ISSN 1830-9674

Statistical information is essential for understanding our complex and rapidly changing world. Eurostat regional yearbook 2009 offers 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 different statistical domains and are accompanied by statistical maps, figures and tables on each subject. A broad set of regional data is presented on the following themes: population, European cities, labour market, gross domestic product, household accounts, structural business statistics, information society, science, technology and innovation, education, tourism and agriculture. The publication is available in English, French and German.

http://ec.europa.eu/eurostat

ISBN 978-92-79-11696-4

9

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Price (excluding VAT) in Luxembourg: EUR 30

Eurostat regional yearbook 2009

KS-HA-09-001-EN-C

Eurostat regional yearbook 2009

Statistical books

Eurostat regional yearbook 2009

Statistical books

Eurostat regional yearbook 2009

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More information on the European Union is available on the Internet (http://europa.eu). Luxembourg: Office for Official Publications of the European Communities, 2009 ISBN 978-92-79-11696-4 ISSN 1830-9674 doi: 10.2785/17776 Cat. No: KS-HA-09-001-EN-C Theme: General and regional statistics Collection: Statistical books © European Communities, 2009 © Copyright for the following photos: cover: © Annette Feldmann; the chapters Introduction, Population, Household accounts, Information society, Education and tourism: © Phovoir.com; the chapter European cities: © Teodóra Brandmüller; the chapters Labour market, Gross domestic product, Structural business statistics and Science, technology and innovation: © the Digital Photo Library of the Directorate-General for Regional Policy of the European Commission; the chapter Agriculture: © Jean-Jacques Patricola. For reproduction or use of these photos, permission must be sought directly from the copyright holder.

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

Walter Radermacher Director-General, Eurostat

Eurostat regional yearbook 2009

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Acknowledgements The editors of the Eurostat regional yearbook 2009 would like to thank all those who were involved in its preparation. We are especially grateful to the following chapter authors at Eurostat for making the publication of this year’s edition possible. • Population: Veronica Corsini, Monica Marcu and Rosemarie Olsson (Unit F.1: Population) • European cities: Teodóra Brandmüller (Unit E.4: Regional statistics and geographical informa­ tion) • Labour market: Pedro Ferreira (Unit E.4: Regional statistics and geographical information) • Gross domestic product: Andreas Krüger (Unit C.2: National accounts — production) • Household accounts: Andreas Krüger (Unit C.2: National accounts — production) • Structural business statistics: Aleksandra Stawińska (Unit G.2: Structural business statistics) • Information society: Albrecht Wirthmann (Unit F.6: Information society and tourism) • Science, technology and innovation: Bernard Félix, Tomas Meri, Reni Petkova and Håkan Wilén (Unit F.4: Education, science and culture) • Education: Sylvain Jouhette, Lene Mejer and Paolo Turchetti (Unit F.4: Education, science and culture) • Tourism: Ulrich Spörel (Unit F.6: Information society and tourism) • Agriculture: Céline Ollier (Unit E.2: Agriculture and fisheries) This publication was edited and coordinated by Åsa Önnerfors (Unit E.4: Regional statistics and geo­ graphical information) with the help of Berthold Feldmann (Unit E.4: Regional statistics and geo­ graphical information) and Pavel Bořkovec (Unit D.4: Dissemination). Baudouin Quennery (Unit E.4: Regional statistics and geographical information) produced all the statistical maps. We are also very grateful to:

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the Directorate-General for Translation of the European Commission, and in particular the German, English and French translation units;

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the Publications Office of the European Union, and in particular Bernard Jenkins in Unit B.1, Cross-media publishing, and the proofreaders in Unit B.2, Editorial services.

Eurostat regional yearbook 2009

Contents Introduction.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Statistics on regions and cities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 The NUTS classification.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Coverage.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 More regional information.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1 POPULATION.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Unveiling the regional pattern of demography. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Population density.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Population change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methodological notes.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

13 14 14 14 23 23

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

25

3 Labour market.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Regional working time patterns.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Brief overview for 2007.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Regional work patterns.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Part-time jobs: lowering the average working time.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Employees spend less time at work. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methodological notes.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Definitions.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

35

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

Eurostat regional yearbook 2009

26 26 26 26 26 28 28 33

36 36 39 41 44 45 46 46 49 50 50 52 52 52 56 56 57 59 59

5

5 Household accounts.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction: measuring wealth.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Private household income.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Results for 2006.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Primary income. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Disposable income.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dynamic development on the edges of the Union. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methodological notes.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Structural business statistics.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Regional specialisation and business concentration.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Specialisation in business services. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Employment growth in business services.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Characteristics of the top 30 most specialised regions in business services. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methodological notes.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Information society.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Access to information and communication technologies.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Use of the Internet and Internet activities.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Non-users of the Internet.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methodological notes.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

61 62 62 62 62 64 68 70 71 73 74 74 80 84 84 87 87 89 90 90 93 96 97 99

8 Science, technology and innovation.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 Introduction.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 Research and development.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 Human resources in science and technology.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 High-tech industries and knowledge-intensive services. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 Patents. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 Conclusion.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 Methodological notes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 9 Education.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Students’ participation in education.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Participation of 4-year-olds in education. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Students in upper secondary education and post-secondary non-tertiary education.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Students in tertiary education. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tertiary educational attainment.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lifelong learning.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methodological notes.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

6

Eurostat regional yearbook 2009

113 114 114 114 116 119 119 119 123 123

10 Tourism.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Accommodation capacity. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Overnight stays.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Average length of stay. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tourism intensity.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tourism development.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Inbound tourism.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methodological notes.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

126 127 127 130 130 133 135 135 137

11 Agriculture.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Utilised agricultural area.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Proportion of area under cereals to the utilised agricultural area.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Proportion of permanent crops to the utilised agricultural area.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Agricultural production. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wheat production. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Grain maize production.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rapeseed production.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methodological notes.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

139 140 140 140 140 143 143 143 146 146 148

AnNEX.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . European Union: NUTS 2 regions.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Candidate countries: statistical regions at level 2.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . EFTA countries: statistical regions at level 2.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

149

Eurostat regional yearbook 2009

125

149 152 153

7

Introduction

Introduction

Statistics on regions and cities Statistical information is essential for under­ standing our complex and rapidly changing world. Eurostat, the Statistical Office of the Euro­ pean Communities, is responsible for collecting and disseminating data at European level, not only from the 27 Member States of the Euro­ pean Union, but also from the three candidate countries (Croatia, the former Yugoslav Repub­ lic 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 EU-27, 30 statisti­ cal 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 Euro­ stat 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 statis­ tical data, selected and sorted in some way (differ­ ent top lists, graphs with regional extreme values within the countries or only giving representative examples) to make it easier to understand.

(1) More information on the NUTS classification can be found at http:// ec.europa.eu/eurostat/ ramon/nuts/splash_ regions.html

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 chap­ ter on Population gives us detailed knowledge of different demographic patterns, such as popula­ tion 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 defini­ tions of the various spatial levels used in the Ur­ ban Audit data collection, with some interesting examples on how people travel to work in nine European capitals. The chapter on the Labour market mainly de­ scribes the differences in weekly working hours

10

throughout Europe and offers a couple of expla­ nations 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 re­ gions, private households and different sectors of the business economy. We are particularly proud to present a new and very interesting chapter on the Information society, 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 innovation 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 knowledge-based economy in the world. In the next chapter we learn more about regional statistics on Tourism, and which tourist desti­ nations 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 classification The nomenclature of territorial units for statistics (NUTS) provides a single uniform breakdown of territorial units for the production of regional sta­ tistics for the European Union. The NUTS classi­ fication 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 Parlia­ ment and the Council (1). Whenever new Member States join the EU, the NUTS regulation is amended to include the re­ gional 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

Eurostat regional yearbook 2009

Introduction

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 ap­ plies 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 lat­ est 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 accom­ panies this publication and it shows all NUTS level 2 regions in the 27 Member States of the European Union (EU-27) and the correspond­ ing 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.

Coverage The Eurostat regional yearbook 2009 mainly con­ tains statistics on the 27 Member States of the European Union but, when available, data is also

Eurostat regional yearbook 2009

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 coun­ tries 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 information In the subject area ‘Regions and cities’ under the heading ‘General and regional statistics’ on the Eurostat website you will find tables with statis­ tics 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 sta­ tistics 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 con­ sult 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 down­ load 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 e-mail address: [email protected]

11

Population

1

Population

Unveiling the regional pattern of demography Demographic 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 gen­ eration 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 school-age population, healthcare, labour force participa­ tion, 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 phe­ nomena might have a stronger impact in some regions than in others. This chapter presents the regional pattern of de­ mographic phenomena as it is today.

Population density On 1 January 2007, 584 million people inhabited the European Union and candidate and EFTA coun­ tries. 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 Janu­ ary 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 Lon­ don was by far the most densely populated, but the Bruxelles-Capitale, 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 change During the last four and a half decades, the pop­ ulation of the 27 countries that make up today’s European Union has grown from around 400

14

million (1960) to almost 500 million (497 million on 1 January 2008). Including candidate coun­ tries 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 compo­ nents: the so-called ‘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 ‘Methodologi­ cal 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 de­ cline 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, five-year averages have been calcu­ lated of the total annual population change and its components. Given that demographic trends are long-term developments, the five-year averages provide a stable and accurate picture. They help to identify regional clusters, which often stretch well beyond national borders. For the sake of compara­ bility, the population change and its components are presented in relative terms, calculating the so-called 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 north-east, east and part of the south-east 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, Slo­ vakia, Hungary and Romania, and to the north the three Baltic States and the northern parts of Sweden and the Finnish region of Itä-Suomi. 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.

Eurostat regional yearbook 2009

Population

1

Map 1.1: Population density, by NUTS 2 regions, 2007

Inhabitants per km2

Eurostat regional yearbook 2009

15

1

Population

Map 1.2: Total population change, by NUTS 2 regions, average 2003–07

16

Per 1 000 inhabitants

Eurostat regional yearbook 2009

1

Population

In nearly all western and south-western 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 popu­ lation change’ is widespread and affects almost 50 % of the EU’s regions. A single extended cross-border region can be identified showing a natural increase of popu­ lation, made up of Ireland, the central United Kingdom, most regions in France, Belgium, Lux­ embourg, 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 Ger­ many, 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 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 1986

0.5

1.0

1.5

2.0

2.5

3.0

3.5

2006

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

Eurostat regional yearbook 2009

17

1

Population

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

18

Per 1 000 inhabitants

Eurostat regional yearbook 2009

1

Population

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 inhabit­ ants of the EU have fewer children. At aggregat­ ed level, in the 27 countries that today form the European Union, the total fertility rate has de­ clined 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 fer­ tility 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 acces­ sible 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 catching-up 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

Figure 1.2: Crude birth rates, by NUTS 2 regions, 2007 Births per 1 000 inhabitants BE BG CZ DK DE EE IE EL ES FR IT CY LV LT LU HU MT NL AT PL PT RO SI SK FI SE UK HR MK TR IS LI NO CH

Région de Bruxelles-Capitale/Brussels Hoofdstedelijk Gewest Prov. West-Vlaanderen Yugoiztochen Severozapaden Střední Čechy Střední Morava Hovedstaden Sjælland Saarland Hamburg Border, Midland and Western Kriti

Ipeiros Principado de Asturias

Southern and Eastern Ciudad Autónoma de Ceuta

Corse

Guyane Provincia Autonoma Bolzano/Bozen

Liguria

Nyugat-Dunántúl

Észak-Alföld Flevoland Vorarlberg Pomorskie Região Autónoma dos Açores Nord-Est Zahodna Slovenija Východné Slovensko Pohjois-Suomi Stockholm Inner London Sjeverozapadna Hrvatska

Limburg (NL) Burgenland (A) Opolskie Alentejo Sud-Vest Oltenia Vzhodna Slovenija Západné Slovensko Itä-Suomi Norra Mellansverige Cornwall and Isles of Scilly Središnja i Istočna (Panonska) Hrvatska

Hedmark og Oppland Ticino

0

5

Oslo og Akershus Région lémanique

10

15

20

25

30

35

National value Source: Eurostat Demographic Statistics. Notes: FR, UK: 2006 TR: national level

Eurostat regional yearbook 2009

19

1

Population

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

20

Eurostat regional yearbook 2009

Population

2.1 children per women is considered to be the replacement level, i.e. the level at which the popu­ lation 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 can­ didate and EFTA countries, with the exception of Turkey and Iceland, are still well below the re­ placement level. The analysis of Map 1.3 can also be refined by iso­ lating the contribution of live births to the natural population change. Figure 1.2 shows the regional differences within each country of the so-called 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 popula­ tion change’ is close to being balanced or nega­ tive, the importance of immigration increases when it comes to maintaining population size. Moreover, migration also contributes indirect­ ly 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 mi­ gration. This is at its most striking in Austria, the United Kingdom, Spain, the northern and central regions of Italy and some regions of western Ger­ many, 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 can­ celled out by negative net migration. Four cross-border regions where more people have left than arrived (negative net migration) can be identified on Map 1.4: • the northernmost regions of Norway and Fin­ land; • an eastern group, comprising most of the re­ gions of eastern Germany, Poland, Lithuania and Latvia and most parts of Slovakia, Hun­ gary, Romania, Bulgaria and Turkey;

Eurostat regional yearbook 2009

1

• regions in the north-east of France and the French overseas departments; • a few regions in the south of Italy, in the Neth­ erlands 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 develop­ ments, upwards or — in some regions — down­ wards. 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 posi­ tive net migration. However, in eastern German regions, Lithuania and Latvia and some regions in Poland, Slovakia, Hungary, Bulgaria and Ro­ mania, 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 EU-27 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 fertil­ ity 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 mortal­ ity in past and future decades. The ageing process can be typified as ageing from the top, as it large­ ly results from projected increases in longevity, moderated by the impact of positive net migra­ tion flows and some recovery in fertility.

21

1

Population

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

22

Eurostat regional yearbook 2009

Population

Conclusion This chapter highlights certain features of region­ al population development in the area made up by the EU-27 Member States and the candidate and EFTA countries over the period from 1 January 2003 to 1 January 2008. As far as possible, typolo­ gies of regions in the different demographic phe­

1

nomena have been identified, spreading across national boundaries. While population decline is evident in several regions, at aggregated level the EU-27 population still increased in that period by around 2 million people every year. The main driver of population growth in this area is migra­ tion, which counterbalanced, as seen in the maps, the negative natural change in many regions.

Methodological notes Sources: Eurostat  — Demographic Statistics. For more information please consult the Eurostat website at http://www.ec.europa.eu/eurostat. Total fertility rate is defined as the average number of children that would be born to a woman during her lifetime if she were to pass through her childbearing years conforming to the age-specific fertility rates that have been measured in a given year. Migration can be extremely difficult to measure. A variety of different data sources and definitions are used in the Member States, meaning that direct comparisons between national statistics can be difficult or misleading. The net migration figures here are not directly calculated from immigration 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 increase between two dates (in the Eurostat database, it is then called net migration including corrections). 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 population and of deaths over the average population. Crude rate of net migration is the ratio of net migration during the year to the average population 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 attributable to births and deaths). Population density is the ratio of the population of a territory to the total size of the territory (including inland waters), as measured on 1 January.

Eurostat regional yearbook 2009

23

European cities

2

European cities

Introduction Data on European cities were collected in the Ur­ ban Audit project. The project’s ultimate goal is to help improve the quality of urban life: it supports the exchange of experience among European cit­ ies; it helps to identify best practices; it facilitates benchmarking at European level; and it provides information on the dynamics both within the cit­ ies and with their surroundings. The Urban Audit has become a core task of Euro­ stat. Even so, the project would not have been pos­ sible 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 Directorate-General 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 rele­ vance. So, where we are now? The list of indica­ tors 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 en­ largement.

Enhanced list of indicators There have been three major revisions of the list so far. Policy relevance, data availability and experi­ ence with previous collections have been reviewed to produce the current list of more than 300 in­ dicators. 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 envi­ ronment 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.

26

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 ret­ rospectively only for a reduced number of 80 var­ iables. Where data for these years were not avail­ able, data from adjacent years were also accepted. In 2009 Eurostat launched an annual Urban Au­ dit, requesting data for a limited number of vari­ ables. 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 EU-27, Croatia, Turkey, Norway and Swit­ zerland. The 321 Urban Audit cities in the EU-27 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 inhab­ itants) were not included. To complement the Urban Audit data collection in this respect, the Large City Audit was launched. The Large City Au­ dit includes all ‘non-Urban Audit cities’ with more than 100 000 inhabitants in the EU-27. For these cities a reduced set of 50 variables is collected. We invite all readers to explore the wealth of in­ formation gathered in the past 10 years by brows­ ing the Urban Audit data on Eurostat’s website.

Discovering the spatial dimension Cities are usually displayed as distinct uncon­ nected dots on a map. This visualisation method increases visibility but it misrepresents reality and distorts the understanding of linkages be­ tween a city and its hinterland and the under­ standing of linkages between cities. Cities can no longer be treated as discrete unrelated enti­ ties without a spatial dimension. The recent de­ velopments in transport, communication and information technology infrastructure ease the flow of people and resources from one area to another considerably. Urban–rural connectivity

Eurostat regional yearbook 2009

European cities

2

Map 2.1: Boundaries of cities participating in the Urban Audit data collection

Eurostat regional yearbook 2009

27

2

European cities

(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).

and inter-urban relations have become critical for balanced regional development. To facilitate the analysis of the interaction be­ tween the city and its surroundings for each participating city, different spatial levels were de­ fined. 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 de­ tail in Map 2.2 while the larger urban zones are shown in Map 2.3. The spatial data used to pro­ duce 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 infor­ mation, 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 usu­ ally 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 politi­ cal entity and the boundary of the built-up 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

28

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 industri­ al 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 ‘built-up’ area. Port areas, airports and sport fa­ cilities were included if they were neighbours of the previously defined ‘built-up’ 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 mean­ ingful for the morphological city as well. To measure spatial inequalities within the city, the area of the core city was divided into sub-city districts. Sub-city districts were defined in such a way as to keep to the population thresholds set — minimum 5 000 and maximum 40 000 in­ habitants — as far as possible. The bottom row of Map 2.2 illustrates the sub-city districts of Ham­ burg and Lyon. Key demographic and social indi­ cators are available in the Urban Audit database for the more than 6 000 sub-city districts.

Larger urban zones City walls, even if they are preserved, no longer function as barriers between the people living in­ side and outside of the city. Students, workers and persons looking for healthcare or for cultural fa­ cilities regularly commute between the city and the surrounding area. Economic activity, transport flows and air pollution clearly cross the adminis­ trative boundaries of a city as well. Consequently, collecting data exclusively at core city level is

Eurostat regional yearbook 2009

European cities

2

Map 2.2: Defining the boundaries of the core city — Hamburg (DE) and Lyon (FR) Hamburg (DE)

Eurostat regional yearbook 2009

Lyon (FR)

29

2

European cities

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

30

Zagreb (HR)

Eurostat regional yearbook 2009

2

European cities

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 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. Municipali­ ties 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 Barce­ lona and Zagreb are displayed in the bottom row.

Map 2.3 uses the examples of Barcelona (Spain) and Zagreb (Croatia) to illustrate how the func­ tional 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

Figures 2.1 and 2.2: Comparison of core city, kernel and larger urban zone in terms of population and area in European capitals, 2004 Share of population living in core cities and kernels (larger urban zone = 100 %)

Share of area of core cities and kernels (larger urban zone = 100 %)

0%

0%

Ankara (TR) Bucureşti (RO) Sofia (BG) Helsinki (FI) Vilnius (LT) Tallinn (EE) Stockholm (SE) Zagreb (HR) Lisboa (PT) Roma (IT) Lefkosia (CY) Riga (LV) Athina (GR) Wien (AT) Budapest (HU) Bratislava (SK) Berlin (DE) København (DK) Warszawa (PL) London (UK) Praha (CZ) Valletta (MT) Paris (FR) Bruxelles/Brussel (BE) Ljubljana (SI) Madrid (ES) Amsterdam (NL) Oslo (NO) Bern (CH) Dublin (IE) Luxembourg (LU) 20 %

core city

40 %

60 %

kernel

80 %

100 %

20 %

40 %

60 %

80 %

100 %

larger urban zone

Notes: HU 2005; FI 2003; HR 2001

Eurostat regional yearbook 2009

31

2

European cities

This demarcation process was used in most par­ ticipating countries, but there were also excep­ tions 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 har­ monised methodology across Europe for which no statistical information is available  — would be completely in vain. Figures 2.1 and 2.2 com­ pare the different spatial levels used for European capitals in terms of population and area. In Bu­ curesti (Romania) more than 80 % of the larger urban zone population lives within the core city. At the other extreme, in Luxembourg (Luxem­ bourg) less than 20  % of the larger urban zone population lives within the core city. This low

percentage suggests that the core city of Luxem­ bourg is slightly under-bounded — meaning that a considerable share of the urban population lives outside the administrative city limits. For very under-bounded capitals — like Paris (France) or Lisboa (Portugal) — an additional spatial level, the ‘kernel’, was introduced. The kernel is an ap­ proximation of the built-up area around the core city. The only exception is London (United King­ dom), 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 cit­ ies 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.

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

Tallinn

Dublin

Madrid

Amsterdam

Bratislava

Helsinki

Stockholm

Bern

by car

by bicycle

on foot

by public transport

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

32

Eurostat regional yearbook 2009

2

European cities

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, envir­ onmental, 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 dif­ ferent 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 consist­ ently 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.

Eurostat regional yearbook 2009

Geography matters The 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 lev­ els, from the local shopping districts that serve residential areas within cities to specialised eco­ nomic regions like Silicon Valley or the City of London that serve the world market as a whole. […] Yet although agglomeration is a clearly pow­ erful force, it is not all-powerful: London is big, but most Britons live elsewhere, in a system of cit­ ies with widely varying sizes and roles. It should not, in other words, be hard to convince econo­ mists that economic geography […] is both an in­ teresting 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 distribu­ tion 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.

(3) Masahisa Fujita, Paul R. Krugman and Anthony Venables, The spatial economy: Cities, regions and international trade. MIT Press, 2001.

33

Labour market

3

Labour market

Regional working time patterns Flexible working hours are one of the most valu­ able 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 dif­ ferent time patterns. First we will give you a snap­ shot of the regional labour market in 2007.

Brief overview for 2007 The EU-27 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 employ­ ment 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 eco­ nomic difficulties we are currently facing, which are highly likely to have a negative impact on em­ ployment in the coming years. The latest quarterly data available at national level confirm this. The employment rate for the EU-27 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 EU-27 for which data was available had already achieved the Lisbon target (shaded with the dark­ est colour in Map 3.1), while 59 regions were still

36

10 percentage points below the overall employ­ ment 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 re­ gional employment rate in 2007 was still signifi­ cant, 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, employ­ ment 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 older-worker 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 EU-27 in­ creased 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 la­ bour market that are likely to be felt in the com­ ing 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. em­ ployed persons aged 55–64 years, was 44.7 % in 2007, which is 1.2 percentage points higher than in 2006. At regional level, older-worker employ­ ment rates ranged from a low of 21.8 % in Śląskie (Poland) to a high of 72.8  % in Småland med öarna (Sweden). The EU-27 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 double-digit 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 re­ corded unemployment rates above 10 % in 2007.

Eurostat regional yearbook 2009

Labour market

3

Map 3.1: Employment rate for the 15–64 age group, by NUTS 2 regions, 2007 Percentage

Eurostat regional yearbook 2009

37

3

Labour market

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

38

Eurostat regional yearbook 2009

3

Labour market

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). Long-term unemployment, which is the worse case of unemployment, also fell in 2007. The share of long-term unemployment, i.e. the share of per­ sons 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 mar­ kets. 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 unemploy­ ment, 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, con­ tinuing the five-year trend.

Regional work patterns Hours usually worked are the hours most com­ monly or typically worked in a short period of time, e.g. during a week. For each employed per­ son, this indicator shows the number of hours spent working, including regular overtime work and excluding regular absences.

public, Poland and Slovakia, tend to spend more time at work, on average, than other European citi­ zens, 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 Nether­ lands) 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 part-time workers has a significant influence in lowering the average hours spent at work. Unfortunately no breakdown of average hours worked into part-time workers and full-time workers is available at regional level. All regions in the Netherlands record a remark­ ably 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 Mar­ tinique (France), the region with the lowest val­ ue of all regions in the EU-27, not counting the Netherlands. This leads us to conclude that the Netherlands is a special case regarding the aver­ age 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 Lon­ don (United Kingdom), both capital regions. In the capital region of Greece, the precise opposite was found, with the capital recording a significant­ ly lower average than in other Greek regions.

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 EU-27 and regional differences are larger between countries than within countries (4).

Significantly lower averages compared with the rest of their respective countries were also observed in Ciudad Autónoma de Ceuta and Ciudad Autóno­ ma de Melilla in Spain, Åland in Finland and in the French overseas departments, Guadeloupe, Marti­ nique, 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 separ­ ation enhanced the marked differences in time patterns, while in contiguous regions the average time spent at work tended to be more similar.

Employed persons living in Greece and in east European countries, e.g. Bulgaria, the Czech Re­

Now let’s look at the factors causing these dif­ ferences to usual weekly hours spent at work at

Working time patterns are influenced by several factors, such as different historical and cultural backgrounds, female participation in regional la­ bour markets, specialisation in a specific industry and the share of part-time workers.

Eurostat regional yearbook 2009

(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 part-time 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.

39

3

Labour market

Map 3.3: Average number of usual weekly hours of work in main job, by NUTS 2 regions, 2007 Hours

40

Eurostat regional yearbook 2009

3

Labour market

regional level. Most differences in the regional working time can be explained by two other re­ gional labour market indicators: the percentage of part-time workers and the percentage of em­ ployees (which means all persons employed, not including self-employed or family workers). The share of part-time 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 aver­ age time that an employed person spends in his or her job, since self-employed and family workers tend to spend more time in their jobs (5).

gion is the share of part-time 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 Neth­ erlands. Having almost a quarter of men and three quarters of women working part time substantially lowers the average of usual weekly hours at work.

Part-time jobs: lowering the average working time

Working part time is more a country-level char­ acteristic, as shown in Map 3.4, which shows scant regional differences within each country. The map also shows well-defined patterns of the share of part-time workers. These patterns are so well defined that the EU-27 regions can be divided into four distinct groups of part-time workers:

The main factor explaining the low average of usual weekly hours of work in main job in a re­

• Group 1: the Dutch regions, with a share of 46.8 % of part-time workers;

(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

EU-27 38.0 30.1 Groningen 45.7 BE 37.1 35.8 Prov. Limburg (B) 38.7 BG 41.6 40.5 Severozapaden 42.4 CZ 41.7 40.4 Moravskoslezsko 43.3 DK 39.5 : : : DE 35.5 34.1 Bremen 37.4 EE 39.5 IE 36.4 36.1 Border, Midland and Western 36.5 EL 42.5 41.4 Attiki 45.7 ES 39.3 37.3 Ciudad Autónoma de Ceuta 40.7 FR 38.0 34.0 Martinique 39.6 IT 38.4 37.2 Calabria 39.1 CY 40.2 LV 40.7 LT 38.8 LU 36.7 HU 40.2 39.8 Dél-Dunántúl 40.6 MT 39.0 NL 30.8 30.1 Groningen 31.6 AT 38.9 38.2 Vorarlberg 39.7 PL 41.0 37.9 Podkarpackie 41.9 PT 39.0 37.2 Centro (P) 40.1 41.4 RO 40.5 39.1 Sud ­­— Muntenia SI 40.3 SK 41.1 40.1 Východné Slovensko 41.7 FI 37.5 36.0 Åland 37.8 SE 36.4 36.2 Västsverige 36.7 UK 36.9 35.3 North Yorkshire 39.5 Notes: NUTS level 2 employment data not available for DK - = not applicable (EE, IE, CY, LV, LT, LU, MT and SI comprise only one or two NUTS level 2 regions)

Eurostat regional yearbook 2009

Regional maximum Notio Aigaio Prov. West-Vlaanderen Severoiztochen Praha : Thüringen Southern and Eastern Notio Aigaio Galicia Basse-Normandie Piemonte Közép-Magyarország Flevoland Kärnten Podlaskie Alentejo Bucureşti — Ilfov Západné Slovensko Länsi-Suomi Övre Norrland Inner London

41

3

Labour market

Map 3.4: Share of employees in overall employment, by NUTS 2 regions, 2007 Percentage

42

Eurostat regional yearbook 2009

Labour market

3

Map 3.5: Share of part-time workers in overall employment, by NUTS 2 regions, 2007 Percentage

Eurostat regional yearbook 2009

43

3

Labour market

• Group 2: regions in the Nordic EU-27 coun­ tries, plus Belgium, Germany, Austria and the United Kingdom, which together have an av­ erage share of 25 %; • Group 3: regions in Ireland, Spain, France, Italy, Luxembourg, Malta and Portugal, with an aver­ age share of 14.2 %; • Group 4: the rest of the EU-27 regions, mainly from the new Member States, with an average share of part-time workers of 7.2 %. Over the past five years, the EU-27 has recorded an increase of 1.6 percentage points in the share of part-time 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 part-time workers of 0.7 percentage points over the last five years.

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 self-employed persons. Map 3.5 shows the re­ gional 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 north-western 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 Pelopon­ nisos (Greece) to a maximum of 96.1 % recorded in Bucureşti — Ilfov (Romania).

Employees spend less time at work

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 part-time workers, the level of employees depends mostly on the country. Nev­ ertheless, there are some region-specific differ­ ences that could be linked to the type of activity predominant in these regions.

Employed persons are classified according to their working status. Regional labour market data are

Employee status is closely related to the type of sector in which a person is employed. For in­

Turkish regions recorded a relatively low share of part-time workers in 2007 as compared with the EU regions, with 8.8 % of employed persons working part time.

60

broken down into three categories: employees (which comprises all personnel with a contract of employment), self-employed and family workers.

Figure 3.1: Share of employees in overall employment versus share of employed persons in the agriculture sector, by NUTS 2 regions, 2007 Percentage

50

40

30

20

10

0 40

44

50

60

70

80

90

Eurostat regional yearbook 2009

100

Labour market

stance, the share of family workers and selfemployed 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 re­ gion where data was available for 2007. The points roughly align on a downward straight line. That means that regions with higher levels of employ­ ment 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 dif­ ferences 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 re­ gional level, especially in rural areas, this is an im­ portant factor to consider in order to have a better understanding of different regional time patterns. To sum up, we can conclude that the average usu­ al time spent at work in a specific region varies significantly throughout the EU-27, which is ex­ plained not only by the share of part-time work­ ers, 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.

Eurostat regional yearbook 2009

3

While part-time 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 in­ fluenced more at regional level.

Conclusion The results presented in this chapter show that 2007 was a year of strong performance regard­ ing both employment and unemployment, and disparities in regional labour markets have nar­ rowed. Nonetheless, the Lisbon employment targets seem unlikely to be achieved. The reces­ sion currently faced by Europe and the rest of the world will make the Lisbon employment targets even more difficult to achieve, since labour mar­ kets 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 par­ ticipation in the labour market, the share of parttime workers, the share of employees and the pre­ dominant sector of activity. All these factors dictate how much free time people have on average. Although it seems like an odd paradox, the aver­ age time people spend at work does not equate to strong labour market or economic performance. In fact, it is precisely the reverse.

45

3

Labour market

Methodological notes The source of regional labour market information down to NUTS level 2 is the European Union labour force survey (LFS). This is a quarterly household sample survey conducted in the Member States of European Union. The LFS target population is made up of all members of private households aged 15 or over. The survey follows the definitions and recommendations of the International Labour Organisation (ILO). To achieve further harmonisation, the Member States also adhere to common principles in drafting questionnaires. All regional results presented here concern NUTS 2 regions and all regional figures are annual averages of the quarterly surveys. For further information on regional labour market statistics, see the metadata on the Eurostat website (http://ec.europa.eu/eurostat).

Definitions Population covers persons aged 15 and over, living in private households (persons living in collective households, i.e. residential homes, boarding houses, hospitals, religious institutions and workers’ hostels, are not included). This comprises all persons living in the households surveyed during 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 business 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 employment (unemployment) rates in a country, weighted by the absolute population (active population) 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 employer, 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

Labour market

3

Self-employed persons are defined as persons who work in their own business, professional practice or farm for the purpose of earning a profit, and who do not employ any other person. Family workers are persons who help another member of the family to run an agricultural holding or other business, provided they are not considered as employees.

Eurostat regional yearbook 2009

47

Gross domestic product

4

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, regard­ less of whether this was attributable to resident or non-resident 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, re­ gional 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 com­ parable 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 artifi­ cial common currency, called purchasing power standard (PPS). This makes it possible to com­ pare the purchasing power of different national currencies (see methodological notes at the end of the chapter).

Regional GDP in 2006 Map 4.1 gives an overview of the regional distri­ bution of per inhabitant GDP (as a percentage of the EU-27 average of 23 600 PPS) for the European Union, Croatia and the former Yugoslav Republic of Macedonia, which has, for the first time, pro­

50

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 EU-27 average (5 800 PPS) per inhabitant in North-East (Romania) to 336 % (79 400 PPS) in the UK capital region of Inner London. The fac­ tor 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 east­ ern 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 EU-27 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 coun­ tries examined here (EU-27 plus Croatia and the former Yugoslav Republic of Macedonia). How­ ever, these two regions must be regarded as ex­ ceptions 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: Közép-Magyarország (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 Ro­ mania), 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 EU-27 average. If we classify the 275 regions considered here by their per inhabitant GDP (in PPS), the follow­ ing picture emerges: in 2006, GDP in 72 regions was less than 75  % of the EU-27 average. These 72 regions are home to 25.2 % of the population (EU-27, Croatia and the former Yugoslav Repub­ lic 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 EU-15 countries.

Eurostat regional yearbook 2009

Gross domestic product

4

Map 4.1: GDP per inhabitant, in PPS, by NUTS 2 regions, 2006 In percentage of EU-27 = 100

Eurostat regional yearbook 2009

51

4

Gross domestic product

At the upper end of the spectrum, 41 regions have a per inhabitant GDP of more than 125  % of the EU-27 average; these regions are home to 20.1 % of the population. The regions with per in­ habitant GDP of between 75 % and 125 % of the EU-27 average are home to 54.7  %, a clear ma­ jority of the population of the 29 countries con­ sidered here. Some 11.5 % of the population live in regions whose per inhabitant GDP is less than 50 % of the EU-27 average; all these regions are in new Member States, Croatia and the former Yugoslav Republic of Macedonia.

Average GDP over the three-year period 2004–06 Map 4.2 gives an overview of the average per inhabitant GDP (in PPS) for the years 2004–06. Three-year 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 EU-27 (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 EU-27 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 EU-27 average. There are 43 of these regions, home to 21.7 % of the population of the EU-27 plus Croatia and the former Yugoslav Re­ public of Macedonia. Contrary to a common mis­ conception, these regions are by no means all in the geographical centre of the Union, but include examples such as Etelä-Suomi (Finland), South­ ern and Eastern (Ireland), Madrid (Spain) and At­ tiki (Greece). However, it is true that many capital cities are among the richest regions, in particular London, Dublin, Bruxelles/Brussel, Paris, Ma­ drid, 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 EU-27 average. Some 33 regions with 12 % of the population have less than 50 %; most of these are in Bulgaria, Ro­

52

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 EU-27 average.

Major regional differences even within the countries themselves There 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 EU-15 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 EU-15 Member States, plus Slovenia and Croatia. In all the new Member States, Croatia and a number of EU-15 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 re­ gional extreme values in the new Member States and Croatia is clearly increasing in some cases, it is falling in one out of every two EU-15 countries.

Dynamic catch-up process in the new Member States Map 4.3 shows the extent to which per inhabitant GDP changed between 2001 and 2006 compared with the EU-27 average (expressed in percent­ age points of the EU-27 average). Economically

Eurostat regional yearbook 2009

Gross domestic product

4

Map 4.2: GDP per inhabitant, in PPS, by NUTS 2 regions, average 2004–06 In percentage of EU-27 = 100

Eurostat regional yearbook 2009

53

4

Gross domestic product

dynamic regions, whose per inhabitant GDP in­ creased by more than 2 percentage points com­ pared 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 EU-27 average) are shown in orange and red. The range is from +33 percentage points for Bratislavský kraj (Slovakia) to -23 per­ centage points for Emilia-Romagna in Italy.

Among the EU-15 Member States, strong growth can be seen in Greece, Spain, Ireland and parts of the United Kingdom, Finland and Sweden in par­ ticular. On the other hand, a trend which started several years ago is continuing: sustained weak growth in certain EU-15 countries. Particularly badly hit have been Italy, Belgium and France, where no region achieved the average growth of the EU-27 during the five-year period 2001–06; half the regions in Germany and Portugal also fell back compared to the EU average.

The map shows that economic dynamism is well above average in the western, eastern and northern peripheral areas of the EU, not only in EU-15 countries but also in the new Member States and Croatia.

Of the new Member States and Croatia, where all of the capital regions are very dynamic, the Baltic States, Romania, the Czech Republic, Slovakia,

Figure 4.1: GDP per inhabitant, in PPS, by NUTS 2 regions, 2006 In percentage of the EU-27 average (EU-27 = 100) Région de Bruxelles-Capitale/ Brussels Hoofdstedelijk Gewest

BE

Hainaut SeveroYugozapaden BG zapaden Střední Morava CZ

DK

Praha Hovedstaden

Sjælland

DE

Brandenburg-Nordost

Hamburg

EE IE

Border, Midland and Western

EL

Southern and Eastern

Dytiki Ellada

ES

Attiki

Extremadura

FR

Madrid

Guyane

IT

Île de France Provincia Autonoma Bolzano/Bozen

Campania

CY LV LT LU HU Észak-Alföld

Közép-Magyarország

MT NL

Groningen

Flevoland

AT

Burgenland (A)

PL

Lubelskie

PT

Wien Mazowieckie

Norte NordEst Vzhodna Slovenija Východné Slovensko Itä-Suomi

RO SI SK FI SE

Lisboa Bucureşti — Ilfov Zahodna Slovenija Bratislavský kraj Åland

Östra Mellansverige

Stockholm

West Wales and Središnja i The Valleys Istočna Sjeverozapadna Hrvatska Hrvatska

UK HR

Inner London

MK 0

50

100

150

200

250

300

350

National average Capital region

54

Eurostat regional yearbook 2009

400

Gross domestic product

4

Map 4.3: Change of GDP per inhabitant, in PPS, by NUTS 2 regions, 2006 as compared with 2001 In percentage points of the average EU-27

Eurostat regional yearbook 2009

55

4

Gross domestic product

Croatia and most regions of Poland have experi­ enced above-average 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 fastest-growing regions are scattered relative­ ly widely across the 29 countries examined here. It is striking, however, that the capital regions continue to have an above-average rate of growth not only in the EU-15 countries but also in the new Member States and in Croatia. The noncapital 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 EU-27 average. A clear concentration in certain Member States is, however, apparent at the lower end of the dis­ tribution curve: of the 35 regions which fell by more than 7 percentage points compared to the EU-27 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 re­ gions fell compared to the EU-27 average between 2001 and 2006: Dél-Dunántúl in Hungary (-1.1 percentage points), Malta (–1.0), Severozapaden in Bulgaria (-0.7) and Kypros/Kıbrıs (-0.6). The catch-up 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 con­ siderably faster than in the 1990s. Per inhabitant GDP (in PPS) in these 13 countries thus rose from 46.0 % of the EU-27 average in 2001 to 53.7 % in 2006. It is feared, however, that the financial cri­ sis which started in mid-2008 may mean that this rate of growth cannot be maintained throughout the first decade of the new century.

Different trends even within the countries themselves A more detailed analysis of trends within the countries between 2001 and 2006 shows that the economic development of regions within a coun­ try 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 EU-27 average for the per inhabitant GDP of the fastest- and slowest-growing 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 EU-15 countries, this significantly diverging regional development was the result mainly of dynamic growth in capi­ tal 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 dy­ namic regions in seven countries attained levels of growth above the EU-27 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 progress This section addresses the question of the extent to which convergence between the regions of the EU-27, Croatia and the former Yugoslav Republic of Macedonia made progress over the five-year period 2001–06. Regional convergence of per in­ habitant GDP (in PPS) can be assessed in various

Table 4.1: Proportions of resident population in economically stronger and weaker regions Percentage of population of EU-27, Croatia and the former Yugoslav Republic of Macedonia resident in regions with a GDP per inhabitant of

56

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

Eurostat regional yearbook 2009

Gross domestic product

ways on the basis of indicators supplied to Euro­ stat by the national statistical institutes. A simple approach is to measure the gap between the highest and the lowest values. By this meth­ od, the gap closed from a factor of 16.0 in 2001 to 13.6 in 2006. The main reason for this clear con­ vergence was the faster economic growth in Bul­ garia 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 con­ vergence consists of classifying the regions accord­ ing to their per inhabitant GDP in PPS. In this way, the proportion of the population of the countries being considered (the EU-27 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 five-year 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 EU-27 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 mid-range (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 com­ parison between the three-year periods 1999–2001 and 2004–06 shows that just five regions managed to exceed the 75 % threshold. These were one re­ gion each in Greece, Spain, Poland, Romania and the UK. These regions are home to almost 16 mil­ lion 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 popula­ tion 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 aver­ age grew by around 10.6 million people. These results close to the 75 % threshold, which is important for regional policy, suggest that

Eurostat regional yearbook 2009

4

poorer regions benefited only marginally during the first half of the decade from increased con­ vergence in the EU. However, a more detailed analysis shows that many regions with a GDP of less than 75 % of the EU-27 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 be­ tween 2001 and 2006 from 28.2 % to 33.2 % of the EU-27 average, as a result in particular of the strong catch-up process in Bulgaria and Romania.

Conclusion In 2006, the highest and lowest values of per in­ habitant GDP (in PPS) for the 275 NUTS 2 re­ gions in 29 countries (EU-27 plus Croatia and the former Yugoslav Republic of Macedonia) exam­ ined 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; re­ gional differences in new Member States tend to be greater than in the EU-15. In 2006, per inhabitant GDP (in PPS) in 72 re­ gions was less than 75  % of the EU-27 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 EU-15 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 EU-27 average. If the trends over the five-year period 2001–06 are considered, dynamic growth can be seen in certain EU-15 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, sig­ nificantly above-average growth can be seen primarily in the Baltic countries, Romania, the Czech Republic, Slovakia, Croatia and most re­ gions of Poland.

57

4

Gross domestic product

Map 4.4: Regions whose GDP per inhabitant, in PPS, moved upwards or downwards over the 75 % threshold of the average EU-27, by NUTS 2 regions, average 2004–06 compared with average 1999–2001

58

Eurostat regional yearbook 2009

Gross domestic product

The catch-up process which has started in the new Member States and Croatia has accelerated significantly compared to the 1990s and contin­ ued until 2006 with an annual rate of around 1.5 percentage points compared to the EU-27 aver­ age. However, not all the regions of the new Mem­ ber States are yet able to benefit from this to the same extent. This is particularly true of Hungary,

4

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 EU-27 average between 2001 and 2006. It is feared, however, that the financial crisis which started in mid-2008 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 between 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 volumes 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 countries 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 regional conversion factors. If such regional PPPs were available, the GDP in PPS for numerous peripheral or rural regions of the EU would be higher than that calculated using national PPPs. The regions may be ranked differently when calculating in PPS instead of euros. For example, in 2006 the Swedish region of Östra Mellansverige had a per inhabitant GDP of EUR 29 600, putting it ahead of Madrid at EUR 29 100. However, in PPS, Madrid at 32 100 PPS per inhabitant is ahead of Östra Mellansverige, at 24 600 PPS per inhabitant. In terms of distribution, the use of PPS rather than the euro has a levelling effect, as countries with a very high per inhabitant GDP also generally have relatively high price levels. The range of per inhabitant GDP in NUTS 2 regions in the EU-27 plus Croatia and the former Yugoslav Republic of Macedonia thus falls from 86 500 in euro to 73 600 in PPS. GDP per inhabitant in PPS is the key variable for determining the eligibility of NUTS 2 regions under the European Union’s structural policy.

Eurostat regional yearbook 2009

59

Household accounts

5

Household accounts

Introduction: measuring wealth One of the primary aims of regional statistics is to measure the wealth of regions. This is of par­ ticular relevance as a basis for policy measures which aim to provide support for less well-off regions. The indicator most frequently used to measure the wealth of a region is regional gross domes­ tic 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 pro­ duced 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 actu­ ally available to the inhabitants of the region. One drawback of regional GDP per inhabitant as an indicator of wealth is that a ‘place-of-work’ fig­ ure (the GDP produced in the region) is divided by a ‘place-of-residence’ figure (the population living in the region). This inconsistency is of rele­ vance 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 In­ ner 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 correspond­ ingly 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, Luxem­ bourg, 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 resi­ dents 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 income In market economies with state redistribution mechanisms, a distinction is made between two stages of income distribution.

62

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 in­ clude in particular the compensation of employ­ ees, 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 oper­ ating surplus and self-employment. 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 bene­fits and transfers other than in kind (monetary trans­ fers) are now added to primary income. From their income, households have to pay taxes on income and wealth, pay their social contributions and ef­ fect 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 re­ gions 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 ap­ plied to the income parameters of private house­ holds. These are therefore converted with specific purchasing power standards for final consump­ tion 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 exam­ ined here. Centres of wealth are clearly evident in southern England, Paris, northern Italy, Austria, Madrid and north-east Spain, Flanders, the west­ ern Netherlands, Stockholm, Nordrhein-West­ falen, Hessen, Baden-Württemberg 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.

Eurostat regional yearbook 2009

Household accounts

5

Map 5.1: Primary income of private households per inhabitant (in PPCS), by NUTS 2 regions, 2006

Eurostat regional yearbook 2009

63

5

Household accounts

In the new Member States, it is mainly the capi­ tal regions that have relatively high income levels, particularly Bratislava and Praha, where income levels are close to the EU-27 average. Közép-Mag­ yarország (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 re­ gions, 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 north-east 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 concen­ tration of regions with the highest incomes in the United Kingdom and Germany is also evident when the ranking is extended to the top 30 re­ gions: 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 Mem­ ber 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 ef­ fects can be observed in the new Member States, particularly in Hungary, Romania, Slovakia and Poland. However, the levelling out of private in­ come levels in the new Member States is generally less pronounced than in the EU-15. In spite of state redistribution and other trans­ fers, most capital regions maintain their promi­

64

nent position with the highest disposable income for the country in question. Of the 10 regions with the highest disposable in­ come per inhabitant, five are in the United King­ dom, 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 re­ gions: 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 north-east Romania to 25 403 PPCS in the UK region of Inner London. State activity and other transfers significantly reduce the dif­ ference 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 signifi­ cant trend in disposable income towards a nar­ rowing 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 oc­ curred 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 consider­ ably 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 dis­ parities 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 north-east Romania. With factors of around 1.8, Slovakia, the United King­ dom, Hungary and Italy also have wide regional

Eurostat regional yearbook 2009

Household accounts

5

Map 5.2: Disposable income of private households per inhabitant (in PPCS), by NUTS 2 regions, 2006 In percentage of EU-27 = 100

Eurostat regional yearbook 2009

65

5

Household accounts

NUTS 2 region also have the highest income val­ ues. This group includes four of the six largest new Member States.

variations. For Spain, Poland and Germany the highest value is about two thirds higher than the respective lowest value. The regional concen­ tration is in general higher in the new Member States than in the EU-15.

The economic dominance of the capital regions is also evident when their income values are com­ pared with the national averages. In four coun­ tries (the Czech Republic, Romania, Slovakia and the United Kingdom), the capital cities exceed the national values by more than a third. Only in Bel­ gium and Germany are the values lower than the national average.

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.

To assess the economic situation in individual re­ gions, 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

Figure 5.1 additionally shows that the capital cit­ ies of 13 of the 18 countries with more than one

Figure 5.1: Disposable income of private households per inhabitant (in PPCS), by NUTS 2 regions, 2006 BE

Vlaams-Brabant

Hainaut

CZ

Severozápad

Praha

DK DE

Mecklenburg-Vorpommern

Hamburg

EE IE

Southern and Eastern

Border, Midland and Western

EL

Ionia Nisia

ES

Attiki Extremadura

FR

País Vasco

Nord — Pas-de-Calais

IT

Île de France

Campania

Prov. Autonoma Bolzano/Bozen

LV LT HU

Közép-Magyarország

Eszak-Alföld

NL

Groningen

Utrecht

AT

Kärnten

PL

Podkarpackie

PT

Norte

RO

Nord-Est

Lisboa

Bucureşti-Ilfov

SI

Zahodna Slovenija

Vzhodna Slovenija

SK

Wien

Mazowieckie

Východné Slovensko

Bratislavský kraj

FI

Itä-Suomi

SE

Åland Stockholm

Övre Norrland

UK

West Midlands

0

2 500

5 000

7 500

10 000

12 500

Inner London

15 000

17 500

20 000

22 500

25 000

National average Capital region Notes: DK: data only available at national level FR: without overseas departments

66

Eurostat regional yearbook 2009

27 500

30 000

Household accounts

5

Map 5.3: Disposable income of private households as % of primary income, by NUTS 2 regions, 2006

Eurostat regional yearbook 2009

67

5

Household accounts

quotient, which gives an idea of the effects of state activity and of other transfer payments. On aver­ age, disposable income in the EU-27 amounts to 87.2 % of primary income. In 2001 this figure had been 87.0 %, so over this five-year period the scale of state intervention and other transfers hardly changed. In general the EU-15 Member States have somewhat lower values than the new Mem­ ber States. On closer inspection, substantial differences can be seen between the regions of the Mem­ ber States. Disposable income in the capital cit­ ies and other prosperous regions of the EU-15 is generally less than 80  % of primary income. Correspondingly higher percentages can be ob­ served in the less affluent areas, in particular on the southern and south-western 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 EU-15. For the capital regions the values are between 80 % and 90 % and are almost without exception at the bottom end of the ranking with­ in 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 particu­ lar to 12 of the 16 regions in Poland and four of the eight regions in Romania. In the EU-15, the most noticeable instances are six regions of east­ ern 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 mon­ etary social benefits from the state which may cause disposable income to exceed primary in­ come. Other transfer payments (e.g. transfers

68

from people temporarily working in other re­ gions) can play a role in some cases.

Dynamic development on the edges of the Union The focus finally turns to an overview of medi­ um-term trends in the regions compared with the EU-27 average. Map 5.4 uses a five-year compari­ son to show how disposable income per inhab­ itant (in PPCS) in the NUTS 2 regions changed between 2001 and 2006 compared to the average for the EU-27. It shows, first of all, powerful dynamic processes in action on the edges of the Union, particularly in Spain and Ireland, the Czech Republic, Slo­ vakia, Hungary and the Baltic States. On the other hand, below-average trends in in­ come 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 percent­ age points in Liguria (Italy). Despite overall clear evidence of a catching-up process in the new Member States, the same posi­ tive 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 re­ gion achieved the highest relative improvement of all regions, with even the Nord-Est 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.

Eurostat regional yearbook 2009

Household accounts

5

Map 5.4: Development of disposable income of private households per inhabitant, by NUTS 2 regions Change between 2001 and 2006 in percentage points of the average EU-27 in PPCS

Eurostat regional yearbook 2009

69

5

Household accounts

Conclusion The regional distribution of disposable house­ hold 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 re­ ceived 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 influ­ ences bring the spread of disposable income be­ tween 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 flatten­ ing 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 EU-15; in only a small number of capital re­

70

gions are income values more than three quarters of the EU average. An analysis over the five-year 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 catching-up process has taken hold — a develop­ ment 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 be­ tween 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 fac­ tor 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 house­ holds could be taken into account alongside GDP statistics when decisions are taken on regional policy measures.

Eurostat regional yearbook 2009

Household accounts

5

Methodological notes Eurostat has had regional data on the income categories of private households for a number of years. The data are collected for the purposes of the regional accounts at NUTS level 2. There are still no data available at NUTS 2 level for the following regions: Bulgaria, Départements d’Outre-Mer (France), Cyprus, Luxembourg and Malta; for Denmark only national data are available. The text in this chapter therefore relates to only 23 Member States, or 254 NUTS 2 regions. Three of these 23 Member States consist of only one NUTS 2 region, namely Estonia, Latvia and Lithuania. Since the beginning of 2008 Denmark has consisted of five NUTS 2 regions, but is shown here only as a single NUTS 1 region, as no data are yet available for the newly defined NUTS 2 regions. Because of the limited availability of data, the EU-27 values for the regional household accounts had to be estimated. For this purpose it was assumed that the share of the missing Member States in household income (in PPCS) for EU-27 was the same as for GDP (in PPS). For the reference year 2005 this share was 1.0 %. Data that reached Eurostat after 28 April 2009 are not taken into account in this chapter of the yearbook.

Eurostat regional yearbook 2009

71

Structural business statistics

6

Structural business statistics

Introduction What effects do the European Union’s economic and regional policies have on the business struc­ ture 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 de­ tailed activity breakdown that can be used for this kind of analysis. The first part of this chap­ ter looks at regional specialisation and business concentration within the EU’s business economy. The second part analyses the activity of the busi­ ness services sector in detail.

Regional specialisation and business concentration There are significant disparities between Euro­ pean 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 busi­ ness economy gives an idea of which regions are the most or least specialised in that activity, re­ gardless of whether the region or the activity con­ sidered is large or small. There are various reasons for relative specialisation. Depending on the type of activity, these can include availability of natu­ ral 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

Figure 6.1: Degree of regional specialisation by activity (NACE sections), EU-27 and Norway, by NUTS 2 regions, 2006 Share of non-financial business economy employment, in percentage Distributive trades (G 50–52)

Dytiki Ellada (GR23)

Západné Slovensko (SK02)

Manufacturing (D 15–37) Real estate, renting and business activities (K 70–74)

Inner London (UKI1)

Construction (F 45)

Andalucía (ES61)

Transport, storage and communication (I 60–64)

Åland (FI20)

Hotels and restaurants (H 55)

Ionia Nisia (GR22)

Electricity, gas and water supply (E 40–41)

Sud-Vest Oltenia (RO41)

Mining and quarrying (C 10–14)

Agder og Rogaland (NO04)

0

5

10

15

20

25

30

35

40

45

50

55

60

Notes: Excluding BG, SI, DK (no data by NUTS 2 regions), MT, North Eastern Scotland (UKM5) and Highlands and Islands (UKM6) (data not available) CY excluding Research and development (K 73)

74

Eurostat regional yearbook 2009

65

Structural business statistics

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 re­ gions, 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 (Slo­ vakia) — 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 non-financial 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 employ­ ment, present in all regions and serving more local clients. Shares ranged from less than 17 % in Åland and Länsi-Suomi (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 com­ munication (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 activi­ ties tend to be of relatively equal importance across most regions. There were only three regions, for example, where the share of employment in trans­ port, storage and communication exceeded 20 %. The highest specialisation of the Finnish island re­ gion 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 particu­ larly important, and Bratislavský kraj (23.8 %), the capital region of Slovakia, owing to the impor­ tance of road and other land transport. Natural endowments play an important role in the activi­ ties 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 de­ posits of metallic ores, coal, oil or gas. Mining and

Eurostat regional yearbook 2009

6

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 non-financial business economy workforce in Śląskie (Poland) and Agder og Rogaland (Norway). Table 6.1 shows which region was the most special­ ised 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 non-financial business economy workforce among all regions within the EU-27 and Norway. Manufacturing activities which involve the pri­ mary 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 Warmińsko-mazurskie in the east­ ern part of Poland, Dél-Alföld 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 Mellans­ verige (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 is­ land regions of Ionia Nisia and Notio Aigaio, the Spanish Illes Balears, the Algarve in the south of Portugal, Provincia Autonoma Bolzano/Bozen in the north-east of Italy on the border with Aus­ tria and the region of Cornwall and Isles of Scilly (United Kingdom). Greek regions were the most specialised in dis­ tributive trades (NACE G 50–52), with the excep­ tion 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 re­ gions. Transport services are also influenced by lo­ cation, 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

75

6

Structural business statistics

Table 6.1: Most specialised region by activity (NACE sections and divisions), EU-27 and Norway, 2006 Share of total non-financial business economy employment of the region and the median and average share of all regions, in percentage All regions Activity (NACE)

Median share (%)

Average share (%)

Most specialised region Name (NUTS 2 region)

Share of the region (%)

Mining and quarrying (C 10–14) 0.3 0.6 Agder og Rogaland (NO04) 10.4 Coal, lignite and peat (10) 0.0 0.2 Śląskie (PL22) c Crude petroleum and natural gas (11) 0.0 0.1 Agder og Rogaland (NO04) 10.0 Uranium and thorium ores (12) 0.0 0.0 Severovýchod (CZ05) c Metal ores (13) 0.0 0.0 Övre Norrland (SE33) c Other mining and quarrying (14) 0.2 0.2 Alentejo (PT18) c Manufacturing (D 15–37) 25.0 26.2 Západné Slovensko (SK02) 56.9 Food and beverages (15) 3.6 3.8 Bretagne (FR52) 11.1 Tobacco products (16) 0.0 0.1 Trier (DEB2) c Textiles (17) 0.4 0.7 Prov. West-Vlaanderen (BE25) 5.6 Wearing apparel; fur (18) 0.3 0.9 Dytiki Makedonia (GR13) 11.5 Leather and leather products (19) 0.1 0.4 Marche (ITE3) 7.7 Wood and wood products (20) 0.8 1.2 Itä-Suomi (FI13) 5.8 Pulp, paper and paper products (21) 0.5 0.6 Norra Mellansverige (SE31) 4.7 Publishing and printing (22) 1.1 1.2 Inner London (UKI1) 4.2 Fuel processing (23) 0.0 0.1 Cumbria (UKD1) c Chemicals and chemical products (24) 1.0 1.3 Rheinhessen-Pfalz (DEB3) 11.6 Rubber and plastic products (25) 1.2 1.4 Auvergne (FR72) 7.8 Other non-metallic mineral products (26) 1.1 1.3 Prov. Namur (BE35) 5.3 Basic metals (27) 0.5 1.0 Norra Mellansverige (SE31) 9.6 Fabricated metal products (28) 2.7 3.0 Arnsberg (DEA5) 8.7 Machinery and equipment (29) 2.2 2.7 Unterfranken (DE26) 12.2 Office machinery and computers (30) 0.0 0.1 Southern and Eastern (IE02) 1.4 Electrical machinery and apparatus (31) 0.9 1.3 Západné Slovensko (SK02) 9.8 Radio, TV and communication equipment (32) 0.3 0.6 Pohjois-Suomi (FI1A) 6.1 Medical, precision and optical equipment (33) 0.6 0.7 Border, Midland and Western (IE01) 5.9 Motor vehicles and (semi)-trailers (34) 0.8 1.7 Braunschweig (DE91) c Other transport equipment (35) 0.5 0.8 Agder og Rogaland (NO04) 6.3 Furniture and other manufacturing (36) 1.1 1.4 Warmińsko-mazurskie (PL62) 8.0 Recycling (37) 0.1 0.1 Brandenburg — Nordost (DE41) 0.7 Electricity, gas and water supply (E 40–41) 1.0 1.3 Sud-Vest Oltenia (RO41) 5.5 Electricity, gas and hot water supply (40) 0.8 1.0 Martinique (FR92) 4.8 Water supply (41) 0.2 0.3 Východné Slovensko (SK04) 1.9 Construction (F 45) 10.4 10.9 Andalucía (ES61) 28.6 Distributive trades (G 50–52) 26.2 26.1 Dytiki Ellada (GR23) 44.8 Motor trades (50) 3.5 3.7 Molise (ITF2) 9.3 Wholesale trade (51) 7.2 7.4 Kentriki Makedonia (GR12) 15.1 Retail trade and repair (52) 14.8 14.9 Dytiki Ellada (GR23) 27.1 Hotels and restaurants (H 55) 7.2 8.1 Ionia Nisia (GR22) 33.8 Transport, storage and communication (I 60–64) 8.4 8.9 Åland (FI20) 47.9 Land transport and pipelines (60) 4.5 4.6 Bratislavský kraj (SK01) 15.8 Water transport (61) 0.1 0.4 Åland (FI20) 38.7 Air transport (62) 0.0 0.2 Outer London (UKI2) 3.9 Supporting transport activities (63) 1.7 1.9 Bremen (DE50) 11.1 Post and telecommunications (64) 1.8 2.0 Köln (DEA2) 24.4 Real estate, renting, business activities (K 70–74) 16.9 18.1 Inner London (UKI1) 49.1 Real estate activities (70) 2.0 2.0 Latvija (LV00) 5.6 Renting (71) 0.4 0.5 Hamburg (DE60) 1.7 Computer activities (72) 1.4 1.7 Berkshire, Buckinghamshire and Oxfordshire (UKJ1) 8.0 Research and development (73) 0.2 0.0 Voreio Aigaio (GR41) 4.8 Other business activities (74) 12.7 13.6 Inner London (UKI1) 38.3 Notes: Excluding BG, SI, DK (no data by NUTS 2 regions), MT, North Eastern Scotland (UKM5) and Highlands and Islands (UKM6) (data not available) CY excluding Research and development (K 73) c = confidential data

76

Eurostat regional yearbook 2009

Structural business statistics

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 Noord-Holland (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 (en­ terprises 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 ser­ vices 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 depart­ ments 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 high­ ly ranked in terms of both specialisation and con­ centration. Figure 6.2 shows the extent to which employment in certain activities was concentrat­ ed 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 employ­ ment in the EU-27 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 em­ ployed 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 em­ ployment respectively. In the case of air transport,

Eurostat regional yearbook 2009

6

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, heav­ ily 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 lar­ gest 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 EU-27 and Norway. In contrast to the more specialised types of min­ ing and quarrying, other mining and quarrying (NACE 14) was among the activities in which the 10 largest regions were least dominant, account­ ing 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 min­ ing 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 quar­ rying, 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 larg­ est 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 frag­ mented. By this measure, the most concentrated regions were generally in countries tradition­ ally associated with tourism (in particular Spain, Greece and Portugal), underlining the impor­ tance of construction, trade, and hotels and res­ taurants in tourism-oriented regions. However, high concentrations were also recorded in several densely populated areas, such as the south-east 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 coun­ tries — the capital region was usually among the

77

6

Structural business statistics

Figure 6.2: Most concentrated activities (NACE divisions), EU-27 and Norway, by NUTS 2 regions, 2006 Share of regions in total sectoral employment, in percentage Uranium and thorium ores (12) Metal ores (13) Coal, lignite and peat (10) Crude petroleum and natural gas (11) Air transport (62) Leather and leather products (19) Post and telecommunications (64) Textiles (17) Water transport (61) Wearing apparel; fur (18) Tobacco prodcuts (16) Office machinery and computers (30) Fuel processing (23) Computer activities (72) Research and development (73) Radio, TV and communication equipment (32) Motor vehicles and (semi)-trailers (34) Basic metals (27) Chemicals and chemical products (24) Supporting transport activities (63) Medical, precision and optical instruments (33) Real estate activities (70) Other business activities (74) Publishing and printing (22) Machinery and equipment (29) Construction (45) Fabricated metal products (28) Other transport equipment (35) Furniture and other manufacturing (36) Electronic machinery and apparatus (31) Renting (71) Other non-metallic mineral products (26) Wood and wood products (20) Electricity, gas and hot water supply (40) Wholesale trade (51) Recycling (37) Land transport and pipelines (60) Hotels and restaurants (H55) Pulp, paper and paper products (21) Water supply (41) Rubber and plastic products (25) Other mining and quarrying (14) Retail trade and repair (52) Food and beverages (15) Motor trades (50)

0

10

Regions ranked:

20 1−10

30

40 11−20

50 21−50

60

70

80

90

100

51−262

Notes: Excluding BG, SI, DK (no data by NUTS 2 regions), MT, North Eastern Scotland (UKM5) and Highlands and Islands (UKM6) (data not available) CY excluding Research and development (K 73)

78

Eurostat regional yearbook 2009

Structural business statistics

6

Map 6.1: Regional business concentration, by NUTS 2 regions, 2006 Share of five largest activities (NACE divisions) in total non-financial business economy employment in percentage

Eurostat regional yearbook 2009

79

6

Structural business statistics

regions with the highest business concentration and was often top of the list.

activities (NACE division 70) are among the top five activities in Inner London (and not construc­ tion), 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 avail­ able, 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.

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 Slova­ kia, 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 lar­ gest activities accounted for less than 40 % of to­ tal employment in Západné Slovensko (Slovakia), Severovýchod (the Czech Republic), Vest (Ro­ mania) and Stredné Slovensko (Slovakia).

Specialisation in business services

Figure 6.3 provides a more detailed analysis of the most specialised regions. Among the top 10 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

The services sector is an important and growing area of the EU economy which in recent years has attracted increasing political and economic inter­ est. In 2006, real estate, renting and business ac­ tivities (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.

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 12.6

Melilla (ES64)

25.0

24.2

33.8

Ionia Nisia (GR22)

22.4

29.9

Notio Aigaio (GR42)

10.0

Ceuta (ES63)

26.1

18.2

Canarias (ES70)

7.5

Comunidad de Madrid (ES30)

22.6

12.7 23.9

Illes Balears (ES53)

12.4

14.5

13.0

Inner London (UKI1)

0

13.5 20

10.5

10.2

8.7 4.8

10.1

24.7

40

22.4

10.4 11.3

22.0

20.8

7.4

7.0

38.3 11.6

14.2

6.5

19.5

8.5

9.4

26.1

19.1

8.0

6.1

19.9

23.4

9.5 6.6

10.1

17.7

19.2

Kriti (GR43)

9.8

24.6

23.1

Algarve (PT15)

9.6

23.6 24.0 25.7

4.9

26.2

6.1

26.3

7.3 60

32.9 80

Hotels and restaurants

Retail trade

Construction

Wholesale trade

Other divisions in top five

Other divisions (not in top five)

100 Other business activities

Notes: Excluding BG, SI, DK (no data by NUTS 2 regions), MT, North Eastern Scotland (UKM5) and Highlands and Islands (UKM6) (data not available) CY excluding Research and development (K 73)

80

Eurostat regional yearbook 2009

Structural business statistics

The importance of this sector, measured as the share in the total workforce of the non-financial 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 work­ force in 2006 was divided between other business services (NACE 74), which include many highly specialised knowledge-intensive activities such as legal, accounting and management services, ar­ chitectural 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 ac­ tivities (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 sec­ tor is at the forefront of the information society, with enterprises that support clients in a broad

6

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 av­ erage growth rate for the whole section. The busi­ ness services sector was quite clearly one of the most dynamic sectors in the non-financial busi­ ness economy in terms of employment growth. One of the prime reasons for the rapid growth of this sector could be the outsourcing phenome­ non. Business services can be produced either in­

Figure 6.4: Structure of employment in real estate, renting and business activities (NACE section K) by divisions, EU-27 and Norway, 2006 Real estate activities (K 70) 11.0 % Renting (K 71) 2.4 %

Computer activities (K 72) 10.6 %

Research and development (K 73) 1.6 %

Other business activities (K 74) 74.4 %

Notes: Excluding MT, North Eastern Scotland (UKM5) and Highlands and Islands (UKM6) (data not available) CY excluding Research and development (K 73)

Eurostat regional yearbook 2009

81

6

Structural business statistics

Map 6.2: Persons employed in business services (NACE divisions K 72 and K 74), by NUTS 2 regions, 2006 Share in non-financial business economy employment of the region, in percentage

82

Eurostat regional yearbook 2009

6

Structural business statistics

ternally by an enterprise itself or they can be pur­ chased. Many enterprises have outsourced some of the services activities they previously produced in-house 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 pat­ tern of high concentration in large metropolitan areas emerges. The capital region is the most spe­ cialised region in all countries except the Neth­ erlands, where Noord-Holland (which includes Amsterdam) was just behind Utrecht. Of the top 20 regions with shares exceeding 25  %, six were British, five Dutch and three German. Luxem­ bourg (23  %) and the Netherlands were particu­ larly 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, part­ ly 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 south-east to Hannover. Figure 6.6 shows the difference in the degree of specialisation in business services across coun­ tries and between the regions with the highest and lowest values in each country. The graph also clearly illustrates the dominance of the capi­ tal 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 aver­ age 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.

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)

6.6

Real estate activities (K 70)

7.8

Renting (K 71)

5.5

Computer activities (K 72)

3.3

Research and development (K 73)

0.1

Other business activities (K 74)

7.2

0

1

2

3

4

5

6

7

8

Notes: Excluding MT, North Eastern Scotland (UKM5) and Highlands and Islands (UKM6) (data not available) CY excluding Research and development (K 73)

Eurostat regional yearbook 2009

83

6

Structural business statistics

Employment growth in business services

Characteristics of the top 30 most specialised regions in business services

Employment in business services in the EU-27 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.

Figure 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 non-financial business econ­ omy 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 re­ gion of the Czech Republic in 26th place.

About one in every six regions recorded negative employment growth rates, but in only 10 cases did the decrease reach 10  %. Half of these were Greek regions and two of them Belgian.

The number of people employed also grew con­ siderably in many of the top-ranked regions in 2006, with by far the highest growth rate, higher

Figure 6.6: Specialisation in business services (NACE divisions K 72 and K 74), EU-27 and Norway, by NUTS 2 regions, 2006 Share of non-financial business economy employment, in percentage NL

Zeeland

Utrecht

LU UK

Cumbria

Inner London Région de Bruxelles-Capitale / Brussels Hoofdstedelijk Gewest Île de France

Prov. Luxembourg (B)

BE FR

Corse

DE

Oberfranken

Berlin

DK SE IE PT IT NO ES HU

Småland med öarna Border, Midland and Western Centro (P) Provincia Autonoma Bolzano/Bozen Nord-Norge Ciudad Autónoma de Ceuta Észak-Magyarország

FI

Stockholm Southern and Eastern Lisboa Lazio Oslo og Akershus Comunidad de Madrid Közép-Magyarország Etelä-Suomi

Åland

AT

Burgenland (A)

CZ

Severovýchod

EL

Wien Praha

Sterea Ellada

Attiki

EE SI PL

Lubelskie Západné Slovensko Nord-Est

SK RO

Mazowieckie Bratislavský kraj Bucureşti — Ilfov

LV BG LT CY 0

5

10

15

20

25

30

35

40

45

50

National average Notes: BG, SI, DK (no data at NUTS 2 level), North Eastern Scotland (UKM5) and Highlands and Islands (UKM6) (data not available) CY excluding Research and development (K 73)

84

Eurostat regional yearbook 2009

Structural business statistics

6

Map 6.3: Growth rates of employment in business services (NACE divisions K 72 and K 74), by NUTS 2 regions, 2005–06

Eurostat regional yearbook 2009

85

6

Structural business statistics

Figure 6.7: Most specialised regions in business services (NACE divisions K 72 and K 74), EU-27 and Norway, by NUTS 2 regions, 2006 Share of non-financial business economy employment of the region and the region's share of total business services employment, in percentage 43.2

Inner London (UKI1)

2.86 34.6

Utrecht (NL31)

0.64

Région de Bruxelles-Capitale/ Brussels Hoofdstedelijk Gewest (BE10)

33.6 0.56 32.7

Noord-Holland (NL32)

1.36

Berkshire, Buckinghamshire and Oxfordshire (UKJ1)

31.7 1.13 31.3

Berlin (DE30)

0.91 30.7

Groningen (NL11)

0.20 30.3

Zuid-Holland (NL33)

1.41 29.9

Île de France (FR10)

5.06 29.3

Prov. Vlaams-Brabant (BE24)

0.35 28.7

Comunidad de Madrid (ES30)

3.69 28.5

Lisboa (PT17)

1.32 28.5

Flevoland (NL23)

0.12

Surrey, East and West Sussex (UKJ2)

28.2 0.94 28.1

Darmstadt (DE71)

1.44 27.8

Stockholm (SE11)

0.86 27.7

Hamburg (DE60)

0.68 27.3

Outer London (UKI2)

1.40 26.3

Noord-Brabant (NL41)

0.97

Hampshire and Isle of Wight (UKJ3)

0.64

Bedfordshire and Hertfordshire (UKH2)

0.62

25.9 25.8 25.4

Gelderland (NL22)

0.64 25.4

Limburg (NL) (NL42)

0.36 24.7

Düsseldorf (DEA1)

1.45 24.6

Cheshire (UKD2)

0.39 24.6

Praha (CZ01)

0.66 24.5

Oslo og Akershus (NO01)

0.42 24.4

Overijssel (NL21)

0.37 24.4

Wien (AT13)

0.56 24.0

Lazio (ITE4)

1.39

0

10 Region's share of total business services employment (%)

20

30

40

Share of non-financial business economy employment of the region (%)

Notes: Excluding BG, SI, DK (no data by NUTS 2 regions), MT, North Eastern Scotland (UKM5) and Highlands and Islands (UKM6) (data not available) CY excluding Research and development (K 73)

86

Eurostat regional yearbook 2009

50

Structural business statistics

than 30 %, in the Dutch regions of Limburg and Groningen. Strong growth of over 20 % was also recorded in Noord-Brabant, Flevoland, NoordHolland and Overijssel (Netherlands), and also in Prov. Vlaams-Brabant (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 ser­vices, but none of them dropped by more than 6 %.

Conclusion Regional 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

6

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 busi­ ness characteristics: the focus, diversity and spe­ cialisation 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 notes Regional structural business statistics (SBS) are collected within the framework of a Council and Parliament regulation, in accordance with the definitions and breakdowns specified in the Commission 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 classification, 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 communication) 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 enterprise situated in a geographically identified place. Local units are classified into sectors (by NACE) according to their main activity. At national level, the statistical unit is the enterprise. An enterprise can consist of several local units. It is possible for the principal activity of a local unit to differ from that of the enterprise to which it belongs. Hence, national and regional structural business statistics are not entirely comparable. It should be noted that in some countries the activity code assigned is based on the principal activity of the enterprise in question. Regional data are available at NUTS 2 level for a limited set of variables: the number of local units, wages and salaries, the number of people employed and investments in tangible goods. The latter variable is collected on an optional basis, except for industry (NACE sections C to E), which has more limited availability of data than for the other variables. Structural business statistics define number of persons employed as the total number of people who work (paid or unpaid) in the observation unit, plus people who work outside the unit who belong to it and are paid by it. It includes working proprietors, unpaid family workers, part-time workers and seasonal workers. Eurostat regional yearbook 2009

87

Information society

7

Information society

Introduction

(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://eur-lex.europa.eu/ LexUriServ/LexUriServ. do?uri=CELEX:52005 DC0229:EN:NOT

During recent decades information and commu­ nication technologies (ICTs) have penetrated all areas of economic and social life. ICTs have ac­ counted 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 communicat­ ing 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 poten­ tial 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 mem­ bers 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, mak­ ing for greater flexibility in work organisation, from which both the enterprise and the employee can benefit. The ubiquitous presence of ICTs car­ ries the potential for completely new ways of par­ ticipating 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 in­ troduced 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 in­ formation society. The digital divide can be clas­ sified according to criteria that describe the dif­ ference in participation according to gender, age, education, income, social groups or geographic location. This chapter puts emphasis on the geo­ graphic aspects of the digital divide.

90

Policies within the European Union at national and European level have recognised the im­ portance of bridging the digital divide to give citizens equal access to information and com­ munication technologies. The Riga ministerial declaration on e-inclusion of November 2006 (6) calls for an inclusive information society and sets the framework for a comprehensive e-inclu­ sion policy addressing different aspects of the digital divide, such as age, accessibility, geogra­ phy, digital literacy and competences, cultural diversity and inclusive online public services. European statistics play the role of benchmark­ ing the development of the European informa­ tion 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 indi­ viduals have been available since 2006.

Access to information and communication technologies Access to information and communication tech­ nologies 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 broad­ band for going online exist at European level. In contrast to supply-side statistics, the Eurostat fig­ ures show the actual uptake of ICTs by the popu­ lation. 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 re­ cent years, with an annual growth rate of 10  % for Internet access and 26  % for broadband ac­ cess between 2006 and 2008. While access to the Internet makes it possible to participate in the in­ formation 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

Eurostat regional yearbook 2009

Information society

7

Map 7.1: Internet access and broadband connections in households, by NUTS 2 regions, 2008 Share of households with Internet access and broadband connection

Eurostat regional yearbook 2009

91

7

Information society

ages, require de facto a broadband connection. Websites are getting richer in content, which in­ creases the demand for traffic volumes constant­ ly, even for less advanced services such as e-mail communication. The regional differences in Internet and broad­ band access are still quite large. They range from 90 % in Noord-Holland (Netherlands) to 17 % in Severozapaden (Bulgaria) for access to the Inter­ net and from 79 % in Groningen and Noord-Hol­ land (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 In­ ternet 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 re­ gions in the Netherlands, the regions in the Scan­ dinavian countries show very high Internet pene­ tration 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 EU-27 level, 65  % of house­ holds in densely populated areas have access to the Internet, while only 51  % of households in thinly populated areas have an Internet connec­ tion. Depending on the structure and size of the regions within the country, this pattern is vis­ ible for some regions on Map 7.1. In general, re­ gions 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 is­ lands in the surrounding regions owing to high­ er 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 pat­ terns to Internet access, with an average lag be­ tween Internet access and broadband connec­ tions of 12 % for the EU-27 in 2008, compared to 19 % in 2006. The lag has lessened during the last two years. Most of the Dutch regions have levels

Figure 7.1: Development of Internet access and broadband connections in households 2006–08 Ratio between increase of connected households between 2006 and 2008 and not-connected households in 2006 50 45 40 35 30 25 20 15 10 5 0

92

EU-27 SE FR IE DE AT UK LU DK LT FI EE SK HU MT SI CZ BE CY NL LV ES PL PT EL IT BG RO IS NO Internet access Broadband connection

Eurostat regional yearbook 2009

7

Information society

of Internet access and broadband connections for households above 70  %, whereas the difference between Internet access and broadband connec­ tion rates for all regions in Germany, Slovakia and Croatia, for most regions in Italy, and for Ire­ land, Luxembourg and Romania at national level is well above the EU-27 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 meth­ od 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 poten­ tial from the levels achieved in 2006 to full satura­ tion. When considering Internet access, Slovakia, France, Austria, Luxembourg, Sweden and the Netherlands developed most strongly within the EU-27, whereas Cyprus, Slovenia, Bulgaria and Greece show the lowest growth rates. Consider­ ing the development of broadband connections, Sweden, France, Ireland, Germany, Austria, the United Kingdom and Luxembourg performed most strongly within the EU-27 while Greece (10), Italy, Bulgaria and Romania are among the weak­ est performers.

Use of the Internet and Internet activities The 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 distribu­ tion of regions according to actual use of the In­ ternet 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 In­ ternet 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 Lux­ embourg, more than three quarters of the pop­ ulation use the Internet at least once a week. A higher share of persons living in densely popu­ lated 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 In­ ternet users. Regions in the east and west of the EU-27 have lower shares of regular Internet users. Almost all regions in Portugal, Italy, Greece, Bul­ garia and Romania as well as the Member State Cyprus had a share of regular Internet users be­ low 40 % in 2008.

(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 Online course (*) Sell goods and services Job search or job application Download software Listen to web radio or television Read online newspapers or magazines Health information search Interaction with public authorities Internet banking Travel and accommodation services Information on goods and services E-mail communication

0

10 2006

20 2008

30

40

50

60

70

80

90

(*) 2007–08

Eurostat regional yearbook 2009

93

7

Information society

Map 7.2: Regular use of the internet by NUTS 2 regions, 2008 Percentage of persons who accessed the Internet, on average, at least once a week

94

Eurostat regional yearbook 2009

Information society

7

Map 7.3: E-commerce by private persons, by NUTS 2 regions, 2008 Percentage of persons who ordered goods or services, over the Internet, for private use, in the last year

Eurostat regional yearbook 2009

95

7

Information society

The most popular activities on the Internet are communication via e-mail and looking for in­ formation 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 ac­ tivities. Internet users are those persons who have used the Internet within the last three months. Obtaining services related to travel and accom­ modation, Internet banking, interacting with public authorities, searching for health-related 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 e-mail communica­ tion, health information searches, Internet bank­ ing and listening to web radio or web TV. The regional differences regarding e-commerce 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 EU-27 aver­ age is 32 % of the target population. Almost all re­ gions in the eastern and southern Member States of the EU-27 show a share of 25 % or less of the total target population. Except for Spain, the var­ iety between the regions in those Member States is quite low, ranging within a maximum difference of one class. All regions in Finland, Sweden, Den­ mark, the United Kingdom and the Netherlands as well as the Member State Luxembourg have a share of e-shoppers 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 Internet (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 EU-27.

96

E-inclusion relates to the participation of all in­ dividuals 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 communica­ tion technologies to overcome digital exclusion and thus improve economic performance, employment opportunities, quality of life, social participation and cohesion. At EU-27 level, one third of the popu­ lation 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 non-users 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 non-users 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 non-users con­ firm 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 non-users have security concerns. It is the explicit objective of European regional policies to facilitate affordable access to the In­ ternet, 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 cover­ age for at least 90  % of the population by 2010. This target describes the supply side, while Euro­ stat figures from the Community ICT-use survey provide information on the take-up of ICTs in the regions, which may lag behind the potentially reachable population figures. In recent years, the share of non-users of the Inter­ net has dropped at EU-27 level from 43 % of the tar­ get population in 2005 to 33 % in 2008. The share of non-users fell in both densely and thinly popu­ lated areas between 2005 and 2008. However, the decrease in thinly populated areas is lagging be­ hind the development in densely populated areas, thereby widening inequality between the regions. The region with the lowest share of non-users 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 non-users in their regions are Bulgaria and Greece, with more than 25 percent­ age points of difference. Denmark, Poland, Fin­ land 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 accord­ ing to the share of persons who have never used the Internet as a deviation from the EU-27 average. Regions in green have fewer non-users than the EU-27 average, while the regions in yellow and or­ ange are above the EU-27 average. The geographic distribution shows similar patterns to those de­ scribed before. All regions in the Scandinavian countries, Norway, Finland, Sweden, Denmark and Iceland, as well as the Netherlands and Lux­

Eurostat regional yearbook 2009

7

Information society

embourg, are at least 15 % below the EU-27 aver­ age, while most of the regions in Bulgaria, Greece, Portugal, Romania, southern Italy and Cyprus are more than 15 % above the EU-27 average. Regions in the east and west of the EU-27 tend to exceed the EU-27 average of non-users of the Internet. Urban regions with higher population density tend to be below the EU-27 average. In the map, this tendency is visible for, for example, Athens, Lisboa, Madrid, Paris, Wien, Budapest, Praha or Berlin.

Conclusion Statistics on use of information and communi­ cation technologies in households and by indi­

viduals are collected annually at level 1 of NUTS on a compulsory basis. Some Member States ad­ ditionally 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 EU-27. Within the last few years, all Member States have increased ac­ cess to and use of ICTs. However, densely popu­ lated areas seem to profit more from the current development than thinly populated areas. In or­ der to overcome this problem, the European Un­ ion has shaped explicit policy targets to achieve an inclusive information society, including the geographic dimension of the digital divide. The

Figure 7.3: Non usage of Internet, by NUTS 2 regions, 2008 In percentage of the population aged between 16 and 74 years EU-27

Flevoland

Sud — Muntenia

RO

Bucureşti — Ilfov

BG

Sud — Muntenia

Yugozapaden

EL

Severozapaden

Attiki

PT

Kentriki Ellada

Lisboa

Região Autónoma dos Açores

CY HR

Središnja i Istočna (Panonska) Hrvatska

IT

Sjeverozapadna Hrvatska

Provincia Autonoma Bolzano/Bozen

Campania

MT PL

Region Centralny

Region Wschodni

LT SI ES

Comunidad de Madrid

HU

Extremadura Alföld és észak

Közép-Magyarország

LV CZ

Praha

Severovýchod

IE BE

Prov. Brabant Wallon

FR

Prov. Hainaut

Île de France

Bassin Parisien

EE AT

Wien

SK

Bratislavský kraj

DE

Burgenland (A) Západné Slovensko

Berlin

Sachsen

UK LU FI

Etelä-Suomi

DK

Itä-Suomi

Hovedstaden

NL

Nordjylland

Flevoland Östra Sverige Vestlandet

SE NO

Zeeland Norra Sverige Agder og Rogaland

IS 0

10

20

30

40

50

60

70

80

National average Notes: EE, IE, CY, LV, LT, LU, MT, SI, UK, IS (national level); DE, EL, FR, HU, PL, SE (by NUTS 1 regions); FI (FI20 combined with FI19)

Eurostat regional yearbook 2009

97

7

Information society

Map 7.4: Non-usage of the Internet, by NUTS 2 regions, 2008 Deviation of the share of persons who never have used the Internet from the EU-27 average

98

Eurostat regional yearbook 2009

7

Information society

policies are benchmarked according to the i2010 benchmarking framework. The maps in this chapter reveal specific spatial patterns that are visible for all indicators present­ ed. Despite the fact that the levels of Internet ac­ cess are highest for households in Dutch regions, there is a clear north–south gradient, with high­ er 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 per­ sons 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 broad­ band Internet as part of the European economic recovery plan to tackle the global economic and financial crisis (13) and has set the goal of achiev­ ing 100 % coverage of the population by 2013.

(13) http://europa.eu/rapid/ pressReleasesAction.do?re ference=DOC/09/1&form at=HTML&aged=0&langu age=EN&guiLanguage=en

Methodological notes European statistical data on use of information and communication technologies have been available 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 regulation describes two modules or areas of statistical data production: statistics on the use of ICT in enterprises 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 regional 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 downloading 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 included in the definition. Orders via manually typed e-mails are excluded. Delivery or payment via electronic means is not a requirement for an e-commerce transaction.

Eurostat regional yearbook 2009

99

Science, technology and innovation

8

Science, technology and innovation

Introduction The Lisbon European Council (2000) and the Bar­ celona European Council (2002) both highlight­ ed the important role of research and develop­ ment (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, technol­ ogy 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 re­ search policy to the broader Lisbon strategy, has also been a highly successful tool for moving re­ search 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 effec­ tiveness of fragmented research efforts and sys­ tems 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 indica­ tors based on a number of data sources available at Eurostat, which provide statistical information in order to compare the evolution and composi­ tion 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 technol­ ogy; and human resources in science and tech­ nology (HRST). More regional indicators for science, technology and innovation are available on the Eurostat web­ page under ‘Science and technology’.

Research and development Increasing 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 in­ dustrial competitiveness of the European Union. Some 20 of the regions shown in Map 8.1 have an R  &  D intensity above the 3  % target speci­ fied in the Lisbon strategy for the EU as a whole. Although this target remains the EU objective for

102

2010, most countries have specified their own tar­ gets 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 re­ spective 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 re­ gions, 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 re­ gions with the fastest-growing 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 in­ put 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 distribu­ tion of researchers (expressed as a percentage of total employment) across Europe. In 15 European regions over 1.8 % of all persons employed are re­ searchers. Trøndelag (Norway) is the leading re­ gion, with a share of 3.16 %, which is more than three times higher than the EU-27 average. This group also comprises one other Norwegian re­ gion, four German regions, three Finnish regions

Eurostat regional yearbook 2009

Science, technology and innovation

8

Map 8.1: Total R & D expenditure as a percentage of GDP, all sectors, by NUTS 2 regions, 2006

Eurostat regional yearbook 2009

103

8

Science, technology and innovation

Map 8.2: Researchers as a percentage of persons employed, all sectors, by NUTS 2 regions, 2006

104

Eurostat regional yearbook 2009

Science, technology and innovation

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 EU-27 average (0.9 %) and, once again, most of these re­ gions (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 EU‑27 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 (Sud-Est, with 0.13  %), the Netherlands (Friesland, with 0.13 %) and the Czech Republic (Severozápad, with 0.15 %). Regional disparities exist not only between coun­ tries 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 Nether­ lands with 0.73 percentage points.

Human resources in science and technology Without sufficient amounts of human resources there can be no growth. As science and technol­ ogy have been recognised as key fields for Euro­ pean 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 sub-group 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

Eurostat regional yearbook 2009

8

presence of the head offices of companies and gov­ ernment institutions. However, another factor is that capitals are often big cities that naturally con­ tain 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 cit­ ies 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 par­ ticular cluster stretches from the Italian region Lazio in the south up through Switzerland to the south-western 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 coun­ tries. 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 sec­ ond, third and fourth-highest 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 services The statistics on high-tech industries and knowl­ edge-intensive services include employment data by sectors of economic activity. Based on the ratio of R & D expenditure to GDP (R & D inten­ sity), sectors can be classified into more specific subsectors so as to analyse employment in sci­ ence and technology. Two subsectors that are of great importance to science and technology are the high-tech manufacturing and medium hightech manufacturing sectors, even though they

105

8

Science, technology and innovation

Map 8.3: Human resources in science and technology by virtue of occupation (HRSTO), by NUTS 2 regions, 2007 Percentage of active population

106

Eurostat regional yearbook 2009

Science, technology and innovation

8

Map 8.4: Employment in high- and medium high-tech manufacturing, by NUTS 2 regions, 2007 Percentage of total employment

Eurostat regional yearbook 2009

107

8

Science, technology and innovation

accounted for only 1.1  % and 5.6 % respectively of EU employment in 2007. High-tech manu­ facturing includes, for example, manufacture of computers, televisions and medical instruments, while medium high-tech manufacturing includes, for example, manufacture of chemicals, machin­ ery and transport equipment. Map 8.4 shows employment in the two subsectors — high-tech and medium high-tech manufactur­ ing — as a percentage of total employment. Em­ ployment in these two subsectors is very high in the central European regions, in a band stretch­

ing from Franche-Comté (France) in the west to Észak-Magyarország (Hungary) in the east. Stutt­ gart 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 re­ gions are all German (in addition to Stuttgart and Braunschweig, they include Karlsruhe, Tübingen, Rheinhessen-Pfalz, Unterfranken and Freiburg). Furthermore, Map 8.4 shows a cluster of four Ital­ ian regions (Piemonte, Emilia-Romagna, Lom­ bardia and Veneto) with relatively high shares

Table 8.1: 25 leading regions in employment in knowledge-intensive services and high-tech knowledgeintensive services, 2007 Knowledge-intensive services (KIS)

108

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

8.9

Stockholm (SE)

55.8

564

84

8.3

Berkshire, Buckinghamshire and Oxfordshire (UK) 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) Région de Bruxelles-Capitale/ Brussels Hoofdstedelijk Gewest (BE) Mellersta Norrland (SE)

47.3

347

56

6.1

Hampshire and Isle of Wight (UK)

47.2

180

133

6.1

Outer London (UK)

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) Berkshire, Buckinghamshire and Oxfordshire (UK) Prov. Brabant Wallon (BE) Gloucestershire, Wiltshire and Bristol/ Bath area (UK)

46.8

132

103

5.8

Darmstadt (DE)

46.5

529

297

5.7

Île de France (FR)

46.1

71

74

5.7

Etelä-Suomi (FI)

46.1

529

70

5.6

Karlsruhe (DE)

Västsverige (SE)

45.8

420

62

5.4

Région lémanique (CH)

45.5

330

110

5.4

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)

Gloucestershire, Wiltshire and Bristol/ Bath area (UK) Oberbayern (DE)

Eurostat regional yearbook 2009

Science, technology and innovation

of employment in high- and medium high-tech manufacturing. In the other parts of Europe only three regions have more than 10 % of their em­ ployment in high- or medium high-tech manu­ facturing; they are Vest (Romania), Bursa (Tur­ key) and Herefordshire, Worcestershire and Warwickshire (United Kingdom). Another subsector of interest is knowledge-in­ tensive services (KIS). KIS can be further split into different categories, of which high-tech knowledge-intensive services (high-tech KIS) is a subsector of special interest when analysing em­ ployment in science and technology. Examples of services in high-tech KIS include computer and related activities, and research and development. KIS, on the other hand, is broader and, in addi­ tion to high-tech 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 high-tech KIS. As KIS generally attracts highly educated persons, there is a similar pat­ tern 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 Swe­ den has a large public sector, which includes the education and healthcare sectors. Looking at the right-hand side of the table, which shows the 25 leading regions in high-tech KIS, only one Swed­ ish region remains. This region, the Swedish capi­ tal region Stockholm, had 8 % of its employment in high-tech KIS, which is the second-highest share after Berkshire, Buckinghamshire and Ox­ fordshire (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 In­ ner London and Outer London).

Eurostat regional yearbook 2009

8

One interesting feature here is that three of the five regions with the highest shares of employ­ ment in high-tech KIS in 2007 were also among the five highest in 2002, when Stockholm (Swe­ den) was the leading region, followed by Berk­ shire, Buckinghamshire and Oxfordshire (United Kingdom). Bratislavský kraj (Slovakia) followed in third place and Île-de-France (Paris) in fourth — which was somewhat surprising compared to its 19th position in 2007. Oslo og Akershus was in fifth place in 2002.

Patents Indicators based on patent statistics are widely used in order to assess the inventive and innova­ tive performance of a country or a region. The current emphasis on innovation as a source of in­ dustrial competitiveness has raised awareness of patents. Patents are used to protect R & D results, but they are just as significant as a source of tech­ nical information, which may avoid reinventing and redeveloping ideas because of a lack of in­ formation. 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 ac­ tivity 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 south-east of France and the north-west of Italy. The most active patenting regions (with 100 to 300 ap­ plications and more than 300 applications per million inhabitants) are situated in the Nordic countries and in the centre of the EU-27. Patent activity varies not only across countries but also across regions. In 2004, Île-de-France (France) was the foremost EU region in terms of total number of patent applications (3 297), while Noord-Brabant (Netherlands) was in the lead for patent applications per million inhabitants (761). In Germany large disparities were observed be­ tween the leading region of Stuttgart in the south and the lowest-performing region of SachsenAnhalt in the east. Regional discrepancies are even wider in the Netherlands, between NoordBrabant and Friesland. Regional disparities, how­ ever, are much lower in countries with comparable national averages, such as Finland and Sweden.

109

8

Science, technology and innovation

Map 8.5: Patent applications to the EPO per million inhabitants, by NUTS 2 regions, 2004

110

Eurostat regional yearbook 2009

Science, technology and innovation

8

Conclusion Relevant and meaningful indicators on science, technology and innovation are of paramount im­ portance 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 them­ selves both at European level and worldwide.

With the aid of the relevant statistics and indica­ tors, this chapter has demonstrated the progress made in recent years on research and develop­ ment activities in European regions. Wide use is also made of statistics on high-tech industries and knowledge-intensive services, patents and human resources in science and technology in order to complete this regional picture.

Methodological notes The data in the maps and tables in this chapter are, wherever possible, by NUTS 2 regions. Data are extracted from the ‘Science, technology and innovation’ domain and, more specifically, from the sub-domains ‘Research and development’, ‘Human resources in science and technology’, ‘Hightechnology 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, frequency 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 annually, 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 intensity, 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 Community (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 received from the European Patent Office (EPO). The patent data reported include the patent applications 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 regionalised 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 includes 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 application details (claimed priorities, application and publication), technology categories, inventors and applicants, title and abstract, patent citations and non-patent literature text.

Eurostat regional yearbook 2009

111

Education

9

Education

Introduction Education, vocational training and lifelong learn­ ing 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 com­ pleted without efficient use of resources, quality improvements in education and training systems and implementation of a coherent lifelong learn­ ing strategy at national level. Securing education and lifelong learning opportunities in every re­ gion and for every inhabitant, wherever they live, is one of the cornerstones of the national strat­ egies to achieve this goal. Eurostat’s regional sta­ tistics 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 edu­ cation 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 informa­ tion on the total number of enrolments by level of education and sex, and by age and sex plus in­ dicators relating enrolments in education to the total population. Data on enrolments in educa­ tion are generally available for the 15 ‘old’ Mem­ ber 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 education In 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 cultur­ al heritage and its accumulated knowledge, values and skills from one generation to another. This chapter gives evidence of the educational en­ rolment of the regional populations as well as their educational attainment levels and their participa­ tion 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

114

of individuals participating in education irrespec­ tive 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 dis­ tribution 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 coun­ tries 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 (north-west 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 education Learning begins at birth. The period from birth to entry into primary education is a critical form­ ative stage for the growth and development of children. The learning outcomes, knowledge and skills of primary education are stronger when ap­ propriate learning and development occur in the years preceding regular schooling. The purpose of pre-primary education is to prepare children physically, emotionally, socially and men­ tally to enter primary school, giving them the abil­ ity and the skills to enter the first level of the edu­ cational system. This preparation is considered the foundation for further educational development. In December 2008, the European Commission proposed a new benchmark, whereby 90  % of 4-year-olds should participate in pre-primary education by 2020. The aim of this proposal is to underpin progress towards the 2002 Barcelona Summit conclusion of increasing participation in pre-primary education to 90 % of all children between 3 years of age and the beginning of com­ pulsory education. Eurostat regional yearbook 2009

Education

9

Map 9.1: Students in all levels of education, as a percentage of total population, by NUTS 2 regions, 2007 ISCED levels 0–6

Eurostat regional yearbook 2009

115

9

Education

The EU-27 rate of participation is already approach­ ing the target (88.5 % in 2007), but this overall high level of participation masks significant variations between the figures for individual countries. When the EU-27 Member States and the candi­ date and EFTA countries are taken into account, approximately 73  % (in 2007) of the European 4-year-olds were enrolled in pre-primary and primary education. The indicator shown here examines the partici­ pation in early childhood education at regional level (NUTS 2) by measuring the percentage of 4-year-olds who are in either pre-primary or pri­ mary education. By far the majority of them at­ tend pre-primary schooling (which in many cases is also non-compulsory). A 4-year-old child can be enrolled either in pre-primary or in primary school. Data highlight that most of them attend pre-primary school. Ireland and the United King­ dom are the only countries where the proportion of 4-year-olds in primary education is relevant. At the age of 4 most children in the European Union are therefore in pre-primary education (80 %), which is generally available from at least 3 to 4 years of age in the EU Member States. Only 5 % of 4-year-olds are enrolled in primary educa­ tion, of which 89  % are in the United Kingdom and 11 % in Ireland. Enrolment in pre-primary 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 Neth­ erlands and Spain, and in regions such as Vlaams Gewest (Belgium), the participation of 4-yearolds in education is nearly 100 %. In contrast, in Croatia, Ireland, Macedonia, Switzerland, Turkey and most of Poland and Finland less than 50 % of the 4-year-olds are enrolled in education. No sig­ nificant 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 education At 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.

116

Map 9.3 shows the percentage of students en­ rolled in upper secondary education (ISCED 3 level) and post-secondary non-tertiary 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 all-round learning and to continue the teaching and educational task of basic education. The objective is often to offer suf­ ficient skills and knowledge with a view to fur­ ther study. It would normally give access to uni­ versity-level programmes. In contrast, vocational streams often provide training for specific labour market occupations. Students generally start upper secondary educa­ tion at the age of 15 to 17, at the end of full-time 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, de­ pending on the country. Post-secondary non-tertiary education pro­ grammes (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 al­ ready gained an upper secondary qualification. In 2007 more than 38 % of the population aged 15–24 years in the EU-27 was enrolled in upper secondary and post-secondary 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 Friuli-Venezia Giulia (Italy), Közép-Magyarország and Dél-Alföld (Hungary) and the Salzburg region (Austria). Taking a wider look at the map, the Nordic coun­ tries (Norway, Sweden, Denmark, Finland and Iceland) show a common pattern with high per­ centages. Many parts of Europe (such as France, Germany, Switzerland, the Netherlands, Poland, Slovakia, Slovenia, Croatia, Romania, Bulgar­ ia 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

Eurostat regional yearbook 2009

Education

9

Map 9.2: Participation rates of 4-year-olds in education, by NUTS 2 regions, 2007 At pre-primary and primary education (ISCED levels 0 and 1). Percentage

Eurostat regional yearbook 2009

117

9

Education

Map 9.3: Students at upper secondary and post-secondary non-tertiary education, as a percentage of the population aged 15 to 24, by NUTS 2 regions, 2007 ISCED levels 3 and 4

118

Eurostat regional yearbook 2009

Education

the rest (lower rates). In contrast, the Iberian pe­ ninsula (Spain and Portugal), Turkey, Lithuania, Malta, Cyprus, Macedonia and some regions in Greece have very low participation rates.

Students in tertiary education Tertiary education refers to levels of education that are provided by universities, vocational universi­ ties, institutes of technology and other institutions that award academic degrees or professional certi­ fications. Access to tertiary-level educational pro­ grammes typically requires successful completion of an upper secondary level and/or a post-second­ ary non-tertiary level programme. The levels of education can be largely theoretical­ ly based and intended to provide sufficient quali­ fications for gaining entry into advanced research programmes and professions with high skills re­ quirements (ISCED level 5A) or more practical, technical and employment-oriented (ISCED level 5B), or can lead to an advanced research qualifi­ cation (ISCED level 6, PhD-like 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 stu­ dents are studying, not on where they come from or live. Regions with universities and other terti­ ary education institutions, often big cities, there­ fore 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 educa­ tion 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 Lux­ embourg, 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 20–24-year-olds. In regions such as Praha, Wien, Région de Bruxelles-Capitale/Brussels Hoofd­ stedelijk Gewest, Brabant Wallon (south of Brus­ sels), Bratislava, Bucureşti, Közép-Magyarország Eurostat regional yearbook 2009

9

(Hungary, Budapest region), Dytiki Ellada (Greece) and Mazowieckie, including the capital Warszawa (Poland), the figures are more than 100 %, signify­ ing 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 tertiary-level student populations below 30 % of the 20–24-year-old 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, tertiary-educa­ tion infrastructure, and the students have to move away in order to obtain higher education.

Tertiary educational attainment The proportion of the population aged 25–64 years who have successfully completed university or uni­ versity-like (tertiary-level) 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 tertiary-level attainment are found in the same regions as the students in tertiary edu­ cation, 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 genera­ tions tend to have higher educational attainment levels than older generations. In 2007 only 23 re­ gions 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 lev­ els 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 learning Continuous refreshing of the skills of the labour force via lifelong learning has repeatedly been underlined in EU policies following up the Lis­ bon objectives. This is reflected in the ‘Education

119

9

Education

Map 9.4: Students in tertiary education, as a percentage of the population aged 20 to 24 years old, by NUTS 2 regions, 2007 ISCED levels 5 and 6

120

Eurostat regional yearbook 2009

Education

9

Map 9.5: Educational attainment level, by NUTS 2 regions, 2007 Percentage of the population aged 25–64 having completed tertiary education

Eurostat regional yearbook 2009

121

9

Education

Map 9.6: Lifelong learning, by NUTS 2 regions, 2007 Percentage of the adult population aged 24 to 64 participating in education and training during the four weeks preceding the survey

122

Eurostat regional yearbook 2009

Education

and training 2010’ programme as well as in the European employment strategy, which empha­ sises the need for comprehensive lifelong learn­ ing 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 pre­ ceding the survey. The data concern the age group 25–64 years for all education or vocational train­ ing, whether or not relevant to current or future employment. As Map 9.6 shows, participation in education and training is largely nationally pro­ filed. In fact, this is the education indicator show­ ing 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

9

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, Portu­ gal, Romania and Slovakia, and also Croatia and Turkey generally have low participation rates in education and training for the age group 25–64.

Conclusion The examples given above are intended merely to highlight a few of the many possible ways of ana­ lysing education and lifelong learning in the re­ gions of the EU and do not constitute a detailed analysis. We hope, however, that they will encour­ age readers to probe deeper into all the data on education freely available on the Eurostat website and to make many further interesting discoveries.

Methodological notes The maps are presented at NUTS 2 level, except for the educational enrolment indicators for Germany 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 indicators 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 programme 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 disseminated by Eurydice (www.eurydice.org) and covers, for example, age of compulsory school attendance and numerous issues relating to the organisation of school life in the Member States (decisionmaking, 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 qualifications similar to the corresponding regular programmes. Apprenticeship programmes are included except those which are entirely work-based and which are not supervised by any formal education 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 average of quarterly 2007 data. The educational attainment level reported is based on ISCED-97. Participation in education and training (lifelong learning) includes participation in all kinds of education and training activities during the four weeks prior to the survey. Eurostat regional yearbook 2009

123

Tourism

10

Tourism

Introduction Tourism is an important and fast-evolving eco­ nomic factor in the European Union, occupying large numbers of small and medium-sized busi­ nesses. Its contribution to growth and employ­ ment varies widely across the EU regions. Par­ ticularly 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 cross-cutting industry. Ser­vices to tourists involve a variety of economic branches: hotels and other accommodation, gastronomy (restaurants, cafes, etc.), the various transport op­ erators and also a wide range of cultural and recre­ ational facilities (theatres, museums, leisure parks, swimming pools, etc.). In many tourism-oriented 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 develop­ ment and structure of tourism since 1995, pur­ suant to Council Directive 95/57/EC on the col­

lection of statistical information in the field of tourism. This includes data both on accommoda­ tion 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 def­ inition of tourism is broader than the common, everyday definition. It encompasses not only pri­ vate travel but also business travel. This is primar­ ily because it views tourism from an economic perspective. Private travellers and business trav­ ellers have broadly similar consumption patterns. They both make significant demands on trans­ port, accommodation and restaurant services. To the providers of these services, it is of secondary interest whether their customers are private tour­ ists or on business. Tourism promotion depart­ ments, 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 promi­ nence to this in their marketing activities.

Figure 10.1: Top 20 EU-27 tourist regions, number of bedplaces by type of accommodation, by NUTS 2 regions, 2007 ES — Cataluña FR — Provence-AlpesCôte d'Azur FR — Languedoc-Roussillon FR — Aquitaine FR — Rhône-Alpes IT — Veneto FR — Bretagne IT — Emilia-Romagna FR — Pays de la Loire ES — Andalucía IT — Toscana FR — Île de France ES — Illes Balears UK — West Wales and The Valleys IT — Lombardia FR — Poitou-Charentes HU — Közép-Magyarorszàg FR — Midi-Pyrénées IT — Lazio AT — Tirol 0

100 000 Hotels

126

200 000

300 000

400 000

500 000

600 000

Campsites

Eurostat regional yearbook 2009

700 000

10

Tourism

Accommodation capacity Figure 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 ac­ commodation 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 ac­ commodation 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 Aus­ tria complete the list of the top regions for accom­ modation capacity, with one region each (West Wales and The Valleys, Közép-Magyarország 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 ho­ tels and on campsites per 1 000 inhabitants (bed

density) for the countries of Europe. This link with the number of inhabitants shows the rela­ tive importance of tourism capacity per head of population. This indicator is therefore affected not only by the number of available beds (bed­ places) 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 stays The 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 cor­ relates 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

Figure 10.2: Top 20 EU-27 tourist regions, number of nights spent in hotels and campsites, by NUTS 2 regions, 2007 Breakdown by residents and non-residents millions

FR — Île de France ES — Cataluña ES — Illes Balears ES — Andalucía ES — Canarias IT — Veneto IT — Emilia-Romagna FR — Provence-Alpes Côte d'Azur IT — Toscana ES — Comunidad Valenciana AT — Tirol IT — Lazio IT — Lombardia FR — Rhône-Alpes FR — Languedoc-Roussillon DE — Oberbayern IT — Provincia autonoma Bolzano/Bozen FR — Aquitaine IT — Campania ES — Comunidad de Madrid 0

10 Residents

Eurostat regional yearbook 2009

20

30

40

50

60

70

80

Non-residents

127

10

Tourism

Map 10.1: Number of bedplaces in hotels and campsites per 1 000 inhabitants, by NUTS 2 regions, 2007

128

Eurostat regional yearbook 2009

Tourism

10

Map 10.2: Nights spent in hotels and campsites, by NUTS 2 regions, 2007

Eurostat regional yearbook 2009

129

10

Tourism

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 Île-de-France region contain­ ing the French capital Paris is well in the lead, fol­ lowed 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 Ober­ bayern in Germany (23.4 million) with the Bavar­ ian 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.

average length of stay. This, however, depends on the character of the region. For example, urban re­ gions 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 to­ wards 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 10.2 gives an overview of numbers of over­ night 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 abovemen­ tioned five countries (Italy, Spain, France, Austria and Germany) represented in the top 20 regions, Croatia, the Netherlands, Portugal, Greece, Cy­ prus, the United Kingdom and the Czech Repub­ lic also have NUTS 2 regions with more than 10 million overnight stays.

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 re­ gions. 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 Provin­ cia Autonoma Bolzano/Bozen. The remaining 21 are either island regions or have long coastlines.

Average length of stay

Tourism intensity

The number of overnight stays in a region is based not only on the number of visitors but also on their

Another important indicator of the touristic na­ ture of a region is tourism intensity. This serves

Figure 10.3: Evolution of nights spent in hotels and campsites 2000–07 in the EU-27 Million nights EU-27

2 000 1 950 1 900 1 850 1 800 1 750 1 700 1 650 1 600 2000

2001

2002

2003

2004

2005

2006

Evolution of nights spent Footnote: EE 2000, 2001; IE 2001; CY 2000, 2002; MT (only hotels)

130

Eurostat regional yearbook 2009

2007

Tourism

10

Map 10.3: Average length of stay in hotels and campsites, by NUTS 2 regions, 2007 Days

Eurostat regional yearbook 2009

131

10

Tourism

Map 10.4: Nights spent in hotels and campsites per 1 000 inhabitants, by NUTS 2 regions, 2007

132

Eurostat regional yearbook 2009

10

Tourism

as an indicator of the relative importance of tour­ ism 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 in­ tensity 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 development Tourism 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 favour­ able economic climate at the time and to special events such as the Holy Year in Italy and the Han­ nover 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 doubledigit 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  %.

Figure 10.4: Nights spent in hotels and campsites, EU-27, average annual change rate 2003–07 Percentage Average annual change rate EU-27 BE BG CZ DK DE EE IE GR ES FR IT CY LV LT LU HU MT NL AT PL PT RO SI SK FI SE UK -5

0

Eurostat regional yearbook 2009

5

10

15

20

25

133

10

Tourism

Map 10.5: Nights spent in hotels and campsites, by NUTS 2 regions, average annual change rate 2003–07

134

Eurostat regional yearbook 2009

Tourism

Only Luxembourg, Slovakia and Cyprus record­ ed declines in the number of overnight stays be­ tween 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 over­ night stays was recorded in the regions of Ro­ mania, Portugal and Spain.

Inbound tourism Inbound 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, particu­ larly 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 visi­ tors 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 over­ night 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

Eurostat regional yearbook 2009

10

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.

Conclusion Analysis of the structure and development of tour­ ism in Europe’s regions confirms the compensa­ tory 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 coun­ try. 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 fac­ tor 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 land­ scapes 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 enhanc­ ing its cultural diversity and providing interesting new destinations for many citizens to discover.

135

10

Tourism

Map 10.6: Share of non-resident nights spent in hotels and campsites, by NUTS 2 regions, 2007

136

Eurostat regional yearbook 2009

Tourism

10

Methodological notes Harmonised statistical data on tourism have been collected since 1996 in the Member States of the European Union on the basis of Council Directive 95/57/EC of 23 November 1995 on the collection of statistical information in the field of tourism. The programme covers both the supply side, i.e. data on available accommodation capacity (establishments, rooms, bedplaces) and its utilisation (number of visitor arrivals and overnight stays), and the demand side, i.e. the travel behaviour of the population. Results by region below Member State level are available only for the supply side, however. The tourism statistics presented in this chapter relate only to ‘hotels and similar establishments’ and ‘tourist campsites’. Statistics for ‘holiday dwellings’ and ‘other collective accommodation’, on which data are also collected under the tourism statistics directive, are not included in this analysis since their comparability must at present still be regarded as limited, particularly at regional level. The analysis of tourism statistics covers data on both private and business travellers. This means that the definition of tourism applied to these statistics is broader than the everyday definition. The reason for this is primarily an economic one, since the two groups of travellers demand similar ser– vices and are thus, for the providers of those services, more or less interchangeable.

Eurostat regional yearbook 2009

137

Agriculture

11

Agriculture

Introduction Crop 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 agri­ culture is increasingly prioritising the kind of high-quality, environmentally friendly produce demanded by the market. This year’s Eurostat regional yearbook concen­ trates 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 concen­ trates 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 pro­ duced crop. European statistics on cereals en­ compass wheat, barley, maize, rye, meslin, oats, rice and other cereals such as triticale, buck­ wheat, 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 re­ gions’ UAA (see Map 11.1), namely Balkan regions such as Sud-Vest Oltenia and Bucureşti — Ilfov in Romania and east European regions, in particular in Hungary (Közép-Dunántúl, Nyugat-Dunántúl and Dél-Dunántúl), Slovakia (Bratislavský kraj and Západné Slovensko) and Poland (Łódzkie, Lubelskie, Wielkopolskie, Zachodnoniopomorskie, Lubuskie, Dolnośląskie, Opolskie, Kujawsko-pomorskie and Pomorskie). Cereal crops also cover over 50  % of the UAA of some regions of northern Europe (Denmark, the EteläSuomi and Länsi‑Suomi regions of Finland and Östra Mellansverige, Småland med öarna and Norra Mellansverige in Sweden) and southern

140

Europe (the Italian region of Basilicata). In west­ ern Europe, the highest proportion of area under cereals to UAA is in the regions of Île-de-France, 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 ac­ counts for less than 10 % of the UAA. Specifically, these regions include almost all re­ gions of Portugal (except Lisboa region), and cer­ tain 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 Prov­ ince), France (Corsica, Limousin and the overseas department of Réunion), the Netherlands (Fries­ land, 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 Medi­ terranean 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 agricul­ ture given that their production generally yields a greater added value per hectare than annual crops and that they are generally intended for hu­ man consumption. Moreover, these crops play an important role not only in shaping the rural landscape (with or­ chards, vines and olive trees) but also in terms of the environmental balance of agriculture.

Eurostat regional yearbook 2009

Agriculture

11

Map 11.1: Cereals (including rice) as a percentage of utilised agricultural area, by NUTS 2 regions, 2007

Eurostat regional yearbook 2009

141

11

Agriculture

Map 11.2: Permanent crops as a percentage of utilised agricultural area, by NUTS 2 regions, 2007

142

Eurostat regional yearbook 2009

Agriculture

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 account­ ing for over 30 % of their UAA, 10 are in the Med­ iterranean basin. They are: Cataluña, Comunidad Valenciana, Illes Balears, Andalucía and Región de Murcia, in Spain (the Comunidad Valenciana region, for example, specialises in cultivating or­ anges and small-fruited citrus, and accounts for over 27 % of the orange-growing surface area and 60  % of the small-fruited citrus surface area of the EU-27); Campania, Puglia, Calabria and Si­ cily, in Italy; Norte, Central, Algarve and the au­ tonomous region of Madeira, in Portugal, and the Languedoc-Rousillon region of France. Similarly, Malta and Cyprus, also in the Mediter­ ranean, 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 production Maps 11.3, 11.4 and 11.5 show the percentage contribution of each region to the total EU pro­ duction of three major crops — wheat, maize and rapeseed. The total regional production of an ag­ ricultural product — even if the figure is heav­ ily 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.

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 Princi­ pado 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 fig­ ures 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 low­ est): Centre, (which accounts for 4.5 % of Com­ munity production of wheat), Picardie, Cham­ pagne-Ardenne, Poitou-Charentes, Pays de la Loire, Nord — Pas-de-Calais, Bourgogne, HauteNormandie, Île-de-France 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, Nordrhein-Westfalen, MecklenburgVorpommern, Baden-Württemberg, Thüringen and Schleswig-Holstein. It can therefore be said that the EU’s wheat ‘gran­ ary’ 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 fourthbiggest producer of wheat, after the United King­ dom (8.3 million tonnes).

Wheat production

Grain maize 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 prima­ rily used in human and animal food, but also for making processed products such as bioethanol and starch.

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 intend­ ed for animal feed but it is also used for industrial products such as starch and glue.

Eurostat regional yearbook 2009

11

Given its physiological needs, this crop covers a smaller geographical range of EU regions. The

143

11

Agriculture

Map 11.3: Wheat production, sum of the regions which together represent x % of the EU-27 production of wheat, by NUTS 2 regions, 2007

144

Eurostat regional yearbook 2009

Agriculture

11

Map 11.4: Grain maize production, sum of the regions which together represent x % of the EU-27 production of grain maize, by NUTS 2 regions, 2007

Eurostat regional yearbook 2009

145

11

Agriculture

most northerly Member States (Ireland, the Uni­ ted Kingdom, Denmark, Estonia, Latvia, Finland and Sweden) produce little or no grain maize.

rapeseed; southern regions (in Spain, Italy and Bulgaria) account for less than 10 % of Commu­ nity production.

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.

The 13 regions (including Denmark) that produce the most rapeseed account for at least 50 % of to­ tal production in the EU-27. 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 14 regions, seven are in France, as fol­ lows (starting with the highest-producing region): Aquitaine (which accounts for 6.3 % of Communi­ ty production), Poitou-Charentes, Midi-Pyrénées, Alsace, Pays de la Loire, Rhône-Alpes and Centre. Four are in the north of Italy (starting with the highest-producing region): Veneto, Lombardia, which accounts for 6.2 % of Community produc­ tion, Piemonte and Friuli-Venezia Giulia. There is one such region in Hungary (Dél-Dunantul, 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 fourth-biggest producer of grain maize in the EU-27 (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 non-edible 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 de­ mand in recent years for renewable energy sour­ ces such as biodiesel. Rapeseed is best suited to a temperate climate. Four countries in the south of the EU — Portu­ gal, Greece, Cyprus and Malta — do not produce

146

Of those regions, eight are in Germany, the big­ gest rapeseed-producing country, with 5.3 mil­ lion tonnes (starting with the highest-producing region): Mecklenburg-Vorpommern (5.8  % of Community production), Bayern, Sachsen-An­ halt, Niedersachsen, Schleswig-Holstein, Sach­ sen, Thüringen and Brandenburg. Four are in France, the second-biggest producer of rapeseed, with 4.6 million tonnes (starting with the highest-producing region): Centre (6  % of Community production), Champagne-Ardenne, 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 third-biggest producer of rapeseed in the EU. Ten Polish regions are in this group: Wielkopol­ skie (2.1 % of Community production), Kujawskopomorskie, Zachononiopomorskie, Dolnośląskie, Opolskie, Pomorskie, Warminsko-mazurskie, Lubelskie, Mazowieckie and Lubuskie. Two Baltic countries, Estonia and Lithuania, also feature in this group.

Conclusion Climate and geography have a major influence on the agricultural use of the land; the choice of ani­ mal and plant production varies from region to region across Europe. It should be emphasised, however, that produc­ tion 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 in­ creasingly important, and could yet alter the cur­ rent face of agriculture in Europe’s regions.

Eurostat regional yearbook 2009

Agriculture

11

Map 11.5: Rape production, sum of the regions which together represent x % of the EU-27 production of rape, by NUTS 2 regions, 2007

Eurostat regional yearbook 2009

147

11

Agriculture

Methodological notes The utilised agricultural area (UAA) comprises arable crops, permanent grassland, permanent crops and other agricultural land such as kitchen gardens. Cereals comprise wheat (common and durum), barley, grain maize, rye and meslin, oats, mixed grain other than meslin, triticale, sorghum and other cereals such as buckwheat, millet, canary seed and rice. Permanent crops are agricultural crops, in particular ligneous crops, that occupy the soil for more than five years (not including permanent pasture). As regards Maps 11.3, 11.4 and 11.5, total EU production and the total number of regions accounting for a particular percentage of EU production do not include countries that have not submitted regional data. Accordingly, for EU wheat production (Map 11.3), the figures do not include production in the Czech Republic, Greece or the United Kingdom. For EU grain maize production (Map 11.4), the figures do not include production figures for the Czech Republic or Greece. Similarly, for EU rapeseed production (Map 11.5), the figures do not include production in the Czech Republic or the United Kingdom.

148

Eurostat regional yearbook 2009

Annex EUROPEAN UNION: NUTS 2 regions Belgium

DK04 Midtjylland

DEB2 Trier

BE10 Région de Bruxelles-Capitale/ Brussels Hoofdstedelijk Gewest

DK05 Nordjylland

DEB3 Rheinhessen-Pfalz DEC0 Saarland

BE21 Prov. Antwerpen

Germany

BE22 Prov. Limburg (B)

DE11 Stuttgart

BE23 Prov. Oost-Vlaanderen

DE12 Karlsruhe

BE24 Prov. Vlaams-Brabant

DE13 Freiburg

BE25 Prov. West-Vlaanderen

DE14 Tübingen

BE31 Prov. Brabant Wallon

DE21 Oberbayern

BE32 Prov. Hainaut

DE22 Niederbayern

BE33 Prov. Liège

DE23 Oberpfalz

Estonia

BE34 Prov. Luxembourg (B)

DE24 Oberfranken

EE00 Eesti

BE35 Prov. Namur

DE25 Mittelfranken

Ireland

DE26 Unterfranken

Bulgaria BG31 Severozapaden BG32 Severen tsentralen BG33 Severoiztochen BG34 Yugoiztochen BG41 Yugozapaden BG42 Yuzhen tsentralen

DE27 Schwaben DE30 Berlin DE41 Brandenburg — Nordost DE42 Brandenburg — Südwest DE50 Bremen DE60 Hamburg DE71 Darmstadt

Czech Republic

DE72 Gießen

CZ01 Praha

DE73 Kassel

CZ02 Střední Čechy

DE80 Mecklenburg-Vorpommern

CZ03 Jihozápad

DE91 Braunschweig

CZ04 Severozápad

DE92 Hannover

CZ05 Severovýchod CZ06 Jihovýchod CZ07 Střední Morava CZ08 Moravskoslezsko

DE93 Lüneburg DE94 Weser-Ems DEA1 Düsseldorf DEA2 Köln

DED1 Chemnitz DED2 Dresden DED3 Leipzig DEE0 Sachsen-Anhalt DEF0 Schleswig-Holstein DEG0 Thüringen

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

Denmark

DEA3 Münster

Spain

DK01 Hovedstaden

DEA4 Detmold

ES11 Galicia

DK02 Sjælland

DEA5 Arnsberg

ES12 Principado de Asturias

DK03 Syddanmark

DEB1 Koblenz

ES13 Cantabria

Eurostat regional yearbook 2009

149

ES21 País Vasco

FR83 Corse

Hungary

ES22 Comunidad Foral de Navarra

FR91 Guadeloupe

HU10 Közép-Magyarország

ES23 La Rioja

FR92 Martinique

HU21 Közép-Dunántúl

ES24 Aragón

FR93 Guyane

HU22 Nyugat-Dunántúl

ES30 Comunidad de Madrid

FR94 Réunion

HU23 Dél-Dunántúl

ES41 Castilla y León 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

ITC1 Piemonte ITC2 Valle d’Aosta/Vallée d’Aoste

HU31 Észak-Magyarország HU32 Észak-Alföld HU33 Dél-Alföld

ITC3 Liguria

Malta

ITC4 Lombardia

MT00 Malta

ITD1 Provincia Autonoma Bolzano/ Bozen

Netherlands

ITD2 Provincia Autonoma Trento ITD3 Veneto ITD4 Friuli-Venezia Giulia ITD5 Emilia-Romagna

NL11 Groningen NL12 Friesland (NL) NL13 Drenthe NL21 Overijssel NL22 Gelderland

France

ITE1 Toscana

FR10 Île-de-France

ITE2 Umbria

FR21 Champagne-Ardenne

ITE3 Marche

FR22 Picardie

NL32 Noord-Holland

ITE4 Lazio

FR23 Haute-Normandie

NL33 Zuid-Holland

ITF1 Abruzzo

NL34 Zeeland

FR24 Centre

ITF2 Molise

NL41 Noord-Brabant

FR25 Basse-Normandie

ITF3 Campania

NL42 Limburg (NL)

FR26 Bourgogne

ITF4 Puglia

FR30 Nord — Pas-de-Calais

ITF5 Basilicata

FR41 Lorraine

ITF6 Calabria

FR42 Alsace

ITG1 Sicilia

FR43 Franche-Comté

ITG2 Sardegna

FR51 Pays de la Loire FR52 Bretagne FR53 Poitou-Charentes

Austria AT11 Burgenland (A) AT12 Niederösterreich AT13 Wien AT21 Kärnten

CY00 Kypros/Kıbrıs

AT31 Oberösterreich

FR62 Midi-Pyrénées

LV00 Latvija

FR72 Auvergne

NL31 Utrecht

AT22 Steiermark

Latvia

FR71 Rhône-Alpes

NL23 Flevoland

Cyprus

FR61 Aquitaine FR63 Limousin

150

Italy

Lithuania LT00 Lietuva

AT32 Salzburg AT33 Tirol AT34 Vorarlberg

Poland PL11 Łódzkie

FR81 Languedoc-Roussillon

Luxembourg

PL12 Mazowieckie

FR82 Provence-Alpes-Côte d’Azur

LU00 Luxembourg (Grand-Duché)

PL21 Małopolskie

Eurostat regional yearbook 2009

PL22 Śląskie

SI02 Zahodna Slovenija

PL31 Lubelskie PL32 Podkarpackie PL33 Świętokrzyskie PL34 Podlaskie PL41 Wielkopolskie PL42 Zachodniopomorskie

Slovakia SK01 Bratislavský kraj SK02 Západné Slovensko SK03 Stredné Slovensko SK04 Východné Slovensko

UKE2 North Yorkshire UKE3 South Yorkshire UKE4 West Yorkshire UKF1 Derbyshire and Nottinghamshire UKF2 Leicestershire, Rutland and Northamptonshire UKF3 Lincolnshire

PL43 Lubuskie

Finland

PL51 Dolnośląskie

FI13 Itä-Suomi

PL52 Opolskie

FI18 Etelä-Suomi

PL61 Kujawsko-pomorskie

FI19 Länsi-Suomi

PL62 Warmińsko-mazurskie

FI1A Pohjois-Suomi

PL63 Pomorskie

FI20 Åland

Portugal

Sweden

UKH3 Essex

PT11 Norte

SE11 Stockholm

UKI1 Inner London

PT15 Algarve

SE12 Östra Mellansverige

UKI2 Outer London

PT16 Centro (P)

SE21 Småland med öarna

PT17 Lisboa

SE22 Sydsverige

UKJ1 Berkshire, Buckinghamshire and Oxfordshire

PT18 Alentejo

SE23 Västsverige

UKJ2 Surrey, East and West Sussex

PT20 Região Autónoma dos Açores

SE31 Norra Mellansverige

UKJ3 Hampshire and Isle of Wight

PT30 Região Autónoma da Madeira

SE32 Mellersta Norrland

UKJ4 Kent

SE33 Övre Norrland

UKK1 Gloucestershire, Wiltshire and Bristol/Bath area

RO11 Nord-Vest

United Kingdom

UKK2 Dorset and Somerset

RO12 Centru

UKC1 Tees Valley and Durham

UKK3 Cornwall and Isles of Scilly

RO21 Nord-Est

UKC2 Northumberland and Tyne and Wear

UKK4 Devon

Romania

RO22 Sud-Est RO31 Sud — Muntenia RO32 Bucureşti — Ilfov RO41 Sud-Vest Oltenia

UKD1 Cumbria UKD2 Cheshire UKD3 Greater Manchester UKD4 Lancashire

RO42 Vest

UKD5 Merseyside

Slovenia SI01 Vzhodna Slovenija

UKE1 East Yorkshire and Northern Lincolnshire

Eurostat regional yearbook 2009

UKG1 Herefordshire, Worcestershire and Warwickshire UKG2 Shropshire and Staffordshire UKG3 West Midlands UKH1 East Anglia UKH2 Bedfordshire and Hertfordshire

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

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

Eurostat regional yearbook 2009

153

European Commission Eurostat regional yearbook 2009 Luxembourg: Publications Office of the European Union 2009 — 153 pp. — 21 × 29.7 cm ISBN 978-92-79-11696-4 ISSN 1830-9674 doi: 10.2785/17776 Price (excluding VAT) in Luxembourg: EUR 30

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ISSN 1830-9674

Statistical information is essential for understanding our complex and rapidly changing world. Eurostat regional yearbook 2009 offers 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 different statistical domains and are accompanied by statistical maps, figures 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.

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ISBN 978-92-79-11696-4

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Price (excluding VAT) in Luxembourg: EUR 30

Eurostat regional yearbook 2009

KS-HA-09-001-EN-C

Eurostat regional yearbook 2009

Statistical books

Eurostat regional yearbook 2009