however. The tourism statistics presented in this chapter relate only to ‘hotels and similar establishments’ and ‘tourist campsites’. Statistics for ‘holiday dwellings’ and ‘other collective accommodation’, on which data are also collected under the tourism statistics directive, are not included in this analysis since their comparability must at present still be regarded as limited, particularly at regional level. The analysis of tourism statistics covers data on both private and business travellers. This means that the definition of tourism applied to these statistics is broader than the everyday definition. The reason for this is primarily an economic one, since the two groups of travellers demand similar ser– vices and are thus, for the providers of those services, more or less interchangeable. Eurostat regional yearbook 2009 137
  • 128. Agriculture
  • 129. 11 Agriculture Introduction Europe (the Italian region of Basilicata). In west­ ern Europe, the highest proportion of area under Crop production plays a key role in human and cereals to UAA is in the regions of Île­de­France, animal food safety. As a major user of the soil, Picardie, Centre and Alsace in France. agriculture shapes the rural landscape. Half of Cereal crops cover a small proportion of the UAA the surface area of the EU is used for agricultural in southern regions (except Basilicata, mentioned purposes, hence the importance of agriculture to above), in certain Alpine regions, on the Atlantic the EU’s natural environment. European agri­ coast of the Iberian peninsula and in the regions culture is increasingly prioritising the kind of of northern Sweden, where this type of crop ac­ high­quality, environmentally friendly produce counts for less than 10 % of the UAA. demanded by the market. Specifically, these regions include almost all re­ This year’s Eurostat regional yearbook concen­ gions of Portugal (except Lisboa region), and cer­ trates on the use of the agricultural area and on the production of certain flagship products in tain coastal areas of Spain (Galicia, Principado de European agriculture. The chapter on agriculture Asturias, Cantabria, Comunidad Valenciana and is thus divided into two main sections: the first Canarias) and Italy (Liguria). focuses on the soil use of certain major (arable The Alpine regions of Austria (Kärnten, Salzburg, and permanent) crops, and the second concen­ Tirol and Vorarlberg) and Italy (Valle d’Aosta/ trates on the production of certain major crops Vallée d’Aoste, Provincia Autonoma Bolzano/ and provides a regional breakdown of wheat, Bozen and Provincia Autonoma Trento) have grain maize and rapeseed production. areas under cereals of less than 10 % of UAA. In certain regions in which the preference is for Utilised agricultural area grassland and, in some cases, green fodder, a small proportion of the area is devoted to cereals. Proportion of area under cereals Those regions are in Belgium (Luxembourg Prov­ to the utilised agricultural area ince), France (Corsica, Limousin and the overseas In terms of the area that they occupy and their department of Réunion), the Netherlands (Fries­ importance in human and animal food, cereals land, Overijssel, Gelderland, Utrecht and Noord­ (including rice) constitute the largest crop group Holland), the whole of Ireland and the region of in the world. Mellersta Norrland in Sweden. In the EU, too, cereals are the most widely pro­ Proportion of permanent crops duced crop. European statistics on cereals en­ to the utilised agricultural area compass wheat, barley, maize, rye, meslin, oats, rice and other cereals such as triticale, buck­ Permanent crops are located mainly in the Medi­ wheat, millet and canary seed. These crops — for terranean regions. The term ‘permanent crops’ which statistics are compiled in all Member States means ligneous crops that occupy the soil for except Malta — accounted for some 30 % of the several — usually more than five — consecutive EU’s utilised agricultural area (UAA) in 2007. years, and refers mainly to fruit and berry trees, bushes, vines and olive trees. Cereals in fact account for over 50 % of some re­ gions’ UAA (see Map 11.1), namely Balkan regions Permanent crops cover a much smaller surface such as Sud­Vest Oltenia and Bucureşti — Ilfov in area than annual crops and cereal crops. They Romania and east European regions, in particular are also much more regionally concentrated, as in Hungary (Közép­Dunántúl, Nyugat­Dunántúl shown in Map 11.2. and Dél­Dunántúl), Slovakia (Bratislavský kraj Permanent crops remain prevalent in agricul­ and Západné Slovensko) and Poland (Łódzkie, ture given that their production generally yields Lubelskie, Wielkopolskie, Zachodnonio­ a greater added value per hectare than annual pomorskie, Lubuskie, Dolnośląskie, Opolskie, crops and that they are generally intended for hu­ Kujawsko­pomorskie and Pomorskie). Cereal man consumption. crops also cover over 50 % of the UAA of some regions of northern Europe (Denmark, the Etelä­ Moreover, these crops play an important role Suomi and Länsi­Suomi regions of Finland and not only in shaping the rural landscape (with or­ Östra Mellansverige, Småland med öarna and chards, vines and olive trees) but also in terms of Norra Mellansverige in Sweden) and southern the environmental balance of agriculture. 140 Eurostat regional yearbook 2009
  • 130. Agriculture 11 Map 11.1: Cereals (including rice) as a percentage of utilised agricultural area, by NUTS 2 regions, 2007 Eurostat regional yearbook 2009 141
  • 131. 11 Agriculture Map 11.2: Permanent crops as a percentage of utilised agricultural area, by NUTS 2 regions, 2007 142 Eurostat regional yearbook 2009
  • 132. Agriculture 11 Map 11.2 clearly shows how the Mediterranean It is also one of the most widely distributed crops regions specialise in permanent crops. Regional in the EU. According to the statistics, only five data on these crops are not available for several regions do not produce wheat, namely Princi­ countries in this area. pado de Asturias in Spain, Valle d’Aosta/Vallée d’Aoste, Provincia Autonoma Bolzano/Bozen in Of the 14 regions with permanent crops account­ Italy and Mellersta Norrland and Övre Norrland ing for over 30 % of their UAA, 10 are in the Med­ in Sweden. iterranean basin. They are: Cataluña, Comunidad Valenciana, Illes Balears, Andalucía and Región In 2007, the EU produced 120 million tonnes of de Murcia, in Spain (the Comunidad Valenciana wheat (including 8.2 million tonnes of durum region, for example, specialises in cultivating or­ wheat), on a total area of 24 million hectares. anges and small­fruited citrus, and accounts for Some 21 regions account for over half of wheat over 27 % of the orange­growing surface area and production in the EU (calculated without the fig­ 60 % of the small­fruited citrus surface area of ures for production in the Czech Republic, Greece the EU­27); Campania, Puglia, Calabria and Si­ and the United Kingdom, for which regional data cily, in Italy; Norte, Central, Algarve and the au­ are not available). tonomous region of Madeira, in Portugal, and the Languedoc­Rousillon region of France. Of those 21 regions, 10 are in France, as follows (ranging from the highest production to the low­ Similarly, Malta and Cyprus, also in the Mediter­ est): Centre, (which accounts for 4.5 % of Com­ ranean, have significant proportions (10–30 %) of munity production of wheat), Picardie, Cham­ permanent crops to their UAA. pagne­Ardenne, Poitou­Charentes, Pays de la In the regions of Aquitaine in France and Rioja in Loire, Nord — Pas­de­Calais, Bourgogne, Haute­ Spain, the large proportion of permanent crops to Normandie, Île­de­France and Bretagne. This UAA is due to vine cultivation. makes France the biggest wheat producer in the EU. France harvested almost 33 million tonnes of In the Belgian region of Limburg, the significant cereal in 2007. proportion of permanent crops to UAA is due to orchards (mainly apple and pear trees). Germany, with 20.9 million tonnes, is the second­ biggest producer. It has eight of the 21 highest­ producing regions, and they are as follows (from Agricultural production the largest producers to the lowest): Bayern (which accounts for 3.6 % of wheat production Maps 11.3, 11.4 and 11.5 show the percentage in the Community), Niedersachsen, Sachsen­ contribution of each region to the total EU pro­ Anhalt, Nordrhein­Westfalen, Mecklenburg­ duction of three major crops — wheat, maize and Vorpommern, Baden­Württemberg, Thüringen rapeseed. The total regional production of an ag­ and Schleswig­Holstein. ricultural product — even if the figure is heav­ ily influenced by the yield and area of the crop It can therefore be said that the EU’s wheat ‘gran­ — remains a good indicator of the contribution ary’ is located in the northern half of France and that a region can make, on a broader level, to the Germany. The next 63 regions contribute 40 % quantity produced in, say, the country and/or the of the EU’s total production. These include all EU. The abovementioned maps and the following but three regions of Poland, which is the fourth­ paragraphs give an overview of the concentration biggest producer of wheat, after the United King­ of the production of these crops. dom (8.3 million tonnes). Wheat production Grain maize production Wheat (common and durum wheat) is the crop In 2007, 47.5 million tonnes of grain maize were with by far the highest production in European produced in the EU, which amounts to 18 % of agriculture. In 2007, wheat accounted for 46 % cereal production. Grain maize is mainly intend­ ed for animal feed but it is also used for industrial of cereal production in the EU. Wheat is prima­ products such as starch and glue. rily used in human and animal food, but also for making processed products such as bioethanol Given its physiological needs, this crop covers a and starch. smaller geographical range of EU regions. The Eurostat regional yearbook 2009 143
  • 133. 11 Agriculture Map 11.3: Wheat production, sum of the regions which together represent x % of the EU-27 production of wheat, by NUTS 2 regions, 2007 144 Eurostat regional yearbook 2009
  • 134. Agriculture 11 Map 11.4: Grain maize production, sum of the regions which together represent x % of the EU-27 production of grain maize, by NUTS 2 regions, 2007 Eurostat regional yearbook 2009 145
  • 135. 11 Agriculture most northerly Member States (Ireland, the Uni­ rapeseed; southern regions (in Spain, Italy and ted Kingdom, Denmark, Estonia, Latvia, Finland Bulgaria) account for less than 10 % of Commu­ and Sweden) produce little or no grain maize. nity production. The 14 regions producing the most grain maize The 13 regions (including Denmark) that produce are responsible for over 50 % of total grain maize the most rapeseed account for at least 50 % of to­ production. This Community production total tal production in the EU­27. This Community was calculated without production figures for the production total was calculated without figures Czech Republic and Greece, given that regional for the Czech Republic and the United Kingdom, data for those countries are not available. given that regional data for those countries are Of those 14 regions, seven are in France, as fol­ not available. lows (starting with the highest­producing region): Of those regions, eight are in Germany, the big­ Aquitaine (which accounts for 6.3 % of Communi­ gest rapeseed­producing country, with 5.3 mil­ ty production), Poitou­Charentes, Midi­Pyrénées, lion tonnes (starting with the highest­producing Alsace, Pays de la Loire, Rhône­Alpes and Centre. region): Mecklenburg­Vorpommern (5.8 % of Four are in the north of Italy (starting with the Community production), Bayern, Sachsen­An­ highest­producing region): Veneto, Lombardia, halt, Niedersachsen, Schleswig­Holstein, Sach­ which accounts for 6.2 % of Community produc­ sen, Thüringen and Brandenburg. tion, Piemonte and Friuli­Venezia Giulia. There is one such region in Hungary (Dél­Dunantul, which Four are in France, the second­biggest producer accounts for 2.3 % of Community production), of rapeseed, with 4.6 million tonnes (starting with one in Spain (Castilla y Leon, 2.2 % of Community the highest­producing region): Centre (6 % of production) and one in Germany (Bayern, 2.1 % of Community production), Champagne­Ardenne, Community production). Bourgogne and Lorraine. Denmark contributes 3.9 % of Community production. The next 40 regions account for 40 % of the EU’s total production. Romania, with 3.9 million The next 34 regions account for 40 % of the EU’s tonnes, is the fourth­biggest producer of grain total production. Poland, with 2.1 million tonnes, maize in the EU­27 (after France, with 14 million is the third­biggest producer of rapeseed in the EU. tonnes, Italy (9.9 million tonnes) and Hungary (4 Ten Polish regions are in this group: Wielkopol­ million tonnes). All regions of Romania except skie (2.1 % of Community production), Kujawsko­ Bucureşti — Ilfov are in this group. Romania pomorskie, Zachononiopomorskie, Dolnośląskie, specialises in grain maize cultivation (2.5 million Opolskie, Pomorskie, Warminsko­mazurskie, hectares, i.e. the largest surface area dedicated to Lubelskie, Mazowieckie and Lubuskie. this crop in the EU), but its yields are not as high Two Baltic countries, Estonia and Lithuania, also as those in the older Member States. feature in this group. Rapeseed production Conclusion In 2007, 18.1 million tonnes of rapeseed were produced in the EU, a 13 % increase on the 2006 Climate and geography have a major influence on figure. Rapeseed is used in the manufacture of oil the agricultural use of the land; the choice of ani­ (mainly non­edible oil such as biodiesel, but also mal and plant production varies from region to edible oil) and animal feed (rapeseed cake from region across Europe. the crushing of rapeseed grain). The increase in It should be emphasised, however, that produc­ rapeseed production is clearly due to the high de­ tion quality and intensity are not the only factors mand in recent years for renewable energy sour­ influencing the development of the agricultural ces such as biodiesel. sector. Other criteria such as rural development, Rapeseed is best suited to a temperate climate. the environment and food safety have become in­ Four countries in the south of the EU — Portu­ creasingly important, and could yet alter the cur­ gal, Greece, Cyprus and Malta — do not produce rent face of agriculture in Europe’s regions. 146 Eurostat regional yearbook 2009
  • 136. Agriculture 11 Map 11.5: Rape production, sum of the regions which together represent x % of the EU-27 production of rape, by NUTS 2 regions, 2007 Eurostat regional yearbook 2009 147
  • 137. 11 Agriculture Methodological notes The utilised agricultural area (UAA) comprises arable crops, permanent grassland, permanent crops and other agricultural land such as kitchen gardens. Cereals comprise wheat (common and durum), barley, grain maize, rye and meslin, oats, mixed grain other than meslin, triticale, sorghum and other cereals such as buckwheat, millet, canary seed and rice. Permanent crops are agricultural crops, in particular ligneous crops, that occupy the soil for more than five years (not including permanent pasture). As regards Maps 11.3, 11.4 and 11.5, total EU production and the total number of regions account- ing for a particular percentage of EU production do not include countries that have not submit- ted regional data. Accordingly, for EU wheat production (Map 11.3), the figures do not include production in the Czech Republic, Greece or the United Kingdom. For EU grain maize production (Map 11.4), the figures do not include production figures for the Czech Republic or Greece. Similarly, for EU rapeseed production (Map 11.5), the figures do not include production in the Czech Republic or the United Kingdom. 148 Eurostat regional yearbook 2009
  • 138. Annex EUROPEAN UNION: NUTS 2 regions Belgium DK04 Midtjylland DEB2 Trier BE10 Région de Bruxelles-Capitale/ DK05 Nordjylland DEB3 Rheinhessen-Pfalz Brussels Hoofdstedelijk Gewest DEC0 Saarland BE21 Prov. Antwerpen Germany DED1 Chemnitz BE22 Prov. Limburg (B) DE11 Stuttgart DED2 Dresden BE23 Prov. Oost-vlaanderen DE12 Karlsruhe DED3 Leipzig BE24 Prov. vlaams-Brabant DE13 Freiburg DEE0 Sachsen-Anhalt BE25 Prov. West-vlaanderen DE14 Tübingen DEF0 Schleswig-Holstein BE31 Prov. Brabant Wallon DE21 Oberbayern DEG0 Thüringen BE32 Prov. Hainaut DE22 Niederbayern BE33 Prov. Liège DE23 Oberpfalz Estonia BE34 Prov. Luxembourg (B) DE24 Oberfranken EE00 Eesti BE35 Prov. Namur DE25 Mittelfranken Ireland DE26 Unterfranken Bulgaria IE01 Border, Midland and Western DE27 Schwaben BG31 Severozapaden IE02 Southern and Eastern DE30 Berlin BG32 Severen tsentralen Greece DE41 Brandenburg — Nordost BG33 Severoiztochen GR11 Anatoliki Makedonia, Thraki DE42 Brandenburg — Südwest BG34 yugoiztochen GR12 Kentriki Makedonia DE50 Bremen BG41 yugozapaden GR13 Dytiki Makedonia DE60 Hamburg BG42 yuzhen tsentralen GR14 Thessalia DE71 Darmstadt Czech Republic GR21 Ipeiros DE72 Gießen CZ01 Praha GR22 Ionia Nisia DE73 Kassel CZ02 Střední Čechy GR23 Dytiki Ellada DE80 Mecklenburg-vorpommern CZ03 Jihozápad GR24 Sterea Ellada DE91 Braunschweig CZ04 Severozápad GR25 Peloponnisos DE92 Hannover CZ05 Severovýchod GR30 Attiki DE93 Lüneburg CZ06 Jihovýchod GR41 voreio Aigaio DE94 Weser-Ems CZ07 Střední Morava GR42 Notio Aigaio DEA1 Düsseldorf CZ08 Moravskoslezsko GR43 Kriti DEA2 Köln Denmark DEA3 Münster Spain DK01 Hovedstaden DEA4 Detmold ES11 Galicia DK02 Sjælland DEA5 Arnsberg ES12 Principado de Asturias DK03 Syddanmark DEB1 Koblenz ES13 Cantabria Eurostat regional yearbook 2009 149
  • 139. ES21 País vasco FR83 Corse Hungary ES22 Comunidad Foral de Navarra FR91 Guadeloupe HU10 Közép-Magyarország ES23 La Rioja FR92 Martinique HU21 Közép-Dunántúl ES24 Aragón FR93 Guyane HU22 Nyugat-Dunántúl ES30 Comunidad de Madrid FR94 Réunion HU23 Dél-Dunántúl ES41 Castilla y León HU31 Észak-Magyarország Italy ES42 Castilla-La Mancha HU32 Észak-Alföld ITC1 Piemonte ES43 Extremadura HU33 Dél-Alföld ITC2 valle d’Aosta/vallée d’Aoste ES51 Cataluña ITC3 Liguria Malta ES52 Comunidad valenciana ITC4 Lombardia MT00 Malta ES53 Illes Balears ITD1 Provincia Autonoma Bolzano/ Netherlands ES61 Andalucía Bozen ES62 Región de Murcia NL11 Groningen ITD2 Provincia Autonoma Trento ES63 Ciudad Autónoma de Ceuta NL12 Friesland (NL) ITD3 veneto ES64 Ciudad Autónoma de Melilla NL13 Drenthe ITD4 Friuli-venezia Giulia ES70 Canarias NL21 Overijssel ITD5 Emilia-Romagna NL22 Gelderland France ITE1 Toscana NL23 Flevoland FR10 Île-de-France ITE2 Umbria NL31 Utrecht FR21 Champagne-Ardenne ITE3 Marche NL32 Noord-Holland FR22 Picardie ITE4 Lazio NL33 Zuid-Holland FR23 Haute-Normandie ITF1 Abruzzo NL34 Zeeland FR24 Centre ITF2 Molise NL41 Noord-Brabant FR25 Basse-Normandie ITF3 Campania NL42 Limburg (NL) FR26 Bourgogne ITF4 Puglia Austria FR30 Nord — Pas-de-Calais ITF5 Basilicata AT11 Burgenland (A) FR41 Lorraine ITF6 Calabria AT12 Niederösterreich FR42 Alsace ITG1 Sicilia AT13 Wien FR43 Franche-Comté ITG2 Sardegna AT21 Kärnten FR51 Pays de la Loire Cyprus AT22 Steiermark FR52 Bretagne Cy00 Kypros/Kıbrıs AT31 Oberösterreich FR53 Poitou-Charentes AT32 Salzburg FR61 Aquitaine Latvia AT33 Tirol FR62 Midi-Pyrénées Lv00 Latvija AT34 vorarlberg FR63 Limousin Lithuania FR71 Rhône-Alpes Poland LT00 Lietuva FR72 Auvergne PL11 Łódzkie FR81 Languedoc-Roussillon Luxembourg PL12 Mazowieckie FR82 Provence-Alpes-Côte d’Azur LU00 Luxembourg (Grand-Duché) PL21 Małopolskie 150 Eurostat regional yearbook 2009
  • 140. PL22 Śląskie SI02 Zahodna Slovenija UKE2 North yorkshire PL31 Lubelskie UKE3 South yorkshire Slovakia PL32 Podkarpackie UKE4 West yorkshire SK01 Bratislavský kraj PL33 Świętokrzyskie UKF1 Derbyshire and SK02 Západné Slovensko PL34 Podlaskie Nottinghamshire SK03 Stredné Slovensko PL41 Wielkopolskie UKF2 Leicestershire, Rutland SK04 východné Slovensko and Northamptonshire PL42 Zachodniopomorskie UKF3 Lincolnshire PL43 Lubuskie Finland UKG1 Herefordshire, Worcestershire PL51 Dolnośląskie FI13 Itä-Suomi and Warwickshire PL52 Opolskie FI18 Etelä-Suomi UKG2 Shropshire and Staffordshire PL61 Kujawsko-pomorskie FI19 Länsi-Suomi UKG3 West Midlands PL62 Warmińsko-mazurskie FI1A Pohjois-Suomi UKH1 East Anglia PL63 Pomorskie FI20 Åland UKH2 Bedfordshire and Hertfordshire Portugal Sweden UKH3 Essex PT11 Norte SE11 Stockholm UKI1 Inner London PT15 Algarve SE12 Östra Mellansverige UKI2 Outer London PT16 Centro (P) SE21 Småland med öarna UKJ1 Berkshire, Buckinghamshire PT17 Lisboa SE22 Sydsverige and Oxfordshire PT18 Alentejo SE23 västsverige UKJ2 Surrey, East and West Sussex PT20 Região Autónoma dos Açores SE31 Norra Mellansverige UKJ3 Hampshire and Isle of Wight PT30 Região Autónoma da Madeira SE32 Mellersta Norrland UKJ4 Kent SE33 Övre Norrland UKK1 Gloucestershire, Wiltshire Romania and Bristol/Bath area RO11 Nord-vest United Kingdom UKK2 Dorset and Somerset RO12 Centru UKC1 Tees valley and Durham UKK3 Cornwall and Isles of Scilly RO21 Nord-Est UKC2 Northumberland and Tyne UKK4 Devon and Wear RO22 Sud-Est UKL1 West Wales and The valleys UKD1 Cumbria RO31 Sud — Muntenia UKL2 East Wales UKD2 Cheshire RO32 Bucureşti — Ilfov UKM2 Eastern Scotland UKD3 Greater Manchester RO41 Sud-vest Oltenia UKM3 South Western Scotland UKD4 Lancashire RO42 vest UKM5 North Eastern Scotland UKD5 Merseyside Slovenia UKM6 Highlands and Islands UKE1 East yorkshire and Northern SI01 vzhodna Slovenija Lincolnshire UKN0 Northern Ireland Eurostat regional yearbook 2009 151
  • 141. CANDIDATE COUNTRIES: Statistical regions at level 2 Croatia HR01 Sjeverozapadna Hrvatska HR02 Središnja i Istočna (Panonska) Hrvatska HR03 Jadranska Hrvatska The former Yugoslav Republic of Macedonia MK00 Poranešnata jugoslovenska Republika Makedonija Turkey TR10 İstanbul TR21 Tekirdağ TR22 Balıkesir TR31 İzmir TR32 Aydın TR33 Manisa TR41 Bursa TR42 Kocaeli TR51 Ankara TR52 Konya TR61 Antalya TR62 Adana TR63 Hatay TR71 Kırıkkale TR72 Kayseri TR81 Zonguldak TR82 Kastamonu TR83 Samsun TR90 Trabzon TRA1 Erzurum TRA2 Ağrı TRB1 Malatya TRB2 van TRC1 Gaziantep TRC2 Şanlıurfa TRC3 Mardin 152 Eurostat regional yearbook 2009
  • 142. EFTA COUNTRIES: Statistical regions at level 2 Iceland IS00 Ísland Liechtenstein LI00 Liechtenstein Norway NO01 Oslo og Akershus NO02 Hedmark og Oppland NO03 Sør-Østlandet NO04 Agder og Rogaland NO05 vestlandet NO06 Trøndelag NO07 Nord-Norge Switzerland CH01 Région lémanique CH02 Espace Mittelland CH03 Nordwestschweiz CH04 Zürich CH05 Ostschweiz CH06 Zentralschweiz CH07 Ticino Eurostat regional yearbook 2009 153
  • 143. European Commission Eurostat regional yearbook 2009 Luxembourg: Publications Office of the European Union 2009 — 153 pp. — 21 × 29.7 cm ISBN 978-92-79-11696-4 ISSN 1830-9674 doi: 10.2785/17776 Price (excluding VAT) in Luxembourg: EUR 30
  • 144. How to obtain EU publications Publications for sale: • via EU Bookshop (http://bookshop.europa.eu); • from your bookseller by quoting the title, publisher and/or ISBN number; • by contacting one of our sales agents directly. You can obtain their contact details on the Internet (http://bookshop.europa.eu) or by sending a fax to +352 2929-42758. Free publications: • via EU Bookshop (http://bookshop.europa.eu); • at the European Commission’s representations or delegations. You can obtain their contact details on the Internet (http://ec.europa.eu) or by sending a fax to +352 2929-42758.
  • 145. KS-HA-09-001-EN-C Eurostat regional yearbook 2009 Statistical information is essential for understanding our complex and rapidly changing world. Eurostat regional yearbook 2009 o ers a wealth of information on life in the European regions in the 27 Member States of the European Union and in the candidate countries and EFTA countries. If you would like to dig deeper into the way the regions of Europe are evolving in a number of statistical domains, this publication is for you! The texts are written by specialists in the di erent statistical domains and are accompanied by statistical maps, gures and tables on each subject. A broad set of regional data is presented on the following themes: population, European cities, labour market, gross domestic product, household accounts, structural business statistics, information society, science, technology and innovation, education, tourism and agriculture. The publication is available in English, French and German. http://ec.europa.eu/eurostat ISBN 978-92-79-11696-4 9 789279 116964 Price (excluding VAT) in Luxembourg: EUR 30

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