2009 Eurostat Regional Yearbook 09
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Transcript of 2009 Eurostat Regional Yearbook 09
Eurostat regional yearbook 2009
KS-HA
-09-001-EN-C
Eurostat regional yearbook 2009
S t a t i s t i c a l b o o k s
ISSN 1830-9674
Price (excluding VAT) in Luxembourg: EUR 30
Eurostat regional yearbook 2009Statistical 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
9 7 8 9 2 7 9 1 1 6 9 6 4
ISBN 978-92-79-11696-4
Eurostat regional yearbook 2009
S t a t i s t i c a l b o o k s
Europe Direct is a service to help you find answers to your questions about the European Union
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Luxembourg: Office for Official Publications of the European Communities, 2009
ISBN 978-92-79-11696-4ISSN 1830-9674doi: 10.2785/17776Cat. No: KS-HA-09-001-EN-C Theme: General and regional statisticsCollection: 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.
3 Eurostat regional yearbook 2009
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 aspects of regional differences and similarities in the 27 Member 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 analyses of economic and social developments.
In addition to the fascinating standard chapters on regional population developments, the regional labour market, regional GDP, etc., this year’s edition features a new contribution on the regional development of information society data. As in recent years, the description of regional developments 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 RadermacherDirectorGeneral, Eurostat
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 information)
• 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 geographical information) with the help of Berthold Feldmann (Unit E.4: Regional statistics and geographical 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
5 Eurostat regional yearbook 2009
Contents
INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
Statistics on regions and cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10The NUTS classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10Coverage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11More regional information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
1 POPULATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
Unveiling the regional pattern of demography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14Population density . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14Population change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23Methodological 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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26Discovering the spatial dimension . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 Core cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 Larger urban zones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28Geography matters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3 LABOUR MARKET . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
Regional working time patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36Brief overview for 2007 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36Regional work patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39Part-time jobs: lowering the average working time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41Employees spend less time at work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45Methodological notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
4 GROSS DOMESTIC PRODUCT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
What is regional gross domestic product?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50Regional GDP in 2006 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50Average GDP over the three-year period 2004–06 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52Major regional differences even within the countries themselves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52Dynamic catch-up process in the new Member States . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52Different trends even within the countries themselves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56Convergence makes progress . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57Methodological notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 Purchasing power parities and international volume comparisons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
6 Eurostat regional yearbook 2009
5 HOUSEHOLD ACCOUNTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
Introduction: measuring wealth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62Private household income . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62Results for 2006 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 Primary income . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 Disposable income . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64Dynamic development on the edges of the Union . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70Methodological notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
6 STRUCTURAL BUSINESS STATISTICS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74Regional specialisation and business concentration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74Specialisation in business services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80Employment growth in business services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84Characteristics of the top 30 most specialised regions in business services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87Methodological notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
7 INFORMATION SOCIETy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90Access to information and communication technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90Use of the Internet and Internet activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93Non-users of the Internet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97Methodological notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
8 SCIENCE, TECHNOLOGy AND INNOvATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102Research and development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102Human resources in science and technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105High-tech industries and knowledge-intensive services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105Patents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111Methodological notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
9 EDUCATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114Students’ participation in education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114Participation of 4-year-olds in education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114Students in upper secondary education and post-secondary non-tertiary education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116Students in tertiary education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119Tertiary educational attainment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119Lifelong learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123Methodological notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
7 Eurostat regional yearbook 2009
10 TOURISM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126Accommodation capacity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127Overnight stays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127Average length of stay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130Tourism intensity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130Tourism development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133Inbound tourism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135Methodological notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137
11 AGRICULTURE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140Utilised agricultural area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140 Proportion of area under cereals to the utilised agricultural area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140 Proportion of permanent crops to the utilised agricultural area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140Agricultural production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 Wheat production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 Grain maize production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 Rapeseed production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146Methodological notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148
ANNEX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
European Union: NUTS 2 regions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149Candidate countries: statistical regions at level 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152EFTA countries: statistical regions at level 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153
Introduction
Statistics on regions and citiesStatistical information is essential for understanding our complex and rapidly changing world. Eurostat, the Statistical Office of the European Communities, is responsible for collecting and disseminating data at European level, not only from the 27 Member States of the European Union, but also from the three candidate countries (Croatia, the former Yugoslav Republic of Macedonia and Turkey) and the four EFTA countries (Iceland, Liechtenstein, Norway and Switzerland).
The aim of this publication, the Eurostat regional yearbook 2009, is to give you a flavour of some of the statistics on regions and cities that we collect from these countries. Statistics on regions enable us to identify more detailed statistical patterns and trends than national data, but since we have 271 NUTS 2 regions in the EU27, 30 statistical regions on level 2 in the candidate countries and 16 statistical regions on level 2 in the EFTA countries, the volume of data is so great that one clearly needs some sorting principles to make it understandable and meaningful.
Statistical maps are probably the easiest way for the human mind to sort and ‘absorb’ large amounts of statistical data at one time. Hence this year’s Eurostat regional yearbook, as in previous editions, contains a lot of statistical maps where the data is sorted by different statistical classes represented by colour shades on the maps. Some chapters also make use of graphs and tables to present the statistical data, selected and sorted in some way (different top lists, graphs with regional extreme values within the countries or only giving representative examples) to make it easier to understand.
We are proud to present a great variety of subjects tackled in the 11 chapters in this years’ edition of the Eurostat regional yearbook. The first chapter on Population gives us detailed knowledge of different demographic patterns, such as population density, population change and fertility rates in the countries examined. This chapter can be considered the key to all other chapters, since all other statistics depend on the composition of the population. The second chapter focuses on European cities and explains in detail the definitions of the various spatial levels used in the Urban Audit data collection, with some interesting examples on how people travel to work in nine European capitals.
The chapter on the Labour market mainly describes the differences in weekly working hours
throughout Europe and offers a couple of explanations for why they vary so much from region to region. The three economic chapters on Gross domestic product, Household accounts and Structural business statistics all give us detailed insight into the general economic situation in regions, private households and different sectors of the business economy.
We are particularly proud to present a new and very interesting chapter on the Information so-ciety, which describes the use of information and communication technologies (ICT) among private persons and households in European regions. This chapter tells us, for example, how many households use the Internet regularly and how many have broadband access. The next two chapters are on Science, technology and innova-tion and Education, three areas of statistics that are often seen as key to monitoring achievement of the goals set in the Lisbon strategy to make Europe the most competitive and dynamic knowledgebased economy in the world.
In the next chapter we learn more about regional statistics on Tourism, and which tourist destinations are the most popular. The last chapter focuses on Agriculture, this time mainly crop statistics, revealing which kind of crop is grown where in Europe.
The NUTS classificationThe nomenclature of territorial units for statistics (NUTS) provides a single uniform breakdown of territorial units for the production of regional statistics for the European Union. The NUTS classification has been used for regional statistics for many decades, and has always formed the basis for regional funding policy. It was only in 2003, though, that NUTS acquired a legal basis, when the NUTS regulation was adopted by the Parliament and the Council (1).
Whenever new Member States join the EU, the NUTS regulation is amended to include the regional classification in those countries. This was the case in 2004, when the EU took in 10 new Member States, and in 2007 when Bulgaria and Romania also joined the European Union.
The NUTS regulation states that amendments of the regional classification, to take account of new administrative divisions or boundary changes in the Member States, may not be carried out more frequently than every three years. In 2006, this review took place for the first time, and the re
10 Eurostat regional yearbook 2009
Introduction
(1) More information on the NUTS classification can be found at http://ec.europa.eu/eurostat/ramon/nuts/splash_regions.html
sults of these changes to the NUTS classification have been valid since 1 January 2008.
Since these NUTS changes were introduced quite recently, the statistical data are still missing in some cases or have been replaced with national values on some statistical maps, as indicated in the footnotes to each map concerned. This applies in particular to Sweden, which introduced NUTS level 1 regions, to Denmark and Slovenia, which introduced new NUTS level 2 regions, and to the two northernmost Scottish regions, North Eastern Scotland (UKM5) and Highlands and Islands (UKM6), where the border between the two regions has changed. The regional data availability for these countries will hopefully soon be improved.
Please also note that some Member States have a relatively small population and are therefore not divided into more than one NUTS 2 region. Thus, for these countries the NUTS 2 value is exactly the same as the national value. Following the latest revision of the NUTS classification, this now applies to six Member States (Estonia, Cyprus, Latvia, Lithuania, Luxembourg and Malta), one candidate country (the former Yugoslav Republic of Macedonia) and two EFTA countries (Iceland and Liechtenstein). In all cases the whole country consists of one single NUTS 2 region.
A folding map on the inside of the cover accompanies this publication and it shows all NUTS level 2 regions in the 27 Member States of the European Union (EU27) and the corresponding level 2 statistical regions in the candidate and EFTA countries. In the annex you will find the full list of codes and names of these regions. This will help you locate a specific region on the map.
CoverageThe Eurostat regional yearbook 2009 mainly contains statistics on the 27 Member States of the European Union but, when available, data is also
given on the three candidate countries (Croatia, the former Yugoslav Republic of Macedonia and Turkey) and the four EFTA countries (Iceland, Liechtenstein, Norway and Switzerland).
Regions in the candidate countries and the EFTA countries are called statistical regions and they follow the same rules as the NUTS regions in the European Union, except that there is no legal base. Data from the candidate and EFTA countries are not yet available in the Eurostat database for some of the policy areas, but the availability of data is constantly improving, and we hope to have even more complete coverage from these countries in the near future.
More regional informationIn the subject area ‘Regions and cities’ under the heading ‘General and regional statistics’ on the Eurostat website you will find tables with statistics on both ‘Regions’ and the ‘Urban Audit’, with more detailed time series (some of them going back as far as 1970) and with more detailed statistics than this yearbook contains. You will also find a number of indicators at NUTS level 3 (such as area, demography, gross domestic product and labour market data). This is important since some of the countries covered are not divided into NUTS 2 regions, as mentioned above.
For more detailed information on the content of the regional and urban databases, please consult the Eurostat publication European regional and urban statistics — Reference guide — 2009 edition, which you can download free of charge from the Eurostat website. You can also download Excel tables containing the specific data used to produce the maps and other illustrations for each chapter in this publication on the Eurostat website. We do hope you will find this publication both interesting and useful and we welcome your feedback at the following email address: estat[email protected]
11 Eurostat regional yearbook 2009
Introduction
Population
Unveiling the regional pattern of demographyDemographic trends have a strong impact on the societies of the European Union. Consistently low fertility levels, combined with extended longevity and the fact that the baby boomers are reaching retirement age, result in demographic ageing of the EU population. The share of the older generation is increasing while the share of those of working age is decreasing.
The social and economic changes associated with population ageing are likely to have profound implications for the EU — and also to be visible at regional level, stretching across a wide range of policy areas and impacting on the schoolage population, healthcare, labour force participation, social protection and social security issues and government finances, etc.
The demographic development is not the same in all regions of the EU. Some demographic phenomena might have a stronger impact in some regions than in others.
This chapter presents the regional pattern of demographic phenomena as it is today.
Population densityOn 1 January 2007, 584 million people inhabited the European Union and candidate and EFTA countries. The population distribution is varied across the 317 NUTS 2 regions that make up this area.
Map 1.1 shows the population density on 1 January 2007. The population density of a region is the ratio of the population of a territory to its size. Generally, capital city regions are among the most densely populated, as Map 1.1 shows. Inner London was by far the most densely populated, but the BruxellesCapitale, Wien, Berlin, Praha, Istanbul, Bucureşti — Ilfov and Attiki (Greece) regions also have densities above 1 000 inhabitants per km². The least densely populated region was the region of Guyane (France), while the next least densely populated regions, with fewer than 10 inhabitants per km², were all in Sweden, Finland, Iceland and Norway. By comparison, the European Union has a population density of 114 inhabitants per km².
Population changeDuring the last four and a half decades, the population of the 27 countries that make up today’s European Union has grown from around 400
million (1960) to almost 500 million (497 million on 1 January 2008). Including candidate countries and EFTA countries, the total population has grown over the same period from under 450 million to 587 million.
The total population change has two components: the socalled ‘natural increase’, which is defined as the difference between the numbers of live births and deaths, and net migration, which ideally represents the difference between inward and outward migration flows (see ‘Methodological notes’). Changes in the size of a population are the result of the number of births, the number of deaths and the number of people who migrate.
Up to the end of the 1980s, natural increase was by far the major component of population growth. However, there has been a sustained decline in the natural increase since the early 1960s. On the other hand, international migration has gained importance and became the major force of population growth from the beginning of the 1990s onwards.
The analysis on the following pages is mainly based on demographic trends observed over the period from 1 January 2003 to 1 January 2008. For this purpose, fiveyear averages have been calculated of the total annual population change and its components. Given that demographic trends are longterm developments, the fiveyear averages provide a stable and accurate picture. They help to identify regional clusters, which often stretch well beyond national borders. For the sake of comparability, the population change and its components are presented in relative terms, calculating the socalled crude rates, i.e. they relate to the size of the total population (see ‘Methodological notes’). Maps 1.2, 1.3 and 1.4 show these figures on total population change and its components.
In most of the northeast, east and part of the southeast of the area made up by the European Union and the candidate and EFTA countries, the population is on the decrease. Map 1.2 is marked by a clear divide between the regions there and in the rest of the EU. Most affected by the decreasing population trend are Germany (in particular the former eastern Germany), Poland, Bulgaria, Slovakia, Hungary and Romania, and to the north the three Baltic States and the northern parts of Sweden and the Finnish region of ItäSuomi. Decreasing population trends are also evident in many regions of Greece. To the east, on the other hand, the total population change is positive in Cyprus and, to a lesser extent, in the former Yugoslav Republic of Macedonia and Turkey.
14 Eurostat regional yearbook 2009
1 Population
Map 1.1: Population density, by NUTS 2 regions, 2007 Inhabitants per km2
15 Eurostat regional yearbook 2009
Population 1
16 Eurostat regional yearbook 2009
1 Population
Map 1.2: Total population change, by NUTS 2 regions, average 2003–07 Per 1 000 inhabitants
In nearly all western and southwestern regions of the EU the population increased over the period 2003–07. This is particularly evident in Ireland and in almost all regions of the United Kingdom, Italy, Spain, France and Portugal, including the French overseas departments and the Spanish and Portuguese islands in the Atlantic Ocean. There has also been positive total population change in Austria, Switzerland, Belgium, Luxembourg and the Netherlands.
The picture provided by Map 1.2 can be refined by analysing the two components of total population change, namely natural change and migration.
Map 1.3 shows that in many regions of the EU more people died than were born in the period
2003–07. The resulting negative ‘natural population change’ is widespread and affects almost 50 % of the EU’s regions.
A single extended crossborder region can be identified showing a natural increase of population, made up of Ireland, the central United Kingdom, most regions in France, Belgium, Luxembourg, the Netherlands, Switzerland, Iceland, Lichtenstein, Denmark and Norway: in these regions, in the period 2003–07, live births were more numerous than deaths.
Deaths are more numerous than births in Germany, the Czech Republic, Slovakia, Hungary, Slovenia, Croatia, Romania and Bulgaria, and also in the Baltic States and Sweden in the north and
Figure 1.1: Total fertility rates by country, 1986 and 2006 Children per woman
1986 2006
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
Source: Eurostat Demographic StatisticsNotes: 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
17 Eurostat regional yearbook 2009
Population 1
18 Eurostat regional yearbook 2009
1 Population
Map 1.3: Natural population change (live births minus deaths), by NUTS 2 regions, average 2003–07 Per 1 000 inhabitants
Greece, Italy and Portugal in the south. The other countries have an overall more balanced situation.
A major reason for the slowdown of the natural increase of the population is the fact that inhabitants of the EU have fewer children. At aggregated level, in the 27 countries that today form the European Union, the total fertility rate has declined from a level of around 2.5 in the early 1960s to a level of about 1.5 in 1993, where it has remained since (for the definition of the total fertility rate, see the ‘Methodological notes’).
At country level, in 2006, a total fertility rate of less than 1.5 was observed in 17 of the 27 Member States. To compare, Figure 1.1 also includes figures for 1986 and for the candidate and EFTA countries.
Relatively high fertility rates tend to be recorded in countries that have implemented a range of familyfriendly policies, such as the introduction of accessible and affordable childcare and/or more flexible working patterns; this is the case for France, the Nordic countries and the Netherlands.
The (slight) increase in the total fertility rate that is observed in some countries between 1986 and 2006 may be partly attributable to a catchingup process following postponement of the decision to have children. When women give birth later in life, the total fertility rate first indicates a decrease in fertility, followed later by a recovery.
By comparison, in the more developed parts of the world today, a total fertility rate of around
National value
BEBG
DKDEEE
CZ
ELESFR
CYIT
LU
LVLT
HU
NLMT
ATPL
ROSI
SKFI
SEUKHR
ISLI
MKTR
NOCH
PT
0 5 10 15 20 25 30 35
Figure 1.2: Crude birth rates, by NUTS 2 regions, 2007 Births per 1 000 inhabitants
Source: Eurostat Demographic Statistics. Notes: FR, UK: 2006 TR: national level
Ciudad Autónoma de CeutaPrincipado de Asturias
Burgenland (A) Vorarlberg
PomorskieOpolskie
Região Autónoma dos AçoresAlentejo
Sjeverozapadna HrvatskaSredišnja i Istočna(Panonska) Hrvatska
Východné Slovensko
Nyugat-Dunántúl Észak-Alföld
Ipeiros Kriti
IE Border, Midland and Western Southern and Eastern
Zahodna SlovenijaVzhodna SlovenijaZápadné Slovensko
Nord-EstSud-Vest Oltenia
Inner London
Norra Mellansverige
Cornwall and Isles of Scilly
Oslo og AkershusHedmark og Oppland
Région lémaniqueTicino
Stockholm
Itä-Suomi Pohjois-Suomi
Limburg (NL) Flevoland
Liguria Provincia Autonoma Bolzano/BozenCorse Guyane
Saarland Hamburg
Sjælland Hovedstaden
Střední Morava Střední ČechyYugoiztochen
Prov. West-Vlaanderen
Severozapaden
Région de Bruxelles-Capitale/Brussels Hoofdstedelijk Gewest
19 Eurostat regional yearbook 2009
Population 1
20 Eurostat regional yearbook 2009
1 Population
Map 1.4: Net migration, by NUTS 2 regions, average 2003–07 Per 1 000 inhabitants
2.1 children per women is considered to be the replacement level, i.e. the level at which the population would remain stable in the long run if there were no inward or outward migration. At present (2006 data), practically all of the EU and the candidate and EFTA countries, with the exception of Turkey and Iceland, are still well below the replacement level.
The analysis of Map 1.3 can also be refined by isolating the contribution of live births to the natural population change. Figure 1.2 shows the regional differences within each country of the socalled crude birth rates (see the ‘Methodological notes’). The largest regional differences in 2007 were in France, where the highest crude birth rate is more than three times the lowest, followed by Spain, where the highest crude birth rate is also three times the lowest. For the other countries, regional differences in crude birth rates are less pronounced but still significant.
The third determinant of population change (after fertility and mortality) is migration. As many countries in the EU are currently at a point in the demographic cycle where ‘natural population change’ is close to being balanced or negative, the importance of immigration increases when it comes to maintaining population size. Moreover, migration also contributes indirectly to natural change, given that migrants have children. Migrants are also usually younger and have not yet reached the age at which death is more frequent.
In some regions of the European Union, negative ‘natural change’ has been offset by positive net migration. This is at its most striking in Austria, the United Kingdom, Spain, the northern and central regions of Italy and some regions of western Germany, Slovenia, southern Sweden, Portugal and Greece, as can be seen in Map 1.4. The opposite is much rarer: in only a few regions (namely in the northern regions of Poland and of Finland and in Turkey) has positive ‘natural change’ been cancelled out by negative net migration.
Four crossborder regions where more people have left than arrived (negative net migration) can be identified on Map 1.4:
• the northernmost regions of Norway and Finland;
• an eastern group, comprising most of the regions of eastern Germany, Poland, Lithuania and Latvia and most parts of Slovakia, Hungary, Romania, Bulgaria and Turkey;
• regions in the northeast of France and the French overseas departments;
• a few regions in the south of Italy, in the Netherlands and in the United Kingdom.
Regions where the two components of population change do not compensate for, but rather add to, one another are often exposed to major developments, upwards or — in some regions — downwards. In Ireland, Luxembourg, Belgium, Malta, Cyprus, Switzerland, Iceland, many regions in France and in Norway and some regions in Spain, the United Kingdom and the Netherlands, a natural increase has been accompanied by positive net migration. However, in eastern German regions, Lithuania and Latvia and some regions in Poland, Slovakia, Hungary, Bulgaria and Romania, both components of population change have moved in a negative direction, as can also be seen from Map 1.2. In these regions this trend has led to sustained population loss.
In 2007, the average population in the EU27 aged 65 and older was 17 %, which means an increase of 2 percentage points in the last 10 years. This ageing population, especially in rural areas, raises issues about infrastructure and the need for social services and healthcare.
The highest percentage of population aged 65 and older can be found in Liguria (Italy), at 27 %. Germany follows with up to 24 % in the region of Chemnitz and a further 14 regions above 20 %. Some regions in Greece, Portugal, France and Spain also show high figures, with up to 23 % of their population aged 65 years and older. These regions also show low and even negative natural population change, with more people dying than being born.
In Turkey the percentage of the population aged 65 and older is as low as 3 % in the region of Van, and on average 8 % in the other regions. Although Turkey has negative net migration, the high fertility results in a young population. Similarly, with high fertility, coupled with high net migration, only 11 % and 12 % of the population in the two regions of Ireland are 65 and older.
According to projections, elderly people would account for an increasing share of the population and this is due to sustained reductions in mortality in past and future decades. The ageing process can be typified as ageing from the top, as it largely results from projected increases in longevity, moderated by the impact of positive net migration flows and some recovery in fertility.
21 Eurostat regional yearbook 2009
Population 1
22 Eurostat regional yearbook 2009
1 Population
Map 1.5: Percentage of population aged 65 years old and more, by NUTS 2 regions, 2007
ConclusionThis chapter highlights certain features of regional population development in the area made up by the EU27 Member States and the candidate and EFTA countries over the period from 1 January 2003 to 1 January 2008. As far as possible, typologies of regions in the different demographic phe
nomena have been identified, spreading across national boundaries. While population decline is evident in several regions, at aggregated level the EU27 population still increased in that period by around 2 million people every year. The main driver of population growth in this area is migration, which counterbalanced, as seen in the maps, the negative natural change in many regions.
Methodological notesSources: 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.
23 Eurostat regional yearbook 2009
Population 1
European cities
IntroductionData on European cities were collected in the Urban Audit project. The project’s ultimate goal is to help improve the quality of urban life: it supports the exchange of experience among European cities; it helps to identify best practices; it facilitates benchmarking at European level; and it provides information on the dynamics both within the cities and with their surroundings.
The Urban Audit has become a core task of Eurostat. Even so, the project would not have been possible without sustained help and support from a wide range of colleagues. In particular, we would like to acknowledge the effort made by the cities themselves, the national statistical institutes and the DirectorateGeneral for Regional Policy of the European Commission.
The Urban Audit celebrates its 10th anniversary this year. The ‘Urban Audit pilot project’ was the first attempt to collect comparable indicators on European cities, and was first conducted by the Commission in June 1999. The past 10 years have brought many changes, and we have constantly made efforts to improve the quality of the data — including coverage, comparability and relevance. So, where we are now? The list of indicators has been enhanced to take account of new policy needs; the periodicity has been reduced to satisfy users; and geographical coverage has been extended following successive rounds of EU enlargement.
Enhanced list of indicators
There have been three major revisions of the list so far. Policy relevance, data availability and experience with previous collections have been reviewed to produce the current list of more than 300 indicators. These indicators cover several aspects of quality of life, such as demography, housing, health, crime, labour market, income disparity, local administration, educational qualifications, the environment, climate, travel patterns, the information society and cultural infrastructure. They are derived from the variables collected by the European Statistical System. Data availability differs from domain to domain: in the domain of demography, for example, data are available for more than 90 % of the cities, whereas for the environment data are available for less than half of the cities. In 2009 we will introduce new indicators to symbolise the relationship between the city and its hinterland.
Moving from five-year periodicity to annual data collection
Four reference years have been defined so far for the Urban Audit: 1991, 1996, 2001 and 2004. For the years 1991 and 1996, data were collected retrospectively only for a reduced number of 80 variables. Where data for these years were not available, data from adjacent years were also accepted. In 2009 Eurostat launched an annual Urban Audit, requesting data for a limited number of variables. The annual data will help users to monitor certain urban developments more closely.
Extended geographical coverage
The pilot study in 1999 covered 58 cities from 15 countries. Since then the number of participating countries has doubled and the number of cities has grown sixfold. At present the Urban Audit covers 362 cities from 31 countries — including the EU27, Croatia, Turkey, Norway and Switzerland. The 321 Urban Audit cities in the EU27 have more than 120 million inhabitants, covering approximately 25 % of the total population. This extended sample ensures that the results give a reliable portrait of urban Europe.
The number of cities was limited and the ones selected should reflect the geographical crosssection of each country. Consequently, in a few countries some large cities (over 100 000 inhabitants) were not included. To complement the Urban Audit data collection in this respect, the Large City Audit was launched. The Large City Audit includes all ‘nonUrban Audit cities’ with more than 100 000 inhabitants in the EU27. For these cities a reduced set of 50 variables is collected.
We invite all readers to explore the wealth of information gathered in the past 10 years by browsing the Urban Audit data on Eurostat’s website.
Discovering the spatial dimensionCities are usually displayed as distinct unconnected dots on a map. This visualisation method increases visibility but it misrepresents reality and distorts the understanding of linkages between a city and its hinterland and the understanding of linkages between cities. Cities can no longer be treated as discrete unrelated entities without a spatial dimension. The recent developments in transport, communication and information technology infrastructure ease the flow of people and resources from one area to another considerably. Urban–rural connectivity
26 Eurostat regional yearbook 2009
2 European cities
27 Eurostat regional yearbook 2009
European cities 2Map 2.1: Boundaries of cities participating in the Urban Audit data collection
and interurban relations have become critical for balanced regional development.
To facilitate the analysis of the interaction between the city and its surroundings for each participating city, different spatial levels were defined. Most of the data are collected at core city level, i.e. the city as defined by its administrative/political boundaries. In addition, a level called the larger urban zone was described. The larger urban zone is an approximation of the functional urban area extending beyond the core city.
Map 2.1 illustrates the cities participating in the Urban Audit data collection, showing the boundaries of core cities and larger urban zones. Not surprisingly, the largest cities in Europe in terms of population — London, Paris, Berlin and Madrid — tend to have the greatest larger urban zones in terms of area, and are readily identifiable on the map. In most cases the larger urban zone includes only one core city. However, there are exceptions, such as the German Ruhr area, which includes several core cities (see inset in Map 2.1). The demarcation of core cities is illustrated in detail in Map 2.2 while the larger urban zones are shown in Map 2.3. The spatial data used to produce most of the maps presented in this chapter are available from the Geographic Information System of the European Commission (GISCO) — a permanent service of Eurostat (for more information, visit Eurostat’s website).
Core cities
Throughout Europe’s history — in ancient Greece, in ancient Rome and in the Middle Ages — a city was as much a political entity as a collection of buildings. This collection of buildings was usually surrounded by fortified walls. As the city grew the walls were expanded. In the modern era the significance of the city walls as part of the defence system declined and most of them were demolished. The boundary of the city as a political entity and the boundary of the builtup area were no longer linked and the location of these boundaries is no longer evident. Nowadays, a city could be designated as an urban settlement or as a legal, administrative entity. The Urban Audit uses this later concept and demarcates the core city by political boundaries. This ensures that data are directly relevant to policymakers.
Map 2.2 illustrates the difference between the two concepts using the examples of Hamburg (Germany) and Lyon (France). Maps in the top row show the land cover based on Corine land cover 2000 (CLC2000) in the area surrounding
the cites. Different land covers were grouped into 44 classes in the CLC2000 (2). Each colour on the map represents a different land cover class. Some of these classes are particularly important for our analysis of cities. Red areas, for instance, are territories covered with urban fabric: roads, residential buildings, buildings belonging to the local administration or to public services, etc. Purple areas are used for commercial or industrial purposes. Light purple represents green urban areas like parks, botanical gardens, etc. The areas of these three land cover classes lying less than 200 m apart were merged together to define ‘builtup’ area. Port areas, airports and sport facilities were included if they were neighbours of the previously defined ‘builtup’ area.
As a next step, road and rail networks and water courses were added if they were within 300 m of the area defined beforehand. The area identified by this procedure is called the ‘urban morphological zone’ (UMZ). The urban morphological zones of Hamburg and Lyon are shown in the middle row of Map 2.2. These maps also make it possible to compare the UMZ and core city in terms of area. In Hamburg 82 %, and in Lyon 73 %, of the area of the UMZ lies within the boundaries of the core city. In terms of population the intersections are even greater: 90 % of the population of the core city of Hamburg lives in the UMZ, and in Lyon the respective figure is 98 %. As we expected, the two areas are not identical but they overlap each other to a large extent, thus ensuring that the data collected at core city level are relevant and meaningful for the morphological city as well.
To measure spatial inequalities within the city, the area of the core city was divided into subcity districts. Subcity districts were defined in such a way as to keep to the population thresholds set — minimum 5 000 and maximum 40 000 inhabitants — as far as possible. The bottom row of Map 2.2 illustrates the subcity districts of Hamburg and Lyon. Key demographic and social indicators are available in the Urban Audit database for the more than 6 000 subcity districts.
Larger urban zones
City walls, even if they are preserved, no longer function as barriers between the people living inside and outside of the city. Students, workers and persons looking for healthcare or for cultural facilities regularly commute between the city and the surrounding area. Economic activity, transport flows and air pollution clearly cross the administrative boundaries of a city as well. Consequently, collecting data exclusively at core city level is
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2 European cities
(2) A detailed description of the CLC2000 project and the UMZ creation is available on the website of the European Environment Agency (http://www.eea.europa.eu).
29 Eurostat regional yearbook 2009
European cities 2Map 2.2: Defining the boundaries of the core city — Hamburg (DE) and Lyon (FR)
Hamburg (DE) Lyon (FR)
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2 European cities
Map 2.3: Defining the boundaries of the larger urban zone — Barcelona (ES) and Zagreb (HR)
Barcelona (ES) Zagreb (HR)
insufficient. It is commonly agreed that we have to widen our territorial perspective. However, the way to measure how far the functional influences of a city go beyond its immediate boundaries varies.
Map 2.3 uses the examples of Barcelona (Spain) and Zagreb (Croatia) to illustrate how the functional urban area was demarcated in the Urban Audit. Maps in the top row are similar to the top row of Map 2.2 portraying the land cover of the selected area. The larger urban zone around the core city tends to be more ‘green’, both on the map and also in real terms. Areas covered with forests and shrubs are coloured green on the map. Yellow and orange indicate areas in agricultural use, such as arable land and fruit trees. As a first step to demarcate the larger urban zones, we looked at the number of people commuting from municipalities to the core city. The middle row of
Map 2.3 displays the different commuting rates. A commuting rate of 10 % means that one in 10 residents living in the municipality commutes to work to the core city. As we can see on the map, large cities like Barcelona and Zagreb attract people living up to 100 kilometres away to work in the city. As a second step, a threshold was set for looking at the commuting pattern. Municipalities above this threshold were to be included but ones below not. Given the different national and regional characteristics, different thresholds were used within the range of 10–20 %. Finally, the list of municipalities to be included in the larger urban zone was revised to ensure spatial contiguity and data availability. By definition the larger urban zone always includes the entire core city. The boundaries of the larger urban zone of Barcelona and Zagreb are displayed in the bottom row.
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European cities 2
0 % 20 % 40 % 60 % 80 % 100 % 0 % 20 % 40 % 60 % 80 % 100 %
core city kernel larger urban zone
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
Luxembourg (LU)
Bern (CH)Dublin (IE)
Amsterdam (NL)Oslo (NO)
Ljubljana (SI)Madrid (ES)
Paris (FR)Bruxelles/Brussel (BE)
Praha (CZ)Valletta (MT)
Warszawa (PL)London (UK)
Berlin (DE)København (DK)
Budapest (HU)Bratislava (SK)
Athina (GR)Wien (AT)
Lefkosia (CY)Riga (LV)
Lisboa (PT)Roma (IT)
Stockholm (SE)Zagreb (HR)
Vilnius (LT)Tallinn (EE)
Sofia (BG)Helsinki (FI)
Ankara (TR)Bucureşti (RO)
Notes: HU 2005; FI 2003; HR 2001
Share of population living in core cities and kernels (larger urban zone = 100 %)
Share of area of core cities and kernels (larger urban zone = 100 %)
This demarcation process was used in most participating countries, but there were also exceptions and departures from this which limit the overall comparability of the larger urban zones to some extent. That said, demarcating a perfect functional urban area — based on a perfectly harmonised methodology across Europe for which no statistical information is available — would be completely in vain. Figures 2.1 and 2.2 compare the different spatial levels used for European capitals in terms of population and area. In Bucuresti (Romania) more than 80 % of the larger urban zone population lives within the core city. At the other extreme, in Luxembourg (Luxembourg) less than 20 % of the larger urban zone population lives within the core city. This low
percentage suggests that the core city of Luxembourg is slightly underbounded — meaning that a considerable share of the urban population lives outside the administrative city limits. For very underbounded capitals — like Paris (France) or Lisboa (Portugal) — an additional spatial level, the ‘kernel’, was introduced. The kernel is an approximation of the builtup area around the core city. The only exception is London (United Kingdom), where the kernel was defined to match the core city of Paris in terms of population to make for easier comparison between the two largest cities in Europe. In terms of area, the picture is more uniform, as for the majority of capitals the core city makes up less than 20 % of the area of the larger urban zone.
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2 European cities
Figure 2.3: Proportion of journeys to work in European capitals, 2004
København DublinTallinn
Notes: SE 2005; DK, NL 2003; CH 2000. For DK, FI and SE the kernel level was used instead of the larger urban zone
Bratislava Madrid
Helsinki Stockholm Bern
by car by bicycle on foot by public transport
Amsterdam
So far we have seen that larger urban zones tend to have a lower population density and a higher percentage of green areas than core cities. Using the indicators calculated in the Urban Audit we can analyse the demographic, economic, environmental, social and cultural characteristics (similarities and differences) of the two spatial levels. To illustrate this, Figure 2.3 compares the travel to work patterns in selected capitals at different levels. The inner circle of the pie charts shows the modal split in the core city. In the core city of København (Denmark), for example, the majority of people ride their bikes to work, 30 % of them use public transport and 25 % travel by car. The outer circle shows the share of transport modes in the larger urban zone. As expected, the proportion of journeys to work by car is consistently higher in the larger urban zone than in the core city, with the exception of Bratislava.
Where do families settle? Where do companies locate? Where do tourists stay? In the core city or in the area of the larger urban zone outside of the core city? We encourage readers to probe deeper into the Urban Audit database and to explore the indicators depicting the spatial dimension.
Geography mattersThe book entitled The Spatial Economy (3), coauthored by Paul Krugman, winner of the 2008 Nobel Memorial Prize in Economic Sciences, states: ‘Agglomeration […] occurs at many levels, from the local shopping districts that serve residential areas within cities to specialised economic regions like Silicon Valley or the City of London that serve the world market as a whole. […] Yet although agglomeration is a clearly powerful force, it is not allpowerful: London is big, but most Britons live elsewhere, in a system of cities with widely varying sizes and roles. It should not, in other words, be hard to convince economists that economic geography […] is both an interesting and important subject.’ In this chapter we have focused on the various spatial levels used in the Urban Audit. These provide a platform for analysing the dramatically uneven distribution of population across the landscape and the agglomeration at district, at city and at regional level. Our intention was to convince readers that ‘statistical geography’ is both an interesting and an important subject.
33 Eurostat regional yearbook 2009
European cities 2
(3) Masahisa Fujita, Paul R. Krugman and Anthony Venables, The spatial economy: Cities, regions and international trade. MIT Press, 2001.
Labour market
Regional working time patternsFlexible working hours are one of the most valuable ways for individuals to reconcile work with other aspects of life, particularly family duties. Working part time can be a positive thing, as long as the decision is voluntary and not due to underemployment. The different legal systems and the different collective agreements across EU countries governing working hours provide some flexibility, providing scope, to a greater or lesser extent, for more free time.
And how about the situation at regional level? Are there significant differences among regions of the same country in how much time people spend at work? It is clear that the national legal system has a big influence in all regions of a country. But on top of this, do any regional factors influence the differences in weekly hours spent at work?
In this chapter we will look at how much time people spend at work in European regions and we will offer some possible explanations for the different time patterns. First we will give you a snapshot of the regional labour market in 2007.
Brief overview for 2007The EU27 employment rate rose from an average of 64.4 % in 2006 to 65.3 % in 2007. It is still 4.6 percentage points short of achieving the Lisbon employment target. Looking back to employment figures for 2000, when the targets were set, it is clear that the rise in employment fell short of ambitions. It now seems increasingly unlikely that the Lisbon targets for employment will be achieved by 2010, since there are only three years left, and especially given the recession and economic difficulties we are currently facing, which are highly likely to have a negative impact on employment in the coming years.
The latest quarterly data available at national level confirm this. The employment rate for the EU27 in the last quarter of 2008 was 65.8 % and 64.6 % in the first quarter of 2009.
Social and territorial cohesion is one of the EU’s goals, so it is important to look at regional labour markets and how they change over time. Map 3.1 shows the regional employment rate for the 15–64 age group, by NUTS 2 regions, in 2007.
In 2007, only 81 of the 264 NUTS 2 regions in the EU27 for which data was available had already achieved the Lisbon target (shaded with the darkest colour in Map 3.1), while 59 regions were still
10 percentage points below the overall employment target set for 2010.
A cluster of regions right in the centre of Europe, comprising regions in southern Germany and in Austria, recorded relatively high employment. The northern EU regions, comprising regions in the Netherlands, the United Kingdom, Denmark, Sweden and Finland, also recorded relatively high employment. Low regional employment rates were mainly found in the southern regions of Spain and Italy and in east European countries.
The range between the lowest and the highest regional employment rate in 2007 was still significant, with the highest employment rate almost twice as high as the lowest. The figures ranged from 43.5 % in Campania (Italy) to 79.5 % in Åland (Finland).
Employment throughout the EFTA regions was above 70 %. In the candidate countries, employment rates ranged from 25.7 % in Mardin (Turkey) to 62.4 % in Sjeverozapadna Hrvatska (Croatia).
The other two Lisbon targets set for employment — 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 increasingly unlikely to be achieved by 2010.
The female employment rate in the EU27 increased in 2007 by 1 percentage point to 58.3 %. Out of the three targets, this seems the most promising, but the negative impacts on the labour market that are likely to be felt in the coming years should not be overlooked. Regional female employment rates varied widely in 2007, from a minimum of 27.9 % in Campania (Italy) to a maximum of 76.4 % in Åland (Finland).
The employment rate of older workers, i.e. employed persons aged 55–64 years, was 44.7 % in 2007, which is 1.2 percentage points higher than in 2006. At regional level, olderworker employment rates ranged from a low of 21.8 % in Śląskie (Poland) to a high of 72.8 % in Småland med öarna (Sweden). The EU27 unemployment rate fell significantly in 2007 by 1 percentage point to 7.2 %, the steepest fall since 2000.
Unemployment is distributed quite evenly throughout the EU. Map 3.2 shows that, in spite of the good performance in 2007, some regions still record a doubledigit unemployment rate. These are mainly located in the south of Spain, the south of Italy and the eastern regions of Germany. Some regions in Slovakia, Poland and Hungary also recorded unemployment rates above 10 % in 2007.
36 Eurostat regional yearbook 2009
3 Labour market
37 Eurostat regional yearbook 2009
Labour market 3Map 3.1: Employment rate for the 15–64 age group, by NUTS 2 regions, 2007
Percentage
38 Eurostat regional yearbook 2009
3 Labour market
Map 3.2: Unemployment rate, by NUTS 2 regions, 2007 Percentage
The lowest levels of unemployment were recorded in all regions in the Netherlands and Austria, the northern parts of Italy and Belgium and the southern parts of the United Kingdom. There are still big differences in regional unemployment rates, ranging in 2007 from 2.1 % in Zeeland (Netherlands) to 25.2 % in Réunion (France).
Longterm unemployment, which is the worse case of unemployment, also fell in 2007. The share of longterm unemployment, i.e. the share of persons looking for a job for more than one year as a percentage of all unemployed, stood at 43 %, a decrease of 2.8 percentage points compared with 2006. This decrease was seen in most EU regions, but two regions recorded a significant increase of more than 10 percentage points in one year, Brabant Wallon (Belgium) and Corse (France).
In all EFTA regions, unemployment was below 5 %. In the candidate countries, the rate ranged from 3.1 % in Kastamonu to 18 % in Mardin (both in Turkey).
Lastly, a brief word on the cohesion of labour markets. In 2007, the dispersion of employment and unemployment rates, which measures regional differences of employment and unemployment levels, decreased from 45.6 to 44.1 for unemployment, and from 11.4 to 11.1 for employment. This means that, overall, the rise in employment and the fall in unemployment were not achieved at the cost of letting some regions lag behind, continuing the fiveyear trend.
Regional work patternsHours usually worked are the hours most commonly or typically worked in a short period of time, e.g. during a week. For each employed person, this indicator shows the number of hours spent working, including regular overtime work and excluding regular absences.
Working time patterns are influenced by several factors, such as different historical and cultural backgrounds, female participation in regional labour markets, specialisation in a specific industry and the share of parttime workers.
Map 3.3 shows the different usual weekly hours of work in a person’s main job. The map reveals two clear facts: the average number of usual weekly hours of work varies considerably among the EU27 and regional differences are larger between countries than within countries (4).
Employed persons living in Greece and in east European countries, e.g. Bulgaria, the Czech Re
public, Poland and Slovakia, tend to spend more time at work, on average, than other European citizens, while employed persons living in the Nordic countries and in the United Kingdom tend to spend less time at work. In 2007 the average number of hours usually spent at work varied from 30.1 hours per week in Groningen and Overijssel (both Netherlands) to 45.7 hours in Notio Aigaio (Greece), which is 1.5 times more than in the two Dutch regions.
It is obvious that the share of parttime workers has a significant influence in lowering the average hours spent at work. Unfortunately no breakdown of average hours worked into parttime workers and fulltime workers is available at regional level.
All regions in the Netherlands record a remarkably low average compared with other regions. The highest value in the Netherlands was found in Flevoland with an average of 31.6 hours per week, which is still 2.4 hours less than in Martinique (France), the region with the lowest value of all regions in the EU27, not counting the Netherlands. This leads us to conclude that the Netherlands is a special case regarding the average time spent at work and the reasons for this will be analysed more in detail later.
Differences in the usual weekly hours of work are not as great among regions in the same country as they are between different EU regions. In fact, the average time spent at work in one region depends less on the region itself than to which country it belongs. Nevertheless, some countries, such as Belgium, Germany and France, record regional differences in the time spent at work.
Two regions recorded significantly higher usual number of hours spent at work than the rest of the country: Praha (Czech Republic) and Inner London (United Kingdom), both capital regions. In the capital region of Greece, the precise opposite was found, with the capital recording a significantly lower average than in other Greek regions.
Significantly lower averages compared with the rest of their respective countries were also observed in Ciudad Autónoma de Ceuta and Ciudad Autónoma de Melilla in Spain, Åland in Finland and in the French overseas departments, Guadeloupe, Martinique, Guyane and Réunion. All these regions are islands or regions that are not contiguous to other country regions (Guyane (France) and the two Spanish autonomous cities). This geographic separation enhanced the marked differences in time patterns, while in contiguous regions the average time spent at work tended to be more similar.
Now let’s look at the factors causing these differences to usual weekly hours spent at work at
39 Eurostat regional yearbook 2009
Labour market 3
(4) This statement can be confirmed in a regression. Some 95 % of the regional variability in time spent at work can be explained with (a) the share of parttime workers, (b) the share of employees, (c) the share of employed persons per economic sector and (d) a country dummy variable. The country effect is very significant in this regression.
40 Eurostat regional yearbook 2009
3 Labour market
Map 3.3: Average number of usual weekly hours of work in main job, by NUTS 2 regions, 2007 Hours
regional level. Most differences in the regional working time can be explained by two other regional labour market indicators: the percentage of parttime workers and the percentage of employees (which means all persons employed, not including selfemployed or family workers). The share of parttime workers in overall employment is responsible for lowering the average weekly hours of work, and the share of employees also seems to have a significant influence on the average time that an employed person spends in his or her job, since selfemployed and family workers tend to spend more time in their jobs (5).
Part-time jobs: lowering the average working timeThe main factor explaining the low average of usual weekly hours of work in main job in a re
gion is the share of parttime workers, and this is quite evident in the Dutch regions. In 2007, the share of employed men working part time was 23.6 % and the share of women working part time was an impressive 75 % in the Netherlands. Having almost a quarter of men and three quarters of women working part time substantially lowers the average of usual weekly hours at work.
Working part time is more a countrylevel characteristic, as shown in Map 3.4, which shows scant regional differences within each country. The map also shows welldefined patterns of the share of parttime workers. These patterns are so well defined that the EU27 regions can be divided into four distinct groups of parttime workers:
• Group 1: the Dutch regions, with a share of 46.8 % of parttime workers;
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Labour market 3
(5) It has, however, to be noted that the statistical measurement of weekly working hours of selfemployed and family workers is quite difficult and hence less reliable than other statistics.
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 AigaioBE 37.1 35.8 Prov. Limburg (B) 38.7 Prov. West-vlaanderenBG 41.6 40.5 Severozapaden 42.4 SeveroiztochenCZ 41.7 40.4 Moravskoslezsko 43.3 PrahaDK 39.5 : : : :DE 35.5 34.1 Bremen 37.4 ThüringenEE 39.5 - - - -IE 36.4 36.1 Border, Midland and Western 36.5 Southern and EasternEL 42.5 41.4 Attiki 45.7 Notio AigaioES 39.3 37.3 Ciudad Autónoma de Ceuta 40.7 GaliciaFR 38.0 34.0 Martinique 39.6 Basse-NormandieIT 38.4 37.2 Calabria 39.1 PiemonteCy 40.2 - - - -Lv 40.7 - - - -LT 38.8 - - - -LU 36.7 - - - -HU 40.2 39.8 Dél-Dunántúl 40.6 Közép-MagyarországMT 39.0 - - - -NL 30.8 30.1 Groningen 31.6 FlevolandAT 38.9 38.2 vorarlberg 39.7 KärntenPL 41.0 37.9 Podkarpackie 41.9 PodlaskiePT 39.0 37.2 Centro (P) 40.1 AlentejoRO 40.5 39.1 Sud — Muntenia 41.4 Bucureşti — IlfovSI 40.3 - - - -SK 41.1 40.1 východné Slovensko 41.7 Západné SlovenskoFI 37.5 36.0 Åland 37.8 Länsi-SuomiSE 36.4 36.2 västsverige 36.7 Övre NorrlandUK 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)
42 Eurostat regional yearbook 2009
3 Labour market
Map 3.4: Share of employees in overall employment, by NUTS 2 regions, 2007 Percentage
43 Eurostat regional yearbook 2009
Labour market 3Map 3.5: Share of part-time workers in overall employment, by NUTS 2 regions, 2007
Percentage
• Group 2: regions in the Nordic EU27 countries, plus Belgium, Germany, Austria and the United Kingdom, which together have an average share of 25 %;
• Group 3: regions in Ireland, Spain, France, Italy, Luxembourg, Malta and Portugal, with an average share of 14.2 %;
• Group 4: the rest of the EU27 regions, mainly from the new Member States, with an average share of parttime workers of 7.2 %.
Over the past five years, the EU27 has recorded an increase of 1.6 percentage points in the share of parttime workers. This increase was recorded in most regions in Group 1 (1.9 percentage points), Group 2 (2.2 percentage points) and Group 3 (2.6 percentage points), as defined above. The opposite trend was recorded in most Group 4 regions, with a decrease in the share of parttime workers of 0.7 percentage points over the last five years.
Turkish regions recorded a relatively low share of parttime workers in 2007 as compared with the EU regions, with 8.8 % of employed persons working part time.
Employees spend less time at workEmployed persons are classified according to their working status. Regional labour market data are
broken down into three categories: employees (which comprises all personnel with a contract of employment), selfemployed and family workers.
The number of hours a person spends at work per week seems to be related to his or her working status, since employees tend to spend less time working per week compared to family workers or selfemployed persons. Map 3.5 shows the regional distribution of the share of employees in overall employment.
The share of employees in total employment tends to be lower compared with other EU regions in almost every region of Greece, Italy, Poland and Romania and in the northwestern part of Spain and in the northern part of Portugal. The share of employees in overall employment at regional level varies from a minimum of 45.8 % in Peloponnisos (Greece) to a maximum of 96.1 % recorded in Bucureşti — Ilfov (Romania).
Apart from some exceptions, like in Romania or in Spain, the share of employees tends to be more or less even within countries, showing that, as with the share of parttime workers, the level of employees depends mostly on the country. Nevertheless, there are some regionspecific differences that could be linked to the type of activity predominant in these regions.
Employee status is closely related to the type of sector in which a person is employed. For in
44 Eurostat regional yearbook 2009
3 Labour market
0
10
20
30
40
50
60
Figure 3.1: Share of employees in overall employment versus share of employed persons in the agriculture sector, by NUTS 2 regions, 2007
Percentage
40 50 60 70 80 90 100
stance, the share of family workers and self employed in agriculture tends to be higher than in other sectors. Agriculture has the lowest share of employees of all sectors. Based on this, we can conclude that rural regions tend to have a lower share of employees, which also tends to lead to a higher average in usual weekly hours of work.
There is a significant negative correlation between the share of employees and the share of employed persons in agriculture, as shown in Figure 3.1.
Each point in Figure 3.1 represents one NUTS 2 region where data was available for 2007. The points roughly align on a downward straight line. That means that regions with higher levels of employment in agriculture are more likely to have lower shares of employees and, consequently, higher averages of weekly time spent at work. At country level, the effect of employment in the agriculture sector is maybe not so significant in explaining differences in the average hours spent at work, since the share of persons working in the agricultural sector is not very high in most countries. But at regional level, especially in rural areas, this is an important factor to consider in order to have a better understanding of different regional time patterns.
To sum up, we can conclude that the average usual time spent at work in a specific region varies significantly throughout the EU27, which is explained not only by the share of parttime workers, the most influential factor, but also by the share of employees, who tend to spend less time at work. The share of employees depends itself on the predominant sector in each region.
While parttime work appears to be influenced more at national level, the average time a person spends at work, the share of employees and the distribution of employment among sectors is influenced more at regional level.
ConclusionThe results presented in this chapter show that 2007 was a year of strong performance regarding both employment and unemployment, and disparities in regional labour markets have narrowed. Nonetheless, the Lisbon employment targets seem unlikely to be achieved. The recession currently faced by Europe and the rest of the world will make the Lisbon employment targets even more difficult to achieve, since labour markets are expected to deteriorate.
The number of hours per week that people usually spend at work was also analysed in this chapter. If we look at working time patterns at regional level, the differences are clearly greater between countries than between regions within the same country, but there are also some regional variations. The average time a person living in a specific region spends at work depends on many factors, such as female participation in the labour market, the share of parttime workers, the share of employees and the predominant sector of activity. All these factors dictate how much free time people have on average.
Although it seems like an odd paradox, the average time people spend at work does not equate to strong labour market or economic performance. In fact, it is precisely the reverse.
45 Eurostat regional yearbook 2009
Labour market 3
Methodological notesThe 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
3 Labour market
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.
47 Eurostat regional yearbook 2009
Labour market 3
Gross domestic product
What is regional gross domestic product?The economic development of a region is, as a rule, expressed in terms of its gross domestic product (GDP). This indicator is also frequently used as a basis for comparisons between regions. But what exactly does it mean? And how can comparability be established between regions of different sizes and with different currencies?
Regions of different sizes achieve different levels of regional GDP. However, a real comparison can be made only by comparing the regional GDP with the population of the region in question. This is where the distinction between place of work and place of residence becomes significant: GDP measures the economic output achieved within national or regional boundaries, regardless of whether this was attributable to resident or nonresident employed persons. The use of GDP per inhabitant is therefore only straightforward if all employed persons involved in generating GDP are also residents of the region in question.
In areas with a high proportion of commuters, regional GDP per inhabitant can be extremely high, particularly in economic centres such as London or Wien, Hamburg, Praha or Luxembourg, and relatively low in the surrounding regions, even if households’ primary income in these regions is very high. Regional GDP per inhabitant should therefore not be equated with regional primary income.
Regional GDP is calculated in the currency of the country in question. In order to make GDP comparable between countries, it is converted into euros, using the official average exchange rate for the given calendar year. However, exchange rates do not reflect all the differences in price levels between countries. To compensate for this, GDP is converted using conversion factors, known as purchasing power parities (PPPs), to an artificial common currency, called purchasing power standard (PPS). This makes it possible to compare the purchasing power of different national currencies (see methodological notes at the end of the chapter).
Regional GDP in 2006Map 4.1 gives an overview of the regional distribution of per inhabitant GDP (as a percentage of the EU27 average of 23 600 PPS) for the European Union, Croatia and the former Yugoslav Republic of Macedonia, which has, for the first time, pro
vided data (for reference years 2004–06) in line with the European system of accounts (ESA 95) transmission programme. It ranges from 25 % of the EU27 average (5 800 PPS) per inhabitant in NorthEast (Romania) to 336 % (79 400 PPS) in the UK capital region of Inner London. The factor between the two ends of the distribution is therefore 13.6:1. Luxembourg at 267 % (63 100 PPS) and Bruxelles/Brussel at 233 % (55 100 PPS) are in positions 2 and 3, followed by Hamburg at 200 % (47 200 PPS) and Groningen (Netherlands) at 174 % (41 000 PPS) in positions 4 and 5.
The regions with the highest per inhabitant GDP are in southern Germany, the south of the UK, northern Italy and Belgium, Luxembourg, the Netherlands, Austria, Ireland and Scandinavia. The capital regions of Madrid, Paris and Praha also fall into this category. The economically weaker regions are concentrated at the southern and western periphery of the Union and in eastern Germany, the new Member States, Croatia and the former Yugoslav Republic of Macedonia.
Praha (Czech Republic), the region with the highest GDP per inhabitant in the new Member States, has 162 % of the EU27 average of 38 400 PPS and is thus in 12th place, whilst Bratislavský kraj (Slovakia) at 149 % (35 100 PPS) is in 19th place among the 275 NUTS 2 regions of the countries examined here (EU27 plus Croatia and the former Yugoslav Republic of Macedonia). However, these two regions must be regarded as exceptions among the regions in the new Member States which joined in 2004, since the next richest regions in the new Member States are far behind: KözépMagyarország (Hungary) at 106 % (24 900 PPS) in position 101, Zahodna Slovenija (Slovenia) at 105 % (24 900 PPS) in position 103 and Cyprus at 90 % (21 300 PPS) in position 161. With the exception of three other regions (Mazowieckie in Poland, Malta and Bucureşti — Ilfov in Romania), all the other regions of the new Member States, Croatia and the former Yugoslav Republic of Macedonia have a per inhabitant GDP in PPS of less than 75 % of the EU27 average.
If we classify the 275 regions considered here by their per inhabitant GDP (in PPS), the following picture emerges: in 2006, GDP in 72 regions was less than 75 % of the EU27 average. These 72 regions are home to 25.2 % of the population (EU27, Croatia and the former Yugoslav Republic of Macedonia), of which three quarters are in the new Member States, Croatia and the former Yugoslav Republic of Macedonia and one quarter are in EU15 countries.
50 Eurostat regional yearbook 2009
4 Gross domestic product
51 Eurostat regional yearbook 2009
Gross domestic product 4Map 4.1: GDP per inhabitant, in PPS, by NUTS 2 regions, 2006
In percentage of EU-27 = 100
At the upper end of the spectrum, 41 regions have a per inhabitant GDP of more than 125 % of the EU27 average; these regions are home to 20.1 % of the population. The regions with per inhabitant GDP of between 75 % and 125 % of the EU27 average are home to 54.7 %, a clear majority of the population of the 29 countries considered here. Some 11.5 % of the population live in regions whose per inhabitant GDP is less than 50 % of the EU27 average; all these regions are in new Member States, Croatia and the former Yugoslav Republic of Macedonia.
Average GDP over the three-year period 2004–06Map 4.2 gives an overview of the average per inhabitant GDP (in PPS) for the years 2004–06. Threeyear averages are particularly important because they are used for the decision as to which regions receive support from the Structural Funds of the European Union.
The map shows a concentration of less developed regions, i.e. with per inhabitant GDP of less than 75 % of the 2004–06 average for the EU27 (22 600 PPS), in southern Italy, Greece and Portugal and in the new Member States, Croatia and the former Yugoslav Republic of Macedonia. In Spain, only Extremadura is still under the 75 % level, and in France only the four overseas departments. All the regions of eastern Germany are now above the 75 % level. Overall, as an average for the period 2004–06, GDP in 72 regions was less than 75 % of the EU27 average; these regions were home to 25.3 % of the population of the 29 countries considered here.
Map 4.2 also shows the particularly prosperous regions of the EU, where GDP is greater than 125 % of the EU27 average. There are 43 of these regions, home to 21.7 % of the population of the EU27 plus Croatia and the former Yugoslav Republic of Macedonia. Contrary to a common misconception, these regions are by no means all in the geographical centre of the Union, but include examples such as EteläSuomi (Finland), Southern and Eastern (Ireland), Madrid (Spain) and Attiki (Greece). However, it is true that many capital cities are among the richest regions, in particular London, Dublin, Bruxelles/Brussel, Paris, Madrid, Wien, Stockholm, Praha and Bratislava.
The new Member States show certain differences in terms of regions with less than 50 % and with between 50 % and 75 % of the EU27 average. Some 33 regions with 12 % of the population have less than 50 %; most of these are in Bulgaria, Ro
mania and Poland. This group also includes two out of the three Croatian regions and the former Yugoslav Republic of Macedonia. On the other hand, all the Czech regions now have GDP of more than 50 % of the EU27 average.
Major regional differences even within the countries themselvesThere are also substantial regional differences 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 22 countries examined here with several NUTS 2 regions. This group includes six of the eight new Member States plus Croatia but only seven of the 14 EU15 Member States.
The largest regional differences are in the United Kingdom, where there is a factor of 4.3 between the highest and lowest values, and in France and Romania, with a factor of 3.5 and 3.4 respectively. The lowest values are in Slovenia, with a factor of 1.5, and in Ireland and Sweden, with a factor of 1.6 in each case. Moderate regional disparities in per inhabitant GDP (i.e. factors of less than 2 between the highest and lowest values) are found only in EU15 Member States, plus Slovenia and Croatia.
In all the new Member States, Croatia and a number of EU15 Member States, a substantial proportion of economic activity is concentrated in the capital regions. Consequently, in 19 of the 22 countries included here in which there are several NUTS 2 regions, the capital regions are also the regions with the highest per inhabitant GDP. For example, Map 4.1 clearly shows the prominent position of the regions around Bruxelles/Brussel, Sofia, Praha, Athens, Madrid, Paris, Lisboa as well as Budapest, Bratislava, London, Warszawa and Zagreb.
A comparison of the extreme values between 2001 and 2006, however, shows that trends in the EU15 have been very different from those in the new Member States. Whilst the gap between the regional extreme values in the new Member States and Croatia is clearly increasing in some cases, it is falling in one out of every two EU15 countries.
Dynamic catch-up process in the new Member StatesMap 4.3 shows the extent to which per inhabitant GDP changed between 2001 and 2006 compared with the EU27 average (expressed in percentage points of the EU27 average). Economically
52 Eurostat regional yearbook 2009
4 Gross domestic product
53 Eurostat regional yearbook 2009
Gross domestic product 4Map 4.2: GDP per inhabitant, in PPS, by NUTS 2 regions, average 2004–06
In percentage of EU-27 = 100
dynamic regions, whose per inhabitant GDP increased by more than 2 percentage points compared with the EU average, are shown in green. Less dynamic regions (those with a fall of more than 2 percentage points in per inhabitant GDP compared with the EU27 average) are shown in orange and red. The range is from +33 percentage points for Bratislavský kraj (Slovakia) to 23 percentage points for EmiliaRomagna in Italy.
The map shows that economic dynamism is well above average in the western, eastern and northern peripheral areas of the EU, not only in EU15 countries but also in the new Member States and Croatia.
Among the EU15 Member States, strong growth can be seen in Greece, Spain, Ireland and parts of the United Kingdom, Finland and Sweden in particular. On the other hand, a trend which started several years ago is continuing: sustained weak growth in certain EU15 countries. Particularly badly hit have been Italy, Belgium and France, where no region achieved the average growth of the EU27 during the fiveyear period 2001–06; half the regions in Germany and Portugal also fell back compared to the EU average.
Of the new Member States and Croatia, where all of the capital regions are very dynamic, the Baltic States, Romania, the Czech Republic, Slovakia,
54 Eurostat regional yearbook 2009
4 Gross domestic product
National average
BE
BG
DK
DE
EE
CZ
EL
ES
FR
CY
IT
LU
LV
LT
HU
NL
MT
AT
PL
RO
SI
SK
FI
SE
UK
HR
MK
PT
0 50 100 150 200 250
Figure 4.1: GDP per inhabitant, in PPS, by NUTS 2 regions, 2006 In percentage of the EU-27 average (EU-27 = 100)
300 350 400
Capital region
MadridExtremadura
Burgenland (A) Wien
MazowieckieLubelskie
LisboaNorte
Sjeverozapadna Hrvatska
Bratislavský krajVýchodnéSlovensko
Észak-Alföld Közép-Magyarország
Dytiki Ellada Attiki
IE Border, Midland and Western Southern and Eastern
Zahodna SlovenijaVzhodna Slovenija
Bucureşti — IlfovNord-Est
West Wales andThe Valleys Inner London
Östra Mellansverige Stockholm
Itä-Suomi Åland
Središnja i Istočna
Hrvatska
Flevoland Groningen
Campania Provincia Autonoma Bolzano/Bozen
Guyane Île de France
Brandenburg-Nordost Hamburg
Sjælland Hovedstaden
Střední Morava Praha
Severo-zapaden Yugozapaden
Hainaut Région de Bruxelles-Capitale/ Brussels Hoofdstedelijk Gewest
55 Eurostat regional yearbook 2009
Gross domestic product 4Map 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
Croatia and most regions of Poland have experienced aboveaverage growth.
Closer analysis of the most dynamic regions shows that 42 of them have growth of more than 7 percentage points above the EU average; of these, 21 are in the new Member States or Croatia.
The fastestgrowing regions are scattered relatively widely across the 29 countries examined here. It is striking, however, that the capital regions continue to have an aboveaverage rate of growth not only in the EU15 countries but also in the new Member States and in Croatia. The non capital region with the strongest growth in the new Member States was Vest (Romania), where per inhabitant GDP (in PPS) increased by 15.3 percentage points between 2001 and 2006, from 29.4 % to 44.7 % of the EU27 average.
A clear concentration in certain Member States is, however, apparent at the lower end of the distribution curve: of the 35 regions which fell by more than 7 percentage points compared to the EU27 average, 20 are in Italy, six in France and three in the UK.
Closer examination of the new Member States and Croatia yields the pleasing result that only four regions fell compared to the EU27 average between 2001 and 2006: DélDunántúl in Hungary (1.1 percentage points), Malta (–1.0), Severozapaden in Bulgaria (0.7) and Kypros/Kıbrıs (0.6).
The catchup process in the new Member States and Croatia was of the order of 1.5 percentage points compared with the EU average per year between 2001 and 2006 and was therefore considerably faster than in the 1990s. Per inhabitant GDP (in PPS) in these 13 countries thus rose from 46.0 % of the EU27 average in 2001 to 53.7 % in 2006. It is feared, however, that the financial crisis which started in mid2008 may mean that this rate of growth cannot be maintained throughout the first decade of the new century.
Different trends even within the countries themselvesA more detailed analysis of trends within the countries between 2001 and 2006 shows that the economic development of regions within a country can be almost as divergent as between regions in different countries.
The largest differences were in the Netherlands, Slovakia and the United Kingdom, where there was a difference of some 30 percentage points relative to the EU27 average for the per inhabitant GDP of the fastest and slowestgrowing regions. The countries with the smallest differences between regions were Ireland and Slovenia, with regional ranges of 0.2 and 4 percentage points respectively, and Croatia and Poland, where the values were around 6 and 9 percentage points respectively.
In both new Member States and EU15 countries, this significantly diverging regional development was the result mainly of dynamic growth in capital regions. However, as the values for Poland and Croatia in particular show, the data available do not confirm the assumption that such regional growth disparities are a typical feature of new Member States or accession countries.
The data also show that the least economically dynamic regions in seven countries attained levels of growth above the EU27 average. It is pleasing to note that, with the exception of Ireland, all of these were in five new Member States or Croatia.
Convergence makes progressThis section addresses the question of the extent to which convergence between the regions of the EU27, Croatia and the former Yugoslav Republic of Macedonia made progress over the fiveyear period 2001–06. Regional convergence of per inhabitant GDP (in PPS) can be assessed in various
56 Eurostat regional yearbook 2009
4 Gross domestic product
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 resident in regions with a GDP per inhabitant of 2001 2006
> 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
ways on the basis of indicators supplied to Eurostat by the national statistical institutes.
A simple approach is to measure the gap between the highest and the lowest values. By this method, the gap closed from a factor of 16.0 in 2001 to 13.6 in 2006. The main reason for this clear convergence was the faster economic growth in Bulgaria and Romania. However, as this approach looks at only the extreme values, it is clear that the majority of shifts between regions are not taken into account.
Another, much more precise, assessment of convergence consists of classifying the regions according to their per inhabitant GDP in PPS. In this way, the proportion of the population of the countries being considered (the EU27 plus Croatia and the former Yugoslav Republic of Macedonia) living in richer or poorer regions, and how this proportion has changed, can be ascertained.
Table 4.1 shows that economic convergence between the regions over the fiveyear period 2001–06 did indeed make clear progress. The proportion of the population living in regions where per inhabitant GDP is less than 75 % of the EU27 average fell from 28.5 % to 25.2 %. At the same time, the proportion of the population living in regions where this value is greater than 125 % fell from 23.0 % to 20.1 %. These shifts at the top and bottom ends of the distribution meant that the proportion of the population in the midrange (per inhabitant GDP of 75–125 %) increased significantly from 48.5 % to 54.7 %, i.e. by more than 35 million persons.
Map 4.4 shows, however, that despite the clear progress made towards convergence overall a comparison between the threeyear periods 1999–2001 and 2004–06 shows that just five regions managed to exceed the 75 % threshold. These were one region each in Greece, Spain, Poland, Romania and the UK. These regions are home to almost 16 million people, or around 3.2 % of the population of the 29 countries considered here. At the same time, however, per inhabitant GDP in four regions fell again below the 75 % threshold in two Italian, one French and one Greek region, with a total population of more than 5 million people, or about 1.1 % of the population of the 29 countries considered here. If both developments are juxtaposed it is found that, as a result of economic development between 1999 and 2006, the population living in regions with a GDP of more than 75 % of the average grew by around 10.6 million people.
These results close to the 75 % threshold, which is important for regional policy, suggest that
poorer regions benefited only marginally during the first half of the decade from increased convergence in the EU.
However, a more detailed analysis shows that many regions with a GDP of less than 75 % of the EU27 average have made considerable progress. The population living in regions with a GDP of less than 50 % of the average fell between 2001 and 2006 by almost a quarter, from 15.3 % to 11.5 %, or 17 million people.
Moreover, examination of the 20 economically weakest regions, where 7.5 % of the population live, shows that this group has progressed as well: per inhabitant GDP in these regions rose between 2001 and 2006 from 28.2 % to 33.2 % of the EU27 average, as a result in particular of the strong catchup process in Bulgaria and Romania.
ConclusionIn 2006, the highest and lowest values of per inhabitant GDP (in PPS) for the 275 NUTS 2 regions in 29 countries (EU27 plus Croatia and the former Yugoslav Republic of Macedonia) examined here differed by a factor of 13.6:1, a figure which is still very high but decreasing over the medium term. Within the individual countries the differences are as much as a factor of 4.3; regional differences in new Member States tend to be greater than in the EU15.
In 2006, per inhabitant GDP (in PPS) in 72 regions was less than 75 % of the EU27 average. Some 25.2 % of the population live in these 72 regions, three quarters of them in new Member States, Croatia and the former Yugoslav Republic of Macedonia and one quarter in EU15 countries. If consideration is broadened to include the threeyear average for 2004–06, an important period for EU structural policy, very similar values are found: 72 regions with 25.3 % of the population achieved less than 75 % of the EU27 average.
If the trends over the fiveyear period 2001–06 are considered, dynamic growth can be seen in certain EU15 countries, particularly in Greece, Spain, Ireland and certain regions of the UK, Finland and Sweden. However, this must be seen against rather disappointing growth in most regions of Belgium, Germany, France, Italy and Portugal.
In the new Member States plus Croatia, significantly aboveaverage growth can be seen primarily in the Baltic countries, Romania, the Czech Republic, Slovakia, Croatia and most regions of Poland.
57 Eurostat regional yearbook 2009
Gross domestic product 4
58 Eurostat regional yearbook 2009
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
The catchup process which has started in the new Member States and Croatia has accelerated significantly compared to the 1990s and continued until 2006 with an annual rate of around 1.5 percentage points compared to the EU27 average. However, not all the regions of the new Member States are yet able to benefit from this to the same extent. This is particularly true of Hungary,
Malta and Poland. All the new Member States and Croatia, considered together, caught up by around 7.7 percentage points to reach 53.7 % of the EU27 average between 2001 and 2006. It is feared, however, that the financial crisis which started in mid2008 may mean that this rate of growth will not be maintained throughout the 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.
59 Eurostat regional yearbook 2009
Gross domestic product 4
Household accounts
Introduction: measuring wealthOne of the primary aims of regional statistics is to measure the wealth of regions. This is of particular relevance as a basis for policy measures which aim to provide support for less welloff regions.
The indicator most frequently used to measure the wealth of a region is regional gross domestic product (GDP). GDP is usually expressed in purchasing power standards (PPS) per inhabitant to make the data comparable between regions of differing size and purchasing power.
GDP is the total value of goods and services produced in a region by the persons employed in that region, minus the necessary inputs. However, owing to a multitude of interregional linkages and state interventions, the GDP generated in a given region does not tally with the income actually available to the inhabitants of the region.
One drawback of regional GDP per inhabitant as an indicator of wealth is that a ‘placeofwork’ figure (the GDP produced in the region) is divided by a ‘placeofresidence’ figure (the population living in the region). This inconsistency is of relevance wherever there are net commuter flows — i.e. more or fewer people working in a region than living in it. The most obvious example is the Inner London region of the UK, which has by far the highest GDP per inhabitant in the EU. Yet this by no means translates into a correspondingly high income level for the inhabitants of the same region, as thousands of commuters travel to London every day to work there but live in the neighbouring regions. Hamburg, Wien, Luxembourg, Praha and Bratislava are other examples of this phenomenon.
Apart from commuter flows, other factors can also cause the regional distribution of actual income not to correspond to the distribution of GDP. These include, for example, income from rent, interest or dividends received by the residents of a certain region, but paid by residents of other regions.
This being the case, a more accurate picture of a region’s economic situation can be obtained only by adding the figures for net income accruing to private households.
Private household incomeIn market economies with state redistribution mechanisms, a distinction is made between two stages of income distribution.
The primary distribution of income shows the income of private households generated directly from market transactions, i.e. the purchase and sale of factors of production and goods. These include in particular the compensation of employees, i.e. income from the sale of labour as a factor of production. Private households can also receive income on assets, particularly interest, dividends and rents. Then there is also income from operating surplus and selfemployment. Interest and rents payable are recorded as negative items for households in the initial distribution stage. The balance of all these transactions is known as the primary income of private households.
Primary income is the point of departure for the secondary distribution of income, which means the state redistribution mechanism. All social bene fits and transfers other than in kind (monetary transfers) are now added to primary income. From their income, households have to pay taxes on income and wealth, pay their social contributions and effect transfers. The balance remaining after these transactions have been carried out is called the disposable income of private households.
For an analysis of household income, a decision must first be made about the unit in which data are to be expressed if comparisons between regions are to be meaningful.
For the purposes of making comparisons between regions, regional GDP is generally expressed in PPS so that meaningful volume comparisons can be made. The same process should therefore be applied to the income parameters of private households. These are therefore converted with specific purchasing power standards for final consumption expenditure called PPCSs (purchasing power consumption standards).
Results for 2006
Primary income
Map 5.1 gives an overview of primary income in the NUTS 2 regions of the 23 countries examined here. Centres of wealth are clearly evident in southern England, Paris, northern Italy, Austria, Madrid and northeast Spain, Flanders, the western Netherlands, Stockholm, NordrheinWestfalen, Hessen, BadenWürttemberg and Bayern. Also, there is a clear north–south divide in Italy and a west–east divide in Germany, whereas in France wealth distribution is relatively uniform between regions. The United Kingdom, too, has a north–south divide, although less marked than the divides in Italy and Germany.
62 Eurostat regional yearbook 2009
5 Household accounts
63 Eurostat regional yearbook 2009
Household accounts 5Map 5.1: Primary income of private households per inhabitant (in PPCS), by NUTS 2 regions, 2006
In the new Member States, it is mainly the capital regions that have relatively high income levels, particularly Bratislava and Praha, where income levels are close to the EU27 average. KözépMagyarország (Budapest), Mazowieckie (Warszawa) and București — Ilfov also have relatively high income levels. The primary income of private households is over half the EU average in all the other Czech regions, in two other Hungarian regions, and in Slovenia and Lithuania, while in all the other regions of the new Member States it is below that level.
The regional values range from 3 197 PPCS per inhabitant in northeast Romania to 35 116 PPCS in the UK region of Inner London. The 10 regions with the highest income per inhabitant include five regions in the UK, three in Germany and one each in France and Belgium. This clear concentration of regions with the highest incomes in the United Kingdom and Germany is also evident when the ranking is extended to the top 30 regions: this group contains 11 German and seven UK regions, along with three each in Italy and Austria, two in Belgium and one each in France, the Netherlands, Spain and Sweden.
It is no surprise that the 30 regions at the tail end of the ranking are all located in the new Member States; the list contains 15 of the 16 Polish regions, seven of the eight Romanian regions, four of the seven Hungarian regions and two of the four Slovakian regions, together with Estonia and Latvia.
In 2006, the highest and lowest primary incomes in the EU regions differed by a factor of 11.0. Five years earlier, in 2001, this factor had been 10.4. There was therefore a slight increase in the gap between the opposite ends of this distribution over the period 2001–06.
Disposable income
A comparison of primary income with disposable income (Map 5.2) shows the levelling influence of state intervention. This particularly increases the relative income level in some regions of Italy and Spain, in the west of the United Kingdom and in parts of eastern Germany and Greece. Similar effects can be observed in the new Member States, particularly in Hungary, Romania, Slovakia and Poland. However, the levelling out of private income levels in the new Member States is generally less pronounced than in the EU15.
In spite of state redistribution and other transfers, most capital regions maintain their promi
nent position with the highest disposable income for the country in question.
Of the 10 regions with the highest disposable income per inhabitant, five are in the United Kingdom, four in Germany, and one in France. The region with the highest disposable income in the new Member States is Bratislavský kraj with 12 309 PPCS per inhabitant, followed by Praha with 12 241 PPCS.
A clear concentration of regions is also evident when the ranking is extended to the top 30 regions: this group contains 11 German and nine UK regions, along with four regions in Austria, three in Italy and one each in Belgium, France and Spain.
The tail end of the distribution is very similar to the ranking for primary income. The bottom 30 include 13 Polish and seven Romanian regions, four in Hungary, two in Slovakia and one in Greece, plus the three Baltic States.
The regional values range from 3 610 PPCS per inhabitant in northeast Romania to 25 403 PPCS in the UK region of Inner London. State activity and other transfers significantly reduce the difference between the highest and lowest regional values in the 23 countries dealt with here from a factor of around 11.0 to 7.0.
In contrast to primary income, there is a significant trend in disposable income towards a narrowing of the range in regional values: between 2001 and 2006 the difference between the highest and lowest values fell from a factor of 8.5 to 7.0.
It can thus be concluded overall that measurable regional convergence between 2001 and 2006 occurred only with regard to the disposable income affected by state intervention; this was not the case with regard to the primary income generated from market transactions.
The regional spread in disposable income within the individual countries is naturally much lower than for the EU as a whole, but varies considerably from one country to another. Figure 5.1 gives an overview of the range of disposable income per inhabitant between the regions with the highest and the lowest value for each country. It can be seen that, with a factor of over 2, the regional disparities are greatest in Romania and Greece. This means that the disposable income per inhabitant in the region of București — Ilfov is more than twice as high as in northeast Romania. With factors of around 1.8, Slovakia, the United Kingdom, Hungary and Italy also have wide regional
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5 Household accounts
65 Eurostat regional yearbook 2009
Household accounts 5Map 5.2: Disposable income of private households per inhabitant (in PPCS), by NUTS 2 regions, 2006 In percentage of EU-27 = 100
variations. For Spain, Poland and Germany the highest value is about two thirds higher than the respective lowest value. The regional concentration is in general higher in the new Member States than in the EU15.
Of the new Member States, Slovenia, with 11 %, has the smallest spread between the highest and lowest values and thus comes very close to Austria, which has the lowest regional income disparities. Ireland, Finland, Sweden and the Netherlands also have only moderate regional disparities, with the highest values ranging between 10 % and 28 % greater than the lowest values.
Figure 5.1 additionally shows that the capital cities of 13 of the 18 countries with more than one
NUTS 2 region also have the highest income values. This group includes four of the six largest new Member States.
The economic dominance of the capital regions is also evident when their income values are compared with the national averages. In four countries (the Czech Republic, Romania, Slovakia and the United Kingdom), the capital cities exceed the national values by more than a third. Only in Belgium and Germany are the values lower than the national average.
To assess the economic situation in individual regions, it is important to know not just the levels of primary and disposable income but also their relationship to each other. Map 5.3 shows this
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National average
BE
DK
DE
EE
CZ
EL
ES
FR
IT
LV
LT
HU
NL
AT
PL
RO
SI
SK
FI
SE
UK
PT
0 2 500 5 000 7 500 10 000 12 500 15 000 17 500 20 000 22 500 25 000 27 500
Figure 5.1: Disposable income of private households per inhabitant (in PPCS), by NUTS 2 regions, 2006
30 000
Notes: DK: data only available at national level FR: without overseas departments
Capital region
HamburgMecklenburg-Vorpommern
Groningen Utrecht
WienKärnten
MazowieckiePodkarpackie
Inner London
Zahodna Slovenija
IE
Severozápad Praha
Bucureşti-IlfovNord-Est
Vzhodna Slovenija
LisboaNorte
Stockholm
Itä-Suomi
Övre Norrland
West Midlands
Åland
Východné Slovensko Bratislavský kraj
Eszak-Alföld Közép-Magyarország
Border, Midland and Western Southern and Eastern
Ionia Nisia Attiki
Extremadura
Campania
País Vasco
Île de France
Prov. Autonoma Bolzano/Bozen
Hainaut Vlaams-Brabant
Nord — Pas-de-Calais
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Household accounts 5Map 5.3: Disposable income of private households as % of primary income, by NUTS 2 regions, 2006
quotient, which gives an idea of the effects of state activity and of other transfer payments. On average, 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 of state intervention and other transfers hardly changed. In general the EU15 Member States have somewhat lower values than the new Member States.
On closer inspection, substantial differences can be seen between the regions of the Member States. Disposable income in the capital cities and other prosperous regions of the EU15 is generally less than 80 % of primary income. Correspondingly higher percentages can be observed in the less affluent areas, in particular on the southern and southwestern peripheries of the EU, in the west of the United Kingdom and in eastern Germany.
This is because in regions with relatively high income levels a larger proportion of primary income is transferred to the state in the form of taxes. At the same time, state social benefits amount to less than in regions with relatively low income levels.
The regional redistribution of wealth is generally less significant in the new Member States than in the EU15. For the capital regions the values are between 80 % and 90 % and are almost without exception at the bottom end of the ranking within each country. This shows that incomes in these regions require much less support through social benefits than elsewhere. The difference between the capital region and the rest of the country is particularly large in Romania and Slovakia, at around 15 percentage points.
In the 23 EU Member States examined here, there is a total of 30 regions in which disposable income exceeds primary income. This applies in particular to 12 of the 16 regions in Poland and four of the eight regions in Romania. In the EU15, the most noticeable instances are six regions of eastern Germany, three regions in Portugal and two in the United Kingdom.
When interpreting these results, however, it should be borne in mind that it is not just monetary social benefits from the state which may cause disposable income to exceed primary income. Other transfer payments (e.g. transfers
from people temporarily working in other regions) can play a role in some cases.
Dynamic development on the edges of the UnionThe focus finally turns to an overview of mediumterm trends in the regions compared with the EU27 average. Map 5.4 uses a fiveyear comparison to show how disposable income per inhabitant (in PPCS) in the NUTS 2 regions changed between 2001 and 2006 compared to the average for the EU27.
It shows, first of all, powerful dynamic processes in action on the edges of the Union, particularly in Spain and Ireland, the Czech Republic, Slovakia, Hungary and the Baltic States.
On the other hand, belowaverage trends in income are apparent in Belgium, Germany, France and especially Italy, where even regions with only average levels of income were affected.
The changes range from +16.4 percentage points for Bucureşti — Ilfov (Romania) to 14.4 percentage points in Liguria (Italy).
Despite overall clear evidence of a catchingup process in the new Member States, the same positive trend is not found everywhere. In seven of Poland’s 16 regions incomes increased by only up to 1.5 percentage points compared with the EU average. The figures for Romania, on the other hand, are very encouraging. With an increase of 16.4 percentage points, the București — Ilfov region achieved the highest relative improvement of all regions, with even the NordEst region (the region with the lowest income in the whole EU) catching up by 4.8 percentage points on average income growth in the EU. The structural problem nevertheless remains that in all the new Member States the wealth gap between the capital city and the less prosperous parts of the country has widened further.
On the whole, the trend between 2001 and 2006 resulted in a slight flattening of the upper edge of the regional income distribution band, caused in particular by substantial relative falls in regions with high levels of income. At the same time, all of the 10 regions at the tail end of the ranking have caught up considerably on the EU average.
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69 Eurostat regional yearbook 2009
Household accounts 5Map 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
ConclusionThe regional distribution of disposable household income differs from that of regional GDP in a large number of NUTS 2 regions, in particular because unlike regional GDP the figures for the income of private households are not affected by commuter flows. In some cases, other transfer payments and flows of other types of income received by private households from outside their region also play a substantial role. In addition, state intervention in the form of monetary social transfers and the levying of direct taxes tends to level out the disparities between regions.
Taken together, state intervention and other influences bring the spread of disposable income between the most prosperous and the economically weakest regions to a factor of about 7.0, whereas the two extreme values of primary income per inhabitant differ by a factor of 11.0. The flattening out of regional income distribution desired by most countries is therefore being achieved.
The income level of private households in the new Member States continues to be far below that in the EU15; in only a small number of capital re
gions are income values more than three quarters of the EU average.
An analysis over the fiveyear period 2001–06 shows that incomes in many regions of the new Member States are catching up only very slowly. This applies in particular to certain regions of Poland. In Romania, on the other hand, a strong catchingup process has taken hold — a development which, happily, extends beyond the capital region of București — Ilfov.
For disposable income there is a measurable trend towards a narrowing of the spread in regional values: between 2001 and 2006 the difference between the highest and lowest values fell from a factor of 8.5 to 7.0, while for primary income the differences between regions increased from a factor of 10.4 to 11.0.
With regard to the availability of data concerning income it may be said that the comprehensiveness of the data and the length of the time series have gradually improved. Once a complete data set is available, data on the income of private households could be taken into account alongside GDP statistics when decisions are taken on regional policy measures.
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5 Household accounts
Methodological notesEurostat 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.
71 Eurostat regional yearbook 2009
Household accounts 5
Structural business statistics
IntroductionWhat effects do the European Union’s economic and regional policies have on the business structure of the regions? What sectors are growing, what sectors are contracting and what regions are likely to be most affected? A detailed analysis of the structure of the European economy can only be made at regional level. Regional structural business statistics (SBS) provide data with a detailed activity breakdown that can be used for this kind of analysis. The first part of this chapter looks at regional specialisation and business concentration within the EU’s business economy. The second part analyses the activity of the business services sector in detail.
Regional specialisation and business concentrationThere are significant disparities between European regions in terms of the importance of dif
ferent activities within the business economy. While some activities are distributed relatively evenly across most regions, many others exhibit a considerable variation in the level of regional specialisation, often with a few regions having a particularly high degree of specialisation.
The share of a particular activity within the business economy gives an idea of which regions are the most or least specialised in that activity, regardless of whether the region or the activity considered is large or small. There are various reasons for relative specialisation. Depending on the type of activity, these can include availability of natural resources, availability of skilled employees, culture and tradition, cost levels, infrastructure, legislation, climatic and topographic conditions and proximity to markets.
Figure 6.1 shows that, on an aggregate activity level (NACE sections), the widest spread in the relative importance of an activity in each region’s nonfinancial business economy (NACE sections C to
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6 Structural business statistics
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
0 5 10 15 20 25 30 35 40 45 50 55 60 65
Hotels and restaurants(H 55)
Transport, storageand communication
(I 60–64)
Construction(F 45)
Real estate, renting andbusiness activities
(K 70–74)
Manufacturing(D 15–37)
Distributive trades(G 50–52)
Electricity, gasand water supply
(E 40–41)
Mining and quarrying(C 10–14)
Západné Slovensko (SK02)
Andalucía (ES61)
Dytiki Ellada (GR23)
Ionia Nisia (GR22)
Åland (FI20)
Inner London (UKI1)
Agder og Rogaland (NO04)
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)
Sud-Vest Oltenia (RO41)
I and K) workforce was in manufacturing (NACE section D). Manufacturing accounted for only 3.1 % of people employed in Ciudad Autónoma de Melilla (Spain) and under 10 % in a further 13 regions, including the capital regions of both Spain and the United Kingdom. The distribution of the remaining regions was relatively symmetrical, from 10 % to almost half of the workforce in two Czech and two Slovak regions: Střední Morava (Czech Republic) and Východné Slovensko (Slovakia) — both 48.0 % — and Severovýchod (Czech Republic) and Stredné Slovensko (Slovakia) — both 48.8 %. Západné Slovensko (Slovakia) was the only region where the share of employment in manufacturing exceeded half the nonfinancial business economy workforce (57.8 %). In contrast, the spread of employment was much narrower in distributive trades (NACE section G), which was the activity displaying the highest median employment, present in all regions and serving more local clients. Shares ranged from less than 17 % in Åland and LänsiSuomi (Finland) to just over 40 % in Anatoliki Makedonia, Thraki, Kriti and Kentriki Makedonia (Greece), and almost 45 % in Dytiki Ellada (Greece).
On the other hand, transport, storage and communication (NACE section I) and mining and quarrying (NACE section C) are two activities with a similar relative size in most regions, but where there are a few strong outlier regions that are highly specialised. Transport, storage and communication accounted for not more than 7.1 % in a quarter of the regions and less than 10.1 % in three quarters of them. These narrow ranges are mainly due to the fact that road transport and post and telecommunications account for a large share of employment in this sector and that these activities tend to be of relatively equal importance across most regions. There were only three regions, for example, where the share of employment in transport, storage and communication exceeded 20 %.The highest specialisation of the Finnish island region of Åland, where almost half of the workforce (47.9 %) was employed in this sector, is due almost exclusively to the importance of water transport. Åland was far ahead of Köln in Germany (31.3 %), where post and telecommunications was particularly important, and Bratislavský kraj (23.8 %), the capital region of Slovakia, owing to the importance of road and other land transport. Natural endowments play an important role in the activities of mining and quarrying. Many regions record little or no such activity, with only a very few of them being highly specialised on account of deposits of metallic ores, coal, oil or gas. Mining and
quarrying accounted for less than 0.2 % of people employed in a quarter of all regions, and between 0.2 % and 0.5 % in half of the regions. However, this sector accounted for over 5 % in six regions and as much as a 10th of the total nonfinancial business economy workforce in Śląskie (Poland) and Agder og Rogaland (Norway).
Table 6.1 shows which region was the most specialised in 2006 on a more detailed activity level (all NACE divisions within each NACE section) and, as a comparison, the median and average share of the nonfinancial business economy workforce among all regions within the EU27 and Norway. Manufacturing activities which involve the primary processing stages of agricultural, fishing or forestry products are particularly concentrated in areas close to the source of the raw material. The regions most specialised in food and beverages manufacturing (NACE 15) were all located in rural areas in or close to agricultural production centres: Bretagne (the most specialised of all the regions) and Pays de la Loire in France, Lubelskie, Podlaskie and Warmińskomazurskie in the eastern part of Poland, DélAlföld in Hungary, and La Rioja in Spain. Heavily forested Nordic and Baltic regions were the regions most specialised in the manufacture of wood and wood products (NACE 20) and in the related manufacturing of pulp, paper and paper products (NACE 21). ItäSuomi (Finland) was the most specialised region in wood and wood products and Norra Mellansverige (Sweden) in pulp and paper.
Regions traditionally associated with tourism, in particular in Spain, Greece and Portugal, were the most specialised in hotels and restaurants (NACE 55). Hotels and restaurants accounted for more than 20 % of the workforce in the Greek island regions of Ionia Nisia and Notio Aigaio, the Spanish Illes Balears, the Algarve in the south of Portugal, Provincia Autonoma Bolzano/Bozen in the northeast of Italy on the border with Austria and the region of Cornwall and Isles of Scilly (United Kingdom).
Greek regions were the most specialised in distributive trades (NACE G 50–52), with the exception of motor trades (NACE 50), where the Italian region of Molise had the highest specialisation. Construction activities (NACE 45) accounted for the highest shares of the workforce in Spanish regions. Transport services are also influenced by location, with water transport (NACE 61) naturally being important for coastal regions and islands, while air transport (NACE 62) is also important for many island regions (especially those with a
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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
Activity (NACE)All regions Most specialised region
Median share (%)
Average share (%) Name (NUTS 2 region) Share of the
region (%)
Mining and quarrying (C 10–14) 0.3 0.6 Agder og Rogaland (NO04) 10.4Coal, lignite and peat (10) 0.0 0.2 Śląskie (PL22) cCrude petroleum and natural gas (11) 0.0 0.1 Agder og Rogaland (NO04) 10.0Uranium and thorium ores (12) 0.0 0.0 Severovýchod (CZ05) cMetal ores (13) 0.0 0.0 Övre Norrland (SE33) cOther mining and quarrying (14) 0.2 0.2 Alentejo (PT18) c
Manufacturing (D 15–37) 25.0 26.2 Západné Slovensko (SK02) 56.9Food and beverages (15) 3.6 3.8 Bretagne (FR52) 11.1Tobacco products (16) 0.0 0.1 Trier (DEB2) cTextiles (17) 0.4 0.7 Prov. West-vlaanderen (BE25) 5.6Wearing apparel; fur (18) 0.3 0.9 Dytiki Makedonia (GR13) 11.5Leather and leather products (19) 0.1 0.4 Marche (ITE3) 7.7Wood and wood products (20) 0.8 1.2 Itä-Suomi (FI13) 5.8Pulp, paper and paper products (21) 0.5 0.6 Norra Mellansverige (SE31) 4.7Publishing and printing (22) 1.1 1.2 Inner London (UKI1) 4.2Fuel processing (23) 0.0 0.1 Cumbria (UKD1) cChemicals and chemical products (24) 1.0 1.3 Rheinhessen-Pfalz (DEB3) 11.6Rubber and plastic products (25) 1.2 1.4 Auvergne (FR72) 7.8Other non-metallic mineral products (26) 1.1 1.3 Prov. Namur (BE35) 5.3Basic metals (27) 0.5 1.0 Norra Mellansverige (SE31) 9.6Fabricated metal products (28) 2.7 3.0 Arnsberg (DEA5) 8.7Machinery and equipment (29) 2.2 2.7 Unterfranken (DE26) 12.2Office machinery and computers (30) 0.0 0.1 Southern and Eastern (IE02) 1.4Electrical machinery and apparatus (31) 0.9 1.3 Západné Slovensko (SK02) 9.8Radio, Tv and communication equipment (32) 0.3 0.6 Pohjois-Suomi (FI1A) 6.1Medical, precision and optical equipment (33) 0.6 0.7 Border, Midland and Western (IE01) 5.9Motor vehicles and (semi)-trailers (34) 0.8 1.7 Braunschweig (DE91) cOther transport equipment (35) 0.5 0.8 Agder og Rogaland (NO04) 6.3Furniture and other manufacturing (36) 1.1 1.4 Warmińsko-mazurskie (PL62) 8.0Recycling (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) 5.5Electricity, gas and hot water supply (40) 0.8 1.0 Martinique (FR92) 4.8Water supply (41) 0.2 0.3 východné Slovensko (SK04) 1.9
Construction (F 45) 10.4 10.9 Andalucía (ES61) 28.6Distributive trades (G 50–52) 26.2 26.1 Dytiki Ellada (GR23) 44.8
Motor trades (50) 3.5 3.7 Molise (ITF2) 9.3Wholesale trade (51) 7.2 7.4 Kentriki Makedonia (GR12) 15.1Retail 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) 33.8Transport, 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) 15.8Water transport (61) 0.1 0.4 Åland (FI20) 38.7Air transport (62) 0.0 0.2 Outer London (UKI2) 3.9Supporting transport activities (63) 1.7 1.9 Bremen (DE50) 11.1Post 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) 49.1Real estate activities (70) 2.0 2.0 Latvija (Lv00) 5.6Renting (71) 0.4 0.5 Hamburg (DE60) 1.7Computer activities (72) 1.4 1.7 Berkshire, Buckinghamshire and Oxfordshire (UKJ1) 8.0Research and development (73) 0.2 0.0 voreio Aigaio (GR41) 4.8Other business activities (74) 12.7 13.6 Inner London (UKI1) 38.3Notes: 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
developed tourism industry), and for regions with or close to major cities. The small island region of Åland (Finland) is a centre for the ferry services between Sweden and Finland and other Baltic Sea traffic. Åland was very highly specialised in water transport, which accounted for almost 40 % of people employed in 2006 — over 10 times more than the next most specialised regions, Hamburg in Germany and Agder og Rogaland in Norway. Outer London was the region most specialised in air transport, followed by NoordHolland (Dutch region of Amsterdam), the French island of Corse, Köln in Germany and the Illes Balears in Spain.
As with air transport, specialisation in real estate, renting and business activities (NACE 70–74) may be based on access to a critical mass of clients (enterprises or households) or to a knowledge base (external researchers and qualified staff). Within the countries themselves, the capital region or other large metropolitan regions were normally among the most specialised in the business services sectors: computer services (NACE 72) and other business activities (NACE 74). A detailed analysis of the business services sector is included in the last part of this chapter. Latvia was most specialised in real estate (NACE 70) in 2006, ahead of Algarve (Portugal) and Inner London (United Kingdom), while Hamburg was most specialised in renting, ahead of the French overseas departments of Guadeloupe and Martinique.
While an analysis of specialisation shows the relative importance of different activities in the regions, regardless of the size of the region or the activity, an analysis of concentration looks at the dominance of certain regions within an activity, or activities, within a region. In most activities, there are many examples of regions that are highly ranked in terms of both specialisation and concentration. Figure 6.2 shows the extent to which employment in certain activities was concentrated in a limited number of regions in 2006. Four of the five mining and quarrying activities topped the rankings based on the share of total employment in the EU27 and Norway, as accounted for by the 10 regions with the largest workforces. The most concentrated was the mining of uranium and thorium ores (NACE 12), with people employed in only seven of the 262 regions (for which data are available) in 2006.
Air transport (NACE 62) and leather and leather products manufacturing (NACE 19) were also highly concentrated in the 10 largest regions, which together accounted for 62 % and 53 % of total employment respectively. In the case of air transport,
this dominance is due to the concentration in large metropolitan regions where the large airports are situated: chief among them the regions of Paris, Outer London, Köln, Amsterdam and Madrid. Leather and leather products manufacturing, on the other hand, is a small activity in Europe, heavily concentrated in Italy, Portugal and Romania: five of the 10 regions with the largest workforces were situated in Italy, three in Romania and one each in Portugal and Spain. The region with the largest workforce was Norte in Portugal, with 43 000 people employed. This region alone accounted for more than 8 % of the total leather manufacturing workforce in the EU27 and Norway.
In contrast to the more specialised types of mining and quarrying, other mining and quarrying (NACE 14) was among the activities in which the 10 largest regions were least dominant, accounting for only 17 % of total sectoral employment. This is due to the widespread availability and local sourcing of many construction materials, such as sand and stone, which dominate this type of mining in most regions. Of all the activities (NACE divisions), only retail trade (NACE 52), food and beverages manufacturing (NACE 15) and motor trades (NACE 50) had a lower concentration in 2006, but, in contrast to other mining and quarrying, these are all major activities in terms of employment in the EU.
Post and telecommunications (NACE 64) and motor vehicles manufacturing (NACE 34) are examples of major activities that were relatively highly concentrated in a few regions.
Map 6.1 gives an indication of how concentrated or diversified the regional business economy was in 2006, measured as the share of the five largest activities (NACE divisions) in the total nonfinancial business economy workforce. The level of concentration tends to be highest in regions where trade and services dominate the business economy, as industrial activities are more fragmented. By this measure, the most concentrated regions were generally in countries traditionally associated with tourism (in particular Spain, Greece and Portugal), underlining the importance of construction, trade, and hotels and restaurants in tourismoriented regions.
However, high concentrations were also recorded in several densely populated areas, such as the southeast of the United Kingdom, most parts of the Netherlands and also the capital region in most countries (at least relative to the national average). The situation was similar in most countries — the capital region was usually among the
77 Eurostat regional yearbook 2009
Structural business statistics 6
78 Eurostat regional yearbook 2009
6 Structural business statistics
0 10 20 30 40 50 60 70 80 90 100
1−10Regions ranked: 11−20 21−50 51−262
Motor trades (50)
Food and beverages (15)
Retail trade and repair (52)
Other mining and quarrying (14)
Rubber and plastic products (25)
Pulp, paper and paper products (21)
Recycling (37)
Hotels and restaurants (H55)
Wholesale trade (51)
Land transport and pipelines (60)
Wood and wood products (20)
Other non-metallic mineralproducts (26)
Furniture and othermanufacturing (36)
Renting (71)
Electronic machineryand apparatus (31)
Water supply (41)
Other transport equipment (35)
Construction (45)
Electricity, gas and hot watersupply (40)
Fabricated metal products (28)
Machinery and equipment (29)
Real estate activities (70)
Medical, precision and opticalinstruments (33)
Publishing and printing (22)
Supporting transport activities (63)
Other business activities (74)
Radio, TV and communicationequipment (32)
Motor vehicles and(semi)-trailers (34)
Basic metals (27)
Chemicals and chemicalproducts (24)
Research and development (73)
Computer activities (72)
Office machineryand computers (30)
Fuel processing (23)
Tobacco prodcuts (16)
Wearing apparel; fur (18)
Water transport (61)
Textiles (17)
Post and telecommunications (64)
Leather and leather products (19)
Air transport (62)
Crude petroleumand natural gas (11)
Coal, lignite and peat (10)
Metal ores (13)
Uranium and thorium ores (12)
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
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)
79 Eurostat regional yearbook 2009
Structural business statistics 6Map 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
regions with the highest business concentration and was often top of the list.
In contrast, the lowest business concentrations were recorded mainly in regions with a relatively small services sector and a large manufacturing sector in eastern Europe (in particular in Slovakia, the Czech Republic, Hungary, Romania and Bulgaria), although low shares were also recorded in Sweden (except the capital region) and Finland (except the island region of Åland). The five largest activities accounted for less than 40 % of total employment in Západné Slovensko (Slovakia), Severovýchod (the Czech Republic), Vest (Romania) and Stredné Slovensko (Slovakia).
Figure 6.3 provides a more detailed analysis of the most specialised regions. Among the top 10 regions, Inner London stands apart as the only large metropolitan region with a fundamentally different business profile. Here, other business activities dominate, accounting for 38 % of total employment, which is much higher than in all the other regions shown. In addition, real estate
activities (NACE division 70) are among the top five activities in Inner London (and not construction), whereas in all other regions shown the top five activities in terms of employment were retail trade, construction, hotels and restaurants, other business activities and wholesale trade. In fact, looking at all regions for which data are available, retail trade is among the five largest activities (NACE divisions) in every region, other business activities is among the five largest in more than 90 % of the regions, construction and wholesale trade in more than 80 % of the regions, and hotels and restaurants in more than 60 % of the regions.
Specialisation in business servicesThe services sector is an important and growing area of the EU economy which in recent years has attracted increasing political and economic interest. In 2006, real estate, renting and business activities (NACE section K) made up a third of this sector in terms of employment, and was second by only 7 percentage points to distributive trades.
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6 Structural business statistics
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)
10.57.5 14.2 13.5 24.7 7.3 32.9
23.9 11.6 22.0 10.1 6.1 26.3
13.0 38.312.7 4.94.8 26.2
18.2 22.614.5 8.710.2 25.7
19.2 12.426.1 11.37.0 24.0
10.0 26.123.4 10.46.5 23.6
23.1 19.917.7 7.49.4 22.4
29.9 10.124.6 8.56.1 20.8
33.8 9.822.4 8.06.6 19.5
12.6 24.225.0 9.59.6 19.1
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)
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)
Ionia Nisia (GR22)
Notio Aigaio (GR42)
Algarve (PT15)
Ceuta (ES63)
Kriti (GR43)
Canarias (ES70)
Inner London (UKI1)
Illes Balears (ES53)
Comunidad de Madrid (ES30)
The importance of this sector, measured as the share in the total workforce of the nonfinancial business economy, has been seen to increase in recent years. The structure of employment in this sector is shown in Figure 6.4.
It can be observed that three quarters of the workforce in 2006 was divided between other business services (NACE 74), which include many highly specialised knowledgeintensive activities such as legal, accounting and management services, architectural and engineering activities, advertising, and the supply of personnel and placement services provided by labour recruitment agencies. Security and industrial cleaning services are also included, as are secretarial, translation, packaging and other professional business services. A significant share, of just over 10 %, was taken up by computer activities (NACE 72), which cover consultancy for hardware and software, data processing, database activities and the maintenance and repair of office and information technology machinery. This sector is at the forefront of the information society, with enterprises that support clients in a broad
range of areas, in almost all economic activities. It is quite common for enterprises to outsource their requirements for hardware and software to specialist providers. The possibility to trade such as services across borders has been increased by improved telecommunications, notably growing access to broadband Internet. Those two divisions together (NACE 72 and 74) make up the business services sector.
All the divisions within the section of real estate, renting and business activities noted positive growth rates in employment in 2006 (see Figure 6.5). Besides research and development (NACE 73), all rates were significant. The growth rate for computer activities reached 3.3 % and for other business activities 7.3 % — and it exceeded the average growth rate for the whole section. The business services sector was quite clearly one of the most dynamic sectors in the nonfinancial business economy in terms of employment growth. One of the prime reasons for the rapid growth of this sector could be the outsourcing phenomenon. Business services can be produced either in
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Structural business statistics 6
Figure 6.4: Structure of employment in real estate, renting and business activities (NACE section K) by divisions, EU-27 and Norway, 2006
Renting (K 71)2.4 %
Computer activities (K 72)10.6 %
Other businessactivities (K 74)
74.4 %
Real estate activities (K 70)11.0 %
Research anddevelopment (K 73)
1.6 %
Notes: Excluding MT, North Eastern Scotland (UKM5) and Highlands and Islands (UKM6) (data not available) CY excluding Research and development (K 73)
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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
ternally by an enterprise itself or they can be purchased. Many enterprises have outsourced some of the services activities they previously produced inhouse in a bid to procure these services on a competitive market and thus to reduce costs and increase flexibility. Business services enterprises enable their clients to focus on their core business activities and lessen their need to employ their own personnel in ancillary or support functions.
Map 6.2 shows how specialised different regions were in business services, from which a clear pattern of high concentration in large metropolitan areas emerges. The capital region is the most specialised region in all countries except the Netherlands, where NoordHolland (which includes Amsterdam) was just behind Utrecht. Of the top 20 regions with shares exceeding 25 %, six were British, five Dutch and three German. Luxembourg (23 %) and the Netherlands were particularly specialised in these activities, which account for a minimum of 17 % of people employed in all Dutch regions. In the United Kingdom, there is a high degree of specialisation in the regions around London and other metropolitan areas such as Greater Manchester and West Midlands. There is also a relatively high share of people employed in business services in South Western Scotland, partly stemming from the location of many call centres in the region. There was also a significant cluster of
regions with very high specialisation in business services in Germany, in a belt from the region of Oberbayern in the southeast to Hannover.
Figure 6.6 shows the difference in the degree of specialisation in business services across countries and between the regions with the highest and lowest values in each country. The graph also clearly illustrates the dominance of the capital region, which is the most specialised in all countries except the Netherlands. There are just as large differences in specialisation within these countries as there are between them.
Business services in the most specialised country, the Netherlands, account on average for 28.5 % of people employed; around four times more than in the least specialised country, Cyprus. The same factor also differentiates between the most and least specialised region in the four countries with the largest regional disparities. Interestingly, these include two of the countries with the lowest average specialisation, Slovakia and Romania, and also one of the most specialised countries, the United Kingdom. The greatest difference between the most and the least specialised region within one country (4.3 times) was observed in Spain. At the other end of the scale are the Netherlands and Ireland, with a factor lower than 2 differentiating between the regions with the highest and lowest values.
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Structural business statistics 6
0 1 2 3 4 8765
Notes: Excluding MT, North Eastern Scotland (UKM5) and Highlands and Islands (UKM6) (data not available) CY excluding Research and development (K 73)
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,business activities (K)
Real estate activities (K 70)
Renting (K 71)
Computer activities (K 72)
Researchand development (K 73)
Other business activities (K 74) 7.2
0.1
3.3
5.5
7.8
6.6
Employment growth in business servicesEmployment in business services in the EU27 grew by an impressive 40 % between 1999 and 2006. Map 6.3 shows the growth rate of employment in 2006 in business services. In total, 18 out of the group of 34 regions with the highest growth rate exceeding 20 % were French and the next six were Dutch. The two Irish regions were also included in this group. Only one region from the countries that joined the EU in 2004 or 2007 is in this top list, namely the Romanian Sud — Muntenia in 33rd place.
About one in every six regions recorded negative employment growth rates, but in only 10 cases did the decrease reach 10 %. Half of these were Greek regions and two of them Belgian.
Characteristics of the top 30 most specialised regions in business servicesFigure 6.7 provides information on the top 30 most specialised regions in business services. The most specialised of all regions is Inner London (United Kingdom), where just under 650 000 people — or over 40 % of the total nonfinancial business economy workforce — are employed in these activities. Only one region from the countries that joined the EU in 2004 or 2007 is in the top 30: the capital region of the Czech Republic in 26th place.
The number of people employed also grew considerably in many of the topranked regions in 2006, with by far the highest growth rate, higher
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6 Structural business statistics
National average
NL
UK
BE
FR
LU
DE
DK
SE
IE
PT
IT
NO
ES
HU
FI
AT
EL
EE
SI
PL
SK
RO
LV
BG
LT
CY
CZ
0 5 10 15 20 25 30 35 40 45 50
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
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)
Sterea Ellada Attiki
Oberfranken Berlin
Corse Île de France
Cumbria Inner London
Zeeland Utrecht
Prov.Luxembourg (B)
Région de Bruxelles-Capitale /Brussels Hoofdstedelijk Gewest
Bratislavský krajZápadnéSlovensko
Nord-Est
MazowieckieLubelskie
Észak-Magyarország Közép-Magyarország
Ciudad Autónoma de Ceuta Comunidad de Madrid
Nord-Norge Oslo og Akershus
Provincia AutonomaBolzano/Bozen Lazio
Severovýchod Praha
Burgenland (A) Wien
Åland Etelä-Suomi
Centro (P) Lisboa
Border, Midlandand Western Southern and Eastern
Småland med öarna Stockholm
Bucureşti — Ilfov
85 Eurostat regional yearbook 2009
Structural business statistics 6Map 6.3: Growth rates of employment in business services (NACE divisions K 72 and K 74),
by NUTS 2 regions, 2005–06
86 Eurostat regional yearbook 2009
6 Structural business statistics
Share of non-financial business economyemployment of the region (%)
Region's share of total business services employment (%)
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)
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
0 10 20 30 40 50
Inner London (UKI1)
Utrecht (NL31)
Région de Bruxelles-Capitale/Brussels Hoofdstedelijk
Gewest (BE10)
Noord-Holland (NL32)
Berkshire, Buckinghamshireand Oxfordshire (UKJ1)
Berlin (DE30)
Groningen (NL11)
Zuid-Holland (NL33)
Île de France (FR10)
Prov. Vlaams-Brabant (BE24)
Comunidad de Madrid (ES30)
Lisboa (PT17)
Flevoland (NL23)
Surrey, East andWest Sussex (UKJ2)
Darmstadt (DE71)
Stockholm (SE11)
Hamburg (DE60)
Outer London (UKI2)
Noord-Brabant (NL41)
Hampshire andIsle of Wight (UKJ3)
Bedfordshire andHertfordshire (UKH2)
Gelderland (NL22)
Limburg (NL) (NL42)
Düsseldorf (DEA1)
Cheshire (UKD2)
Praha (CZ01)
Oslo og Akershus (NO01)
Overijssel (NL21)
Wien (AT13)
Lazio (ITE4)
0.62
0.68
0.86
1.44
0.94
0.12
0.20
0.64
2.86
0.56
0.91
1.36
0.35
1.13
1.41
1.32
3.69
5.06
1.45
0.97
1.40
0.39
0.66
0.42
24.01.39
0.3724.4
24.5
24.6
24.6
24.7
25.4
25.4
25.8
25.9
26.3
28.1
28.2
28.5
28.5
28.7
30.3
31.7
32.7
33.6
34.6
43.2
29.3
30.7
31.3
29.9
27.7
27.8
27.3
24.4
0.56
0.36
0.64
0.64
than 30 %, in the Dutch regions of Limburg and Groningen. Strong growth of over 20 % was also recorded in NoordBrabant, Flevoland, NoordHolland and Overijssel (Netherlands), and also in Prov. VlaamsBrabant (Belgium). Regions with already high concentrations in business services were aiming for even greater specialisation. Only four regions from the top 30, three British and the capital region of France, recorded reductions in the number of people employed in business ser vices, but none of them dropped by more than 6 %.
ConclusionRegional structural business statistics offer users wanting to know more about the structure and development of the regional business economy a detailed, harmonised data source, describing for
each activity the number of workplaces, number of people employed, wage costs and investments made. This chapter has shown how some of these data can be used to analyse different regional business characteristics: the focus, diversity and specialisation of the regional business economies and the nature and characteristics of regional business services activities. The analysis in this chapter has generally confirmed the positive expectations for the business services sector, reinforcing the belief that this area will remain one of the key drivers of competitiveness and job creation within the EU economy in the coming years.
Globalisation, international market liberalisation and further technological gains are likely to lead to further integration among Europe’s regions (and beyond), bringing buyers and sellers of these services closer together.
Methodological notesRegional 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.
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Structural business statistics 6
Information society
IntroductionDuring recent decades information and communication technologies (ICTs) have penetrated all areas of economic and social life. ICTs have accounted for a significant increase in productivity of the economy and growth of GDP. As a driver for social modernisation they are transforming our societies in a profound and unprecedented way. The introduction of the Internet and the Word Wide Web has led the development of the information society. With access to the Internet it is very easy to obtain information on almost all topics. Search engines provide easy, fast access to websites and information sources on the World Wide Web. Many activities such as communicating and selling or buying goods and services can be performed online. These developments have created new dimensions of economic, social or political participation for individuals or groups of individuals. As these activities are not bound to any specific geographic place, they have the potential of bridging large distances. In principle, the geographic place from where these activities are performed does not matter any more as long as there is a connection to the Internet. Nowadays, it is possible to keep up contacts with family members or friends via social networking sites, share 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 via the Internet. ICTs support working from home or from other places outside the enterprise, making for greater flexibility in work organisation, from which both the enterprise and the employee can benefit. The ubiquitous presence of ICTs carries the potential for completely new ways of participating in the economy and society.
As a basic condition, the participation of citizens and businesses in the information society depends on access to ICTs, i.e. the presence of electronic devices, such as computers, and connections to the Internet. The term ‘digital divide’ has been introduced to distinguish between those who have access to the Internet and are able to make use of new services offered on the World Wide Web and those who are excluded from these services. The term explicitly includes access to ICTs as well as the related skills needed to participate in the information society. The digital divide can be classified according to criteria that describe the difference in participation according to gender, age, education, income, social groups or geographic location. This chapter puts emphasis on the geographic aspects of the digital divide.
Policies within the European Union at national and European level have recognised the importance of bridging the digital divide to give citizens equal access to information and communication technologies. The Riga ministerial declaration on einclusion of November 2006 (6) calls for an inclusive information society and sets the framework for a comprehensive einclusion policy addressing different aspects of the digital divide, such as age, accessibility, geography, digital literacy and competences, cultural diversity and inclusive online public services. European statistics play the role of benchmarking the development of the European information society towards these political goals. The key benchmarking indicators are defined in the European Commission’s i2010 benchmarking framework (7), which followed on from the i2010 strategy ‘A European information society for growth and employment’ (8). The i2010 strategy promotes the positive contribution that ICTs can make to the economy, society and quality of life.
Statistics for the European Union and EFTA countries on the access to and use of ICTs in households/by individuals and in enterprises have been collected annually by Eurostat since 2003. Regional statistics for households and individuals have been available since 2006.
Access to information and communication technologiesAccess to information and communication technologies is at the heart of the digital divide and geographic location is one aspect of that divide. Regional statistical data on access to the Internet within households and the availability of broadband for going online exist at European level. In contrast to supplyside statistics, the Eurostat figures show the actual uptake of ICTs by the population. On average, 60 % of households in Europe with members aged 16–74 years had access to the Internet at home and almost half (49 %) of households accessed the Internet via broadband in 2008. These figures have grown rapidly in recent years, with an annual growth rate of 10 % for Internet access and 26 % for broadband access between 2006 and 2008. While access to the Internet makes it possible to participate in the information society, broadband connections enable Internet users to fully exploit the potential of the Internet. Many of the advanced Internet services, such as social networking sites, uploading and downloading of media content (video and audio files) or the use of online maps and satellite im
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7 Information society
(6) http://ec.europa.eu/information_society/events/ict_riga_2006/doc/declaration_riga.pdf
(7) http://ec.europa.eu/information_society/eeurope/i2010/benchmarking/index_en.htm
(8) http://eurlex.europa.eu/LexUriServ/LexUriServ.do?uri=CELEX:52005 DC0229:EN:NOT
91 Eurostat regional yearbook 2009
Information society 7Map 7.1: Internet access and broadband connections in households, by NUTS 2 regions, 2008 Share of households with Internet access and broadband connection
ages, require de facto a broadband connection. Websites are getting richer in content, which increases the demand for traffic volumes constantly, even for less advanced services such as email communication.
The regional differences in Internet and broadband access are still quite large. They range from 90 % in NoordHolland (Netherlands) to 17 % in Severozapaden (Bulgaria) for access to the Internet and from 79 % in Groningen and NoordHolland (both Netherlands) to 12 % in Severozapaden (Bulgaria) for broadband access. The six leading regions in terms of Internet access are located in the Netherlands, whereas the six regions with the lowest share of households with Internet access are located in Bulgaria and Greece.
Map 7.1 shows the share of households with Internet access and broadband connections in Europe. A closer look at the map reveals three different patterns of digital divide. Firstly, there is a north–south gradient. Although the highest shares of Internet access are associated with regions in the Netherlands, the regions in the Scandinavian countries show very high Internet penetration rates, while regions in southern Europe have lower penetration rates.
The second pattern is in a latitudinal direction. Regions in the west and east of the European
Union have lower Internet penetration rates than regions in the centre.
Lastly, households in urban regions tend to have higher Internet access rates than households in rural regions. At EU27 level, 65 % of households in densely populated areas have access to the Internet, while only 51 % of households in thinly populated areas have an Internet connection. Depending on the structure and size of the regions within the country, this pattern is visible for some regions on Map 7.1. In general, regions with big cities, e.g. Lisbon (PT17), Madrid (ES30) and Barcelona (ES51), Rome (ITE4) and Milan (ITC4), Vienna (AT13), Budapest (HU1), Prague (CZ01) or Berlin (DE3), show up as islands in the surrounding regions owing to higher levels of Internet access. The visibility of the effect is stronger if the region only includes the area of the respective conurbation. Exceptions to this rule are Brussels (BE10) and London (UKI1), where neighbouring regions have equal or higher Internet access rates.
Broadband connection rates show similar patterns to Internet access, with an average lag between Internet access and broadband connections of 12 % for the EU27 in 2008, compared to 19 % in 2006. The lag has lessened during the last two years. Most of the Dutch regions have levels
92 Eurostat regional yearbook 2009
7 Information society
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
Internet access Broadband connection
0
5
10
15
20
25
30
35
40
45
50
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
of Internet access and broadband connections for households above 70 %, whereas the difference between Internet access and broadband connection rates for all regions in Germany, Slovakia and Croatia, for most regions in Italy, and for Ireland, Luxembourg and Romania at national level is well above the EU27 average. The regions in these countries would profit considerably from increased broadband access.
Figure 7.1 illustrates the growth rates of Internet access and broadband connections between 2006 and 2008 at national level. The calculation method considers the levels that had been reached in 2006, taking into account the fact that efforts have to be higher when reaching saturation (9). The increases in Internet access and broadband connection are set against the remaining potential from the levels achieved in 2006 to full saturation. When considering Internet access, Slovakia, France, Austria, Luxembourg, Sweden and the Netherlands developed most strongly within the EU27, whereas Cyprus, Slovenia, Bulgaria and Greece show the lowest growth rates. Considering the development of broadband connections, Sweden, France, Ireland, Germany, Austria, the United Kingdom and Luxembourg performed most strongly within the EU27 while Greece (10), Italy, Bulgaria and Romania are among the weakest performers.
Use of the Internet and Internet activitiesThe share of households with Internet access or broadband connections shows the potential for private use of the Internet from home. Map 7.2 provides an overview of the geographic distribution of regions according to actual use of the Internet in 2008. Regular users of the Internet are defined as those persons who use the Internet at least once a week, regardless of the place of Internet usage. The spatial pattern which has been described for Internet access is again visible for regular Internet use. In regions in Scandinavia, the Netherlands, the United Kingdom and Luxembourg, more than three quarters of the population use the Internet at least once a week. A higher share of persons living in densely populated areas regularly uses the Internet compared to the share of regular Internet users living in thinly populated areas. As with Map 7.1, there is a latitudinal gradient in the share of regular Internet users. Regions in the east and west of the EU27 have lower shares of regular Internet users. Almost all regions in Portugal, Italy, Greece, Bulgaria and Romania as well as the Member State Cyprus had a share of regular Internet users below 40 % in 2008.
93 Eurostat regional yearbook 2009
Information society 7
(9) For example, an increase of 10 percentage points at a penetration level of 20 % would exploit 10 out of 80 % (100 % 20 %) of the remaining potential whereas the same increase at a penetration level of 80 % would exploit 10 out of 20 % (100 % 20 %) of the remaining potential.
(10) However, Greece has the strongest annual growth rates, starting from a 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
E-mail communication
Information on goodsand services
Travel and accommodationservices
Internet banking
Interaction withpublic authorities
Health information search
Read online newspapersor magazines
Listen to web radioor television
Download software
Job searchor job application
Sell goods and services
Online course (*)
2006 2008
0 10 20 30 40 50 60 908070
(*) 2007–08
94 Eurostat regional yearbook 2009
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
95 Eurostat regional yearbook 2009
Information society 7Map 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
The most popular activities on the Internet are communication via email and looking for information on goods and services (see Figure 7.2). More than 80 % of Internet users had used the Internet within the last three months for these activities. Internet users are those persons who have used the Internet within the last three months. Obtaining services related to travel and accommodation, Internet banking, interacting with public authorities, searching for healthrelated information and reading online newspapers or magazines are activities engaged in by more than 40 % of Internet users. The biggest rise from 2006 to 2008 is accounted for by email communication, health information searches, Internet banking and listening to web radio or web TV.
The regional differences regarding ecommerce activity by persons are illustrated on Map 7.3. The geographic patterns already described are again visible on the map. All regions in Norway have a share of more than 55 % of the population buying goods or services online, while the EU27 average is 32 % of the target population. Almost all regions in the eastern and southern Member States of the EU27 show a share of 25 % or less of the total target population. Except for Spain, the variety between the regions in those Member States is quite low, ranging within a maximum difference of one class. All regions in Finland, Sweden, Denmark, the United Kingdom and the Netherlands as well as the Member State Luxembourg have a share of eshoppers higher than 45 % of the total target population, whereas in almost all regions in Bulgaria and Romania the share is below 5 %.
Non-users of the InternetEinclusion relates to the participation of all individuals and communities in all aspects of the information society (11). The respective policies of the European Union aim to reduce gaps in and promote the use of information and communication technologies to overcome digital exclusion and thus improve economic performance, employment opportunities, quality of life, social participation and cohesion. At EU27 level, one third of the population aged 16–74 years do not use the Internet.
The Community survey on ICT use in households asks for the reasons for not using the Internet. In 2008, 38 % of nonusers said that they had no need to use the Internet. According to this figure, it seems that there is a deliberate choice not to go online. However, only 14 % of nonusers explicitly state that they do not want to use the Internet. The
reply of having no need could just as well reveal a lack of information as regards the possibilities offered by the Internet. In addition to the reasons already mentioned, one fourth of nonusers confirm that equipment costs, e.g. buying a computer for accessing the Internet, were too high and 21 % stated that connection costs were too expensive. Almost one fourth (24 %) report a lack of required skills for accessing the Internet, whereas only 5 % of nonusers have security concerns.
It is the explicit objective of European regional policies to facilitate affordable access to the Internet, including access to the network, terminal equipment, contents and services, especially in remote and rural areas of the European Union. The EU is aiming to achieve broadband coverage for at least 90 % of the population by 2010. This target describes the supply side, while Eurostat figures from the Community ICTuse survey provide information on the takeup of ICTs in the regions, which may lag behind the potentially reachable population figures.
In recent years, the share of nonusers of the Internet has dropped at EU27 level from 43 % of the target population in 2005 to 33 % in 2008. The share of nonusers fell in both densely and thinly populated areas between 2005 and 2008. However, the decrease in thinly populated areas is lagging behind the development in densely populated areas, thereby widening inequality between the regions.
The region with the lowest share of nonusers in 2008 was Flevoland (Netherlands), with 7 %, and the region with the highest share was Sud — Muntenia (Romania), with 69 % (see Figure 7.3). The Member States with the highest differences between shares of nonusers in their regions are Bulgaria and Greece, with more than 25 percentage points of difference. Denmark, Poland, Finland and Sweden are the countries with less than 10 percentage points of difference between their regions (12). The highest shares of Internet nonusers are reported by Cyprus, Portugal, Greece, Bulgaria and Romania, with more than half of the total target population.
Map 7.4 shows the distribution of regions according to the share of persons who have never used the Internet as a deviation from the EU27 average. Regions in green have fewer nonusers than the EU27 average, while the regions in yellow and orange are above the EU27 average. The geographic distribution shows similar patterns to those described before. All regions in the Scandinavian countries, Norway, Finland, Sweden, Denmark and Iceland, as well as the Netherlands and Lux
96 Eurostat regional yearbook 2009
7 Information society
(11) http://ec.europa.eu/information_society/events/ict_riga_2006/doc/declaration_riga.pdf
(12) Although these figures give an impression of the issue, they are heavily influenced by the delimitation of the regions and the number of regions in a country. With an increasing number of regions, the size of the regions diminishes and the probability of higher variations increases. Moreover, statistics at regional level are not available for nine Member States, which limits comparability within the EU27.
embourg, are at least 15 % below the EU27 average, while most of the regions in Bulgaria, Greece, Portugal, Romania, southern Italy and Cyprus are more than 15 % above the EU27 average. Regions in the east and west of the EU27 tend to exceed the EU27 average of nonusers of the Internet. Urban regions with higher population density tend to be below the EU27 average. In the map, this tendency is visible for, for example, Athens, Lisboa, Madrid, Paris, Wien, Budapest, Praha or Berlin.
ConclusionStatistics on use of information and communication technologies in households and by indi
viduals are collected annually at level 1 of NUTS on a compulsory basis. Some Member States additionally provide information at NUTS 2 level. The available statistics illustrate that there are considerable differences regarding access and use of information and communication technologies between the regions of the EU27. Within the last few years, all Member States have increased access to and use of ICTs. However, densely populated areas seem to profit more from the current development than thinly populated areas. In order to overcome this problem, the European Union has shaped explicit policy targets to achieve an inclusive information society, including the geographic dimension of the digital divide. The
97 Eurostat regional yearbook 2009
Information society 7
National average
BE
FR
LU
PT
EU-27
UK
IS
NL
DE
DK
SE
IE
NO
IT
MT
ES
HU
FI
AT
EL
EE
SI
PL
SK
RO
LV
BG
LT
CY
HR
CZ
Figure 7.3: Non usage of Internet, by NUTS 2 regions, 2008 In percentage of the population aged between 16 and 74 years
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)
0 10 20 30 40 50 60 8070
Flevoland Sud — Muntenia
Sud — Muntenia
Severozapaden
Kentriki Ellada
Região Autónoma dos Açores
Sjeverozapadna Hrvatska
Campania
Region Wschodni
Extremadura
Alföld és észak
Severovýchod
Prov. Hainaut
Bassin Parisien
Burgenland (A)
Západné Slovensko
Sachsen
Itä-Suomi
Nordjylland
Zeeland
Norra Sverige
Agder og Rogaland
Bucureşti — Ilfov
Yugozapaden
Attiki
Lisboa
Provincia Autonoma Bolzano/Bozen
Region Centralny
Comunidad de Madrid
Közép-Magyarország
Praha
Prov. Brabant Wallon
Île de France
Wien
Berlin
Etelä-Suomi
Flevoland
Vest-landet
ÖstraSverige
Hovedstaden
Bratislavský kraj
Središnja i Istočna (Panonska) Hrvatska
98 Eurostat regional yearbook 2009
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
policies are benchmarked according to the i2010 benchmarking framework.
The maps in this chapter reveal specific spatial patterns that are visible for all indicators presented. Despite the fact that the levels of Internet access are highest for households in Dutch regions, there is a clear north–south gradient, with higher values of Internet access and use in northern Member States. The second pattern is a latitudinal one. Regions in the west and east of the European Union tend to have lower shares of Internet access and use than regions in the centre. Finally, urban or densely populated regions reveal a higher share
of population accessing and using the Internet than thinly populated areas. In order to achieve the policy goals of inclusive participation in the information society, it will be necessary to keep up existing efforts to provide affordable access to the Internet via broadband and to educate persons with the necessary skills to enable them to access and exploit the richness of the Internet. The European Council announced on 20 March 2009 further support for projects in the field of broadband Internet as part of the European economic recovery plan to tackle the global economic and financial crisis (13) and has set the goal of achieving 100 % coverage of the population by 2013.
Methodological notesEuropean 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.
99 Eurostat regional yearbook 2009
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(13) http://europa.eu/rapid/pressReleasesAction.do?reference=DOC/09/1&format=HTML&aged=0&language=EN&guiLanguage=en
Science, technology and innovation
IntroductionThe Lisbon European Council (2000) and the Barcelona European Council (2002) both highlighted the important role of research and development (R & D) and innovation in the EU. Against this background, the 2005 initiative ‘Working together for growth and jobs’ relaunched the Lisbon strategy. ‘Knowledge and innovation for growth’ thus became one of the three main areas for action in the new Lisbon partnership for growth and jobs, which put science, technology and innovation at the heart of EU national and regional policies.
The concept of a European research area (ERA), introduced in 2000 as the contribution by research policy to the broader Lisbon strategy, has also been a highly successful tool for moving research higher up on the political agenda. Eight years of developing ERA have transformed it from a theoretical concept to a practical policy approach for improving the efficiency and effectiveness of fragmented research efforts and systems in Europe, increasing the attractiveness of Europe to researchers and research investment, and raising the coherence and synergies between research policy and other EU policies in order to implement the renewed Lisbon strategy.
This chapter presents statistical data and indicators based on a number of data sources available at Eurostat, which provide statistical information in order to compare the evolution and composition of science, technology and innovation (STI) in European regions and their position relative to other regions. The domains covered are: research and development (R & D); patents; high technology; and human resources in science and technology (HRST).
More regional indicators for science, technology and innovation are available on the Eurostat webpage under ‘Science and technology’.
Research and developmentIncreasing investment in R & D is one of the key objectives of the Lisbon strategy. A substantial increase in investment in R & D is important as a means of providing a significant boost to the industrial competitiveness of the European Union.
Some 20 of the regions shown in Map 8.1 have an R & D intensity above the 3 % target specified in the Lisbon strategy for the EU as a whole. Although this target remains the EU objective for
2010, most countries have specified their own targets in national reform programmes. The national targets range from 0.75 % in the case of Malta to 4 % for Finland and Sweden, and — if met — they will bring the average R & D performance in the EU to around 2.6 % by 2010.
On the map, the largest cluster of regions with a relatively high R & D intensity, i.e. above 2 %, can be found in southern Germany, spreading out to Austria and through Switzerland into France all the way to the Pyrenees. It is also clear from the map that regions containing capital cities tend to be relatively R & D intensive. The regions containing the capitals Sofia, Bucureşti, Budapest, Warszawa, Wien, Madrid and Roma are the most R & D intensive regions in their respective countries. This fact is further illustrated by the region that surrounds Praha, and to some extent by the region containing Paris, which is the second most R & D intensive of the French regions. However, when ranking the German regions, Berlin comes only sixth, even though its R & D intensity is well above 3 %.
Regions with a lower R & D intensity are found mainly in the southern and eastern parts of the EU. It is also here that we find many of the regions with the fastestgrowing R & D intensities. Of the 30 regions that have recorded an annual average growth rate of over 10 % since 2000, six are Greek, two are Czech, two are Spanish, one is Portuguese and one is Romanian. Estonia, Malta and Slovenia are also among these fastgrowing regions.
R & D personnel is the other basic R & D input indicator (besides R & D expenditure) that measures the human resources going directly into R & D activities. R & D personnel comprise three categories: researchers, technicians and other support staff. Of these, researchers are the most important in terms of R & D activities. They are professionals engaged in the conception or creation of new knowledge, products, processes, methods and systems, and in the management of the projects concerned.
Map 8.2 shows the regional pattern of distribution of researchers (expressed as a percentage of total employment) across Europe. In 15 European regions over 1.8 % of all persons employed are researchers. Trøndelag (Norway) is the leading region, with a share of 3.16 %, which is more than three times higher than the EU27 average. This group also comprises one other Norwegian region, four German regions, three Finnish regions
102 Eurostat regional yearbook 2009
8 Science, technology and innovation
103 Eurostat regional yearbook 2009
Science, technology and innovation 8Map 8.1: Total R & D expenditure as a percentage of GDP, all sectors, by NUTS 2 regions, 2006
104 Eurostat regional yearbook 2009
8 Science, technology and innovation
Map 8.2: Researchers as a percentage of persons employed, all sectors, by NUTS 2 regions, 2006
and one region each from the Czech Republic, Austria, Slovakia, Belgium, Iceland and France. Sweden, for which only data at the country level is available, also has more than 1.8 % researchers in total employment. In a further 48 regions, the concentration of researchers is above the EU27 average (0.9 %) and, once again, most of these regions (18) are in Germany.
The number of researchers as a percentage share of all persons employed in the foremost region of nine countries is below the EU27 average (0.9 %): these countries are Bulgaria, Cyprus, Latvia, Lithuania, Malta, the Netherlands, Slovenia, Croatia and Turkey. The regions with the lowest concentration of researchers are in Bulgaria (Severozapaden, with 0.08 %), Romania (SudEst, with 0.13 %), the Netherlands (Friesland, with 0.13 %) and the Czech Republic (Severozápad, with 0.15 %).
Regional disparities exist not only between countries but also between regions of the same country. The largest difference between the leading region and the bottom region is observed in the Czech Republic (2.88 percentage points between Praha and Severozápad). Austria, Germany, Finland, Slovakia and Norway also present disparities of more than 2 percentage points. At the other end of the scale, the smallest gap is in Ireland, with 0.03 percentage points, followed by the Netherlands with 0.73 percentage points.
Human resources in science and technologyWithout sufficient amounts of human resources there can be no growth. As science and technology have been recognised as key fields for European development, it is therefore of considerable importance for policymakers at a regional level (as well as at EU and national levels) to analyse the stock of highly qualified people.
One way to measure the concentration of highly qualified people in the regions is by looking at the human resources in science and technology (HRST). HRST defines those who have completed a tertiary level of education and/or are employed in a science and technology occupation where a tertiary level of education is normally required. HRSTO is a subgroup of HRST denoting those employed in a science and technology occupation.
As Map 8.3 shows, there is an urban concentration of HRSTO in particular around the capital regions. In such regions there is often a high concentration of highly qualified jobs, for example owing to the
presence of the head offices of companies and government institutions. However, another factor is that capitals are often big cities that naturally contain large groups of higher education facilities, and thus a large number of highly educated people. This makes these and the nearby regions safe places for new companies to open up businesses, thanks to the supply of highly skilled human resources that are already present in the region. At the same time, highly skilled people can be attracted to larger cities as they are also more likely to find a skilled job that meets their requirements in a region where there are many companies.
This urban concentration of human resources employed in science and technology can be seen in Map 8.3, by looking at the capital regions and also at two of the three large regional clusters with shares of HRSTO exceeding 30 %. This particular cluster stretches from the Italian region Lazio in the south up through Switzerland to the southwestern parts of Germany. In the main, the regions in this cluster are very densely populated, as are the regions in the second distinct cluster which contains the regions of the Benelux countries. The third cluster is in the Scandinavian countries, where the regions — apart from the capital regions — are very sparsely populated. In Scandinavia we also find the regions with the second, third and fourthhighest share of HRSTO; they are Stockholm in Sweden (48 %), Oslo og Akershus in Norway (48 %) and Hovedstaden in Denmark (44 %) respectively. The highest share, however, is found in Praha (Czech Republic), where 52 % of the labour force are HRSTO. It is interesting to note that, two years previously, the top three regions were the same and that their shares have since increased. The share for Praha has increased the most, up from 47 % of HRSTO two years ago. Stockholm and Oslo og Akershus have each increased their shares by 2 percentage points during the past two years.
High-tech industries and knowledge-intensive servicesThe statistics on hightech industries and knowledgeintensive services include employment data by sectors of economic activity. Based on the ratio of R & D expenditure to GDP (R & D intensity), sectors can be classified into more specific subsectors so as to analyse employment in science and technology. Two subsectors that are of great importance to science and technology are the hightech manufacturing and medium hightech manufacturing sectors, even though they
105 Eurostat regional yearbook 2009
Science, technology and innovation 8
106 Eurostat regional yearbook 2009
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
107 Eurostat regional yearbook 2009
Science, technology and innovation 8Map 8.4: Employment in high- and medium high-tech manufacturing, by NUTS 2 regions, 2007 Percentage of total employment
accounted for only 1.1 % and 5.6 % respectively of EU employment in 2007. Hightech manufacturing includes, for example, manufacture of computers, televisions and medical instruments, while medium hightech manufacturing includes, for example, manufacture of chemicals, machinery and transport equipment.
Map 8.4 shows employment in the two subsectors — hightech and medium hightech manufacturing — as a percentage of total employment. Employment in these two subsectors is very high in the central European regions, in a band stretch
ing from FrancheComté (France) in the west to ÉszakMagyarország (Hungary) in the east. Stuttgart and Braunschweig (both Germany) are the only regions with more than one in five employed persons working in these subsectors; both regions have a share of 22 %. In fact, the seven leading regions are all German (in addition to Stuttgart and Braunschweig, they include Karlsruhe, Tübingen, RheinhessenPfalz, Unterfranken and Freiburg).
Furthermore, Map 8.4 shows a cluster of four Italian regions (Piemonte, EmiliaRomagna, Lombardia 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)
% of total employment
Total number (1 000s)
Total number (1 000s)
% of total employment
Inner London (UK) 59.7 785 101 8.9Berkshire, Buckinghamshire and
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/ Brussels Hoofdstedelijk Gewest (BE)
47.2 180 133 6.1 Outer London (UK)
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 Oxfordshire (UK)
46.5 529 297 5.7 Île de France (FR)
Prov. Brabant Wallon (BE) 46.1 71 74 5.7 Etelä-Suomi (FI)Gloucestershire, Wiltshire and Bristol/Bath area (UK)
46.1 529 70 5.6 Karlsruhe (DE)
västsverige (SE) 45.8 420 62 5.4Gloucestershire, Wiltshire and Bristol/
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
8 Science, technology and innovation
of employment in high and medium hightech manufacturing. In the other parts of Europe only three regions have more than 10 % of their employment in high or medium hightech manufacturing; they are Vest (Romania), Bursa (Turkey) and Herefordshire, Worcestershire and Warwickshire (United Kingdom).
Another subsector of interest is knowledgeintensive services (KIS). KIS can be further split into different categories, of which hightech knowledgeintensive services (hightech KIS) is a subsector of special interest when analysing employment in science and technology. Examples of services in hightech KIS include computer and related activities, and research and development. KIS, on the other hand, is broader and, in addition to hightech KIS, also includes water and air transport, financial intermediation, education and health and social work, for example.
Table 8.1 shows the 25 leading regions in KIS and in hightech KIS. As KIS generally attracts highly educated persons, there is a similar pattern to that seen in Map 8.3 for human resources in science and technology (HRST), namely that urban regions, especially capital regions, often exhibit high shares of employment in KIS and high shares of HRST.
Looking at Table 8.1, the four leading regions were all capital regions, with Inner London (United Kingdom) showing the highest percentage of KIS (59.7 %). By far the majority of the leading regions are urban, or within commuting distance of an urban region. The one exception is Åland, an autonomous province of Finland consisting of islands. As shipping is an important part of this region’s economy, it is one of the major reasons behind the high share of KIS in Åland.
Another feature that stands out is the fact that six of Sweden’s eight regions are represented among the 25 regions with the highest shares of KIS. This can be explained in part by the fact that Sweden has a large public sector, which includes the education and healthcare sectors. Looking at the righthand side of the table, which shows the 25 leading regions in hightech KIS, only one Swedish region remains. This region, the Swedish capital region Stockholm, had 8 % of its employment in hightech KIS, which is the secondhighest share after Berkshire, Buckinghamshire and Oxfordshire (United Kingdom), with 9 %. Further examination shows that 13 of the 25 regions with the highest percentage of employment in hightech KIS were capital regions (including both Inner London and Outer London).
One interesting feature here is that three of the five regions with the highest shares of employment in hightech KIS in 2007 were also among the five highest in 2002, when Stockholm (Sweden) was the leading region, followed by Berkshire, Buckinghamshire and Oxfordshire (United Kingdom). Bratislavský kraj (Slovakia) followed in third place and ÎledeFrance (Paris) in fourth — which was somewhat surprising compared to its 19th position in 2007. Oslo og Akershus was in fifth place in 2002.
PatentsIndicators based on patent statistics are widely used in order to assess the inventive and innovative performance of a country or a region. The current emphasis on innovation as a source of industrial competitiveness has raised awareness of patents. Patents are used to protect R & D results, but they are just as significant as a source of technical information, which may avoid reinventing and redeveloping ideas because of a lack of information. Patent statistics at regional level are confined to applications to the European Patent Office (EPO). The data are regionalised by linking postcodes or city names to the nomenclature of territorial units for statistics (NUTS).
Map 8.5 illustrates the regional patenting activity in the EU. In most European countries, national patenting is concentrated in certain regions. Regions that are active in patenting are often situated close together, i.e. they form economic clusters. This is the case, for example, in the southern part of Germany, the southeast of France and the northwest of Italy. The most active patenting regions (with 100 to 300 applications and more than 300 applications per million inhabitants) are situated in the Nordic countries and in the centre of the EU27.
Patent activity varies not only across countries but also across regions. In 2004, ÎledeFrance (France) was the foremost EU region in terms of total number of patent applications (3 297), while NoordBrabant (Netherlands) was in the lead for patent applications per million inhabitants (761). In Germany large disparities were observed between the leading region of Stuttgart in the south and the lowestperforming region of Sachsen Anhalt in the east. Regional discrepancies are even wider in the Netherlands, between NoordBrabant and Friesland. Regional disparities, however, are much lower in countries with comparable national averages, such as Finland and Sweden.
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Science, technology and innovation 8
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8 Science, technology and innovation
Map 8.5: Patent applications to the EPO per million inhabitants, by NUTS 2 regions, 2004
ConclusionRelevant and meaningful indicators on science, technology and innovation are of paramount importance for informing policymakers about where European regions stand on the path towards more knowledge and growth. This information is also necessary in order to gain a better picture of how regions are evolving, compared between themselves both at European level and worldwide.
With the aid of the relevant statistics and indicators, this chapter has demonstrated the progress made in recent years on research and development activities in European regions. Wide use is also made of statistics on hightech industries and knowledgeintensive services, patents and human resources in science and technology in order to complete this regional picture.
Methodological notesThe 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.
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Science, technology and innovation 8
Education
IntroductionEducation, vocational training and lifelong learning play a vital role in the economic and social strategy of the European Union. The relaunched Lisbon process, implemented by the ‘Education and training 2010’ programme, cannot be completed without efficient use of resources, quality improvements in education and training systems and implementation of a coherent lifelong learning strategy at national level. Securing education and lifelong learning opportunities in every region and for every inhabitant, wherever they live, is one of the cornerstones of the national strategies to achieve this goal. Eurostat’s regional statistics on enrolment in education, educational attainment and participation in lifelong learning make it possible to measure progress at regional level and monitor regions lagging behind.
Comparable regional data on enrolment in education from 1998 onwards are available from Eurostat’s website, while data on educational attainment levels and participation in lifelong learning are available for the period since 1999.
The Eurostat website contains regional information on the total number of enrolments by level of education and sex, and by age and sex plus indicators relating enrolments in education to the total population. Data on enrolments in education are generally available for the 15 ‘old’ Member States for the period since 1998 and for the 12 ‘new’ Member States plus Norway since 2000 or 2001. Information on the educational attainment of the population and on participation in lifelong learning is available for all the Member States and also for Norway.
Students’ participation in educationIn its broad sense, education refers to any act or experience that has a formative effect on the mind, character, or physical ability of an individual. In its technical sense, education is the process by which society, through schools, colleges, universities and other institutions, deliberately transmits its cultural heritage and its accumulated knowledge, values and skills from one generation to another.
This chapter gives evidence of the educational enrolment of the regional populations as well as their educational attainment levels and their participation in lifelong learning, reflecting how education touches persons throughout life in all regions.
Map 9.1 shows the number of students in all levels of education as a percentage of the total population at regional level. This indicator reveals the number
of individuals participating in education irrespective of the level in which they are enrolled. In 2007 roughly 21 % of the total European population (the 27 EU Member States and the candidate and EFTA countries) was enrolled in education. It means that one person in five is involved in formal education. This indicator is influenced by the age distribution of the population: ‘old’ populations have relatively low enrolment rates and, conversely, if the age distribution of the population under consideration is younger the figures are higher.
Some of the regions with the highest percentages of students in education are around capital cities in eastern Europe such as Praha, Bucureşti, Bratislava and Lubjiana. These cities represent the focal point of their region in terms of education. Some countries such as Belgium, Sweden, Norway, Iceland and Lithuania display figures that are higher than anywhere else, whereas in Denmark, in the north of Italy and in some regions of Spain, Greece and Germany the rates are relatively low, below 18 %.
Furthermore, the differences within the countries are at times small, as in Poland and France, while in other countries there are noticeable dissimilarities, as in Italy (northern regions compared to southern regions), Spain (northwest regions compared to the others), Germany (eastern area compared to the western regions) and Greece (where the southern area has lower rates than the rest of the country).
Participation of 4-year-olds in educationLearning begins at birth. The period from birth to entry into primary education is a critical formative stage for the growth and development of children. The learning outcomes, knowledge and skills of primary education are stronger when appropriate learning and development occur in the years preceding regular schooling.
The purpose of preprimary education is to prepare children physically, emotionally, socially and mentally to enter primary school, giving them the ability and the skills to enter the first level of the educational system. This preparation is considered the foundation for further educational development.
In December 2008, the European Commission proposed a new benchmark, whereby 90 % of 4yearolds should participate in preprimary education by 2020. The aim of this proposal is to underpin progress towards the 2002 Barcelona Summit conclusion of increasing participation in preprimary education to 90 % of all children between 3 years of age and the beginning of compulsory education.
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Education 9Map 9.1: Students in all levels of education, as a percentage of total population, by NUTS 2 regions, 2007 ISCED levels 0–6
The EU27 rate of participation is already approaching the target (88.5 % in 2007), but this overall high level of participation masks significant variations between the figures for individual countries.
When the EU27 Member States and the candidate and EFTA countries are taken into account, approximately 73 % (in 2007) of the European 4yearolds were enrolled in preprimary and primary education.
The indicator shown here examines the participation in early childhood education at regional level (NUTS 2) by measuring the percentage of 4yearolds who are in either preprimary or primary education. By far the majority of them attend preprimary schooling (which in many cases is also noncompulsory). A 4yearold child can be enrolled either in preprimary or in primary school. Data highlight that most of them attend preprimary school. Ireland and the United Kingdom are the only countries where the proportion of 4yearolds in primary education is relevant.
At the age of 4 most children in the European Union are therefore in preprimary education (80 %), which is generally available from at least 3 to 4 years of age in the EU Member States. Only 5 % of 4yearolds are enrolled in primary education, of which 89 % are in the United Kingdom and 11 % in Ireland.
Enrolment in preprimary education is almost always voluntary. Nevertheless, many countries have participation rates of 100 % or close to this.
Map 9.2 shows that in some countries, such as Denmark, France, Iceland, Italy, Malta, the Netherlands and Spain, and in regions such as Vlaams Gewest (Belgium), the participation of 4yearolds in education is nearly 100 %. In contrast, in Croatia, Ireland, Macedonia, Switzerland, Turkey and most of Poland and Finland less than 50 % of the 4yearolds are enrolled in education. No significant regional differences within the countries can be noted except for England, Germany and Portugal, where there are some slight differences in levels of participation between the regions.
Students in upper secondary education and post-secondary non-tertiary educationAt the age of 16 young people are faced with the choice of whether to remain in education, go into vocational training or seek employment. Over the last decade young people have become more likely to continue with their education at this age.
Map 9.3 shows the percentage of students enrolled in upper secondary education (ISCED 3 level) and postsecondary nontertiary education (ISCED level 4) as a percentage of the population aged 15–24 years old in the region.
The task of general upper secondary education is to provide extensive allround learning and to continue the teaching and educational task of basic education. The objective is often to offer sufficient skills and knowledge with a view to further study. It would normally give access to universitylevel programmes. In contrast, vocational streams often provide training for specific labour market occupations.
Students generally start upper secondary education at the age of 15 to 17, at the end of fulltime compulsory education, and finish it three or four years later. The starting/finishing ages and the age range depend on the national educational programmes. However, students can normally attend upper secondary education programmes relatively close to where they have grown up. For this indicator a broad age group has been defined to cover the relatively wide spread in ages, depending on the country.
Postsecondary nontertiary education programmes (ISCED level 4) lie between the upper secondary and tertiary levels of education from an international point of view, even though they might clearly be considered upper secondary or tertiary programmes in a national context. Although their content may not be significantly more advanced than upper secondary programmes, they serve to expand the knowledge of participants who have already gained an upper secondary qualification.
In 2007 more than 38 % of the population aged 15–24 years in the EU27 was enrolled in upper secondary and postsecondary education.
The highest rates are found in Belgium, Finland, Iceland, the Praha region, some regions of Sweden (Mellersta Norrland and Norra Mellansverige), Valle d’Aosta Basilicata and FriuliVenezia Giulia (Italy), KözépMagyarország and DélAlföld (Hungary) and the Salzburg region (Austria).
Taking a wider look at the map, the Nordic countries (Norway, Sweden, Denmark, Finland and Iceland) show a common pattern with high percentages. Many parts of Europe (such as France, Germany, Switzerland, the Netherlands, Poland, Slovakia, Slovenia, Croatia, Romania, Bulgaria and Greece) have low rates of participation, whereas Italy, Austria, the Czech Republic and Hungary show high rates. The United Kingdom is split in two parts — England (high rates) and
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117 Eurostat regional yearbook 2009
Education 9Map 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
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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
the rest (lower rates). In contrast, the Iberian peninsula (Spain and Portugal), Turkey, Lithuania, Malta, Cyprus, Macedonia and some regions in Greece have very low participation rates.
Students in tertiary educationTertiary education refers to levels of education that are provided by universities, vocational universities, institutes of technology and other institutions that award academic degrees or professional certifications. Access to tertiarylevel educational programmes typically requires successful completion of an upper secondary level and/or a postsecondary nontertiary level programme.
The levels of education can be largely theoretically based and intended to provide sufficient qualifications for gaining entry into advanced research programmes and professions with high skills requirements (ISCED level 5A) or more practical, technical and employmentoriented (ISCED level 5B), or can lead to an advanced research qualification (ISCED level 6, PhDlike studies).
Map 9.4 shows the number of students in tertiary education (ISCED levels 5 and 6) as a percentage of the population aged 20–24 years old in the region. The student population is related to the population in the relevant age group in order to see the relative size of the student population at regional level.
This indicator is based on data on where the students are studying, not on where they come from or live. Regions with universities and other tertiary education institutions, often big cities, therefore tend to have high percentages of students, as students often travel or move to them for higher education. This is in contrast to younger pupils and students in lower levels of education, who usually attend a school close to where they live. Therefore, the first thing which this indicator shows is an uneven distribution of higher education institutions across regions (and not uneven participation in higher education by region).
In 2007, 58 % of the population aged 20–24 years in the European Union was in tertiary education. Some countries, such as Malta, Cyprus and Luxembourg, have relatively low rates because many students at tertiary level go abroad to study and hence are not included in the statistics of their home countries but in the countries where they study.
In the regions with the highest percentages, students in tertiary education outnumber the population of 20–24yearolds. In regions such as Praha, Wien, Région de BruxellesCapitale/Brussels Hoofdstedelijk Gewest, Brabant Wallon (south of Brussels), Bratislava, Bucureşti, KözépMagyarország
(Hungary, Budapest region), Dytiki Ellada (Greece) and Mazowieckie, including the capital Warszawa (Poland), the figures are more than 100 %, signifying a large student population among the younger cohorts. Many of these regions are around capital cities where big universities are located.
Relatively few regions have tertiarylevel student populations below 30 % of the 20–24yearold age group and those that do are spread out among many Member States. Many of them have features which easily explain the low percentages, such as being in the rural parts of a country or being islands. Most of these regions have little, if any, tertiaryeducation infrastructure, and the students have to move away in order to obtain higher education.
Tertiary educational attainmentThe proportion of the population aged 25–64 years who have successfully completed university or universitylike (tertiarylevel) education is shown in Map 9.5. The pattern in this map is similar to the pattern in Map 9.4. In most countries the highest proportions of tertiarylevel attainment are found in the same regions as the students in tertiary education, i.e. where the tertiary education institutions as well as the largest enterprises and institutions and their providers are located. The demographic profile of a region also has some influence on the educational attainment levels, as younger generations tend to have higher educational attainment levels than older generations. In 2007 only 23 regions in the EU had a proportion of persons with higher education above 35 %; these included large cities such as Bruxelles/Brussel, London, Paris, Helsinki, Stockholm, Madrid and Amsterdam; Oslo (Norway), Geneva and Zurich (Switzerland) also fell into this category. In EU Member States such as Ireland, Sweden, Finland, the Netherlands, Belgium and Germany educational attainment levels are generally high across the whole country. The regions with the lowest percentages of people with tertiary education are largely concentrated in the rural parts of 10 EU countries, with a significant contrast with their larger cities: this is this case in Portugal, as well as Romania, Croatia and Turkey, and to a lesser extent Bulgaria, the Czech Republic, Greece, Italy, Hungary, Poland and Slovakia and includes islands such as Sardegna and Sicilia (Italy), Açores and Madeira (Portugal) and Malta.
Lifelong learningContinuous refreshing of the skills of the labour force via lifelong learning has repeatedly been underlined in EU policies following up the Lisbon objectives. This is reflected in the ‘Education
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Education 9
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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
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Education 9Map 9.5: Educational attainment level, by NUTS 2 regions, 2007 Percentage of the population aged 25–64 having completed tertiary education
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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
and training 2010’ programme as well as in the European employment strategy, which emphasises the need for comprehensive lifelong learning strategies to ensure the continual adaptability and employability of workers. Adult learning can be measured via the labour force survey through specific questions on participation in education or training activities during the four weeks preceding the survey. The data concern the age group 25–64 years for all education or vocational training, whether or not relevant to current or future employment. As Map 9.6 shows, participation in education and training is largely nationally profiled. In fact, this is the education indicator showing the smallest regional variation compared with the others discussed earlier in this chapter. The participation is high in every region of Denmark, the Netherlands, Slovenia, Finland, Sweden and the United Kingdom and also in Iceland, Norway and Switzerland. Within countries, the highest rates of participation in education and training are often found around the largest cities, which
are usually also the regions with the highest levels of educational attainment (see previous section) and the regions where the supply of education and training activities is wider and continuing vocational training activities are most frequent (e.g. in large enterprises). On the other hand, EU Member States on the fringes of the continent, such as Greece, Hungary, Malta, Poland, Portugal, Romania and Slovakia, and also Croatia and Turkey generally have low participation rates in education and training for the age group 25–64.
ConclusionThe examples given above are intended merely to highlight a few of the many possible ways of analysing education and lifelong learning in the regions of the EU and do not constitute a detailed analysis. We hope, however, that they will encourage readers to probe deeper into all the data on education freely available on the Eurostat website and to make many further interesting discoveries.
Methodological notesThe 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.
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Education 9
Tourism
IntroductionTourism is an important and fastevolving economic factor in the European Union, occupying large numbers of small and mediumsized businesses. Its contribution to growth and employment varies widely across the EU regions. Particularly in rural regions, usually peripheral to the economic centres of their countries, tourism is often one of the main sources of income for the population and a prominent factor in creating and securing an adequate level of employment.
Tourism is a typical crosscutting industry. Ser vices to tourists involve a variety of economic branches: hotels and other accommodation, gastronomy (restaurants, cafes, etc.), the various transport operators and also a wide range of cultural and recreational facilities (theatres, museums, leisure parks, swimming pools, etc.). In many tourismoriented regions the retail sector also benefits considerably from the demand created by tourists in addition to that of the resident population.
Eurostat has been collecting data on the development and structure of tourism since 1995, pursuant to Council Directive 95/57/EC on the col
lection of statistical information in the field of tourism. This includes data both on accommodation capacity and its utilisation and on the travel behaviour of the population. The travel behaviour data are, however, only available at national level. In contrast, the data collected on accommodation capacity and its utilisation are also available by region. The regionalised data are outlined below.
It is important to point out that the statistical definition of tourism is broader than the common, everyday definition. It encompasses not only private travel but also business travel. This is primarily because it views tourism from an economic perspective. Private travellers and business travellers have broadly similar consumption patterns. They both make significant demands on transport, accommodation and restaurant services. To the providers of these services, it is of secondary interest whether their customers are private tourists or on business. Tourism promotion departments, on the other hand, are keen to combine the two aspects by emphasising the attractiveness of conference locations as tourist destinations in their own right, and they give particular prominence to this in their marketing activities.
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Figure 10.1: Top 20 EU-27 tourist regions, number of bedplaces by type of accommodation, by NUTS 2 regions, 2007
Hotels Campsites
ES — CataluñaFR — Provence-Alpes-
Côte d'AzurFR — 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 BalearsUK — West Wales
and The ValleysIT — 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
Accommodation capacityFigure 10.1 shows the 20 NUTS 2 regions of the EU with the highest accommodation capacities, measured by the number of bedplaces in hotels and similar establishments and on campsites. Numbers of pitches on campsites are multiplied by four to make them comparable with hotel accommodation capacity. This gives a theoretical number of bedplaces, assuming that four people occupy the average pitch.
The ranking of the 20 regions with the largest accommodation capacities reveals the dominance of three main tourist destinations in Europe, namely France, Italy and Spain. Nine of the 20 regions on this list are in France, five are in Italy and three are in Spain. The United Kingdom, Hungary and Austria complete the list of the top regions for accommodation capacity, with one region each (West Wales and The Valleys, KözépMagyarország and Tirol). It is clear that the strong position of the French regions on this list reflects a very heavy preponderance of campsite accommodation.
Map 10.1 shows the number of bedplaces in hotels and on campsites per 1 000 inhabitants (bed
density) for the countries of Europe. This link with the number of inhabitants shows the relative importance of tourism capacity per head of population. This indicator is therefore affected not only by the number of available beds (bedplaces) but also by the population figure. It can be seen that the highest bed densities are to be found primarily in coastal regions and on islands, but also in most Alpine regions and in Luxembourg, together with its two neighbouring regions to the east (Trier in Germany) and west (the Province of Luxembourg in Belgium).
Overnight staysThe central indicator for accommodation services is the number of overnight stays in establishments. This figure reflects both the length of stay and the number of visitors. Furthermore, expenditure by tourists during their stay at their destination correlates closely with the number of overnight stays.
Figure 10.2 shows the 20 regions in Europe with the highest numbers of overnight stays, broken down by domestic and foreign visitors. The dominance in European tourism of Italy, Spain and France is
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Tourism 10
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
Residents Non-residents
FR — Île de France
ES — Cataluña
ES — Illes Balears
ES — Andalucía
ES — Canarias
IT — Veneto
IT — Emilia-RomagnaFR — Provence-Alpes
Côte d'AzurIT — Toscana
ES — Comunidad Valenciana
AT — Tirol
IT — Lazio
IT — Lombardia
FR — Rhône-Alpes
FR — Languedoc-Roussillon
DE — OberbayernIT — Provincia autonoma
Bolzano/BozenFR — Aquitaine
IT — Campania
ES — Comunidad de Madrid
0 20 10 30 50 70 40 60 80
millions
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10 Tourism
Map 10.1: Number of bedplaces in hotels and campsites per 1 000 inhabitants, by NUTS 2 regions, 2007
129 Eurostat regional yearbook 2009
Tourism 10Map 10.2: Nights spent in hotels and campsites, by NUTS 2 regions, 2007
even more pronounced for overnight stays than for accommodation capacities; these three countries accounting for 18 of the 20 regions. At 68.7 million overnight stays, the ÎledeFrance region containing the French capital Paris is well in the lead, followed by the four Spanish regions of Cataluña (56.4 million), Illes Balears (50.9 million), Andalucía (48.6 million) and Canarias (48.5 million). Tirol in Austria, at 30.4 million overnight stays, and Oberbayern in Germany (23.4 million) with the Bavarian metropolitan area of München are the only regions on the list of 20 that are not in one of the three leading tourism countries mentioned before.
Map 10.2 gives an overview of numbers of overnight stays in the regions of Europe. Here, too, it is clear that the focus of European tourism is in the Mediterranean. The Alpine regions also occupy a strong position. In addition to the abovementioned five countries (Italy, Spain, France, Austria and Germany) represented in the top 20 regions, Croatia, the Netherlands, Portugal, Greece, Cyprus, the United Kingdom and the Czech Republic also have NUTS 2 regions with more than 10 million overnight stays.
Average length of stayThe number of overnight stays in a region is based not only on the number of visitors but also on their
average length of stay. This, however, depends on the character of the region. For example, urban regions frequently tend to have very large numbers of visitors, but these visitors tend to stay for only a few days and nights. A big share of visitors to these regions are often there on business. But even in the case of private tourists there is a trend towards shorter stays. In contrast, stays are generally substantially longer in the typical holiday regions visited chiefly for recreational purposes. To that extent, an overview of average lengths of stay can also indicate the touristic nature of a region.
Map 10.3 shows the NUTS 2 regions in Europe according to the average length of stay of visitors. Once again, it can be seen that the holiday areas in the European Union with the greatest average length of visitor stays are very often maritime regions. They either have extensive coastlines or are islands and therefore encircled by the sea. Of the 22 NUTS 2 regions where the average length of stay of visitors is five nights or more, only one is completely landlocked, namely the Italian Provincia Autonoma Bolzano/Bozen. The remaining 21 are either island regions or have long coastlines.
Tourism intensityAnother important indicator of the touristic nature of a region is tourism intensity. This serves
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10 Tourism
Evolution of nights spent
Figure 10.3: Evolution of nights spent in hotels and campsites 2000–07 in the EU-27 Million nights
EU-27
2000 2001 2002 2003 2004 2005 2006 2007 1 600
1 650
1 700
1 750
1 800
1 850
1 900
1 950
2 000
Footnote: EE 2000, 2001; IE 2001; CY 2000, 2002; MT (only hotels)
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Tourism 10Map 10.3: Average length of stay in hotels and campsites, by NUTS 2 regions, 2007 Days
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10 Tourism
Map 10.4: Nights spent in hotels and campsites per 1 000 inhabitants, by NUTS 2 regions, 2007
as an indicator of the relative importance of tourism for a region. Tourism intensity is calculated by comparing the number of overnight stays in a region with the size of the resident population. It is generally a better guide to the economic weight of tourism for a region than the absolute number of overnight stays. The huge importance of tourism to many of Europe’s coastal regions and, even more so, to its islands, as well as to most of the Alpine regions of Austria and Italy, is evident here too.
Of the 25 regions in Europe with a tourism intensity of more than 10 000 overnight stays per 1 000 inhabitants, 10 are island regions, seven are Alpine regions and six are coastal regions. The Spanish region of Illes Balears shows the highest tourism intensity, at 50 178 overnight stays per 1 000 inhabitants, followed by the Greek region of Notio Aigaio (48 168), the Italian Provincia Autonoma Bolzano/Bozen (47 438), the Austrian Tirol (43 527), the Portuguese Algarve (39 132), the Greek Ionia Nisia (33 304) and the Austrian region of Salzburg (30 487).
Tourism developmentTourism in the European Union increased overall from 2000 to 2007. Two particular phases stand out. The years 2000 and 2001 were both record years, each recording 1.75 billion overnight stays in hotels and on campsites, thanks to the favourable economic climate at the time and to special events such as the Holy Year in Italy and the Hannover World EXPO. Tourism declined in 2002 and 2003, due in part to the economic slowdown but certainly also due to the 9/11 attacks. The number of overnight stays decreased to 1.73 billion in 2003 but then increased markedly from 2004 to 2007. In 2007 the number of overnight stays in the EU Member States’ hotels and campsites was just below the 2 billion mark, at 1.94 billion.
The biggest beneficiaries were the three Baltic States and Poland, all of which recorded double digit growth in overnight stays. Bulgaria, Greece, Romania, Spain, Finland, Portugal, the United Kingdom and Hungary also recorded growth figures above the EU average of 2.8 %.
133 Eurostat regional yearbook 2009
Tourism 10
Figure 10.4: Nights spent in hotels and campsites, EU-27, average annual change rate 2003–07 Percentage
EU-27BEBGCZDKDEEEIE
GRESFRIT
CYLVLTLU
HUMTNLATPLPTRO
SISKFI
SEUK
-5 0 5 10 15 20 25
Average annual change rate
134 Eurostat regional yearbook 2009
10 Tourism
Map 10.5: Nights spent in hotels and campsites, by NUTS 2 regions, average annual change rate 2003–07
Only Luxembourg, Slovakia and Cyprus recorded declines in the number of overnight stays between 2003 and 2007.
Map 10.5 illustrates the trend in overnight stays 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 States of the Baltic States, Poland and Bulgaria.Most regions in these countries achieved growth rates of over 10 %. Equally strong growth in overnight stays was recorded in the regions of Romania, Portugal and Spain.
Inbound tourismInbound tourism, i.e. visits from abroad, is of particular interest to most analyses of tourism in a given region. The statistically important factor here is the usual place of residence of the visitors, not their nationality. Foreign visitors, particularly those from distant countries, usually spend more per day than domestic visitors during their stays and thus carry greater weight as a demand factor for the local economy. Their expenditure also contributes to the balance of payments of the country visited. They may therefore help to offset foreign trade deficits.
Map 10.6 shows overnight stays by foreign visitors as percentages of total overnight stays in the various regions. The values differ very widely from region to region, from less than 5 % to well over 90 %. Europe’s island regions, or at least those in the south, show particularly high figures for foreign visitors as a percentage of total overnight stays. This is true not only for the island states of Malta and Cyprus but also for the Greek island regions, the Spanish Illes Balears and Ca
narias and the Portuguese Região Autónoma da Madeira. Foreign visitors also account for more than 90 % of overnight stays in Luxembourg and Praha, the Croatian region of Jadranska Hrvatska and the Austrian region of Tirol.
ConclusionAnalysis of the structure and development of tourism in Europe’s regions confirms the compensatory role which this sector of the economy plays 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 country. Here, tourism services are often an important factor in creating and securing employment and are one of the main sources of income for the population. This applies especially to Europe’s island states and island regions, to many coastal regions, particularly in southern Europe, and to the whole Alpine region. The particularly dynamic growth in tourism in most of the new central and east European Member States is a significant factor in helping their economies to catch up more rapidly with those of the old Member States.
According to the World Tourism Organisation, Europe is the most frequently visited region on earth. Five of the top 10 countries for visitors worldwide are European Union Member States. The wealth of its cultures, the variety of its landscapes and the exceptional quality of its tourist infrastructure are some of the probable reasons for this prominent position. The accession of the new Member States has hugely enriched the European Union’s tourism potential by enhancing its cultural diversity and providing interesting new destinations for many citizens to discover.
135 Eurostat regional yearbook 2009
Tourism 10
136 Eurostat regional yearbook 2009
10 Tourism
Map 10.6: Share of non-resident nights spent in hotels and campsites, by NUTS 2 regions, 2007
Methodological notesHarmonised 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.
137 Eurostat regional yearbook 2009
Tourism 10
Agriculture
IntroductionCrop production plays a key role in human and animal food safety. As a major user of the soil, agriculture shapes the rural landscape. Half of the surface area of the EU is used for agricultural purposes, hence the importance of agriculture to the EU’s natural environment. European agriculture is increasingly prioritising the kind of highquality, environmentally friendly produce demanded by the market.
This year’s Eurostat regional yearbook concentrates on the use of the agricultural area and on the production of certain flagship products in European agriculture. The chapter on agriculture is thus divided into two main sections: the first focuses on the soil use of certain major (arable and permanent) crops, and the second concentrates on the production of certain major crops and provides a regional breakdown of wheat, grain maize and rapeseed production.
Utilised agricultural area
Proportion of area under cereals to the utilised agricultural area
In terms of the area that they occupy and their importance in human and animal food, cereals (including rice) constitute the largest crop group in the world.
In the EU, too, cereals are the most widely produced crop. European statistics on cereals encompass wheat, barley, maize, rye, meslin, oats, rice and other cereals such as triticale, buckwheat, millet and canary seed. These crops — for which statistics are compiled in all Member States except Malta — accounted for some 30 % of the EU’s utilised agricultural area (UAA) in 2007.
Cereals in fact account for over 50 % of some regions’ UAA (see Map 11.1), namely Balkan regions such as SudVest Oltenia and Bucureşti — Ilfov in Romania and east European regions, in particular in Hungary (KözépDunántúl, NyugatDunántúl and DélDunántúl), Slovakia (Bratislavský kraj and Západné Slovensko) and Poland (Łódzkie, Lubelskie, Wielkopolskie, Zachodnonio pomorskie, Lubuskie, Dolnośląskie, Opolskie, Kujawskopomorskie and Pomorskie). Cereal crops also cover over 50 % of the UAA of some regions of northern Europe (Denmark, the EteläSuomi and LänsiSuomi regions of Finland and Östra Mellansverige, Småland med öarna and Norra Mellansverige in Sweden) and southern
Europe (the Italian region of Basilicata). In western Europe, the highest proportion of area under cereals to UAA is in the regions of ÎledeFrance, Picardie, Centre and Alsace in France.
Cereal crops cover a small proportion of the UAA in southern regions (except Basilicata, mentioned above), in certain Alpine regions, on the Atlantic coast of the Iberian peninsula and in the regions of northern Sweden, where this type of crop accounts for less than 10 % of the UAA.
Specifically, these regions include almost all regions of Portugal (except Lisboa region), and certain coastal areas of Spain (Galicia, Principado de Asturias, Cantabria, Comunidad Valenciana and Canarias) and Italy (Liguria).
The Alpine regions of Austria (Kärnten, Salzburg, Tirol and Vorarlberg) and Italy (Valle d’Aosta/Vallée d’Aoste, Provincia Autonoma Bolzano/Bozen and Provincia Autonoma Trento) have areas under cereals of less than 10 % of UAA.
In certain regions in which the preference is for grassland and, in some cases, green fodder, a small proportion of the area is devoted to cereals. Those regions are in Belgium (Luxembourg Province), France (Corsica, Limousin and the overseas department of Réunion), the Netherlands (Friesland, Overijssel, Gelderland, Utrecht and NoordHolland), the whole of Ireland and the region of Mellersta Norrland in Sweden.
Proportion of permanent crops to the utilised agricultural area
Permanent crops are located mainly in the Mediterranean regions. The term ‘permanent crops’ means ligneous crops that occupy the soil for several — usually more than five — consecutive years, and refers mainly to fruit and berry trees, bushes, vines and olive trees.
Permanent crops cover a much smaller surface area than annual crops and cereal crops. They are also much more regionally concentrated, as shown in Map 11.2.
Permanent crops remain prevalent in agriculture given that their production generally yields a greater added value per hectare than annual crops and that they are generally intended for human consumption.
Moreover, these crops play an important role not only in shaping the rural landscape (with orchards, vines and olive trees) but also in terms of the environmental balance of agriculture.
140 Eurostat regional yearbook 2009
11 Agriculture
141 Eurostat regional yearbook 2009
Agriculture 11Map 11.1: Cereals (including rice) as a percentage of utilised agricultural area, by NUTS 2 regions, 2007
142 Eurostat regional yearbook 2009
11 Agriculture
Map 11.2: Permanent crops as a percentage of utilised agricultural area, by NUTS 2 regions, 2007
Map 11.2 clearly shows how the Mediterranean regions specialise in permanent crops. Regional data on these crops are not available for several countries in this area.
Of the 14 regions with permanent crops accounting for over 30 % of their UAA, 10 are in the Mediterranean basin. They are: Cataluña, Comunidad Valenciana, Illes Balears, Andalucía and Región de Murcia, in Spain (the Comunidad Valenciana region, for example, specialises in cultivating oranges and smallfruited citrus, and accounts for over 27 % of the orangegrowing surface area and 60 % of the smallfruited citrus surface area of the EU27); Campania, Puglia, Calabria and Sicily, in Italy; Norte, Central, Algarve and the autonomous region of Madeira, in Portugal, and the LanguedocRousillon region of France.
Similarly, Malta and Cyprus, also in the Mediterranean, have significant proportions (10–30 %) of permanent crops to their UAA.
In the regions of Aquitaine in France and Rioja in Spain, the large proportion of permanent crops to UAA is due to vine cultivation.
In the Belgian region of Limburg, the significant proportion of permanent crops to UAA is due to orchards (mainly apple and pear trees).
Agricultural productionMaps 11.3, 11.4 and 11.5 show the percentage contribution of each region to the total EU production of three major crops — wheat, maize and rapeseed. The total regional production of an agricultural product — even if the figure is heavily influenced by the yield and area of the crop — remains a good indicator of the contribution that a region can make, on a broader level, to the quantity produced in, say, the country and/or the EU. The abovementioned maps and the following paragraphs give an overview of the concentration of the production of these crops.
Wheat production
Wheat (common and durum wheat) is the crop with by far the highest production in European agriculture. In 2007, wheat accounted for 46 % of cereal production in the EU. Wheat is primarily used in human and animal food, but also for making processed products such as bioethanol and starch.
It is also one of the most widely distributed crops in the EU. According to the statistics, only five regions do not produce wheat, namely Principado de Asturias in Spain, Valle d’Aosta/Vallée d’Aoste, Provincia Autonoma Bolzano/Bozen in Italy and Mellersta Norrland and Övre Norrland in Sweden.
In 2007, the EU produced 120 million tonnes of wheat (including 8.2 million tonnes of durum wheat), on a total area of 24 million hectares.
Some 21 regions account for over half of wheat production in the EU (calculated without the figures for production in the Czech Republic, Greece and the United Kingdom, for which regional data are not available).
Of those 21 regions, 10 are in France, as follows (ranging from the highest production to the lowest): Centre, (which accounts for 4.5 % of Community production of wheat), Picardie, ChampagneArdenne, PoitouCharentes, Pays de la Loire, Nord — PasdeCalais, Bourgogne, Haute Normandie, ÎledeFrance and Bretagne. This makes France the biggest wheat producer in the EU. France harvested almost 33 million tonnes of cereal in 2007.
Germany, with 20.9 million tonnes, is the secondbiggest producer. It has eight of the 21 highestproducing regions, and they are as follows (from the largest producers to the lowest): Bayern (which accounts for 3.6 % of wheat production in the Community), Niedersachsen, SachsenAnhalt, NordrheinWestfalen, MecklenburgVorpommern, BadenWürttemberg, Thüringen and SchleswigHolstein.
It can therefore be said that the EU’s wheat ‘granary’ is located in the northern half of France and Germany. The next 63 regions contribute 40 % of the EU’s total production. These include all but three regions of Poland, which is the fourth biggest producer of wheat, after the United Kingdom (8.3 million tonnes).
Grain maize production
In 2007, 47.5 million tonnes of grain maize were produced in the EU, which amounts to 18 % of cereal production. Grain maize is mainly intended for animal feed but it is also used for industrial products such as starch and glue.
Given its physiological needs, this crop covers a smaller geographical range of EU regions. The
143 Eurostat regional yearbook 2009
Agriculture 11
144 Eurostat regional yearbook 2009
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
145 Eurostat regional yearbook 2009
Agriculture 11Map 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
most northerly Member States (Ireland, the United Kingdom, Denmark, Estonia, Latvia, Finland and Sweden) produce little or no grain maize.
The 14 regions producing the most grain maize are responsible for over 50 % of total grain maize production. This Community production total was calculated without production figures for the Czech Republic and Greece, given that regional data for those countries are not available.
Of those 14 regions, seven are in France, as follows (starting with the highestproducing region): Aquitaine (which accounts for 6.3 % of Community production), PoitouCharentes, MidiPyrénées, Alsace, Pays de la Loire, RhôneAlpes and Centre. Four are in the north of Italy (starting with the highestproducing region): Veneto, Lombardia, which accounts for 6.2 % of Community production, Piemonte and FriuliVenezia Giulia. There is one such region in Hungary (DélDunantul, which accounts for 2.3 % of Community production), one in Spain (Castilla y Leon, 2.2 % of Community production) and one in Germany (Bayern, 2.1 % of Community production).
The next 40 regions account for 40 % of the EU’s total production. Romania, with 3.9 million tonnes, is the fourthbiggest producer of grain maize in the EU27 (after France, with 14 million tonnes, Italy (9.9 million tonnes) and Hungary (4 million tonnes). All regions of Romania except Bucureşti — Ilfov are in this group. Romania specialises in grain maize cultivation (2.5 million hectares, i.e. the largest surface area dedicated to this crop in the EU), but its yields are not as high as those in the older Member States.
Rapeseed production
In 2007, 18.1 million tonnes of rapeseed were produced in the EU, a 13 % increase on the 2006 figure. Rapeseed is used in the manufacture of oil (mainly nonedible oil such as biodiesel, but also edible oil) and animal feed (rapeseed cake from the crushing of rapeseed grain). The increase in rapeseed production is clearly due to the high demand in recent years for renewable energy sources such as biodiesel.
Rapeseed is best suited to a temperate climate. Four countries in the south of the EU — Portugal, Greece, Cyprus and Malta — do not produce
rapeseed; southern regions (in Spain, Italy and Bulgaria) account for less than 10 % of Community production.
The 13 regions (including Denmark) that produce the most rapeseed account for at least 50 % of total production in the EU27. This Community production total was calculated without figures for the Czech Republic and the United Kingdom, given that regional data for those countries are not available.
Of those regions, eight are in Germany, the biggest rapeseedproducing country, with 5.3 million tonnes (starting with the highestproducing region): MecklenburgVorpommern (5.8 % of Community production), Bayern, SachsenAnhalt, Niedersachsen, SchleswigHolstein, Sachsen, Thüringen and Brandenburg.
Four are in France, the secondbiggest producer of rapeseed, with 4.6 million tonnes (starting with the highestproducing region): Centre (6 % of Community production), ChampagneArdenne, Bourgogne and Lorraine. Denmark contributes 3.9 % of Community production.
The next 34 regions account for 40 % of the EU’s total production. Poland, with 2.1 million tonnes, is the thirdbiggest producer of rapeseed in the EU. Ten Polish regions are in this group: Wielkopolskie (2.1 % of Community production), Kujawskopomorskie, Zachononiopomorskie, Dolnośląskie, Opolskie, Pomorskie, Warminskomazurskie, Lubelskie, Mazowieckie and Lubuskie.
Two Baltic countries, Estonia and Lithuania, also feature in this group.
ConclusionClimate and geography have a major influence on the agricultural use of the land; the choice of animal and plant production varies from region to region across Europe.
It should be emphasised, however, that production quality and intensity are not the only factors influencing the development of the agricultural sector. Other criteria such as rural development, the environment and food safety have become increasingly important, and could yet alter the current face of agriculture in Europe’s regions.
146 Eurostat regional yearbook 2009
11 Agriculture
147 Eurostat regional yearbook 2009
Agriculture 11Map 11.5: Rape production, sum of the regions which together represent x % of the EU-27 production
of rape, by NUTS 2 regions, 2007
Methodological notesThe 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.
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11 Agriculture
Annex
EUROPEAN UNION: NUTS 2 regions
Belgium
BE10 Région de Bruxelles-Capitale/Brussels Hoofdstedelijk Gewest
BE21 Prov. Antwerpen
BE22 Prov. Limburg (B)
BE23 Prov. Oost-vlaanderen
BE24 Prov. vlaams-Brabant
BE25 Prov. West-vlaanderen
BE31 Prov. Brabant Wallon
BE32 Prov. Hainaut
BE33 Prov. Liège
BE34 Prov. Luxembourg (B)
BE35 Prov. Namur
Bulgaria
BG31 Severozapaden
BG32 Severen tsentralen
BG33 Severoiztochen
BG34 yugoiztochen
BG41 yugozapaden
BG42 yuzhen tsentralen
Czech Republic
CZ01 Praha
CZ02 Střední Čechy
CZ03 Jihozápad
CZ04 Severozápad
CZ05 Severovýchod
CZ06 Jihovýchod
CZ07 Střední Morava
CZ08 Moravskoslezsko
Denmark
DK01 Hovedstaden
DK02 Sjælland
DK03 Syddanmark
DK04 Midtjylland
DK05 Nordjylland
Germany
DE11 Stuttgart
DE12 Karlsruhe
DE13 Freiburg
DE14 Tübingen
DE21 Oberbayern
DE22 Niederbayern
DE23 Oberpfalz
DE24 Oberfranken
DE25 Mittelfranken
DE26 Unterfranken
DE27 Schwaben
DE30 Berlin
DE41 Brandenburg — Nordost
DE42 Brandenburg — Südwest
DE50 Bremen
DE60 Hamburg
DE71 Darmstadt
DE72 Gießen
DE73 Kassel
DE80 Mecklenburg-vorpommern
DE91 Braunschweig
DE92 Hannover
DE93 Lüneburg
DE94 Weser-Ems
DEA1 Düsseldorf
DEA2 Köln
DEA3 Münster
DEA4 Detmold
DEA5 Arnsberg
DEB1 Koblenz
DEB2 Trier
DEB3 Rheinhessen-Pfalz
DEC0 Saarland
DED1 Chemnitz
DED2 Dresden
DED3 Leipzig
DEE0 Sachsen-Anhalt
DEF0 Schleswig-Holstein
DEG0 Thüringen
Estonia
EE00 Eesti
Ireland
IE01 Border, Midland and Western
IE02 Southern and Eastern
Greece
GR11 Anatoliki Makedonia, Thraki
GR12 Kentriki Makedonia
GR13 Dytiki Makedonia
GR14 Thessalia
GR21 Ipeiros
GR22 Ionia Nisia
GR23 Dytiki Ellada
GR24 Sterea Ellada
GR25 Peloponnisos
GR30 Attiki
GR41 voreio Aigaio
GR42 Notio Aigaio
GR43 Kriti
Spain
ES11 Galicia
ES12 Principado de Asturias
ES13 Cantabria
149 Eurostat regional yearbook 2009
ES21 País vasco
ES22 Comunidad Foral de Navarra
ES23 La Rioja
ES24 Aragón
ES30 Comunidad de Madrid
ES41 Castilla y León
ES42 Castilla-La Mancha
ES43 Extremadura
ES51 Cataluña
ES52 Comunidad valenciana
ES53 Illes Balears
ES61 Andalucía
ES62 Región de Murcia
ES63 Ciudad Autónoma de Ceuta
ES64 Ciudad Autónoma de Melilla
ES70 Canarias
France
FR10 Île-de-France
FR21 Champagne-Ardenne
FR22 Picardie
FR23 Haute-Normandie
FR24 Centre
FR25 Basse-Normandie
FR26 Bourgogne
FR30 Nord — Pas-de-Calais
FR41 Lorraine
FR42 Alsace
FR43 Franche-Comté
FR51 Pays de la Loire
FR52 Bretagne
FR53 Poitou-Charentes
FR61 Aquitaine
FR62 Midi-Pyrénées
FR63 Limousin
FR71 Rhône-Alpes
FR72 Auvergne
FR81 Languedoc-Roussillon
FR82 Provence-Alpes-Côte d’Azur
FR83 Corse
FR91 Guadeloupe
FR92 Martinique
FR93 Guyane
FR94 Réunion
Italy
ITC1 Piemonte
ITC2 valle d’Aosta/vallée d’Aoste
ITC3 Liguria
ITC4 Lombardia
ITD1 Provincia Autonoma Bolzano/Bozen
ITD2 Provincia Autonoma Trento
ITD3 veneto
ITD4 Friuli-venezia Giulia
ITD5 Emilia-Romagna
ITE1 Toscana
ITE2 Umbria
ITE3 Marche
ITE4 Lazio
ITF1 Abruzzo
ITF2 Molise
ITF3 Campania
ITF4 Puglia
ITF5 Basilicata
ITF6 Calabria
ITG1 Sicilia
ITG2 Sardegna
Cyprus
Cy00 Kypros/Kıbrıs
Latvia
Lv00 Latvija
Lithuania
LT00 Lietuva
Luxembourg
LU00 Luxembourg (Grand-Duché)
Hungary
HU10 Közép-Magyarország
HU21 Közép-Dunántúl
HU22 Nyugat-Dunántúl
HU23 Dél-Dunántúl
HU31 Észak-Magyarország
HU32 Észak-Alföld
HU33 Dél-Alföld
Malta
MT00 Malta
Netherlands
NL11 Groningen
NL12 Friesland (NL)
NL13 Drenthe
NL21 Overijssel
NL22 Gelderland
NL23 Flevoland
NL31 Utrecht
NL32 Noord-Holland
NL33 Zuid-Holland
NL34 Zeeland
NL41 Noord-Brabant
NL42 Limburg (NL)
Austria
AT11 Burgenland (A)
AT12 Niederösterreich
AT13 Wien
AT21 Kärnten
AT22 Steiermark
AT31 Oberösterreich
AT32 Salzburg
AT33 Tirol
AT34 vorarlberg
Poland
PL11 Łódzkie
PL12 Mazowieckie
PL21 Małopolskie
150 Eurostat regional yearbook 2009
PL22 Śląskie
PL31 Lubelskie
PL32 Podkarpackie
PL33 Świętokrzyskie
PL34 Podlaskie
PL41 Wielkopolskie
PL42 Zachodniopomorskie
PL43 Lubuskie
PL51 Dolnośląskie
PL52 Opolskie
PL61 Kujawsko-pomorskie
PL62 Warmińsko-mazurskie
PL63 Pomorskie
Portugal
PT11 Norte
PT15 Algarve
PT16 Centro (P)
PT17 Lisboa
PT18 Alentejo
PT20 Região Autónoma dos Açores
PT30 Região Autónoma da Madeira
Romania
RO11 Nord-vest
RO12 Centru
RO21 Nord-Est
RO22 Sud-Est
RO31 Sud — Muntenia
RO32 Bucureşti — Ilfov
RO41 Sud-vest Oltenia
RO42 vest
Slovenia
SI01 vzhodna Slovenija
SI02 Zahodna Slovenija
Slovakia
SK01 Bratislavský kraj
SK02 Západné Slovensko
SK03 Stredné Slovensko
SK04 východné Slovensko
Finland
FI13 Itä-Suomi
FI18 Etelä-Suomi
FI19 Länsi-Suomi
FI1A Pohjois-Suomi
FI20 Åland
Sweden
SE11 Stockholm
SE12 Östra Mellansverige
SE21 Småland med öarna
SE22 Sydsverige
SE23 västsverige
SE31 Norra Mellansverige
SE32 Mellersta Norrland
SE33 Övre Norrland
United Kingdom
UKC1 Tees valley and Durham
UKC2 Northumberland and Tyne and Wear
UKD1 Cumbria
UKD2 Cheshire
UKD3 Greater Manchester
UKD4 Lancashire
UKD5 Merseyside
UKE1 East yorkshire and Northern Lincolnshire
UKE2 North yorkshire
UKE3 South yorkshire
UKE4 West yorkshire
UKF1 Derbyshire and Nottinghamshire
UKF2 Leicestershire, Rutland and Northamptonshire
UKF3 Lincolnshire
UKG1 Herefordshire, Worcestershire and Warwickshire
UKG2 Shropshire and Staffordshire
UKG3 West Midlands
UKH1 East Anglia
UKH2 Bedfordshire and Hertfordshire
UKH3 Essex
UKI1 Inner London
UKI2 Outer London
UKJ1 Berkshire, Buckinghamshire and Oxfordshire
UKJ2 Surrey, East and West Sussex
UKJ3 Hampshire and Isle of Wight
UKJ4 Kent
UKK1 Gloucestershire, Wiltshire and Bristol/Bath area
UKK2 Dorset and Somerset
UKK3 Cornwall and Isles of Scilly
UKK4 Devon
UKL1 West Wales and The valleys
UKL2 East Wales
UKM2 Eastern Scotland
UKM3 South Western Scotland
UKM5 North Eastern Scotland
UKM6 Highlands and Islands
UKN0 Northern Ireland
151 Eurostat regional yearbook 2009
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
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
153 Eurostat regional yearbook 2009
European Commission
Eurostat regional yearbook 2009
Luxembourg: Publications Office of the European Union
2009 — 153 pp. — 21 × 29.7 cm
ISBN 978-92-79-11696-4ISSN 1830-9674doi: 10.2785/17776
Price (excluding VAT) in Luxembourg: EUR 30
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Eurostat regional yearbook 2009
KS-HA
-09-001-EN-C
Eurostat regional yearbook 2009
S t a t i s t i c a l b o o k s
ISSN 1830-9674
Price (excluding VAT) in Luxembourg: EUR 30
Eurostat regional yearbook 2009Statistical 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
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ISBN 978-92-79-11696-4