Statistical books , Product code: KS-HA-16-001, published on 14-Sep-2016

Statistical information is an important tool for understanding and quantifying the impact of political decisions in a specific territory or region. The Eurostat Regional yearbook 2016 gives a detailed picture relating to a broad range of statistical topics across the regions of the EU Member States

Statistical books , Product code: KS-HA-15-001, published on 09-Oct-2015

Statistical information is an important tool for understanding and quantifying the impact of political decisions in a specific territory or region. The Eurostat Regional Yearbook 2015 gives a detailed picture relating to a broad range of statistical topics across the regions of the Member States

Statistical books , Product code: KS-GP-15-001, published on 25-Jun-2015

EURONA – an open access, peer-reviewed, scholarly journal on concepts, techniques, standards, methods and practices on National Accounts and macroeconomic indicators, which aims to bring together users, producers and researchers in the field. In this first issue of 2015, four articles offer thought-provoking views on a wide range of subjects, including natural capital accounts, government finance statistics, goodwill and seasonal adjustment.

Statistical books , Product code: KS-GP-13-002, published on 17-Dec-2014

of the national accounts in Europe 24 EURONA — Eurostat Review on National Accounts and Macroeconomic Indicators Figure 13: Revisions to non-financial corporations’ profit shares by country (%) 5. The impact of ESA 2010 on government deficit and debt data This section presents the most important

Manuals and guidelines , Product code: KS-GQ-16-003, published on 29-Jun-2016

Requirements EDP tables, questionnaire tables and other related data Timeliness: T+3 months; Periodicity: B Candidate countries are required to deliver EDP notifications on a best effort basis. 4. Methodology ESA2010 and legal acts above Manual on government deficit and debt, Eurostat 2016

Compact guides and catalogues , Product code: KS-FM-13-005, published on 13-Mar-2013

Statistics Explained is an official Eurostat website presenting all statistical topics in an easily understandable way. This publication presents a glossary of terms included in Statistics Explained in December 2012. Volume 5 contains all terms A to E in all available languages.

Compact guides and catalogues , Product code: KS-76-06-324, published on 30-May-2007

Mini-guide KS-76-06-324-EN -C ISSN 1725-5961 Eurostat publications and databases 2007-2008 edition EuropEaN CommISSIoN ISBN 92-79-02952-5 9 789279 029523 cover.indd 1 27-04-2007 16:18:58 terzaPocket.indd 2 30-03-2007 16:12:13 Eurostat publications and databases Mini-guide EuropEan Commission 2007-2008 edition Europe Direct is a service to help you find answers to your questions about the European Union Freephone number (*): 00 800 6 7 8 9 10 11 (*) Certain mobile telephone operators do not allow access to 00 800 numbers or these calls may be billed. more information on the European union is available on the internet (http://europa.eu). Luxembourg: office for official publications of the European Communities, 2007 isBn 92-79-02952-5 issn 1725-5961 Cat. no. Ks-76-06-324-En-n (printed publication Ks-76-06-324-En-C) © photos: European Commission’s audiovisual service © European Communities, 2007 Introduction � Mini-guide Eurostat, the Statistical Office of the European Communities, is com- mitted to providing you with a high-quality statistical information service. To facilitate the research task of our users, we have compiled this mini-guide which presents an overview of Eurostat’s activities, publications and databases. Hard copies of the existing publications can be purchased through the Publications Office dissemination network, while free access to all interested parties will be given via Eurostat’s Internet site. Our website offers not only the option of consulting and downloading all the electronically available publications in PDF format, but also that of consulting the existing databases directly and free of charge. Predefined tables in HTML format (which already cover the majority of general statistical requests) are available, as well as very detailed tables to meet the needs of specialists. From spring 2007, the display of the predefined tables on the website will be enhanced with cutting-edge functionalities. With the new, easy-to-use interface, you will be able to view the data in various graphical representations and by means of coloured maps, customise the graphs and maps to your needs and also store them for future reference. An alert service informing you of the release of new publications is also at your disposal. Eurostat’s main publications and databases are broken down by theme. This mini-guide presents the current selection of our prod- ucts and databases. For a complete overview and daily update, please consult our Internet site (http://ec.europa.eu/eurostat). To receive regular information on Eurostat products, simply request a free subscription to the information booklet ‘Statistical references’ or subscribe to the electronic ‘Monthly news’. Thank you for your interest in Eurostat’s activities. Pedro Diaz Muñoz Director of statistical methods and tools; dissemination EUROSTAT Mini-gUidE � 2007 Contents introduction to Eurostat and its statistical information . . . . . . . . . . . . . . . . . . . . 6 Eurostat databases and products How to access Eurostat’s statistical information SELECTION OF EUROSTAT PRODUCTS: PUBLICATIONS, CD-ROMS AND DATABASES 1. General and regional statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Structural indicators Euro-indicators Regions Non-EU countries Urban audit 2. Economy and finance.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 National accounts Prices Exchange rates and interest rates Balance of payments Public finance Monetary and financial statistics 3. population and social conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 Population Health Education and training Labour market Living conditions and welfare Information society statistics Tourism 4. industry, trade and services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 Business structures Industry and construction Distributive trade Services Financial services Introduction � 5. agriculture and fisheries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 Agriculture Forestry Fisheries Food 6. External trade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 7. Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 8. Environment and energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 Environment Energy 9. science and technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 Research and development Patents order form . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 Key to the Eurostat mini-guide Graphs Tables maps Each publication is either published in a multilingual version or avail- able in one or more language versions. When ordering publications offering a choice of language, please insert the abbreviation of the language version you wish to receive instead of the ‘EN’ indication given in the catalogue number of this edition (DE or FR). EUROSTAT Mini-gUidE � 2007 WHO USES EUROSTAT’S STATISTICS? WHAT DOES EUROSTAT DO? Introduction to Eurostat and its statistical information Eurostat is the Statistical Office of the European Communities. It publishes official harmonised statistics on the European Union and the euro area and offers a comparable, reliable and objective portrayal of a changing Europe. A vast range of data broken down by region and country of the enlarged European Union and by candidate coun- try is available to you. To produce these statistics, Eurostat collects data from the national statistical institutes and harmonises them according to a single meth- odology. The data thus obtained are genuinely comparable for the European Union as a whole. Decision-makers in the fields of politics, higher education and eco- nomics use Eurostat services and products to obtain the informa- tion that is vital for their activities. Eurostat also enables the general public and the media to obtain an objective view of developments in European society. WHAT IS EUROSTAT? Introduction 7 DATABASES Eurostat databases and products With over 300 million statistical data, Eurostat is a mine of statistical information unique in the world and covers all areas of European society. From October 2004, the data has been directly available from Eurostat’s Internet site. According to the needs of the users, there are two ways to access the data: a general user can find the data he is looking for via access through the ‘Key indicators’ entry, while a specialist can find more sophisticated data via the entry to detailed databases. Key indicators are predefined tables in HTML format. They include: Short-term indicators Some 300 tables showing the short-term economic data available for the euro area and the EU, as well as for Member States. They show the ‘euro indicators’ and cover the following topics: n balance of payments; n business surveys; n consumer prices; n external trade; n industry, commerce and services; n labour market; n monetary and financial indicators; n national accounts. A release calendar is also available. Long-term indicators Some 600 tables on many areas of life, work, the economy and the environment in the EU can easily be accessed. KEY INDICATORS EUROSTAT Mini-gUidE � 2007 PUBLICATIONS DATABASES AND METADATA Sustainable development indicators The data offered in this following special topic present information on economic development, poverty and social exclusion, ageing society, public health, climate change and energy, production and consumption patterns, management of natural resources, transport, good governance and global partnership. Structural indicators Some 100 tables extracted from the Commission’s synthesis report. The indicators cover the domains of employment, innovation and re- search, economic reform, social cohesion and the environment. The macroeconomic and social databases were created for all those who require high-quality statistical information as an aid to decision- making. More than 300 million data are available. They are subdi- vided into several domains, each covering a specific sector. Links to the methodology applied following the ‘special data dissemination standards (SDDS)’ provide full information to specialised users. The data are available in multi-dimensional tables. The dimensions of the table specify n countries; n units; n variables; n periodicity, etc. The data are organised into the nine areas covered by Eurostat (see contents list). Examples: Data on external trade of the European Union and on trade between the Member States are based on the combined nomenclature and cover some 11 000 products traded each year with some 250 partner countries. Regional data cover the main aspects of economic and social life for the regions and provinces of the European Community (NUTS clas- sification levels 2 and 3) and relate to, among other things, popula- tion, economic accounts and employment. The following products are presented according to the level of information they provide. Introduction � NEWS RELEASES The ‘News releases’ provide recent information on the euro indicators and on social, demographic, regional, agricultural or environmental topics. They can be obtained by direct access in PDF format on the Internet or via a subscription to our alert service. OVERVIEW PUBLICATIONS n Statistical books Overview publications replacing former ‘Panoramas’ and ‘Detailed tables’. n Statistics in focus This collection, published regularly by Eurostat, provides updated summaries of the main results of surveys, studies and statistical anal- yses. It is published for all the themes and comprises 4–12 pages per issue. About 200 issues of ‘Statistics in focus’ are published per year. They are available free of charge in PDF format from the website. They can also be obtained in paper format by means of a subscrip- tion, for which a fee is charged. n Pocketbooks ‘Pocketbooks’ are free-of-charge publications with the objective of giving users a set of basic figures on a specific topic. Pocketbooks aim at a large distribution, but should also motivate the user to look for additional information either from more sophisticated publications or from the website. SPECIALISED PUBLICATIONS n Data in focus ‘Data in focus’ are similar to ‘Statistics in focus’ in layout and produc- tion process, but they contain mainly data and little text and are pro- duced for expert readers who are just interested in the newest data. n Methodologies and working papers Methodologies and working papers are technical publications in A4 format, essentially for use by a small number of statistical experts. They include former working papers and studies publications, and methods and nomenclatures publications. EUROSTAT Mini-gUidE 10 2007 ACCESS TO HARD COPY PRODUCTS DIRECT ACCESS Select the content and type of information in which you are interested and choose the type of access which suits you best. How to access Eurostat’s statistical information There are two ways of doing this: n direct access via the Internet; n access to hard copy products via the Publica- tions Office’s sales network. via the Internet site: http://www.ec.europa.eu/eurostat Consultation, seven days a week and 24 hours a day, of Eurostat’s statistical information and data. This type of consultation is referred to as ‘direct’ because no intermediary is involved. All you need is a computer and Internet access. Information published on the Eurostat site is available in English, French and German and can be downloaded free of charge. European statistical data support Eurostat established with the members of the European statistical sys- tem a network of support centres, which exist in nearly all Member States as well as in some EFTA countries. Their mission is to provide help and guidance to Internet users of European statistical data. The complete details concerning this user support network can be found on our Internet site. The Office for Official Publications of the European Communities operates an extensive network of sales offices throughout Europe and beyond, from which you can purchase all Eurostat publications in paper and CD-ROM format. Order processes see: http://bookshop.europa.eu All prices indicated are exclusive of VAT and shipping charges. Ë 11 Catalogue GENERAL AND REGIONAL STATISTICS ´ Europe in figuresEurostat yearbook 2006–07 (with CD-ROM) Languages available: DE, EN, FR Format: paper with CD-ROM, 370 pages ISBN 92-79-02489-2 ISSN 1681-4789 Cat. No: KS-CD-06-001-EN-C Format: PDF; also available by chapters: Price (excluding VAT): 30 € Europe in figuresEurostat yearbook 2006–07 — presents a com- prehensive selection of statistical data on the European Union, its Member States and candidate countries. Most data cover the period 1995–2005 and some data include other countries such as the USA and Japan. With almost 400 statistical tables, graphs and maps, the yearbook treats areas such as population, education, health, living conditions and welfare, the labour market, the economy, interna- tional trade, industry and services, science and technology, the environment, agriculture, forestry and fisheries, and European re- gions. This edition’s spotlight chapter deals with energy statistics. The paper version includes a CD-ROM with the electronic version of the yearbook in PDF, all tables and graphs in Excel format, as well as further information. The yearbook may be viewed as an in- troduction to European statistics and provides guidance to the vast range of data freely available from the Eurostat website at http:// ec.europa.eu/eurostat. ´ Measuring progress towards a more sustainable Europe — Sustainable development indicators for the European Union — Data 1990–2005 Languages available: DE, EN, FR Format: paper ISBN 92-894-9768-8 Format: PDF Cat. No: KS-68-05-551-EN-C Price (excluding VAT): € 30 This publication, aimed at the general public as well as decision-mak- ers, provides a first progress report on the implementation of the sus- tainable development strategy, launched by the European Council in Gothenburg in 2001. It focuses on quantitative trends, restricting the analysis to the set of sustainable development indicators (SDI) adopt- 12 2007EUROSTAT Mini-gUidE 2007 ed by the European Commission in February 2005, and provides a useful complement to the Commission’s communication on the re- view of the sustainable development strategy. Trends are assessed against policy objectives to inform the readers about the achieve- ments, trade-offs, and failures in achieving the commonly agreed objectives. The data presented cover the period 1990–2005 (or the latest year available). The emphasis is on the visualisation of trends the actual data can be downloaded from the Eurostat SDI webpage (special topic ‘Sustainable development’ on Eurostat’s website). The wide range of themes covered illustrates both the practical implica- tions of sustainable development for EU citizens, and the complexity of issues involved. This report should contribute to raising awareness of the opportunities and challenges lying ahead. ´ EU integration seen through statistics — Key facts of 18 policy areas Language available: EN only Format: PDF ISBN 92-79-00453-0 ISSN 1725-2784 Cat. No: KS-71-05-691-EN-N This publication presents key statistical facts on 18 major European policy areas. The statistics show the progress the European Union has made but also the issues that remain. Subjects such as world trade, government debt, migration, environment, information soci- ety and dependency on energy imports are treated. The publication provides an overview on similarities and differences between the economies and societies of the EU. The statistical information is generally given both for the total of the European Union (EU-25 or EU-15) and for its Member States. When available, statistics are also provided for EU candidate countries, Japan, the United States and other countries. 1� Catalogue ´ Statistical portrait of the European Union 2007: 50 years of the Treaty of Rome establishing the European Economic Community Language available: EN only Format: paper ISBN 92-79-02770-0 Cat. No: KS-76-06-276-EN-C Format: PDF Size: 2 000 Kb This brochure is the third edition of a series of publications that are distributed at the end of each year. It celebrates the 50th anniver- sary of the signing of the Treaty of Rome establishing the European Economic Community (EEC), which occurs on 25 March 2007. This publication presents a collection of statistical indicators relating to the different areas covered by the preamble to the Treaty, tracing the evolution of the European Community over the last five decades. Each of the eight points covered by the preamble to the Treaty is covered in this publication with a short summary of policy develop- ments and a description of the accompanying statistics. In addition, three other sections have been added to the publication, in relation to the European Coal and Steel Community (ECSC), Euratom and Amsterdam Treaties, with information on coal and steel, energy, and education and lifelong learning opportunities. ´ Key figures on Europe — Statistical pocketbook 2006 — Data 1995–2005 Language available: EN only Format: paper, 209 pages ISBN 92-79-01849-3 Cat. No: KS-EI-06-001-EN-C Format: PDF Size: 2 864 Kb This publication has the objective of providing users with a balanced set of statistical data about the economic and social development of the European Union. It covers mainly data from 1995 to 2005. The presentation largely follows the statistical themes of Eurostat’s dis- semination database. Data are generally provided for the European Union, the euro area and the EU Member States. When available and appropriate, data are added for candidate countries, EEA/EFTA countries, Japan and the United States. 1� 2007EUROSTAT Mini-gUidE 2007 ´ Eurostatistics — Data for short-term economic analysis — Monthly Language available: EN only Format: paper ISSN 1725-811 Cat. No: KS-BJ-07-00 -EN-C Subscription code: VHI000 Price per copy (excluding VAT): € 20 Annual subscription price (excluding VAT): € 180 Format PDF Size: 6 534 Kb Eurostatistics — Data for short-term economic analysis shows the evolution of the economic activity in the European Union, euro area and Member States. This monthly review gives a synthetic pic- ture of the macroeconomic situation in the recent past. It contains monthly and quarterly macroeconomic data presented following an economic classification of the indicators such as output, demand, income, prices, labour market, external transactions, monetary and financial markets. ´ Monograph of official statistics — Work Session on Statistical Data Confidentiality (Geneva, 9–11 November 2005) Language available: EN only Format: paper ISBN 92-79-01108-1 Cat. No: KS-73-05-623-EN-C Format: PDF Size: 5 966 Kb This publication contains the proceedings of the work session, jointly organised by Eurostat and UN Economic Commission for Europe, on statistical data confidentiality held in Geneva, 9–11 November 2005. Different aspects of statistical confidentiality are covered, namely: web/online remote access, statistical disclosure risks, disclosure control methods and software, access to microdata and general statistical confidentiality issues. Papers submitted for the work session constitute a very important contribution to the development of applied procedures in the domain of statistical con- fidentiality. 1 1� Catalogue ´ Medstat — Euro-Mediterranean statistics Languages available: EN, FR and Arabic Format: paper ISBN 92-79-00327-5 ISSN 1561-4034 Cat. No: KS-DI-05-001-EN-C Format: PDF Size: 2 072 Kb This is a presentation of the main socio-economic indicators for Med- iterranean countries. It is the latest update of general interest data in the framework of actions to launch the Medstat programme on the occasion of the 10th anniversary of the Barcelona Declaration. ´ Software to perform temporal disaggregation of economic time series Language available: EN only Format: PDF Cat. No: KS-DT-05-004-EN-N Size: 3 180 Kb Paper presented at the OECD/Eurostat Workshop on Frontiers in Benchmarking Techniques and their Application to Official Statistics. ´ Constructing internationally comparable real income aggregates by combining Sparse benchmark data with annual national accounts data: a state-space approach Language available: EN only Format: PDF Cat. No: KS-DT-05-005-EN-N Size: 1 160 Kb Paper presented at the OECD/Eurostat Workshop on Frontiers in Benchmarking Techniques and their Application to Official Statistics. 1� 2007EUROSTAT Mini-gUidE 2007 ´ Pocketbook on candidate countries and western Balkan countries — 2006 Language available: EN only Format: paper, 142 pages ISBN 92-79-00338-0 Cat. No: KS-70-05-899-EN-C Format: PDF Size: 3 628 Kb This extensive Pocketbook on candidate countries and western Balkan countries should serve as a working tool for all interested in the en- largement process and cooperation with western Balkan countries. Covering the years 1995–2004, the pocketbook contains tables and graphs on demography, education, social conditions and labour force, national accounts and finance, agriculture, energy, industry, construction and services, transport, communications and informa- tion society, as well as external trade, research and development and environment. A short commentary on the data and methodological notes are also included. The pocketbook contains most of the struc- tural indicators that were adopted by the European Council to moni- tor the Lisbon competitiveness strategy. ´ Regions — Statistical yearbook 2006 Languages available: DE, EN, FR Format: paper with CD-ROM ISBN 92-79-01799-3 ISSN 1681-9306 Cat. No: KS-AF-06-001-EN-C Format: PDF Size: 29 000 Kb Price (excluding VAT): € 30 The 2006 edition of this publication covers the more than 250 regions of the EU Member States. The regions are defined according to level 2 of the nomenclature of territorial units for statistics (NUTS 2003). The publication contains chapters on population, GDP, household ac- counts, labour market, labour productivity, urban statistics, science, technology and innovation, structural business statistics, health, transport and agriculture. New in the 2006 version is the chapter on labour productivity. The regional diversity of Europe is shown in the form of maps and graphs, commented by texts. A CD-ROM contains 17 Catalogue the data series used to draw the maps, the PDF versions of each of the three language editions of the yearbook and documentation on the NUTS 2003 nomenclature. ´ Regions — Nomenclature of territorial units for statistics — NUTS — 2003/EU-25 Languages available: trilingual edition DE/EN//FR Format: PDF (4 files) ISBN 92-894-8031-9 Cat. No: KS-BD-04-005-3A-N Size: 3 128 Kb + 3 723 Kb + 3 233 Kb + 3 405 Kb Edition: 2004 This document presents the current NUTS nomenclature, which subdivides the territory of the European Union after the enlargement into 89 regions at NUTS level 1, 254 at NUTS level 2 and 1 214 at NUTS level 3. NUTS is the official division of the EU for regional statistics. The regional breakdown can be visualised on maps, coun- try by country. ´ Urban audit methodological handbook Language available: EN only Format: PDF ISBN 92-894-7079-8 Cat. No: KS-BD-04-002-EN-N Size: 1 600 Kb, 88 pages Edition: 2004 The Urban audit methodological handbook provides both the in- formation required by the data suppliers to achieve coherence and comparability of the urban audit data on the one hand, and helps users understand the methods that have been applied in data com- pilation, and assess the relevance of the data for their own purposes on the other. The handbook contains descriptions of the relevant aspects of the urban audit project, that is, the method for selection of spatial units for the three spatial levels (administrative city, larger urban zone and sub-city district) per country, the list of partici- pating towns/cities, the glossary of variables and indicators (defi- nitions and references) and basic information on the estimation methods applied. 1� 2007EUROSTAT Mini-gUidE 2007 ´ European regional and urban statistics — Reference guide — 2006 edition Languages available: DE, EN, FR Format: PDF Cat. No: KS-BD-06-001-EN-N Size: 1 456 Kb The reference guide is designed to serve as a vade-mecum, explaining the background of European regional and urban statistics, including its regional classification NUTS. The structure of the data stored in the public database is comprehensively described. ´ Statistical requirements compendium — 2006 edition Language available: EN only Format: PDF Cat. No: KS-BD-06-002-EN-N Size: 1 261 Kb This annual publication serves as a reference document for the ac- quis communautaire in statistics. The new compendium intends, as did its predecessors, to indicate the reference information for the European statistical production. The structure follows the Commu- nity statistical programme in its current version (2003–07), which is sub-divided by chapters, sub-chapters, themes and modules. Each module includes description of statistical subjects, key priorities for 2006, legal basis, data requirements, methodology and international cooperation issues. ´ Stat-Lex — 1998–2002 legislation Languages available: DA, DE, EL, EN, ES, FI, FR, IT, NL, PT, SV Format: CD-ROM ISBN 92-894-4731-1 Cat. No: KS-CW-02-001-1F-Z Price (excluding VAT):€ 100 Edition: 2003 The CD-ROM Stat-Lex 1998–2002 legislation aims to give an over- view of the texts (in 11 languages) of all Community legislation re- lating to statistics for the years 1998–2002. This represents about 35 legislative acts per year. This publication is intended to be a practi- 1� Catalogue STATISTICS IN FOCUS (selection of issues published up to the end of October 2006) (for the daily update, please consult our website) • The impact of other transport equipment on the new orders index — Issue No 2�/200� A special aggregate covering manufacturing industries working on new orders excluding the manufacture of other transport equipment (NACE division 35) was introduced by Eurostat at the start of 2006. This aggregate was created as a result of observing considerable fluc- tuations in the index of new orders for other transport equipment. This publication shows the latest developments of the total manufacturing working on orders and of this special aggregate. Product code: KS-NP-06-025 PDF file: 120 Kb — Issue date: 27.10.2006 • Social protection in the European Union — Issue No 1�/200� This Statistics in Focus describes and analyses the expenditure and the receipts of social protection in different European countries. In 2003, social protection expenditure accounted for 28 % of GDP in the EU-25. This average masks high disparities between countries. Expenditure on old-age and survivors’ benefits account for a large part of social benefits in most countries. Different countries have markedly different systems for financing social protection. Product code: KS-NK-06-014 PDF file: 230 Kb — Issue date: 23.10.2006 cal and handy tool for all actors involved in the field of statistics at European level: national statistical offices, other public bodies pro- ducing official statistics, organisations and businesses complying with responding obligations in the field of statistics but also users of Community statistics both in the public and private sectors and finally European citizens themselves. It may also serve as an aid to the accession process by acquainting candidate countries with the acquis communautaire in statistics. A CD-ROM ‘historique’, which contains all Community legislation relating to statistics until 1997, was also published a few years ago and is still available. ´ Stat-Lex — Legislation up to 1997 Languages available: DA, DE, EL, EN, ES, FI, FR, IT, NL, PT, SV Format: CD-ROM ISBN 92-828-6536-3 Cat. No: CA-25-99-916-1F-Z Price (excluding VAT): € 100 Edition: 2001 This CD-ROM contains the secondary legislation on statistics from the early days of the Statistical Office of the European Communities until 1997. All acts are reproduced in the 11 official languages of the European Union. System requirements: MS-Windows — Pentium, Win 9.x /Win NT/2000; Macintosh — PowerPC, MacOS 8.x. 20 2007EUROSTAT Mini-gUidE 2007 • Statistics in Focus — Global subscription 200� (all themes) — Paper Subscriptions always refer to a calendar year (January to December) and are payable in advance. This collection is published regularly by Eurostat and provides up-to-date summaries of the main results of sta- tistical surveys, studies and analysis. It is published for all themes and consists of four or eight pages per issue. Eurostat issues around 200 Statistics in Focus per year. Subscription code: VAZ000EN-06 Price: € 240 Please note that an extended selection of Statistics in Focus are listed under each specific theme further in this mini-guide 8 DATABASES European and national short-term indicators With most of the old EU Member States participating in economic and monetary union since 1999, infra-annual economic statistics for the euro area and the European Union as a whole have gained and will continue to gain ever more operational importance for collective and private decision-making. The momentum of the EU economies and most notably the euro-area economy has to be assessed continuously. The Euro-Indicators special topic (www.ec.europa.eu/euroindica- tors) is exclusively dedicated to infra-annual economic statistics such as consumer prices; national accounts; balance of payments; external trade; industry, energy, commerce and services; labour market; as well as a selection of monetary and financial indica- tors of the European Central Bank and business and consumer 21 Catalogue surveys results from the European Commission’s Economic and Financial Affairs DG. Data are updated daily and present key aggregate indicators for the EU and the euro area. The database contains an unrivalled volume of mostly harmonised and, above all, uniformly struc- tured and documented national and European series. Data can be extracted online with the help of a user-friendly browser. Structural indicators This domain presents the database of structural indicators used to underpin the Commission’s analysis in the 2005 spring report to the European Council. The structural indicators cover the five domains of employment, innovation and research, economic reform, social cohesion and environment, as well as the general economic background. The Commission is presenting a shortlist of 14 indicators to be presented in the statistical annex to its spring report. Both the database of structural indicators and the shortlist are available free of charge on the Eurostat structural indicators web- site (www.ec.europa.eu/eurostat/structuralindicators). Regional statistics This domain relates to the main aspects of economic life in the European Union at regional level. Created in 1975, it is subdivid- ed into 12 statistical domains: demography, migration, economic accounts, unemployment, labour market, transport and energy statistics, agriculture, education, health, tourism, structural busi- ness statistics and statistics concerning science and technology (including research and development). The regions are classified in line with a specific system called NUTS (nomenclature of ter- ritorial units for statistics). Urban audit The urban audit is a response to the growing demand for an as- sessment of the quality of life in European cities. The database allows a comparison of cities in terms of certain characteristics (demography, economic activity, employment, public transport, culture, environment, education level, etc.). The urban audit com- prises information for 284 cities. A total of 258 cities participated in the initial urban audit data collection 2003/04, of which 189 were from the 15 EU Member States and 69 from the 12 (at that time) candidate countries. Twenty-six Turkish cities were added to the urban audit at the beginning of 2006. Sustainable Development Indicators The EU sustainable development strategy, adopted by the Euro- pean Council in Gothenburg in June 2001, aims to reconcile eco- 22 2007EUROSTAT Mini-gUidE 2007 nomic development, social cohesion and protection of the envi- ronment. Monitoring progress towards this overarching goal is an essential part of the strategy. The aim of these pages is to present a set of indicators being developed to monitor, assess and review the EU’s sustainable development strategy. A parallel objective is to inform the general public about progress in attaining the com- monly agreed objectives of sustainable development. The indicators were developed with the help of a group of na- tional experts, known as the ‘Sustainable Development Indicators Task Force’. They are organised within 10 themes reflecting the political priorities of the strategy, and related subsequent political commitments. 2� Catalogue ´ EC economic data pocketbook — Quarterly publication Language available: EN only Format: paper ISSN 1026-0846 Cat. No: KS-CZ-07-001-EN-C Format: PDF The EC economic data pocketbook is a handy collection of economic data from different domains, covering the European aggregates, EU Member States and its main economic partners. The publication fo- cuses on the structural aspects of the EU economy; consequently, most of the data given are annual, complemented by selected month- ly and quarterly indicators. ´ Asymmetries in EU current account data Language available: EN only Format: PDF Cat. No: KS-DB-06-002-EN-N Size: 1282 Kb The document describes the asymmetries in the EU current account in general and in data on international trade in services in particular. It also raises the need to identify possible future actions to reduce the asymmetries. ECONOMY AND FINANCE 2� 2007EUROSTAT Mini-gUidE 2007 ´ European Union international trade in services — Analytical aspects — Data 1996–2004 Language available: EN only Format: PDF ISBN 92-79-01855-8 Cat. No: KS-EB-06-001-EN-N Size: 940 Kb This publication concerns EU international transactions in services between 1996 and 2004, and is divided into two parts. The first part is analytical and gives the most recent portrait of European Union (EU- 25, EU-15) international transactions. It highlights the main trends of EU trade in services in 2004 and underlines the results of the EU with its main partner zones. The second part presents statistics on international trade in services of the EU-25 and EU-15 with partner world, extra-EU-25/EU-15, intra-EU-25/EU-15, Canada, the United States and Japan, for the main services items and 44 items of interna- tionally tradable services. The period covered is 1996–2004. Statistics are also available for 2004 on the geographical breakdown (in relation to 50 countries and partner zones and 17 items) of services of the EU- 25, the EU-15, Norway, the United States and Japan. ´ Manual on quarterly non-financial accounts for general government Language available: EN only Format: PDF ISBN 92-79-01867-1 ISSN 1725-0048 Cat. No: KS-BE-06-001-EN-N Size: 2 637 Kb The Manual on quarterly non-financial accounts for general govern- ment complements the already existing Manual on compilation of taxes and social payments on a quarterly basis (published in 2002). These manuals establish an inventory of sources and methods used for compiling short-term public finance statistics but do not recom- mend ‘best practice’. The compilation of the short-term public finance statistics is required by Commission Regulation (EC) No 264/2000 of 3 February 2000 and by Regulation (EC) No 1221/2002 of the European Parliament and of the Council of 10 June 2002. 2� Catalogue The work on the manual was undertaken with the help of the mem- bers of the Working Group on Short-term Public Finance Statistics. ´ Methodological soundness questionnaire — Report on responses to the Eurostat/OECD questionnaire on the measurement of trade in services in the balance of payments Language available: EN only Format: PDF Cat. No: KS-DB-06-001-EN-N Size: 1 891 Kb This working paper presents the responses to the Eurostat/OECD questionnaire on the measurement of trade in services in the balance of payments. It provides a unique source of comparative information on national methodological practices in 36 EU and OECD Member States. The questionnaire was constructed in reference to the recom- mendations of the new Manual on statistics of international trade in services, and, in particular, the implementation of the extended bal- ance of payments services (EBOPS) classification. ´ Structures of the taxation systems in the European Union — Data 1995–2004 Language available: EN only Format: paper, 417 pages ISBN 92-79-01850-7 Cat. No: KS-DU-06-001-EN-C Format: PDF Size: 2 473 Kb Price (excluding VAT): € 25 In the publication Structures of the taxation systems in the European Union, Taxation and Customs Union DG and Eurostat present sev- eral classifications of tax revenues and tax rates with a view to moni- toring and comparing the structures of the taxations systems in the European Union. The publication covers the period 1995–2004. 2� 2007EUROSTAT Mini-gUidE 2007 ´ European Union foreign direct investment yearbook 2006 — Data 1999–2004 Language available: EN only Format: paper, 140 pages ISBN 92-79-01856-6 ISSN 1605-2935 Cat. No: KS-BK-06-001-EN-C Format: PDF Size: 5 669 Kb Foreign direct investment (FDI) plays a key role in the globalisation process and is an important element affecting international relations. An international investment is classified as FDI when at least 10 % of the capital of the target enterprise is acquired. The pocketbook pro- vides detailed data on EU FDI for recent years (mainly 1999–2004), for both EU FDI abroad and FDI into the EU. It provides an overview of the position of the EU in world FDI and a comparison with the United States. For EU FDI abroad, a particular focus is put on EU FDI in emerging countries. Finally, FDI data with major partners are detailed according to the kind of activity in which the investment takes place. Data focus on the EU as whole and, to a lesser extent, on the Member States. ´ European system of accounts — ESA 1995 Language available: DA, DE, EL, EN, ES, FI, FR, IT, NL, PT, SV Format: paper Catalogue No: CA-15-96-001-EN-C Price: € 50 The European system of national and regional accounts (ESA 1995) defines the accounting rules which need to be introduced so that the economies of the Member States can be described in quanti- tative terms in a consistent reliable and comparable manner. It is designed for Community institutions, government departments and others involved in economic and social affairs who base their decisions on harmonised statistics. ESA 1995 is an essential tool for administering the whole range of European Union policies and for the instruction of those who are interested in the operation, analysis and understanding of the European economy. Compared with the former version which dates from 1979, the new version provides clarification and explanation, with concepts and defini- 27 Catalogue tions, and also covers quarterly and regional accounts. ESA 1995 is the result of collaboration between the European Commission, the European Monetary Institute and government statisticians in the Member States. ´ Harmonised indices of consumer prices (HICPs) — A short guide for users Languages available: DE, EN, FR Format: PDF ISBN 92-894-7081-X Cat. No: KS-BE-04-001-EN-N Current version: 2004 This guide provides a brief description of the harmonised indices of consumer prices (HICPs). Its target audience are non-specialist us- ers of the HICPs, including analysts and commentators, who wish to gain a general overview of these price indices. This guide also pro- vides references and links to more detailed information on the HICPs and a list of contact points for further information as well as a list of all the legally binding regulations. ´ Compendium of HICP reference documents (2/2001/B/5) Language available: EN only Format: PDF Cat. No: KS-AO-01-005-EN-N Current version: 2002 This compendium is intended to share with users a comprehensive reference for the harmonised methodology and to support not only those concerned with managing, understanding and analysing the European economy, but also those generally interested in inflation measurement issues. The set of compiled texts includes technical and non-technical documents, as well as the legal framework at the start of the harmonised indices of consumer prices (HICPs). 2� 2007EUROSTAT Mini-gUidE 2007 STATISTICS IN FOCUS (selection of issues published up to the end of September 2006) (for the daily update, please consult our website) • Regional GDP in the EU, the accession countries and Croatia in 200� — Issue No 17/200� This Statistics in Focus analyses the structure of regional gross domes- tic product (GDP) in the European Union in 2003. Data on Bulgaria and Romania have been integrated as well. In addition, Eurostat pub- lishes data on Croatia for the first time. The analysis focuses on a com- parison among the regions for the year 2003. In addition mid-term developments are illustrated by way of a comparison of the situation with the year 2001. Product code: KS-NJ-06-017 PDF file: 420 Kb — Issue date: 28.8.2006 • Harmonised indices of consumer prices — Issue No 1�/200� This Statistics in Focus is the monthly publication of harmonised indi- ces of consumer prices (HICP) for July 2006. It contains indices and rates of change for individual Member States, the EU, the EEA and the euro area. Product code: KS-NJ-06-016 PDF file: 109 Kb – Issue date: 28-08-2006 • EU-2� current account deficit widened to € ��.2 billion in 200� — Issue No 1�/200� This Statistics in Focus describes the 2005 preliminary results for the EU-25 balance of payments current account. Detailed figures on the current account main components (goods, services, income, current transfers) are presented, together with an analysis of the develop- ments between 2004 and 2005 and of the geographical breakdown of the flows between the main partners. A detailed breakdown of trade in services is also included. Product code: KS-NJ-06-014 PDF file:343 Kb – Issue date: 20.07.2006 • EU foreign direct investment in 200� — Issue No 1�/200� The document gives an overview of the EU foreign direct invest- ment (FDI) statistics over the period 1999–2004. Results for annual FDI flows, stocks at year-end and annual income are presented, for both EU FDI abroad and FDI into the EU. A detailed analysis com- pleted with illustrative tables and graphs is provided for FDI data with major partners and according to the activity in which the in- vestment takes place. Product code: KS-NJ-06-013 PDF file: 175 Kb – Issue date: 20.7.2006 2� Catalogue • Comparative price levels for selected consumer services in Europe for 200� — Issue No 12/200� This Statistics in Focus presents price level indices for five selected consumer service categories — energy, transport, communication, restaurants and hotels and recreational and cultural services — for 2005. This article analyses the comparative price levels of the listed service categories against the data on general price levels and general levels of economic development (measured in terms of gross domestic product — GDP) in European countries. Product code: KS-NJ-06-012 PDF file: 252 Kb — Issue date: 19.7.2006 8 DATABASES Balance of payments — International transactions This domain provides monthly and quarterly balance of payment statistics, annual data on international trade in services, annual data on foreign direct investment and on the activity of foreign affiliates, and detailed data on international transactions involv- ing the European institutions. For all these subjects, this domain provides harmonised data for the European Union and for the euro area, but also for each EU Member State, for the candidate countries, for Norway, the USA, Japan and Switzerland. Exchange rates and interest rates Exchange rates and interest rates are basic financial statistics in their own right. They are also used in a wide range of calculations and for deriving other time series. The bilateral exchange rates and conversion factors included in the exchange rates collection are those which should officially be used throughout Eurostat in calculations. Bilateral exchange rates are available, updated daily. In addition to these rates, exchange rates include bilateral and effective exchange rate indices, data on fluctuations in the exchange rate mechanism of the EU, and con- version factors for euro fixed series into euro/ecu. The interest rates collection covers short-, medium- and long- term interest rates. These include central bank interest rates, money market rates, bond yields, and commercial (retail) bank rates. The official series used for the EMU convergence criteri- on on long-term interest rates is available on a daily basis. The euro yield curve information calculated daily by Eurostat is also housed in this collection. Financial accounts This domain covers annual financial accounts of the European Union, focusing on financial transactions between institutional sectors within the economy and between them and the rest of the �0 2007EUROSTAT Mini-gUidE 2007 world, and balance sheets of financial assets and liabilities within the economy and vis-à-vis the rest of the world. The data are on a consolidated and non-consolidated basis. Financial accounts form an important tool for analysing the ef- fect of monetary policy transmission and for assessing the role of financial intermediation. They are also the best tool for showing the structure of financial investment and financing in an econo- my, and for following its development. Monetary and other financial statistics The collections of this domain cover many of the elements re- quired for understanding monetary and financial developments: monetary aggregates, external assets and liabilities (including for- eign official reserves), stock and bond market information and banking transactions. For many series, annual, quarterly, and monthly data are available. Normally, euro-area and EU aggre- gates are available, along with data for individual countries in the European Economic Area, plus the candidate countries, the USA and Japan. However, some euro-area country data end with the start of EMU. Prices This domain comprises four collections: harmonised indices of consumer prices (monthly and annual data), national consumer price indices (monthly and annual data), cost of living compari- son in the European Union, Brussels = 100 (annual data), and purchasing power parities for private consumption (comparison by country, annual data). Harmonised indices of consumer prices Harmonised indices of consumer prices (HICP) are economic indicators constructed to measure the changes over time in the �1 Catalogue prices of consumer goods and services acquired by households. The HICP give comparable measures of inflation in the euro area, the EU, the European Economic Area and other countries includ- ing some accession and candidate countries. They are calculated according to a harmonised approach and a single set of definitions. HICPs provide the official measure of consumer price inflation in the euro area for the purposes of monetary policy and assessing inflation convergence as required under the Maastricht criteria. HICP information can also be found in the ‘HICP section’ hosted in Eurostat’s website on the ‘Special topics’ window. That section gathers data, explanations and reference documents. HICP data is available from 1996 and the database includes monthly and annual indices, rates, special aggregates and country and items weights. National accounts (including GDP) Annual national accounts This domain includes data on national accounts aggregates on an annual basis. It includes variables covering the three approaches of GDP and other important macroeconomic variables such as gross national income (GNI), net saving, net lending and bor- rowing or employment. There is also more detailed information in the form of breakdowns for final consumption, gross value added and employment by branch, gross fixed capital formation by investment product. These figures are in accordance with ESA 95 and are available at current and constant prices, expressed in national currency, in euro and in PPS, supplemented by the re- spective growth rates, deflators, indices and ratios with respect to principal totals. Auxiliary indicators, like population and conver- sion rates, used to calculate the different evaluations are available in the domain itself. Geographical coverage includes the euro area, the EU, the Member States and candidate countries, as well as the main economic partners of the European Union. Quarterly national accounts This domain includes data on national accounts aggregates on a quarterly basis. It includes variables covering the three approach- es of GDP and other important macroeconomic variables such as GNI, net saving, net lending and borrowing or employment. There is also more detailed information, although less than in the annual national accounts domain, in the form of breakdowns for final consumption, gross value added and employment by branch and gross fixed capital formation by investment product. These figures are in accordance with ESA 95 and are available at cur- rent and constant prices, expressed in national currency, in euro and in PPS, supplemented by the respective growth rates, defla- tors, indices and ratios with respect to principal totals. Auxiliary indicators, such as population and conversion rates, used to cal- �2 2007EUROSTAT Mini-gUidE 2007 culate the different evaluations are available in the domain itself. Geographical coverage includes the euro area, the EU, the Mem- ber States and candidate countries, as well as the main economic partners of the European Union. National accounts by institutional sector This domain comprises national accounts data on an annual ba- sis. Sector accounts record all transactions between economic agents with broadly similar behaviour grouped into the following institutional sectors: n S11 Non-financial corporations n S12 Financial corporations n S13 General government n S14 Households n S15 Non-profit institutions serving households n S2 Rest of the world. The sector accounts show the interactions among the different sectors of the domestic economy, and between them and the rest of the world. Transactions are classified according to their economic nature (such as payment and receipt of wages or taxes, consumption, etc.). They are grouped into a sequence of accounts covering a specific aspect of the economic process, ranging from production, generation and (re)distribution of income, consumption and in- vestment to borrowing and lending. Each account leads to a bal- ancing item (gross domestic product, operating surplus/mixed income, gross national income, gross disposable income, saving, net lending/borrowing), which is calculated as total resources minus total uses or total changes in financial assets minus total changes in liabilities. Geographical coverage includes the euro area, the EU, the Mem- ber States and Norway. Figures are provided at current prices. The euro area-12/EU-25 accounts are based on, but are not the simple sum of, the national accounts of the Member States. Sev- eral steps are necessary for conversion of national accounts of the Member States into European accounts: − validation of the national sector accounts; − conversion from national currency to euro (where relevant); − addition of the accounts of European institutions and other European bodies, which are not considered to be part of the domestic economy in the national accounts of Member States; �� Catalogue − elimination, from the rest of the world account, of cross- border transactions between euro-area / European Union countries. National accounts — Supply, use and input-output tables This domain contains the supply, use and symmetric input-out- put tables at current and constant prices. The breakdown covers 60 products and/or 60 industries. Supply and use data are re- corded on an annual basis, whereas the symmetric input-output tables are recorded in intervals of five years. Data are published for Member States of the European Union and other European countries as far as available. Government statistics This domain contains the following: n main aggregates of general government; n government deficit and debt; n taxes and social contributions; n expenditure of general government by COFOG function and by type; n quarterly statistics; n state aids; n public procurements. �� 2007EUROSTAT Mini-gUidE 2007 POPULATION AND SOCIAL CONDITIONS ´ Population statistics (with CD-ROM) Languages available: DE, EN, FR Format: paper, 181 pages ISBN 92-79-01642-3 ISSN 1725-8670 Cat. No: KS-EH-06-001-EN-C Format: PDF Size: 7 955 Kb Price (excluding VAT): € 20 This publication provides statistical information on all major demo- graphical aspects in the EU: population change, composition, fertil- ity, mortality, international migration, nuptiality, population projec- tions, regional data. Alongside the tables, there are explanatory texts, graphs and maps. The paper version includes a CD-ROM (English/ French/German). ´ Consumers in Europe — Facts and figures — Data 1999–2004 Language available: EN only Format: paper, 309 pages ISBN 92-894-8790-9 Cat. No: KS-DY-04-001-EN-C Format: PDF Size: 6 359 Kb Price (excluding VAT): € 30 This publication brings together the most relevant and useful infor- mation for the evaluation and development of consumer policy. The material includes data from various sources including Eurostat, other Commission services as well as other surveys and studies. Although the prime objective of this publication is to help policymakers at the �� Catalogue European level to better understand the needs of consumers in general, the publication should also be of use to other stakeholders interested in consumer affairs, such as consumer organisations, other public au- thorities and even suppliers of goods and services. This is the second edition of a series of publications. Data cover the period 1999–2004. ´ European social statistics — Social protection — Expenditure and receipts — Data 1996–2004 Language available: EN only Format: PDF only ISBN 92-79-04582-2 ISSN 1681-9365 Cat. No: KS-DC-07-001-EN-N Size: 1316 Kb The publication includes data (1996–2004) on expenditure and re- ceipts of social protection schemes. The data are drawn up according to the Esspros manual 1996. Esspros stands for ‘European system of integrated social protection statistics’, a harmonised system provid- ing a means of analysing and comparing social protection financial flows. Expenditure of social protection schemes is broken down into social benefits, administration costs and other expenditure. Social benefits are classified by function: sickness/healthcare, disability, old age, survivors, family/children, unemployment, housing and social exclusion. Receipts of social protection schemes comprise social con- tributions, general government contributions and other receipts. ´ Living conditions in Europe (pocketbook) — Data 2002–2005 Language available: EN only Format: paper, 107 pages ISBN 92-79-03262-3 ISSN 1725-5988 Cat. No: KS-76-06-390-EN-C Format: PDF Size: 2873 Kb This pocketbook provides a comprehensive picture of the current liv- ing conditions in the 25 Member States, the two acceding countries and three candidate countries of the European Union as well as the four EFTA States. Different areas of the social field are described by a selection of indicators which are presented in tables and graphs and ac- companied by a short commentary. Data are drawn from sources avail- able in Eurostat, such as the European Union Labour Force Survey. �� 2007EUROSTAT Mini-gUidE 2007 ´ European social statistics — Labour market policy — Expenditure and participants — Data 2004 Languages available: EN only Format: paper ISBN 92-79-01802-7 ISSN 1725-602X Cat. No: KS-DO-06-001-EN-C Format: PDF Size: 1 078 Kb This year’s publication is noteworthy for two reasons. Firstly, for the first time, it includes data from the new EU Member States (except Cyprus, Malta, Poland and Slovenia) and for two of the candidate countries (Bulgaria and Romania). Secondly, it is also the first year that LMP data collection was launched jointly with OECD. Both organisations have agreed to adopt the Eurostat methodology. The publication presents data on public expenditure on labour market policy (LMP) measures and numbers of participants in these meas- ures (stocks, entrants and exits). Policy measures covered are only those targeted to people who are unemployed, or in risk of involun- tary job-loss, and inactive persons who are currently not part of the labour force, but who would like to enter the labour market and are disadvantaged in some way. Labour market policy measures are clas- sified in two ways: by type of action and by type of expenditure. The type of action refers to the way in which the measure acts to achieve its objectives (e.g. training measures or employment incentives). The type of expenditure indicates how the payment is made and who the main recipient is. ´ Labour market policy database — Methodology — Revision of June 2006 Languages available: DE, EN, FR Format: paper ISBN 92-79-02273-3 ISSN 1725-0056 Cat. No: KS-BF-06-003-EN-C Format: PDF Size: 1 772 Kb A methodological manual is an essential tool for assisting statisti- cians in their work, but one should be aware that all manuals should be revised at regular intervals. We have great pleasure in presenting here the Labour market policy database — Methodology — Revision of June 2006, as an improvement of the first methodology which was published in May 2000. During these years of development, the la- �7 Catalogue bour market policy database has moved from a kind of ‘exploratory’ status to maturity. Moreover, since 2005 (reference year 2004), the LMP data collection is jointly launched by Eurostat and OECD. Both organisations agreed to use the Eurostat methodology, and OECD contributed actively to the revision process. The result is a simplifi- cation of the Member States’ workload as well as an overall improve- ment in the quality of data for international comparisons. Joint work with the Employment, Social Affairs and Equal Opportunities DG was maintained throughout this process of development, reflection and revision. As a result, the present version of the LMP method- ology takes into account the needs expressed by the Employment Committee for the LMP database to improve its contribution to the monitoring of the European employment strategy. The LMP meth- odology provides guidelines for the collection of data on public ex- penditure on labour market policy (LMP) measures and numbers of participants in these measures (stocks, entrants and exits). The scope covers measures targeted to people who are unemployed; people in employment but in risk of involuntary job-loss, and inactive per- sons who are currently not part of the labour force, but who would like to enter the labour market. The LMP methodology classifies la- bour market policy measures in two ways: by type of action and by type of expenditure. The type of action refers to the way in which the measure acts to achieve its objectives (e.g. training measures or employment incentives). The type of expenditure indicates how the payment is made and who the main recipient is. All these data are published yearly. ´ Final report of the task force for evaluating the 2004 LFS ad hoc module on work organisation and working time arrangements Language available: EN only Format: paper ISBN 92-79-02559-7 ISSN 1725-065X Cat. No: KS-CC-06-008-EN-C Format: PDF Size: 1 239 Kb The task force on evaluating the LFS ad hoc module on work organi- sation and working time arrangements was created by the Labour Market Statistics Working Group in 2005. The task force delivered its report to the working group in 2006. The report provides a detailed description of the implementation of the module and the main prob- lems encountered in the field. The report makes recommendations for future ad hoc modules on the same subject and provides a sum- mary of the main quality indicators, which are: relevance, accuracy, accessibility and clarity, timeliness and punctuality, comparability, and coherence. �� 2007EUROSTAT Mini-gUidE 2007 ´ Panorama on tourism (with CD-Rom) Language available: EN only Format: paper, 61 pages, with CD-ROM ISBN 92-79-01522-2 Cat. No: KS-74-06-912-EN-C Format: PDF Size: 2 024 Kb Price (excluding VAT): € 20 This publication gives an overview of tourism in the EU Member States, candidate countries and EFTA countries. It focuses on general facts in tourism that remain stable over time, including recent trends, the tourism behaviour of Europeans and details on tourism in the different countries. The period 2000–04 and the year 1995 are covered. This pub- lication consists of tables and graphs with short analysis and includes a CD-ROM containing more detailed tables. The CD-ROM has been published for several years under the title Tourism statistics yearbook. ´ Tourism — Statistical pocketbook — Data 2000–2005 Language available: EN only Format: paper, 113 pages ISBN 978-92-79-04960-6 Cat. No: KS-DS-07-001-EN-C Format: PDF Size: 3 394 Kb This pocketbook consists mainly of tables with short texts highlight- ing key facts and features of tourism in Europe. Information on re- cent policy issues is also provided. The pocketbook covers the period 2000-05 and includes information on all EU and EFTA Member States and on the candidate countries. �� Catalogue ´ Medstat programme — Adapting residence permits registers in the Maghreb countries to measure international migration flows and stocks Languages available: EN, FR Format: paper ISBN 92-79-01525-7 ISSN 1725-065X Cat. No: KS-CC-06-002-EN-C Format: PDF Size: 4 133 Kb MED-Migration (1997–2002), the specific component of Medstat I, the Euro-Mediterranean statistical cooperation programme, notably focused its activities on the following aspects: • the evaluation of migration statistics in the Mediterranean coun- tries; • the evaluation of the existing or potential sources relevant for the production of migration statistics; • the setting-up of recommendations/manuals — common to the Mediterranean region or to a sub-region (Maghreb) — aiming at developing these sources to enhance the production of migration statistics. These recommendations/manuals are concentrated on three statisti- cal and/or administrative sources: • border cards; • residence permits; • surveys (censuses): emigration module. These various recommendations/manuals are the subject of a series of four working papers. ´ Medstat programme — Adapting border card systems in the Mediterranean region to measure international migration and international tourism: new initiatives and technical guidelines Languages available: EN, FR Format: paper ISBN 92-79-01528-1 ISSN 1725-065X Cat. No: KS-CC-06-003-EN-C Format: PDF Size: 2 995 Kb MED-Migration (1997–2002), the specific component of Medstat I, the Euro-Mediterranean statistical cooperation programme, notably focused its activities on the following aspects: �0 2007EUROSTAT Mini-gUidE 2007 • the evaluation of migration statistics in the Mediterranean coun- tries; • the evaluation of the existing or potential sources relevant for the production of migration statistics; • the setting-up of recommendations/manuals — common to the Mediterranean region or to a sub-region (Maghreb) — aiming at developing these sources to enhance the production of migration statistics. These recommendations/manuals are concentrated on three statisti- cal and/or administrative sources: • borders cards; • residence permits; • surveys (censuses): emigration module. These various recommendations/ manuals are the subject of a series of four working papers. ´ Medstat programme — Census migration question in the Mediterranean countries — An inventory and comparative overview Languages available: EN, FR Format: paper: ISBN 92-79-01580-X ISSN 1725-065X Cat. No: KS-CC-06-004-EN-C Format: PDF Size: 14 113 Kb MED-Migration (1997–2002), the specific component of Medstat I, the Euro-Mediterranean statistical cooperation programme, notably focused its activities on the following aspects: • the evaluation of migration statistics in the Mediterranean coun- tries; • the evaluation of the existing or potential sources relevant for the production of migration statistics; • the setting-up of recommendations/manuals — common to the Mediterranean region or to a sub-region (Maghreb) — aiming at developing these sources to enhance the production of migration statistics. These recommendations/manuals are concentrated on three statisti- cal and/or administrative sources: • borders cards; • residence permits; • surveys (censuses): emigration module. These various recommendations/manuals are the subject of a series of four working papers. �1 Catalogue ´ Medstat programme — estimating emigration through an emigration module in census — A proposal for next censuses Languages available: EN, FR Format: paper ISBN 92-79-01583-4 ISSN 1725-065X Cat. No: KS-CC-06-005-EN-C Format: PDF Size: 941 Kb MED-Migration (1997–2002), the specific component of Medstat I, the Euro-Mediterranean statistical cooperation programme, notably focused its activities on the following aspects: • the evaluation of migration statistics in the Mediterranean coun- tries; • the evaluation of the existing or potential sources relevant for the production of migration statistics; • the setting-up of recommendations/manuals — common to the Mediterranean region or to a sub-region (Maghreb) — aiming at developing these sources to enhance the production of migration statistics. These recommendations/manuals are concentrated on three statisti- cal and/or administrative sources: • border cards; • residence permits; • surveys (censuses): emigration module. These various recommendations/manuals are the subject of a series of four working papers. ´ Comparable time use statistics — Main results for Spain, Italy, Latvia, Lithuania and Poland — February 2006 Language available: EN only Format: paper ISBN 92-79-01682-2 Cat. No: KS-CC-06-006-EN-C Format: PDF Size: 609 Kb This working paper presents comparable time use statistics collected for Spain, Italy, Latvia, Lithuania and Poland. This document com- pletes the previous working paper on time use presenting compara- ble statistics for 10 European countries (Belgium, Germany, Estonia, �2 2007EUROSTAT Mini-gUidE 2007 France, Hungary, Slovenia, Finland, Sweden, the United Kingdom and Norway) which was published in March 2005. The aim of this working paper is to make available for further analysis some com- parable data produced by these five countries who conducted a time use survey more recently. Information on how to prepare compara- ble tables in compliance with harmonised European time use surveys (HETUS) guidelines is also included. ´ Classification of learning activities — Manual Language available: EN only Format: paper: ISBN 92-79-01806-X ISSN 1725-0056 Cat. No: KS-BF-06-002-EN-C Format: PDF Size: 525 Kb CLA is intended to cover all types of learning opportunities and edu- cation/learning pathways. It is intended to be universal in nature, applicable in countries irrespective of their level of development or systems of education and learning. It is designed to serve as an instrument for compiling and presenting comparable statistics and indicators on learning activities both with- in individual countries and across countries. It covers all intentional and organised learning activities for all age groups. CLA is to be applied to statistical surveys to collect quantitative infor- mation on different aspects of participation of individuals in learn- ing. The CLA has been designed to cover and serve the scope of the European Union adult education survey (AES). However, other EU household surveys (e.g. LFS, TUS, etc) as well as specific enterprise surveys (e.g. CVTS) may use it if it is adequate for their needs. ´ Labour force survey in the EU, candidate and EFTA countries — Main characteristics of the national surveys — 2004 Language available: EN only Format: PDF Cat. No: KS-BF-06-001-EN-N Size: 868 Kb This publication presents the methodological framework of the la- bour force surveys in the EU, candidate and EFTA countries in 2004. �� Catalogue Information is provided on the history and background of the sur- veys, the sample design and rotation pattern, the weighting proce- dure and methods of data collection. ´ Health in Europe — Data 1998–2003 (new version will be released during 2007) Language available: EN only ISBN 92-79-00410-7 Cat. No: KS-71-05-182-EN-N Format: PDF Size: 638 Kb This pocketbook provides a selection of figures on health and health determinants. It responds to a growing demand of EU health policy for comprehensive, consistent and comparable health data and indi- cators. A more complete overview of health indicators can be found in the publication Key data on health. The present selection in pock- etbook format focuses on some core items. ´ European social statistics — Income, poverty and social exclusion: second report — Data 1994–97 Languages available: DE, EN, FR Format: paper ISBN 92-894-4333-2 Cat. No: KS-BP-02-008-EN-C Format: PDF Size: 1 500 Kb Price (excluding VAT): € 28 Income, poverty and social exclusion is a topic of general, persist- ent and increasing interest. This report is aimed at a wide range of users, including policymakers, the research community and the gen- eral public. It gives a comprehensive picture of income poverty and social exclusion in the European Union during the mid-1990s. A first report was published in 2001. The current report presents updated cross-sectional information and introduces a longitudinal analysis. Calculations are based on the latest version of the European Com- munity Household Panel user database which has been validated by participant countries (December 2002, waves 1–4). Indicator calcu- lations are consistent with those adopted at the Laeken European Council in December 2001. The publication includes an executive summary and a detailed explanation of the conceptual and methodo- logical framework. There are chapters dealing with income distribu- tion, income poverty and the dynamics of income poverty. These are �� 2007EUROSTAT Mini-gUidE 2007 complemented by an analysis of non-monetary ‘lifestyle’ deprivation and its interaction with income poverty. Further sections are devoted to the role of social transfers and country-specific results and con- clusions. The presentation comprises text and graphs. Appendices include detailed statistical tables. The work has been undertaken in collaboration with a team of recognised experts in the field. ´ The production of data on homelessness and housing deprivation in the European Union: survey and proposals Language available: EN only Format: PDF ISBN 92-894-8170-6 Cat. No: KS-CC-04-008-EN-N Size: 1 900 Kb Homelessness and housing deprivation is perhaps the most extreme example of poverty and social exclusion in society today, both as a symptom and as a cause. However, so far, there are few official sta- tistics on homelessness and housing deprivation, and these are rarely comparable between countries. This report highlights the various ob- stacles to a pan-European comparison, discusses the definition(s) of homelessness and housing deprivation and reviews systems for data collection, supporting the analysis with extensive appendices. It con- cludes with a series of concrete proposals. The report is considered as making an important contribution to the progress of efforts to gauge the scale and extent of homelessness and housing deprivation in a European context. ´ European social statistics — Labour force survey results 2002 — Data 2002 Languages available: DE, EN, FR Format: paper, 227 pages ISBN 92-894-5662-0 Cat. No: KS-BP-03-001-EN-C Format: PDF Size: 1 125 Kb Price (excluding VAT): € 34 This publication presents the detailed results of the 2002 (second quarter for countries having a quarterly survey or yearly results) la- bour force survey conducted in the EU Member States in accord- ance with Council Regulation (EC) No 577/98 and in the following EFTA countries: Iceland, Norway and Switzerland. It contains four �� Catalogue chapters: population and households, employment, unemployment, inactivity. At the end of every chapter, a table presents the main indi- cators at NUTS 2 level. ´ The European Union labour force survey Language available: EN only Format: PDF ISBN 92-894-8602-3 ISSN 1725-0056 Cat. No: KS-BF-05-001-EN-N Size: 1 905 Kb, This publication presents the methodological framework of the la- bour force surveys in the Member States, as well as the candidate countries and EFTA countries in 2003. Information is provided on the history and background of the surveys, the sample design and rotation pattern and the weighting procedure. The explanatory notes for the new and revised set of variables on education and training are published in the annex. ´ Key data on education in Europe — 2005 Languages available: EN, FR Format: paper, 332 pages ISBN 92-894-9422-0 ISSN 1725-1621 Cat. No: NC-AF-05-001-EN-C Format: PDF Size: 5 700 Kb Price (excluding VAT): € 30 This sixth edition provides an exceptionally wide-ranging overview of the functioning of education systems and the participation of young people at all levels of education. It contains 153 indicators in six sub- ject-based chapters. Besides data on the context, participation in edu- cation at all educational levels, graduation rates and human and finan- cial resources, the book also offers detailed information on several key aspects of how education is administered and structured. This applies in particular to decision-making levels, the autonomy of schools and procedures for evaluating the education system. One chapter is also devoted to teaching time, the grouping of pupils and their assessment. Each topic is discussed using information from Eurydice on methods of management and implementation. This is supplemented by statis- tical material derived from various Eurostat data collections. Certain data obtained from international surveys (PISA 2003 and 2000, and PIRLS 2001) provide further insight into the topics examined. �� 2007EUROSTAT Mini-gUidE 2007 ´ Methodological manual for statistics on the information society Language available: EN only Format: PDF Cat. No: KS-BG-06-004-EN-N Size: 2 000 Kb The methodological manual has been drawn up as a tool for help- ing national statistical institutes (NSIs) to implement Community surveys concerning individuals and enterprises and the information society, with emphasis on the survey 2006. It is a hands-on tool with recommended guidelines to follow a harmonised methodology and it does not replace statistical handbooks. Annexes with the Eurostat model questionnaires, the transmission formats and the reporting templates as well as the legal background documents are included. The concept is to use it as a rolling document, which will be adapted and improved in line with the annual revision of the Eurostat model questionnaires. ´ Methodological work on measuring the sustainable development of tourism — Part 1 & Part 2 Language available: EN only Format: paper ISBN 92-79-01688-1 Cat. No: KS-DE-06-001-EN-C KS-DE-06-002-EN-C Format: PDF Size: 466 Kb and 354 Kb In May 2004, Eurostat commissioned Statistics Sweden to conduct a methodological study on the sustainable development of tourism, the environment being a factor that is increasingly influencing tour- ist demand. The result is presented in a report and a manual for a set of 20 core indicators for sustainable tourism, including detailed descriptions of each indicator. This core set forms a base of indicators that may be used both at national, regional and local levels in EU countries, and at Community level. �7 Catalogue STATISTICS IN FOCUS (selection of issues published up to the end of 2006) • Causes of death in the EU — Issue No 10/200� This Statistics in Focus presents an analysis of the main causes of death by age group throughout the EU and the observed related differences between Member States. An overview of the characteristics of the mor- tality for all causes together at the national level is also given. Lastly, a specific study on the deaths of European residents due to the tsunami of December 2004 in south-east Asia is provided in this issue. Product code: KS-NK-06-010 PDF file: 520 Kb — Issue date: 7.7.2006 • Winter season tourism trends 200�–0� — Issue No �0/200� The annual Statistics in Focus ‘Winter season tourism trends’ provides information on the number of nights spent in hotels and similar es- tablishments in the EU and EFTA countries and on the net occupancy of these establishments during the winter months (from November to April). The current publication compares the figures for the 2005–06 winter season to those of the previous year, showing an increase in terms of guest flows in the majority of the given countries. Product code: KS-NP-06-030 PDF file: 100 kb — Issue date: 6.12.2006 • Regional tourism in the European Union — Issue No 27/200� This publication focuses on the regional aspects of tourism in the Member States of the European Union. It describes the volume and the structure of the accommodation capacities as well as their use at the level of the NUTS 2 regions in the EU. Information is given on the different relative incidence of tourism in the various regions. Product code: KS-NP-06-027 PDF file: 275 Kb — Issue date: 23.11.2006 �� 2007EUROSTAT Mini-gUidE 2007 • Summer tourism trends in 200� — Issue No 1�/200� The annual Statistics in Focus ‘Summer tourism trends’ provides in- formation on the number of nights spent in hotels and similar estab- lishments in the EU and EFTA countries and on the net occupancy of these establishments during the summer months (from June to September). The current publication compares the figures of the 2005 summer season to those of the previous year, showing an increase in terms of guest flows in the majority of the given countries. Product code: KS-NP-06-019 PDF file: 89 Kb — Issue date: 20.7.2006 • How Europeans go on holiday — Issue No 1�/200� This Statistics in Focus ‘How Europeans go on holiday’ describes the behaviour of EU tourists in the year 2004. It focuses on the prefer- ences of tourists travelling inside and outside their country, includ- ing their seasonal behaviour and length of stay. It also examines the distribution of trips and nights by type of accommodation and reports details about the habits of the EU residents as regards the transport modes they use and how they organise their holidays. Product code: KS-NP-06-018. PDF file: 110 Kb — Issue date: 20.7.2006 • Inbound and outbound tourism in the European Union — Issue No �/200� This volume of Statistics in Focus deals with inbound and outbound tourism in the European Union, EFTA and candidate countries. It ex- amines some major aspects of this phenomenon country by country, especially the number of inbound and outbound nights and arrivals, the main holiday periods and the favourite destinations of outbound tourists from the EU countries. All information presented in this volume of Statistics in Focus will soon be complemented by two new Eurostat publications, entitled Pocketbook on tourism and Panorama on Tourism. These two upcoming publications cover detailed infor- mation available in Eurostat’s free dissemination database. This infor- mation is presented in the form of tables, graphs and analytical texts. Both publications cover data for a longer period. The ‘Pocketbook’ is written and designed for the broad public, while the ‘Panorama’ is intended for the specialised user. The ‘Panorama’ publication will also include a CD-ROM containing most of the data in Eurostat’s database on tourism and some general economic information related to tour- ism for the EU-25, EFTA and candidate countries. Product code: KS-NP-06-005. PDF file: 480 Kb — Issue date: 2.2.2006 • 17 million tertiary students in the European Union — Issue No 1�/200� The number of tertiary students in the European Union has increased by 17 % and the number of graduates by more than 30 % between 1998 and 2003. The differences between countries are marked. The participation and graduation rates and the age span among students vary by a factor of three as well as the tertiary education attainment in younger age-groups. These and other data on tertiary education are presented in this issue of Statistics in Focus. Product code: KS-NK-05-019 PDF file: 474Kb – Issue date: 15.12.2005 �� Catalogue • The digital divide in Europe — Issue No ��/200� This issue of Statistics in Focus takes a closer look at the differences between societal groups and their access to and usage of information and communication technologies (ICTs). It discusses the effect of so- cio-demographic characteristics such as age, gender, educational level or degree of urbanisation on the take-up of ICTs. In addition, the issue looks at whether the digital divides among different groups of house- holds, individuals or enterprises have narrowed over recent years. Product code: KS-NP-05-03 PDF file: 280 Kb —Issue date: 7.11.2005 8 DATABASES Demography This domain gives detailed figures on population, fertility, mor- tality, nuptiality and divorce and covers the main demographic indicators. It also provides some information on population, la- bour force and household projections. Education The aim of this domain is to provide comparable data, statistics and indicators on education for the EU-25, the candidate coun- tries, EEA countries, Switzerland, Albania, the former Yugoslav Republic of Macedonia, USA and Japan. The main data source is the set of joint UOE (Unesco Institute of Statistics (UIS), OECD, Eurostat) questionnaires on education and other Eurostat-spe- cific tables. The statistics refer to public and private, full-time and part-time education in the ordinary school and university system as defined in the international standard classification of education (ISCED). The statistics cover enrolments, entrants, graduates, personnel, language learning and expenditure. Training CVTS1 was the first survey on continuing vocational training in enterprises carried out at EU level in a coordinated form. CVTS1 describes the continuing vocational training activities in the then 12 EU Member States in the reference year 1993. CVTS2, the second European survey of continuing vocational training in enterprises was conducted in 2000/01 in all the Mem- ber States, Norway and nine candidate countries. The survey pro- vided comparable statistical results on training and non-training enterprises, the supply of and the demand for vocational skills, the need for CVT and the forms, content and volume of CVT, the use of enterprises’ own training resources and of external providers, and the cost of CVT courses. The survey covered enterprises with 10 and more employees in Sections C to K and O of the statistical classification of economic activities in the European Community (NACE Rev. 1) in the reference year 1999. �0 2007EUROSTAT Mini-gUidE 2007 Labour market The labour market domain covers a wide range of statistics that are presented in the following five sections: Employment and unemployment This collection contains a wide scope of data on employment and unemployment. The main data source is the European Union La- bour Force Survey (EU LFS). This survey is a quarterly household sample survey carried out in the Member States of the European Union, candidate countries and EFTA countries (except Liech- tenstein). It provides population estimates for the main labour market characteristics, such as employment, unemployment, in- activity, hours of work, occupation, economic activity and much else as well as important socio-demographic characteristics, such as sex, age, education, households and regions of residence. The definitions of employment and unemployment, as well as other survey characteristics follow the definitions and recom- mendations of the International Labour Organisation (ILO). In addition, harmonisation is achieved through adherence of the Member States to common principles of questionnaire construc- tion, unemployment definition and common definitions of main variables and reply categories. Annual and quarterly unemployment rates are derived from the monthly harmonised series based on the EU LFS and national monthly sources. The results are seasonally adjusted while all other LFS results are not. Employment growth results present national accounts data (domestic concept) using the EU LFS for social breakdowns. For the main employment indicators, miss- ing quarters of past series are estimated by Eurostat to produce adjusted EU aggregates and annual averages. Job vacancy survey This collection includes available national statistics on the number of job vacancies, number of occupied jobs and job vacancy rates in enterprises belonging to sections A to O of the European clas- sification of economic activities (NACE Rev 1.1). Activities of households, and extra-territorial organisations and bodies are ex- cluded. National breakdowns are provided by size of enterprises (all establishments, establishments with 10 employees or more) and economic activity (NACE Rev 1.1 sections). Earnings and labour costs This domain covers a wide range of data on labour costs, gross and net earnings, tax rates, minimum wages and the gender pay gap. The parts on labour costs and gross earnings are hierarchi- cally organised giving structural information on a four-yearly basis, annual level data, and information on the short-term devel- opment via the labour cost index. The structural data in addition �1 Catalogue include information on hours worked and hours paid. The data are usually broken down by economic activity and further cat- egories such as size of enterprise and labour cost components for labour cost data or occupation for gross earnings data. Labour market policy The labour market policy database (LMP) provides data on ‘la- bour policy measures’ implemented in each country to combat unemployment, as well as data for its follow-up in the frame- work of the European employment strategy. Data come from administrative sources, and refer to public expenditure and to participants (stock and flows). Data on participants are broken down by gender, by age and by duration of unemployment be- fore participation. The database also includes detailed qualitative information concerning each labour market measure (around 600 descriptions for 16 countries for the year 2003), which allow the reader to understand the kind of measures which are more common in each country and to compare different models of interventions. Industrial disputes This section contains the available data on the number of work- ing days lost and the number of workers involved in strikes and lockouts (in 1 000s and in rates per 1 000 workers). Breakdown by economic activity (NACE Rev.1.1) is also available. Health Eurostat presents here, in the framework of the ‘Health’ domain, a systematic and, as far as possible, harmonised set of regular and official statistics which are directly relevant to Community ac- tions in the field of health. The domain is divided into two main items: public health and health and safety at work. The collection ‘Public health’ has been divided into six chap- ters: (i) structural indicators on health; (ii) causes of death; (iii) health care: data from administrative sources and other surveys (tables on human resources on health, hospitals beds, in-patient care, ambulatory care and treatments); (iv) health care: indicators from the national health interview surveys (ta- bles on consultation, hospitalisation and cancer screening); (v) health status: indicators from the national health interview sur- veys (which include mainly tables on self-perceived health, life styles and restrictions); (vi) health status: indicators from other surveys and sources. Some of these tables are also at NUTS II level. In the near future, a chapter on health accounts will also be incorporated. The tables compiled for the collection ‘Public health’ are the prod- uct of data provided mainly by the national statistical institutes and the ministries of health or other national competent insti- �2 2007EUROSTAT Mini-gUidE 2007 tutes. Disaggregation by age, sex and other variables are provided where available. The emphasis is on basic data and well-known common measures, such as rates and ratios. The collection ‘Health and safety at work’ is divided into four chapters (groups): (i) accidents at work (accidents at the work- place or in the course of an occupational activity); (ii) commut- ing accidents (on the journey to or from work); (iii) occupa- tional diseases (recognised cases from national authorities); (iv) work-related health problems and accidental injuries (self-re- ported cases). The data presented in the tables for the first three groups of the collection ‘Health and safety at work’ are based on national ad- ministrative sources, from declarations to the insurance (public insurance, social security scheme, or private insurance scheme) or to another competent authority (usually the labour inspector- ate). These harmonised data are available from 1993 (accidents at work), 1995 (occupational diseases) or 1996 (commuting ac- cidents). The data for the last group of the collection ‘Health and safety at work’ (work-related health problems and accidental inju- ries) were provided by an ad hoc module in the 1999 Community labour force survey. Income and living conditions (including housing statistics) This revised domain contains statistical information about in- come, poverty and social exclusion — notably indicators adopted under the ‘open method of coordination’ on social inclusion and on adequacy of pensions. The current focus is on income inequal- ity and relative monetary poverty, and is presented at aggregate level and with breakdowns according to various socio-demo- graphic variables. Work is ongoing to develop additional indica- tors of poverty and exclusion. The housing sub-domain contains a wide range of information on housing conditions (housing type, tenure status, cost and qual- ity of housing) and additional data on consumer durables. A lot of the information is cross-tabulated with several socio-demo- graphic variables. The primary source of data is currently the European Com- munity Household Panel (ECHP). This pioneering survey cov- ers private households in the Member States of the European Union, with effect from 1994, and is now being replaced by a new data collection instrument: the EU–SILC. Where available, comparable data are included for EFTA countries, accession countries and candidate countries. Some indicators are also drawn from other sources. Household budget survey This domain gathers cross-sectional data on final consumption expenditure of non-collective private households. �� Catalogue These data are useful for the production of indicators for the management of some politics at EU level relating to consump- tion, employment, social exclusion and social protection, as well as for carrying out research studies on consumption and living conditions. In this domain, there are data available for three reference years: 1988 with the participation of 10 Member States; 1994 with the participation of 15 Member States; 1999 with a full participation of 15 Member States and a limited participation (partial supply of data) of the 10 new Member States and two candidate counties. These statistics have no legal basis hence each Member State has its own targets, methodology and survey programming. Data supplied by the Member States are not perfectly harmonised. After each round, some harmonisation efforts have been carried out and each new round of data collection is better harmonised than the previous one. However, some problems of comparabil- ity among countries still remain. In order to assess the impact of these problems of comparability on data analyses, we recommend that you consult the following Eurostat publications: n Family budget surveys in the EC — Methodology and recommendations for harmonisation (ISBN 92-826-6193- 8) for the reference year 1988; n Household budget surveys in the EU — Methodology and recommendations for harmonisation, 1997 (ISBN 92-827- 9805-4) for the reference year 1994; n Household budget surveys in the EU — Methodology and recommendations for harmonisation, 2003 (ISBN 92-894- 5435-0) for the reference year 1999; n Household budget surveys in the candidate countries — Methodological analysis, 2003 (ISBN 92-894-7087-9) for the reference year 1999. Social protection Harmonised data on social protection expenditure (such as pen- sions, unemployment benefits, healthcare, family allowances) and receipts (such as social contributions by employers and employ- ees, government contributions) in the 15 Member States of the European Union, Iceland, Norway and Switzerland. International migration and asylum The data refer to asylum applications by citizenship, acquisition of citizenship, immigration and emigration by sex, citizenship, age group, country of previous residence and next residence. �� 2007EUROSTAT Mini-gUidE 2007 Tourism Available statistics include variables on capacity of tourist accom- modation establishments, occupancy in these establishments and data on residents’ tourism demand. Recently, data on employ- ment in the tourist accommodation sector have been added to the set of available statistics. Telecommunication services This database contains annual information on telecommunica- tions in the EU (including the new Member States), the candidate countries and EFTA countries. For selected variables, time series since 1980 are provided. Mainly the data covers the period from 1995 on. This database contains statistics on operators, network access, employment, economy and traffic. Information society statistics This domain presents key figures on the information society. It is at present subdivided into two collections with more collections being added throughout the coming year. Because of its wide- ranging content covering various fields of interest, information society statistics can be found under the three themes of ‘popula- tion and social conditions’, ‘industry, trade and services’ and ‘sci- ence and technology’. 1. Policy indicators. The data given here is collected by the national statistical institutes or ministries of the EU Member States and is based on Eurostat’s annual surveys on informa- tion and communication technologies (ICT) usage and e- commerce in enterprises and ICT usage in households and by individuals. These data are collected in the context of the Europe 2005 action plan, born from the Lisbon European Council in March 2000 and launched at the Seville European Council in June 2002. The action plan aims to develop mod- �� Catalogue ern public services and a dynamic environment for e-busi- ness through widespread availability of broadband access at competitive prices and a secure information infrastructure. These statistics provides policy or ‘benchmarking’ indicators by offering harmonised and comparable statistical informa- tion at a European level. 2. Structure: information society These statistics-related structural indicators are used in the Commission’s annual spring report to the European Council and are also directly related to the Lisbon European Council wherein the Council also invited the Commission to draw up an annual synthesis report on the basis of the structural indicators, which provide an instrument for an objective as- sessment of the progress made towards the Lisbon objectives and support the key messages of the report. �� 2007EUROSTAT Mini-gUidE 2007 INDUSTRY, TRADE AND SERVICES ´ Panorama on tourism (with CD-Rom) Language available: EN only Format: paper, 61 pages, with CD-ROM ISBN 92-79-01522-2 Cat. No: KS-74-06-912-EN-C Format: PDF Size: 2 024 Kb Price (excluding VAT): € 20 This publication gives an overview on tourism in the EU Member States, candidate and EFTA countries. It focuses on general facts in tourism that remain stable over time. This includes recent trends, the tourism behaviour of Europeans and details on tourism in the differ- ent countries. The period 2000–2004 and the year 1995 are covered. This publication consists of tables and graphs with short analysis and includes a CD-ROM containing more detailed tables. The CD-ROM has been published for several years under the title Tourism statistics yearbook. ´ Tourism — Statistical pocketbook — Data 2000–2005 Language available: EN only Format: paper, 113 pages ISBN 978-92-79-04960-6 Cat. No: KS-DS-07-001-EN-C Format: PDF Size: 3 394 Kb This pocketbook consists mainly of tables with short texts highlight- ing key facts and features of tourism in Europe. Information on re- cent policy issues is also provided. The pocketbook covers the period 2000–05 and includes information on all EU and EFTA Member States and on the candidate countries. �7 Catalogue ´ Quarterly panorama of European business statistics Language available: EN only Format: PDF ISSN 1725-485X Cat. No: KS-DL-06-001-EN-N Size: 2 853 Kb The Quarterly panorama of European business statistics is a tool to fol- low the evolution of the short-term trends of the European economy in the industrial, construction, trade and other service sectors. The panorama is now a web-publication, available through a dedicated section on the Eurostat website. ´ Key figures on European business — with a special feature section on SMEs — Data 1995–2005 Language available: EN only Format: paper, 130 pages ISBN 92-79-02576-7 Cat. No: KS-DH-06-001-EN-C Format: PDF Size: 3 311 Kb This publication summarises the main features of European business and its different activities in a concise and simple manner. It includes a special feature section on SMEs, which presents an analysis of the different characteristics of micro, small, medium and large enterpris- es. The publication is also intended to function as a showcase for and introduction to the data available in this field. The focus is on struc- tural business statistics: both the more traditional business statistics which are disseminated regularly, but also specific information com- piled on a multi-yearly basis and the latest results from development projects on topics of key political interest. �� 2007EUROSTAT Mini-gUidE 2007 ´ European business — Facts and figures — 2006 edition, with CD-ROM Language available: EN only Format: paper, 425 pages, with CD-Rom ISBN 92-79-03351-4 Cat. No: KS-BW-06-001-EN-C Format: PDF Price (excluding VAT): € 40 The new edition of European business - Facts and figures gives a com- prehensive picture of the structure, development and characteristics of European business and its different activities: from energy and the extractive industries to communications, information services and media. It presents the latest available statistics from a wide selection of statistical sources describing, for each activity, production and employment, country specia-lisation and regional distribution, cost structures, productivity and profitability, the importance of small and medium sized enterprises (SMEs), external trade etc. The accompa- nying CD-ROM presents the paper publication in an easily accessible electronic format, including all the data, graphs and tables in Excel- format. It also contains more complete, detailed datasets on which the publication is based, a means of easily accessing the most up-to- date live data, as well as a large amount of background information. ´ Methodology of short-term business statistics — Interpretation and guidelines Language available: EN only Format: paper ISBN 92-79-01295-9 ISSN 1725-0099 Cat. No: KS-BG-06-001-EN-C Format: PDF Size: 7 774 Kb Short-term business statistics are in great demand for economic analysis by a large number of users. Considerable progress has been achieved in recent years to improve their coverage, their content and their timeliness. The bases of those improvements are Council Regulation (EC) No 1165/98 in 1998 which set the legal basis and the amending Council Regulation (EC) No (1158/2005) in 2005. As part from this new regulation, the Commission has to publish an updated version of the methodological manual, taking into account these changes. The present volume is the third edition of the Methodology of short-term statistics — Interpretation and guidelines, updated to in- clude the new variables and to ensure as far as possible a consistency with national accounts definitions. �� Catalogue ´ Methodology of short-term business statistics — Associated documents Language available: EN only Format: paper ISBN 92-79-01296-7 ISSN 1725-0099 Cat. No: KS-BG-06-002-EN-C Format: PDF Size: 2 555 Kb The third edition of the Methodology of short-term statistics — Interpre- tation and guidelines is supported by a number of documents including the texts of the Council regulations, the implementing Commission regulations, a detailed description of the data delivery requirements resulting from the regulations, the NACE activity classification, the construction classification, various recommendations by the Working Group on Short-term Statistics and the detailed transmission protocol (Gesmes) which ensures reliable and speedy transmission of the data between national statistical offices and Eurostat. ´ Methodological manual for statistics on the information society Languages available: DE, EN, FR Format: PDF Cat. No: KS-BG-06-004-EN-N Size: 2 000 Kb This methodological manual is drawn up as a tool for helping na- tional statistical institutes (NSIs) to implement Community surveys concerning individuals and enterprises and the information society, with emphasis on the survey 2006. It is a hands-on tool with recom- mended guidelines to follow a harmonised methodology and does not replace statistical handbooks. Annexes with the Eurostat model questionnaires, the transmission formats and the reporting templates as well as the legal background documents are included. The con- cept is to use it as a rolling document, which will be adapted and improved in line with the annual revision of the Eurostat model ques- tionnaires. �0 2007EUROSTAT Mini-gUidE 2007 ´ Methodological work on measuring the sustainable development of tourism — Part 1 & Part 2 Language available: EN only Format: paper ISBN 92-79-01688-1 Cat. No: KS-DE-06-001-EN-C KS-DE-06-002-EN-C Format: PDF Size: 466 Kb and 354 Kb In May 2004, Eurostat commissioned Statistics Sweden to conduct a methodological study on the sustainable development of tourism, the environment being a factor that is increasingly influencing tour- ist demand. The result is presented in a report and a manual for a set of 20 core indicators for sustainable tourism, including detailed descriptions of each indicator. This core set forms a base of indicators that may be used both at national, regional and local levels in EU countries, and at Community level. ´ Methodological guide for developing producer price indices for services Language available: EN only Format: paper ISBN 92-79-01297-5 ISSN 1725-0099 Cat. No: KS-BG-06-003-EN-C Format: PDF Size: 4 308 Kb The Methodological guide for developing producer price indices for services is a two-fold complement to the International producer price index manual (PPI manual) published by the IMF in 2004: it focuses on service-specific aspects in PPI compilation by developing the conceptual framework further and it adds detailed descriptions of PPI measurement for a series of service industries. This guide has been jointly produced by the OECD, Eurostat and the members of a taskforce with delegates from 19 OECD/EU Member States. The electronic version of the guide is made available on both the OECD and Eurostat websites. The guide is seen as a ‘living document’ which could be amended and updated to incorporate additional service in- dustries and to address particular points in greater detail. The guide will permit the development of services producer price indices in the EU and OECD regions and beyond provide better information for decision-making and analysis. �1 Catalogue ´ Employment in the market economy in the European Union — An analysis based on the structural business statistics — 2004 edition Language available: EN only Format: PDF ISBN 92-894-7495-5 Cat. No: KS-59-04-944-EN-N Size 4 625 Kb The structural business statistics (SBS) represent the most complete source of data on business in the European Union. The SBS provide information about most aspects of enterprise activity on a detailed breakdown of economic sector. The purpose of the publication is to increase knowledge and understanding of employment and business- related issues across the European Union. The publication will present a fairly complete and coherent picture on, for example, the scale of business output and employment in different parts of the Union. It will also indicate the division of employment and output between different economic activities, the relative importance of different-sized enter- prises, their productivity and costs of production and the amount of investment undertaken. Since the data on employment and business- related variables (output, value-added etc.) are compiled and classi- fied on the same basis within the SBS framework, their combined use ensures that analysis done is internally consistent. ´ Iron and steel — Yearly statistics — Concluding edition — Data 1993–2002 Languages available: trilingual edition DE/EN/FR Format: paper, 73 pages ISSN 1609-4107 ISBN 92-894-5265-X Cat. No: KS-BL-03-001-3A-C Format: PDF Size: 1 000 Kb Price (excluding VAT): € 17.50 Iron and steel — Yearly statistics provides a statistical overview of de- velopments in the European Union’s iron and steel industry in recent years. This publication contains detailed tables presenting annual sta- tistics on the structure and economic situation of this industry at EU level and in each of the Member States. It collates the latest available data on such topics as production of iron ore, pig iron, finished steel products etc., the size of enterprises and plants, employment, indirect foreign trade and others. �2 2007EUROSTAT Mini-gUidE 2007 NB: the ECSC (European Coal and Steel Community) Treaty expired in July 2002, and steel statistics under the ECSC Treaty came to an end at 31 December 2002. Therefore the publication on Iron and steel — Yearly statistics is ending with this ‘Concluding edition’. STATISTICS IN FOCUS (selection of issues published up to the end of September 2006) • Short-term statistics — Issue No 22/200� The aim of this Statistics in Focus on ‘Short-term statistics’ is to draw attention to labour input data, mainly employment, and the possible use of these data for analysis. As an example, the publication studies the proposal that developments of the index of the number of persons em- ployed reflect more general developments in the economy as a whole. Product code: KS-NP-06-022 PDF file: 113 Kb – Issue date: 22.9.2006 • Manufacture of glass, ceramics and cement in Europe — Issue No 21/200� The manufacture of glass, ceramics and cement (NACE 26) provided jobs to 1.5 million people and generated EUR 69 billion in value- added in the EU-25 in 2003. This was respectively 1.4 % of the total number employed and 1.5 % of total value-added in the non-financial business economy as a whole. Manufacture of cement and concrete was the largest sub-sector. Energy costs accounted for 6 % of the op- erating costs. Small and medium-sized enterprises (1–249 persons employed) employed more than 63 % of the workforce and 53 % of EU-25 value added in this sector. Product code: KS-NP-06-021 PDF file: 130 Kb — Issue date: 1.9.2006 • Tourism and the Internet in the European Union — Issue No 20/200� This publication takes a look at the specific usage the accommodation sector makes of information technologies in general and of the Inter- net in particular, using the results of the 2005 Community surveys on ICT usage. While the accommodation sector is largely ahead of other sectors in its usage of information technologies for managing client relationships, it lags behind other sectors as regards the full im- plementation of e-business. One explanatory factor might be the low uptake of broadband technologies that would allow more information to be conveyed at a faster speed. Product code: KS-NP-06-020 PDF file: 850 Kb — Issue date: 18.8.2006 • How skilled are Europeans in using computers and the Internet ? Issue No 17/200� This publication takes a look at Europeans’ inclusion in the informa- tion society, more specifically at their ‘e-skills’ to use information and communication technologies, using the results of the 2005 Commu- nity survey on ICT usage in households and by individuals ‘The dig- ital divide in Europe’ (Issue No 38/2005). Product code: KS-NP-06-017 PDF file: 140 Kb — Issue date: 20.6.2006 �� Catalogue • Use of the Internet among individuals and enterprises — Issue No 12/200� This issue sets out to highlight some of the initial results of the 2005 Community surveys on ICT usage in enterprises and households. In- cluded are Internet access and broadband connectivity, regular Inter- net usage by individuals and electronic commerce on the Internet. Product code: KS-NP-06-012 PDF file: 420 Kb — Issue date: 6.4.2006 • Winter season tourism trends 200�–200� — Issue No �0/200� The annual Statistics in Focus ‘Winter season tourism trends’ provides information on the number of nights spent in hotels and similar es- tablishments in the EU and EFTA countries and on the net occupancy of these establishments during the winter months (from November to April). The current publication compares the figures of the 2005–06 winter season to those of the previous year, showing an increase in terms of guest flows in the majority of the given countries. Product code: KS-NP-06-030 PDF file: 100 kb – Issue date: 6.12.2006 • Regional tourism in the European Union — Issue No 27/200� This publication focuses on the regional aspects of tourism in the Member States of the European Union. It describes the volume and the structure of the accommodation capacities as well as their use at the level of the NUTS 2 regions in the EU. Information is given on the different relative incidence of tourism in the various regions. Product code: KS-NP-06-027 PDF file: 275 Kb – Issue date: 23.11.2006 • Summer tourism trends in 200� — Issue number 1�/200� The annual Statistics in Focus ‘Summer tourism trends’ provides in- formation on the number of nights spent in hotels and similar estab- lishments in the EU and EFTA countries and on the net occupancy of these establishments during the summer months (from June to September). The current publication compares the figures of the 2005 summer season to those of the previous year, showing an increase in terms of guest flows in the majority of the given countries. Product code: KS-NP-06-019 PDF file: 89 Kb — Issue date: 20.7.2006 • How Europeans go on holiday — Issue number 1�/200� This Statistics in Focus ‘How Europeans go on holiday’ describes the behaviour of EU tourists in 2004. It focuses on the preferences of tour- ists travelling inside and outside their country, including their sea- sonal behaviour and length of stay. It also examines the distribution of trips and nights by type of accommodation and reports details about the habits of EU residents as regards the transport modes they use and how they organise their holidays. Product code: KS-NP-06-018. PDF file: 110 Kb — Issue date: 20.7.2006 �� 2007EUROSTAT Mini-gUidE 2007 • Inbound and outbound tourism in the European Union — Issue No �/200� This volume of Statistics in Focus deals with inbound and outbound tourism in the European Union, EFTA and candidate countries. It ex- amines some major aspects of this phenomenon country by country, especially the number of inbound and outbound nights and arrivals, the main holiday periods and the favourite destinations of outbound tourists from the EU countries. All information presented in this vol- ume of Statistics in Focus will soon be complemented by two new Eu- rostat publications, entitled Pocketbook on tourism and Panorama on tourism. These two upcoming publications cover detailed information available in Eurostat’s free dissemination database. This information is presented in form of tables, graphs and analytical texts. Both publica- tions cover data for a longer period. The ‘Pocketbook’ is written and designed for the broad public, while the ‘Panorama’ is intended for the specialised user. The ‘Panorama’ publication will also include a CD- ROM containing most of the data in Eurostat’s database on tourism and some general economic information related to tourism for the EU-25, EFTA and candidate countries. Product code: KS-NP-06-005. PDF file: 480 Kb — Issue date: 2.2.2006 8 DATABASES Structural business statistics (industry, construction, trade and services) Now the only reference domain for business structural data. All business structural data regarding industry, trade, construc- tion and services (other than financial services) from 1995 on- wards on the legal basis of Council Regulation (EC, Euratom) No 58/97 as well as some long time series (NACE Rev. 1 G to K from 1990, NACE Rev. 1 C to F from 1985). It includes statistics broken down by size class and regional statistics. Data on new member countries are generally available from 1998 onwards (for a number of new member countries, data are also available for earlier reference years). The domain also contains detailed data on business services and a breakdown of annual business statistics by nationality of owner- ship. European business trends — Monthly and quarterly short-term statistics (industry, construction, retail trade and other services) This domain contains information for the analysis of the short- term evolution of supply and demand, production factors and prices in all market activities in industry, construction, retail trade, repair and other services. The short-term variables collected by Eurostat are produced by the national statistical institutes of the Member States on a �� Catalogue monthly or quarterly basis and shall be transmitted between one and three months after the end of the reference period. For industry, all variables are to be transmitted at the two-digit level of NACE Rev. 1. For construction, retail trade and repair and other services, specific levels of detail are required. The first mandatory reference period is the beginning of 1998. Eurostat disseminates working days and seasonally adjusted se- ries and estimates EU-25 and euro-area aggregates. Statistics by product (Prodcom) These are annual statistics on the volume and value of production of a list of about 5 000 manufactured products, from 1995 on- wards. The legal bases are Council Regulation (EEC) No 3924/91 and Commission Regulation (EC) No 912/2004, together with annual Commission regulations defining the Prodcom list of products for each year. Data on new member countries are generally available from 2002 onwards (for a number of new member countries, data are also available for earlier reference years). Where possible, the external trade statistics corresponding to each Prodcom heading are also published. �� 2007EUROSTAT Mini-gUidE 2007 AGRICULTURE AND FISHERIES ´ Agricultural statistics — Data 1995–2005 — Pocketbook Language available: EN only Format: paper ISBN 92-79-02955-X ISSN 1830-463X Cat. No: KS-ED-07-001-EN-C Format: PDF This publication presents selected tables and graphs providing an overview on developments and the situation in the agricultural sec- tor of the European Union and also presents some data on rural de- velopment. Most data are presented for the 27 Member States of the European Union. ´ Food: from farm to fork statistics — Pocketbook Language available: EN only Format: paper, 100 pages ISBN 92-79-00429-8 Cat. No: KS-51-05-473-EN-C Format: PDF Size: 6 259 Kb This pocketbook provides the reader with statistical information on how the food chain evolves in Europe. It gives a summary of the data currently available in the ‘Food: from farm to fork’ database of Euro- stat, including different indicators for each step of the production- consumption chain. The structure follows the approach taken by the European Commission on food safety policy. �7 Catalogue ´ Validation of Eurostat’s meat statistics using the Agricultural Information System (AGRIS) — Analysis of individual time series and consistency Language available: EN only Format: PDF Cat. No: KS-AZ-05-002-EN-N Size: 1 492 Kb Data validation is an essential part of work in official statistics which contributes to the assurance of high-quality data and reinforces the reliability of statistics. The working paper presents an automated method for data validation in agricultural statistics. The presented methods were originally applied on meat statistics but can be easily extended to other agricultural statistics. The theoretical background and applications of software are explained. ´ Fishery statistics — Data 1990–2005 Language available: EN only Format: paper ISBN 92-79-02954-1 ISSN 1830-5075 Format: PDF Size:1 628 Kb This pocketbook contains summary tables for EEA and EU candidate countries on catches by fishing region, on aquaculture production, on total production, on landings in EEA ports, on trade in fishery products, on supply balance sheets, on the EEA fishing fleet and on the number of fishers. �� 2007EUROSTAT Mini-gUidE 2007 STATISTICS IN FOCUS (selection of issues published up to the end of September 2006) • Farm structure in Malta — 200� — Issue No 1�/200� This Statistics in Focus is the third issue of a series of country-specific publications on the results of the farm structure survey (FSS) 2005. It provides a brief but nevertheless comprehensive insight into the farm structure in Malta. A similar publication was issued in November 2004 presenting the results of the FSS 2003 in Malta. Product code: KS-NN-06-013 PDF file: 117 Kb — Issue Date: 23.8.2006 • Farm structure in Lithuania — 200� — Issue No 12/200� This Statistics in Focus is part of a series of country-specific publica- tions on the results of the farm structure survey (FSS) 2005. It pro- vides a brief but nevertheless comprehensive insight into the farm structure in Lithuania. A similar publication was issued in March 2005 presenting the results of the FSS 2003 in Lithuania. Product code: KS-NN-06-012 PDF file: 200 Kb — Issue date: 22.8.2006 • 200� harvest in EU-2�: early estimates — Issue No �/200� This publication presents areas and production 2006 early estimates for cereals, sugar beets and rape seeds. Product code: KS-NN-06-009 PDF file: 312 Kb — Issue date: 7.7.2006 �� Catalogue 8 DATABASES Economic accounts for agriculture and forestry (European Union and candidate countries) This database contains four collections: n economic accounts for agriculture; n economic accounts for forestry; n agricultural labour input; n unit values of agricultural products. Structure of agricultural holdings This summarises the main data in the Eurofarm database and contains the results of Community surveys on the structure of agricultural holdings, i.e. labour force, size of holdings, land use, livestock, subsistence farming. This domain also contains the main indicators on the structure of agricultural holdings by region. Agricultural information system This domain contains annual data on agricultural production, ab- solute selling prices and the economic accounts for agriculture. All the data are taken from the other agricultural domains under the theme ‘Agriculture and fisheries’ of the dissemination data- base and brought together in a harmonised framework. Animal feed These data concern the supply side of the balances of animal feed- ing stuffs: total available, home production, imports (from EU and third countries); annual data. Fishery statistics This domain contains data on catches by fishing region, on aquac- ulture production, on total production, on landings in EEA ports, on trade in fishery products, on supply balance sheets, on the EEA fishing fleet and on the number of fishers. • Roundwood production in EU-2� — EU-2� and the world — Issue No �/200� The EU-25 ranks second in the world for roundwood production, just behind the United States. The EU-25 produces mainly conifer- ous roundwood. The trend in such production has shown a marked increase in recent years. Product code: KS-NN-06-008 PDF file: 75 Kb — Issue date: 16.6.2006 70 2007EUROSTAT Mini-gUidE 2007 Agricultural prices and price indices (EU and candidate countries) This domain contains monthly and annual data on agricultural price indices and absolute prices. The price indices comprise the EU index of producer prices of agricultural products and the EU index of goods and services currently consumed in agriculture and the goods and services contributing to agricultural invest- ment. The absolute prices comprise the prices of agricultural products and the prices of some selected products used in agri- culture (energy, fertilisers and animal feedingstuffs). Agricultural products This domain includes data shown in physical units (number, weight, quantity): n land use, annual data; n livestock (number and structure), annual data; n crop production (area, yield, production), annual data; n meat, milk and milk products production and activity of hatcheries (incubation and trade), monthly and annual data; n summary data (balance sheets), annual data. The countries concerned are the EU-27, candidate countries (Turkey) and other countries (Albania, countries from the former Yugoslavia, Norway). Database on orchards This domain contains data on the plantations of seven species of fruit trees: dessert apples, dessert pears, peaches, apricots, oranges, lemons and small-fruited citrus fruits; certain information is also available on cooking apples and cooking pears. The results referring 71 Catalogue to the characteristics of these species are: age classes, density and varieties, which are recorded per country and production zone. Database on viticulture This database contains the results of surveys of areas under vines (basic, every 10 years, and intermediate, annually) carried out by Member States with at least 500 ha under vines in the open air (Germany, Greece, France, Italy, Luxembourg, Portugal, Spain, Austria and the United Kingdom) in accordance with Council Regulation (EEC) No 357/79 of February 1979. The aim is to as- sess the situation and developments in the grape-growing sector in the European Union. The structure of the database is based on the tables defined in the legislation (Commission Regulation (EEC) No 991/79 and Commission Decisions 79/491/EEC, 80/763/EEC, 80/764/EEC and 80/765/EEC). Each table comprises n dimensions, usually five dimensions, one of which corresponds to the regional breakdown (NUTS and wine-growing regions). The dimensions of the tables are given in the form of key words. At regional level, results are given for the 210 wine-growing re- gions defined in accordance with Council Regulation (EEC) No 357/79 and, since 1989, for the 13 new Greek wine-growing regions corresponding to a second Greek breakdown; there are therefore two breakdowns for Greece until 1993. Forestry statistics The domain contains data on production and trade of roundwood and forest industry products. The data cover the area of EU-25 and EFTA countries, candidate countries, Canada, USA and Rus- sian Federation. The major groups of primary forest products in- cluded are: roundwood, sawnwood, wood-based panels, pulp and paper and paperboard. 72 2007EUROSTAT Mini-gUidE 2007 Food 1��1–200� This domain provides access to various sets of statistics related to food products and collected from different statistical sources and covering ‘From farm to fork’. Only statistics providing information on food products and the food sector and relevant for food safety purposes are included. As an example, only importers from countries outside the EU are presented. Also, priority has been given to presenting data in volume terms rather than in value. The domain also includes statistics on ‘products with distinctive marks’ such as products issued from organic farming and GMO (genetically modified or- ganisms). The domain is structured in four main chapters: n from consumption to health: food consumption figures and food-borne and water-borne infectious diseases; n from production to distribution: organic production, volume of production and trade of quality wines, volume of production and sales of food products, relative price levels of food products, etc. n inputs to the food chain: volume of agricultural primary production, volume of seeds, consumption of pesticides, main countries from which food products enter the EU territory, etc. n which actors are involved in the food chain?: number of enterprises and agricultural holdings, etc. Organic farming ‘Organic farming’ can be defined as a method of production which places the highest emphasis on environmental protection and, with regard to livestock production, animal welfare consid- erations. It avoids or largely reduces the use of synthetic chemical units such as fertilisers, pesticides, additives and medicinal prod- ucts. It has to be understood as a part of a sustainable farming system and a viable alternative to the more traditional approaches to agriculture. The database contains a set of summary tables intended to pro- vide an overview of the situation regarding organic farming within the European Economic Area. It includes the number of certified operators (producers, transformers and importers), fully converted and under conversion organic agricultural area and organic livestock. The statistical information contained in the da- tabase refers only to organic farming practices that comply with EU legislation. 7� Catalogue EXTERNAL TRADE ´ External and intra-European Union trade — Statistical yearbook — Data 1958–2005 Language available: EN only Format: PDF ISBN 92-79-00841-2 ISSN 1606-3481 Cat. No: KS-CV-06-002-EN-C Size: 1 879 Kb The yearbook on external and intra-European Union trade provides data on long-term trends in the trade of the European Union and its Member States. In particular, it contains annual statistics on the trade flows of the EU with its main trading partners on the one hand and between the Member States on the other. These statistics are broken down by major product groups. The publication also includes extra chapters on the trade of candidate countries and EFTA members. ´ External and intra-European Union trade — Pocketbook — Data 1958–2004 Language available: EN only Format: paper, 100 pages ISBN 92-894-9956-7 ISSN 1606-3481 Cat. No: KS-CV-05-001-EN-C Format: PDF Size: 829 Kb The pocketbook on external and intra-European Union trade con- tains annual time series on trade of the European Union, the euro zone and the 25 Member States. In particular, it provides statistics on trade flows between the EU and its main trading partners with a breakdown by major product groups. 7� 2007EUROSTAT Mini-gUidE 2007 ´ External trade by enterprise characteristics — Data 2002 Language available: EN only Format: PDF ISBN 92-79-01619-9 ISSN 1725-0749 Cat. No: KS-AS-06-002-EN-N Size: 2 600 Kb By linking external trade statistics with general business registers it is possible to reconcile trade flows by enterprise characteristics, for instance by sector of activity or by size of traders. In 2005, Eurostat launched a pilot study to test the feasibility of linking and to produce predefined tables. This paper provides an overview of the methodol- ogy and a summary of the main findings of the pilot study. ´ External and intra-European Union trade –– Monthly statistics Language available: EN only Format: PDF ISSN 1725-700X Cat. No: KS-AR -06-000-EN-N Periodical Size: 2 180 Kb The monthly bulletin on external and intra-European Union trade provides data on short-term trends in the trade of the European Un- ion and its Member States. In particular, it contains monthly statistics on the trade flows of the EU with its main trading partners on the one hand and between the Member States on the other. These statistics are broken down by major product groups. The publication now in- cludes the first estimates for the EU and the euro area. ´ Statistics on the trading of goods — User guide Language available: EN only Format: paper ISBN 92-79-01577-X ISSN 1725-0153 Cat. No: KS-BM-06-001-EN-C Format: PDF Size: 2 311 Kb The guide describes the basic methodology used for the compilation of Community statistics on the trading of goods. This publication is 7� Catalogue aimed at the general user of these statistics and does not require a specialist background to be understood. The 2006 edition provides some updated information on indices and on the differences between EU statistics and those published by Member States.” ´ Quality report on international trade statistics Language available: EN only Format: paper ISBN 92-79-02150-8 ISSN 1725-0749 Cat. No: KS-AS-06-001-EN-C Format: PDF Size: 3 317 Kb The purpose of this quality report is to provide the users of the Eu- ropean Union foreign trade statistics with a tool for assessing the quality of these statistics. It provides a summary of the main quality indicators which are: timeliness, accuracy, accessibility, clarity, com- parability and coherence. The quality report is updated annually. ´ Panorama of European Union trade — Data 1988–2001 Languages available : EN + DE/FR in PDF only Format: paper, 44 pages ISBN 92-894-5318-4 Cat. No: KS-DJ-03-001-EN-C Format: PDF Size: 697 Kb Price (excluding VAT): € 15 This ‘Panorama’ sets out to describe the features and trends of the EU external trade during the period 1988–2001. It emphasises the place of the European Union on the world market and analyses its trade with its main trading partners as well as the goods exchanged. The Panorama also looks into the trade between the Member States and of the euro area. The impact of the enlargement is tackled in a specific chapter. 7� 2007EUROSTAT Mini-gUidE 2007 ´ Intra- and extra-EU trade data — Combined nomenclature (DVD) — Comext Format: Monthly DVD Languages available: Trilingual edition in DE/EN/FR ISSN 1017-6594 Cat. No: KS-CK-06-000-3A-Z Periodical Subscription code: OCDR00 Single copy price (excluding VAT): € 40 Annual subscription price (excluding VAT): € 210 This DVD-ROM is published monthly. It contains statistics of trade of Member States (25 countries), classifications of countries and products, methodological notes, notes on the state of data availabil- ity and the user manual. In addition, it includes the Europroms data (European production and market statistics). ´ Geonomenclature Languages available: DE, EN, FR Format: PDF ISBN 92-894-9347-X ISSN 1725-0153 Cat. No: KS-BM-05-002-EN-N Size: 3 210 Kb The purpose of this publication is to provide users with all the sup- plementary information on the Geonomenclature 2005 that is essen- tial to understand its content and to illustrate the changes in country codes in order to facilitate analysis of the results of the statistics on the European Union’s external trade. 77 Catalogue STATISTICS IN FOCUS (selection of issues published up to the end of September 2006) • Extra EU-2� trade in goods by mode of transport — Issue No 2/200� This publication on trade by mode of transport focuses on the extra- EU trade of goods between the EU-25 and its main partners and with world regions such as Africa, America and Asia. For the reference years 1999 and 2004, the following modes of transport are considered: air, fixed installation (including pipelines), rail, road and sea. Product code: KS-NO-06-002 PDF file: 106 Kb — Issue date: 20.9.2006 • Trade between EU-2� and neighbouring countries by mode of transport — Issue No 1/200� This publication on trade by mode of transport focuses on imports and exports of goods between the EU-25 Member States and their neigh- bouring countries such as the candidate countries, CARDS, EFTA, TACIS, Mediterranean and other European countries. For the refer- ence period 1999–2004, the following modes of transport are consid- ered: air, fixed installation (including pipelines), rail, road and sea. Product code: KS-NO-06-001 PDF file: 182 Kb — Issue date: 20.9.2006 8 DATABASES External trade long-term indicators This domain contains annual time series for the EU and the euro area as well as for each of the EU Member States starting from 1990 (for the longest series) up to the last year available. It presents gross values and indices with breakdowns according to the one- digit level of the SITC classification and main trading partners. Series are available for imports, exports and trade balances. External trade short-term indicators This domain contains macroeconomic series for the EU and the euro area as well as for each of the EU Member States from Janu- ary 1989 (for the longest series) until the last month published in the external trade press release. It presents, simultaneously, gross values, indices and the corresponding seasonally adjusted data. The data are given at the one-digit level of the SITC clas- sification, by broad economic categories (BEC) and for the main trading partners, with series for each flow: imports, exports and trade balances. 7� 2007EUROSTAT Mini-gUidE 2007 External trade detailed data This domain contains monthly time series for the EU and the euro area as well as for each of the EU Member States from Janu- ary 1995 (for the longest series) until the last month available. It presents the gross values and quantities of the imported and exported goods. Data are given by trading partner, with products classified according to each level of the combined nomenclature (CN8, HS6, HS4 and HS2) and according to the levels 1, 2, 3 and 5 of the SITC nomenclature. 7� Catalogue TRANSPORT ´ Transport by air and sea — National and international intra- and extra-EU — CD-ROM — 2007 edition Format: CD-ROM/DVD ISBN 978-92-79-04620-9 ISSN 1830-6209 Cat. No: KS-DG-07-001-EN-Z Detailed statistics on passenger and freight transport by air and sea for the EU Member States, candidate and EEA countries 2004/05. ´ Road freight transport methodology — Volume 1: Reference manual for the implementation of Council Regulation (EC) No 1172/98 on statistics on the carriage of goods by road Languages available: DE, EN, FR Format: paper ISBN 92-894-9880-3 ISSN 1725-0188 Cat. No: KS-BI-05-001-EN-C Format: PDF Size: 2 393 Kb The adoption of Council Regulation (EC) No 1172/98 in May 1998 marked an important step forward in the evolution of Community transport statistics in the European Union. This regulation provides a legal base for the collection of a wide range of data on road freight transport. This reference manual aims to provide detailed guidance for Member States and candidate countries engaged in the imple- mentation of this regulation. This guidance falls into three parts: Part A — recommendations for sample surveys of the transport of goods by road; Part B — recommendations for the variables, defi- nitions and explanatory notes; Part C — rules for transmission of data to Eurostat, data validations carried out by Eurostat and rules for data dissemination by Eurostat. The current edition is for the first time published in 2 volumes: Volume 1: Reference manual in English, French and German and Volume 2: Methodologies used in surveys of road freight transport in Member States and candidate countries (in English only). Language available: EN only �0 2007EUROSTAT Mini-gUidE 2007 ´ Road freight transport methodology — Volume 2: Methodologies used in surveys of road freight transport in Member States and candidate countries Language available: EN only Format: paper ISBN 92-894-9925-7 ISSN 1725-0188 Cat. No: KS-BI-05-002-EN-C Format: PDF Size: 594 Kb The adoption of Council Regulation (EC) No 1172/98 in May 1998 marked an important step forward in the evolution of Community transport statistics in the European Union. This regulation provides a legal base for the collection of a wide range of data on road freight transport. This reference manual aims to provide detailed guidance for Member States and candidate countries engaged in the imple- mentation of this regulation. This guidance falls into three parts: Part A — recommendations for sample surveys of the transport of goods by road; Part B — recommendations for the variables, defi- nitions and explanatory notes; Part C — rules for transmission of data to Eurostat, data validations carried out by Eurostat and rules for data dissemination by Eurostat. The current edition is for the first time published in 2 volumes: Volume 1: Reference manual in English, French and German and Volume 2: Methodologies used in surveys of road freight transport in Member States and candidate countries (in English only). ´ Energy, transport and environment indicators pocketbook Language available: EN only Format: paper ISBN 92-79-02260-1 ISSN 1725-4566 Cat. No: KS-DK-06-001-EN-C Format: PDF Size: 4 322 Kb This pocketbook comprises a broad set of data collected by Eurostat and the European Environment Agency. The objective of this publi- cation is to provide an overview of the most relevant indicators on energy, transport and environment, with particular focus on sustain- able development. It presents data for the EU Member States, candi- date and EFTA countries. �1 Catalogue ´ Glossary for transport statistics Languages available: EN, FR Format: paper ISBN 92-894-4942-X Cat. No: KS-BI-03-002-EN-C Format: PDF Edition: 2004 The Glossary for transport statistics was published for the first time in 1994 with the purpose of assisting member countries during the collection of data on transport made by UNECE, ECMT and Eurostat through the common questionnaire. The glossary now comprises 533 definitions and represents a point of reference for all those involved in transport statistics. STATISTICS IN FOCUS (selection of issues published up to the end of September 2006) • Passenger transport in the European Union — Issue No �/200� This publication includes data on passenger transport in the EU Mem- ber States, and EFTA countries. The figures are presented at country level and include all inland transport modes. Product code: KS-NZ-06-009 PDF file: 124 Kb — Issue date: 19.9.2006 • Road freight transport 1���–200�: cabotage and transport with non-EU countries — Issue No �/200� This new publication on road freight transport focuses on cabotage transport and transport with non-EU countries. It covers data report- ed by EU Member States and Norway for the years 1999–2004. Product code: KS-NZ-06-008 PDF file: 422 Kb — Issue date: 5.7.2006 • Air transport between the EU and the USA — Issue No 7/200� This publication includes data on air passenger transport between the EU and the USA mainly for 2003 and 2004 covering the number of passengers at country, airport and route level as well as the number of flights. Data on air freight transport is also shown for 2004. Product code: KS-NZ-06-007 PDF file: 498 Kb — Issue date: 30.6.2006 8 DATABASES Railway transport This domain presents aggregated data from the common ques- tionnaire (Eurostat/UNECE/ECMT) and detailed annual and monthly freight transport data from Council Directive 80/1177/ EEC of 4 December 1980. �2 2007EUROSTAT Mini-gUidE 2007 Road transport This domain contains aggregated data from the common ques- tionnaire (Eurostat/UNECE/ECMT) and detailed annual and quarterly freight transport data from Council Directive 78/546/ EEC of 12 June 1978, as amended by Council Directive 89/462/ EEC of 18 July 1989. Since 1999, the data included in the ‘romegood’, ‘romegonr’ and ‘romecabo’ tables are derived from microdata collected under Council Regulation (EC) No 1172/98. Inland waterways transport This domain gives information on aggregated data from the com- mon questionnaire (Eurostat/UNECE/ECMT) and detailed an- nual, quarterly and monthly freight transport data from Council Directive 80/1119/EEC of 17 November 1980. Oil pipeline transport This domain gives information on aggregated data from the com- mon questionnaire (Eurostat/UNECE/ECMT) on oil pipelines following the definition set up in the Eurostat–UNECE–ECMT Glossary for transport statistics. Maritime transport This domain presents quarterly and annual data based on Council Directive 95/64/EC of 8 December 1995. It contains information on seaborne transport of goods, passenger movements and ves- sels calling at ports. Air transport This domain gives information on the total passengers and ton- nage of freight transported (in tons) at the levels of airport pairs, airports, countries or regions of the world. �� Catalogue ENVIRONMENT AND ENERGY ´ Environmental statistics in the Mediterranean countries — Compendium 2005 Languages available: EN, FR Format: paper, 140 pages ISBN 92-79-01539-7 Cat. No: KS-74-06-823-EN-C Format: PDF Size: 3 939 Kb Price (excluding VAT): € 30 This publication is the second environmental statistics compendium produced by Eurostat with data coming from the national statistics of- fices (NSOs) of the EU’s 12 southern and eastern Mediterranean part- ner countries. It presents the data and the metadata collected within these 12 countries in the framework of the Medstat Environment project, including a whole set of general indicators plus more specific indicators concerning soils, forests, water, marine environment, biodi- versity, atmospheric pollution and production and treatment of solid wastes. Certain tables help to make comparisons at a regional level, while others illustrate the historical evolution at a national level. ´ Gas and electricity market statistics — Data 1990–2006 Language available: EN only Format: paper ISBN 92-79-02837-5 ISSN 1830-0472 Cat. No: KS-76-06-289-EN-C Format: PDF Size: 7 883 Kb This is a new upgraded Eurostat publication which substitutes the previous publications Energy prices, Gas prices and Electricity prices and gives an overall quantitative overview of the evolution especially of the liberalised gas and electricity markets. This new publication consists of a ‘classical’ paper part and an accompanying CD-ROM, where time series of relevant data, tables, older relevant publications in PDF format, legal acts etc. and other added-value features are included. It is enriched with illustrative texts and graphs, analyses referring to trends and data and provides basic quantitative infor- mation on gas and electricity prices, as well as on structures for gas and electricity existing in each country. It also includes statistical information on selected indicators. Reporting countries are all 25 Member States. �� 2007EUROSTAT Mini-gUidE 2007 ´ Panorama of energy — Energy statistics to support EU policies and solutions — 2007 edition Language available: EN only Format: paper ISBN 92-79-03894-X Format: PDF Cat. No: KS-76-06-604-EN-C This first edition of the Panorama of energy endeavours to deliver global characteristics of the energy situation in Europe, using the most recent official data available in Eurostat. It covers the main energy themes for the EU-25 as well as for each individual Mem- ber State and quantifies them. Community energy policies receive deserved attention, and, in order to demonstrate the dynamic na- ture of the subject and how new policies call for new solutions, a few statistical projects will illustrate recent development work in cooperation with the Member States. A CD is included which, apart from a substantial amount of documentary information, invites the reader to review statistical data by means of an easy-to-use numeri- cal presentation tool. ´ Energy — Yearly statistics — Data 2004 Language available: EN only Format: paper, 468 pages ISBN 92-79-02064-1 ISSN 1609-4190 Cat. No.: KS-CN-06-01-3A-C Format: PDF Size: 1 356 Kb Price (excluding VAT): € 30 The first chapter of this yearbook covers the characteristic data of energy economics in recent years. The second chapter gives an overall view of the trends for the principal aggregates, taken from the ‘energy supplied’ balance-sheets of the European Union in tonnes of oil equivalent. Chapters 3–7 give historical series for each energy source for the principal aggregates characterising the structure of energy economics. All EU-25 Member States are cov- ered, the accession countries Bulgaria and Romania, the candidate countries Croatia and Turkey, as well as the EEA countries Iceland and Norway. �� Catalogue ´ Energy balance sheets — Data 2003–2004 Language available: EN only Format: PDF ISBN 92-79-02063-3 ISSN 1725-3144 Cat. No: KS-DM-06-001-3A-N Size: 1 900 Kb The current publication, which is exclusively devoted to the global energy balance sheets, presents, for the years 2003 and 2004, the bal- ance sheets expressed in specific units and in tonnes of oil equivalent, for each Member State and candidate country of the European Un- ion, Norway and Iceland. The balance sheets have been constructed according to Eurostat’s methodology for energy balances, where all the operations are harmonised on the basis of the energy content of each source and form of energy, without any hypothetical substitu- tions, nor any calculation of equivalence. ´ Energy — Monthly statistics Languages available: DE/EN/FR Format: PDF ISSN 0258-3569 Cat. No: KS-BX-06-000-3A-N Periodical Size: 1 435 Kb The latest monthly statistics available in Eurostat for energy are pre- sented for each Member State, Bulgaria, Romania and Norway. The statistics for the corresponding months in the previous year are also included, as well as the annual statistics for the preceding year as summed-up for the statistics on individual months. Additionally, the statistics for the EU, EU-15 and the euro area are given. �� 2007EUROSTAT Mini-gUidE 2007 ´ Waste generated and treated in Europe — Data 1995–2003 Language available: EN only Format: paper ISBN 92-894-9996-6 Cat. No: KS-69-05-755-EN-C Format: PDF Size: 4 065 Kb The 2005 edition presents an update of the successful 2003 publica- tion on Waste generated and treated in Europe. It is the last publication based on the data collection via the joint OECD/Eurostat question- naire. In 2006, this questionnaire will be replaced by reporting under the waste statistics regulation. This publication provides important baseline data for the implementation of national and European poli- cies on waste management. In addition, it informs about the new le- gally covered reporting scheme.” ´ Environmental expenditure statistics — Industry data collection handbook Languages available: DE, EN, FR Format: paper ISBN 92-894-9687-8 ISSN 1725-0218 Cat. No: KS-EC-05-002-EN-C Format: PDF Size: 2 867 Kb In order to respond to the need for structural business statistics, ex- tended under Council Regulation (EC, Euratom) No 58/97 to include environmental protection expenditure, Eurostat has produced this publication in collaboration with the Member States and the acces- sion and EFTA countries. It is designed as a practical aid to compiling statistics on environmental protection expenditure by industry and describes each step in the process, either by means of an explanatory text or on the basis of examples taken from the experience of various countries. �7 Catalogue STATISTICS IN FOCUS (selection of issues published up to the end of September 2006) • The coal industry in the European Union in 200� and 200� — Issue No 1�/200� Describes the evolution of coal industry in the European Union from 2004 to 2005, including primary production, colliery stocks, con- sumption, imports and exports of coal, production, stocks at coking plants, imports and exports of coke-oven coke. Product code: KS-NQ-06-014 PDF file: 87 Kb — Issue date: 23.9.2006 • Statistical aspects of the energy economy in 200� — Issue No 1�/200� This publication provides a first overview of the energy economy in 2005 based on monthly cumulated data for each EU Member State, as well as the EU-25, EU-15 and the euro area. Production and con- sumption trends are outlined for the principal energy commodities — oil, natural gas, solid fuels, nuclear and primary electrical energy. The main indicators such as energy dependence rate and energy in- tensity are presented. In addition, heating degree days calculated on the basis of a common Eurostat methodology are also included. Product code: KS-NQ-06-013 PDF file:147 Kb — Issue date: 21.9.2006 ´ OECD/Eurostat environmental protection expenditure and revenue joint questionnaire / SERIEE environmental protection expenditure account — Conversion guidelines Language available: EN only Format: PDF Cat. No: KS-EC-05-011-EN-N Size: 391 Kb The conversion guideline describes the differences between the sys- tem of environmental protection expenditure account (EPEA) and the joint questionnaire (JQ) for environmental protection expendi- tures. A number of countries are already using EPEA results as a basis for data reporting in the JQ. However, comparison between EPEA publications and data reported in the JQ shows that there are often differences both in terms of data quantity and in actual figures. This publication is aimed at the compilers of these statistics and might require a specialist background to be understood. �� 2007EUROSTAT Mini-gUidE 2007 8 DATABASES Environment statistics The data in this domain focus on the environment and the impact of economic activities on it. The following areas are covered: n waste generation and treatment, including recycling; n water resources and use wastewater treatment; n environmental protection expenditure; n environmental taxes; n plant protection products (sales and usage); n air pollution and climate change; n land use. Two additional data collections cover the impact of specific eco- nomic sectors. The part on agriculture includes data on pesti- cides, fertilisers and organic farming, the part on transport covers various environmental aspects including transport efficiency and prices. • Electricity prices for EU households and industrial con- sumers on 1 January 200� — Issue No 11/200� This publication on electricity prices in the European Union includes prices for household and industrial consumers referring to 1 January 2006. Details on the price evolution over the last three years are given for the EU-15 average, as well as on the evolution of all national prices by comparing them to price data of 12 months ago. Detailed tables with price data for all household and industrial consumers are avail- able for EU Member States, some candidate countries and Norway. Product code: KS-NQ-06-011 PDF file: 110 — Issue date: 11.7.2006 • Air pollutant emissions in the Mediterranean partner countries — Issue No �/200� This Statistics in Focus results from data collection initiated by the Medstat environment programme. It presents the emissions of green- house gases and other air pollutants in the Mediterranean partner countries including their sources and trends over time. This is im- portant information with regard to the discussion about the driving forces for climate change. Product code: KS-NQ-06-009 PDF file: 160 Kb — Issue date: 9.6.2006 �� Catalogue The chapter on environmental protection expenditure delivers detailed data on private and public spending on the protection of the environment. Energy The domain ‘Energy’ covers a broad spectrum of data. Energy quantities: Annual data on crude oil, oil products, natural gas, electricity, solid fuels and renewables covering the full spec- trum of the energy balance positions from supply through trans- formation to final energy consumption by sector and fuel type. Monthly data on crude oil, oil products, natural gas, electricity and solid fuels, covering mainly the supply side. Energy prices: Half-yearly data on electricity and natural gas prices both for industrial end-users as well as for households; to- gether with pump prices of premium unleaded gasoline 95 RON and diesel oil, as well as prices of heating oil and residual fuel oil. Prices are provided without taxes, with VAT and with all taxes included in monetary units (euro, national currencies and pur- chasing power parities). Energy indicators: Seven selected energy indicators belonging to the major collection ‘Structural indicators’ and four indicators be- longing to the collection ‘Euro indicators’ are included. Nuclear power stations (historical collection): This histori- cal collection comprises monthly and annual data (last update: December 2001) on selected indicators, such as load factors, maximum output capacity, net thermal efficiency, etc. by nuclear power operator. �0 2007EUROSTAT Mini-gUidE 2007 SCIENCE AND TECHNOLOGY ´ Science, technology and innovation in Europe Language available: EN only Format: paper ISBN 92-79-02577-5 Cat. No: KS-76-06-203-EN-C Format: PDF Size: 5 561 Kb Price (excluding VAT): € 25 This publication presents statistics on Europe’s recent per-formance in research and development, innovation, high-tech industries and knowledge-based services, patenting and human resources in science and technology. Relevant and meaningful indicators in these areas are paramount in informing the public and policy-makers as to whe- re Europe stands in moving towards more knowledge and growth. This information is also necessary to better gauge how Europe is evolving, compared with the United States, Japan, China, the Russian Federation and other main economies. ´ Science and technology in Europe — Data 1990–2004 — Pocketbook Language available: EN only Format: paper ISBN 92-894-8798-4 ISSN 1725-5821 Cat. No: KS-EA-06-001-EN-C Format: PDF Size: 3 913 Kb This publication presents statistical data and indicators based on a number of data sources available at Eurostat (mainly related to sci- ence, technology, innovation and regions). It provides the reader with statistical information to appreciate the evolution and composition of science and technology (S & T) in Europe and its position with re- gard to its partners. The pocketbook is divided into nine chapters in- cluding: key R & D input and output indicators (R & D expenditure, R & D personnel and government budget appropriations or outlays on R & D (GBAORD)); patents; high technology; regional overview of S & T, looking at the top European regions and at the best-per- forming region of each country; human resources in S & T fields (HRST); main conclusions of the Community innovation statistics 2002/03. Another chapter gives some general statistics concerning population, gross domestic product (GDP) and employment. �1 Catalogue ´ Data production methods for harmonised patent statistics: assignee sector allocation Language available: EN only Format: PDF ISBN 92-79-02499-X ISSN 1725-0838 Cat. No: KS-AV-06-001-EN-N Size: 2 163 Kb In 2004, the interinstitutional Patent Statistics Task Force decided to create a worldwide statistical database under the acronym of Patstat, which has to be understood as one single patent statistics raw database, held by the EPO and developed in cooperation with WIPO, OECD and Eurostat. Considering that industrial innovative activity has created a need for refinements in patent indicators, sec- tor assignment has thus become a necessary condition for further analysis of the dynamics underlying technological performance. The document presents the methodology developed to produce an exhaustive sector assignment aiming to identify whether patentees are companies, universities and higher education institutions, or governmental agencies within Patstat. The paper also delineates paths for further improvements of the methodology within the world of researchers and analysts. �2 2007EUROSTAT Mini-gUidE 2007 ´ Data production methods for harmonised patent statistics: patentee name harmonisation Language available: EN only Format: PDF ISBN 92-79-02500-7 ISSN 1725-0838 Cat. No: KS-AV-06-002-EN-C Size: 1 878 Kb In 2004, the interinstitutional Patent Statistics Task Force decided to create a worldwide statistical database under the acronym of Patstat, which has to be understood as one single patent statistics raw da- tabase, held by the EPO and developed in cooperation with WIPO, OECD and Eurostat. The document presents a method for cleaning and harmonising the names of patent applicants within Patstat. This method was defined on the basis of already existing approaches. Its main working steps are related to character cleaning and standardis- ing, legal form indication removal, non-significant character removal, approximate string searching, keyword searching etc. The application of the method leads to a considerable reduction of name diversity after name cleaning. This improves the data quality of the patent raw data and the aggregated patent statistics thoroughly. ´ Oslo manual 2005 Languages available: EN, FR Co-editor: OECD Guidelines for collecting and interpreting innovation data. The ability to determine the scale of innovation activities, the char- acteristics of innovating firms, and the internal and systemic factors that can influence innovation is a prerequisite for the pursuit and analysis of policies aimed at fostering technological innovation. The Oslo manual is the foremost international source of guidelines for the collection and use of data on innovation activities in industry. This second edition has been updated to take account of the progress made in understanding the innovation process, the experience gained from the previous round of innovation surveys, the extension of the field of investigation to other sectors of industry and the latest revi- sions of international standard classifications. �� Catalogue ´ The EU-15’s new economy: a statistical portrait Language available: EN only Format: paper ISBN 92-894-9058-6 ISSN 1725-0838 Cat. No: KS-AV-05-001-EN-C Format: PDF Size: 732 Kb The working paper The EU-15’s new economy: a statistical portrait offers a statistical portrait of the EU-15 which makes it possible to benchmark the countries involved in terms of how they are managing to achieve the goals set in the Lisbon strategy. It is both comprehen- sive — covering a broad spectrum of phenomena relevant to the new economy — but at the same time compact, restricting itself to a set of 50 key indicators. Because of this, the publication provides the reader with a well-considered and practical overview of how Europe is per- forming with regard to the Lisbon agenda. This working paper is one of the results of the project ‘New economy statistical information system’ (NESIS), which was carried out under the fifth EU frame- work programme for research and technological development (FP5). Further information on the NESIS project is available at the project website http://nesis.jrc.it. This publication exists only in English. STATISTICS IN FOCUS (selection of issues published up to the end of September 2006) • Trade in high-tech products — Issue No 1�/200� This Statistics in Focus publication analyses the high-tech trade in the EU Member States, the United States and Japan. In 2004, the EU-25 was the leading economy in the world when looking at the value of high-tech exports and imports. Good progress was, in particular, ob- served in the new EU Member States. Products related to electronics and telecommunications had the highest trade share in all the three economies of the triad. Product code: KS-NS-06-014 PDF file: 215 Kb — Issue date: 15.9.2006 • High-tech industries and knowledge-based services — Issue No 1�/200� This publication presents data showing the importance of R & D and human resources in science and technology. HRST represented 47 % of total employment in services, but only 29 % in the manufacturing sector in 2004, the share of scientists and engineers (S & E) among HRST varying markedly across sectors. In the total services sector, the proportion of women among HRST was higher than in the manu- facturing sector. In 2003, the proportion of researchers among busi- ness enterprise R & D personnel was generally higher in the high-tech manufacturing sector than in the manufacturing sector as a whole. Product code: KS-NS-06-013 PDF file: 200 Kb — Issue date: 23.8.2006 �� 2007EUROSTAT Mini-gUidE 2007 • Measuring gender differences among Europe’s knowle- dge workers — Issue No 12/200� In 2004, only 29 % of Europe’s scientists and engineers were women, working with a share of 88 % in services. Out of the male scientists and engineers, the corresponding proportion was 67 %. The unem- ployment rate is higher with female scientists compared to male ones, even if these differences seem to have diminished in recent years. Product code: KS-NS-06-012 PDF file: 280 Kb — Issue date: 21.8.2006 • Tourism and the Internet in the European Union — Issue No 20/200� This publication takes a look at the specific usage the accommodation sector makes of information technologies in general and of the Inter- net in particular, using the results of the 2005 Community surveys on ICT usage. While the accommodation sector is largely ahead of other sectors in its usage of information technologies for managing client relationships, it lags behind other sectors as regards the full im- plementation of e-business. One explanatory factor might be the low uptake of broadband technologies that would allow more information to be conveyed at a faster speed. Product code: KS-NP-06-020 PDF file: 850 Kb —Issue date: 18.8.2006 • How skilled are Europeans in using computers and the Internet? — Issue No 17/200� This publication takes a look at Europeans’ inclusion in the informa- tion society, more specifically at their ‘e-skills’ in using information and communication technologies, using the results of the 2005 Com- munity survey on ICT usage in households and by individuals ‘The digital divide in Europe’ (Issue No 38/2005) Product code: KS-NP-06-017 PDF file: 140 Kb — Issue date:20.6.2006 • Use of the Internet among individuals and enterprises — Issue No 12/200� This issue sets out to highlight some of the initial results of the 2005 Community surveys on ICT usage in enterprises and households. In- cluded are Internet access and broadband connectivity, regular Inter- net usage by individuals and electronic commerce on the Internet. Product code: KS-NP-06-012 PDF file: 420 Kb — Issue date: 6.4.2006 8 DATABASES Research and development R & D indicators are often considered as a main driver for eco- nomic development, innovation and growth. This domain pro- vides users with data concerning R & D expenditure and R & D personnel, broken down by the following institutional sectors: business enterprise, government, higher education, private non- profit and the total of all sectors. Data are compiled and broken �� Catalogue down further using the guidelines laid out in the Proposed stand- ard practice for surveys of research and experimental development, ‘Frascati manual’, OECD, 2002. Data are available for the EU Member States, Iceland, Norway, the candidate countries, Japan and the United States. Until 2003, data on R & D were collected under gentleman’s agreement. From the reference year 2003 onwards, the data collection is based on the Commission Regulation (EC) No 753/2004 on statistics on science and technology. Employment in high-technology sectors This domain provides users with data concerning employment in both manufacturing and service high-technology sectors accord- ing to region (up to NUTS 2 level) for the EU-15 Member States. The data are obtained from the Community labour force survey (theme ‘Population and social conditions’) and are expressed in absolute terms and as a percentage of total employment. Human resources in science and technology This domain provides users with data concerning human resourc- es in science and technology (HRST). Breakdowns are given ac- cording to gender, age, region, sector of activity, occupation and educational attainment, although it should be noted that not all combinations are possible. The data on stocks and also mobility are obtained from the Community labour force survey while data on education come from the education database, both of which are in the theme ‘Population and social conditions’. Survey on innovation in EU enterprises This domain covers statistics on the number of enterprises hav- ing introduced new or improved products or processes, turno- ver of new and improved products, innovation expenditure, objectives and hampering factors for innovation in the business enterprise sector (all manufacturing industries and several serv- ice industries). Data from the CIS 2 covers the period 1996–98, CIS 3 data covers the period 1998–2000. The Community innovation survey (CIS) is a survey on innovation activity in enterprises covering EU Member States, EU candidate countries, Iceland and Norway. All aggregations and indicators presented in this collection are based on data from the national CIS 2 and CIS 3 data collections. Research and development: government budget appropria- tions or outlays on R & D Government budget appropriations or outlays for R & D (GBAORD) are a way of measuring government support to R & D activities, or, in other words, how much priority govern- ments place on publicly funding R & D. �� 2007EUROSTAT Mini-gUidE 2007 GBAORD data include all appropriations allocated to R & D in central government or federal budgets. Government R & D ap- propriations are broken down by socio-economic objectives on the basis of NABS (Nomenclature for the analysis and comparison of scientific programmes and budgets, Eurostat, 1994). The data are obtained from the Member States, candidate coun- tries, Iceland and Norway whereas those for Japan and the United States come from the OECD — Main science and technology in- dicators (MSTI). European and US patenting systems This domain provides users with data concerning patent applica- tions to the European Patent Office (EPO) and patents granted by the United States Patent and Trademark Office (USPTO). In the ‘Patent applications to EPO by date of filing’ collection, data are given at national and regional levels for both total patents and patents in high-technology fields. Data have been provided by the EPO and are broken down according to the international patent classification (IPC). Data in the ‘Patent granted by USPTO by date of publication’ collection have been provided by the USPTO and are available at national level only. �7 Catalogue Order form for products on sale To be sent to the sales office in your country. See addresses on the Internet (http://publications.europa.eu). Please send me the following publication(s): QUANTITY TITLE CATALOGUE NO. payment is due on receipt of invoice. Name and address Date and signature European Commission Mini-guide – Eurostat databases and publications Luxembourg: Office for Official Publications of the European Communities 2007 — 97 pp. — 21 x 10.5 cm ISBN 92-79-02952-5 ISSN 1725-5961 How to obtain EU publications Our priced publications are available from EU Bookshop (http://bookshop.europa.eu), where you can place an order with the sales agent of your choice. The Publications Office has a worldwide network of sales agents. You can obtain their contact details by sending a fax to (352) 29 29-42758. terzaPocket.indd 3 30-03-2007 16:12:14 Mini-guide KS-76-06-324-EN - ISSN 1725-5961 Eurostat publications and databases 2007-2008 edition EuropEaN CommISSIoN ISBN 92-79-02952-5 9 789279 029523 cover.indd 1 27-04-2007 16:18:58 N Eurostat publications and databases - 2007-2008 edition Introduction Contents Introduction to Eurostat and its statistical information SELECTION OF EUROSTAT PRODUCTS:PUBLICATIONS, CD-ROMS AND DATABASES 1. General and regional statistics 2. Economy and finance 3. Population and social conditions 4. Industry, trade and services 5. Agriculture and fisheries 6. External trade 7. Transport 8. Environment and energy 9. Science and technology Order form

Compact guides and catalogues , Product code: KS-FM-12-001, published on 11-Jun-2012

Statistics Explained is an official Eurostat website presenting all statistical topics in an easily understandable way This publication presents all validated articles included in Statistics Explained in May 2012. Volume 1 contains all articles related to European general and economic statistics.

Statistical working papers , Product code: KS-44-02-068, published on 01-Oct-2002

Catalogue number: KS-44-02-068-EN-C ISBN number: 92-894-3733-2 E N L A R G IN G T H E E U S T A T IS T IC A L N E T W O R K Turkey Slovenia Romania Lithuania Estonia Czech Republic Cyprus Bulgaria ENLARGING THE EU STATISTICAL NETWORK Slovak Republic Poland Malta Latvia Hungary E U R O P E A N COMMISSION Multi-beneficiary programme on Statistical Co-operation Programme Secretariat Services This publication has been prepared in the framework of the project entitled: ‘Programme Secretariat Services for Phare Candidate Countries (Phare ZZ9916)’ and was financed by Phare funds. The chapter components for Cyprus, Malta and Turkey were financed by Commission’s budget. The project was entrusted for execution to CAMIRE, Estadistica y Analisis, S.L. Editorial team — Programme Secretariat: Jolanta Szczerbinska, François Bigot and Amina Kafaï Writer : Kenneth Smith (People Knowledge Limited) Concept & Desktop Publishing: Doumig Le Cuziat The findings, conclusions and interpretations expressed in this publication should in no way be taken to reflect the policies or opinions of the European Commission. ISBN 92-894-3733-2 Catalogue Number : KS-44-02-068-EN-C © European Commission, Luxembourg, August 2002 Turkey Slovenia Romania Lithuania Estonia Czech Republic Cyprus Bulgaria ENLARGING THE EU STATISTICAL NETWORK Slovak Republic Poland Malta Latvia Hungary E U R O P E A N COMMISSION Multi-beneficiary programme on Statistical Co-operation Programme Secretariat Services G France United Kingdom Portugal Spain Liechtenstein Switzerland Ireland Norwa Iceland Luxembourg Den Netherlands Belgium 2 Enlarging the EU Statistical Network Turkey Cyprus Hungary Malta Bulgaria Romania Finland Estonia Latvia Sweden Germany Czech Republic Poland Slovak Republic Slovenia Austria Lithuania ay mark Italy Greece Enlarging the EU Statistical Network 3 4 Enlarging the EU Statistical Network Enlarging the EU Statistical Network 5 The enlargement’s political challenges need high quality statistics. This implies a particular effort for developing the national statistical systems of the Candidate Countries for the adoption of the acquis. It also implies an assessment of each. Take 17 European statistical experts, analysing the efforts of 13 different nations to develop systems of national statistics to the European standards. Add the full co-operation of government officials across the nations. Collect all their data and findings.Tabulate hundreds of statistics. Edit about one thousand pages of reports.And you have here a unique book of reference that provides information on the national statistical systems of all the Candidate Countries to the European Union. This publication is a source of reference on the organisation and capacity of the national statistical systems and on the methods with which they produce their statistics.The level of the harmonisation of the statistical systems with the EU requirements is given special emphasis. It is a joint effort of the Member States’ statisticians who contributed with their expertise in assessing the systems, the Candidate Countries who were willing to share the information on their daily work and Eurostat staff who monitored the process. It documents the progress made by the Candidate Countries over the last twelve years of intensive co-operation with Eurostat. It is evident that they made a good use of the assistance provided and capable of producing good quality and comparable statistics. The facts presented in the publication prove that, at the threshold of accession, the Candidate Countries are ready to take up the challenge of being active participants in the European statistical system and are able to contribute to its further development. Yves Franchet Director General Eurostat Foreword 6 Enlarging the EU Statistical Network Enlarging the EU Statistical Network 7 In many respects the EU is about sharing and exchanging.With goods and services, ideas and inspiration, languages and cultures, peoples are coming together and being enriched by the diversity that is the very essence of Europe. Nowhere is the sharing more important than in respect of information and data: the statistics that allow us to acquire knowledge. Each country provides this knowledge in a standardised form to enable comparisons between Member States and also the elements with which to create a picture of the Union as a whole. With the emphasis on the data comparability and quality as one of the priority objectives of the European statistics it is important to know the standing of the Candidate Countries in this respect. Very soon they will be providers of regular statistical data series for a large variety of the Eurostat publications.To fully benefit from the information the users should be equipped with some background knowledge on the production process to make informed judgements on the data. The information contained in the publication aims at a comprehensive presentation of national statistical systems against the general country profile and attempts also to indicate what they have in common and how they differ. Accordingly, the publication contains the country specific chapters and summary annex tables in which main characteristics of the systems are presented for all the countries. I would like to express my sincere thanks to all colleagues in the Member States and in our partner countries for their contributions and hope that this publication will serve its purpose of providing a closer insight into the statistical systems of the newcomers. Photios Nanopoulos Director Eurostat Introduction 8 Enlarging the EU Statistical Network Enlarging the EU Statistical Network 9 The Programme Secretariat would like to express their sincere thanks to all those who have made this publication possible. It was prepared within the framework of the 1999 Phare Multi-Beneficiary Phare Programme in Statistical Cooperation under the supervision of Heikki Salmi and Nikolaus Wurm of Eurostat Unit A5 with the contribution of respective country desk-officers in the unit. Project management was ensured by Elisabeth Lamp, Director of Camire and the co-ordination by Amina Kafaï of Camire. The chapters were written by Kenneth Smith of People Knowledge Limited and the desktop publishing and layout were carried out by Dominique le Cuziat. Cristina Perreira de Sa of Eurostat Unit C1 and Beatrice Paccoud contributed with advice on the publishing standards. The map was provided by the GISCO team of Eurostat. The content was drafted on the basis of reports provided by teams of experts from the national statistical systems of the majority of Member States and Norway, namely:Tim Holt and David Wroe of UK, Pedro Dias of Portugal, Matti Niva and Gösta Guteland of Sweden, Michel Blanc, Patrice Roussel and Alain Tranap of France, Etienne Chapron of UN/ECE, Reinhold Schwarzl of Austria, Ronald Luttikhuizen of the Netherlands, Günter Kopsch and Ligia Frankfort of Germany, Kirsten Wismer of Denmark, Jan Byfuglien of Norway, Heli Jeskanen-Sundström and Hilkka Vihavainen of Finland. The review of the whole content was done by Alain Chaintraine, former Director of Eurostat. Finally, the heads of international relations in all 13 Candidate Countries patiently verified the facts and figures, contributing with essential comments and suggestions. It was the enthusiasm and commitment of all the persons that made this publication possible. Jolanta Szczerbinska François Bigot The Programme Secretariat Acknowledgements 10 Enlarging the EU Statistical Network Table of contents Foreword .............................................................................................. 5 Introduction .................................................................................... 7 Acknowledgements ................................................................... 9 Reviewing the process ........................................................... 11 Country chapters: Bulgaria ................................................................................................ 19 Cyprus ..................................................................................................... 29 Czech Republic .............................................................................. 39 Estonia .................................................................................................... 49 Hungary ................................................................................................ 61 Latvia ....................................................................................................... 71 Lithuania .............................................................................................. 81 Malta .......................................................................................... ................ 91 Poland ..................................................................................................... 101 Romania ................................................................................................ 109 Slovak Republic............................................................................... 119 Slovenia ................................................................................................. 129 Turkey ..................................................................................................... 139 Annex Tables ................................................................................... 147 Contact Addresses .................................................................... 158 Glossary ................................................................................................. 159 Enlarging the EU Statistical Network 11 Reviewing the process Enlargement is a simple word that describes far more than just the growth of the European Union. Never in its short history has the EU faced the integration of so many different nations with such divergent cultures and political experiences. The thirteen nations – Bulgaria, Cyprus, the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Romania, the Slovak Republic, Slovenia and Turkey – have moved from co-operating with the EU, to viewing accession into the Union as a fundamental goal for the near future. For many of these nations, the transition in thirteen years from command economies to open market economies, which can be considered alongside the existing Member States of the European Union, has required an immense effort. Some groundwork started before 1989 through participation by countries in central Europe with the Economic Commission for Europe at the UN, yet the achievement is impressive. The Phare Programme of the European Union operating since 1989 has been the main financial instrument in sup- porting the Central European countries as they strive to adopt the full range of EU rules and practices laid down in the acquis communautaire. Statistical co-operation pro- grammes are a part of the Phare assistance, where the Candidate Countries (CCs) aim at strengthening their capacity to implement the acquis through institution- building, transfer of know-how and setting up of sustain- able statistical systems based on European standards and methods. When considering these CCs for membership of the EU, it is obviously crucial to their integration that they can communicate data, measure development, make comparisons in a way that is in harmony with the existing Member States.To understand the capacity of each CC to meet these needs, it has been necessary to study the statistical systems of each country in depth and report on the progress being made to harmonise national measures and methods with those now used throughout the EU. Assessing the national statistical systems Within the 1997 and 1999 Phare Multi-Beneficiary Programme for Statistical Co-operation, a set of global assessments of the statistical systems of EU Candidate Countries (CCs) was carried out between 1999 and 2002. Some of these assessments were financed by Phare and others by Eurostat. The objective was to evaluate three aspects related to compliance by assessing: • The administrative capacity of the country in the field of statistics • The legal compliance level with the acquis communautaire in statistics • The technical aspect of the national statistical system in the country For each CC, the assessors included a team of two experts from EU Member States, who visited the CCs twice during two months and have compiled in-depth analyses with the full co-operation and commitment of the local officials.This exercise has been unprecedented in its scope and impressive in its thoroughness. It has required people who are not only EU experts and in their field but who can also apply objectivity to the research, discussion and conclusions. It should also be noted that these analyses have necessitated the most open co-operation from the national statistics and government officials in all thirteen states. This has been given with enthusiasm and energy that has clearly displayed the eagerness to become integrated into the EU. In addition to the meetings and discussions, the resulting global assessment reports were also based on documentation on the national statistical systems (legal framework, statistical work programmes, operational procedures) provided by the CCs and the country desk officers of Eurostat. It should be noted that the global assessments did not cover the entirety and all the methodological and institutional detail of the statistical system but focussed rather on specific topics approved by Eurostat. These included legal aspects, administrative relationships, staff and dissemination policy. The actual statistical systems have been studied as well as the process of data production that is currently carried out. The functioning of each National Statistical Institute (NSI) and the legal framework within which it works have been analysed to review compliance with EU demands. In terms of the statistical output itself, quite clearly, the key issue has been the harmonisation of measurement and reporting systems with those of Eurostat and strict adherence to fundamental principles that ensure the maximum accuracy of survey results. The Candidate Countries Accession negotiations were first opened in March 1998 with six countries: Cyprus, the Czech Republic, Estonia, Hungary, Poland and Slovenia. In February 2000, the second wave of negotiations was launched for a further six countries: Bulgaria, Latvia, Lithuania, Malta, Romania and the Slovak Republic.The EU adopted the accession partnership for Turkey only in March 2001. For the purposes of the membership negotiations, the acquis communautaire is divided into 31 chapters, of which chapter 12 refers to the ‘statistical acquis’. This states that the CCs need to have sound statistical bases to produce accurate and harmonised data in a permanent and sustainable way. To produce accurate comparable statistics, it is necessary for the CCs to build on the general administrative capacity of a country, on its public service performance and on the capacity to recruit and retain qualified people. By December 2000, the chapter on statistics had been negotiated and was provisionally closed with twelve of the CCs. At the time, negotiations with Turkey had not yet begun. The eclectic group of 13 CCs therefore includes nations that formerly struggled under dominated planned economies, which had different forms of government administration and had diverse legal structures. It is with the legal aspect that we should start to take a closer look at the complexities of this work and the overall conclusions. The legal background Every national statistical system has to be founded on a clear legal framework. Among the essential elements is the independence of the system so that integrity and veracity in all respects are never questioned. In addition, only a clear guarantee of confidentiality encourages free and active participation in surveys by individuals, firms and others. Some of the CCs were using statistical laws that had been on the statute books for quite some time. Turkey’s law, for example, dates from 1962 although it is currently being substantially revised. Most of the countries that have chosen the transition to a market economy after 1989 introduced new laws covering statistics in the first few years of the 1990s. In nearly all such cases, amendments have been made in the last two years to bring them into line with EU standards. Most of these amendments have clearly provided for confidentiality of data, including strict rules of the supply of anonymous statistics for research use. In addition, all the CCs, except Romania, have laws which include statistical obligation to provide data as well as personal data protection. National statistical systems Carrying out surveys and the accumulating statistical information have in one form or another existed for many years in all the countries concerned. Therefore, statistical systems and National Statistical Institutes (NSIs) have histories that date back, in some instances, to the first quarter of the 20th century or even earlier. The Hungarian Statistical Office exists since 1867 whereas the Central Statistical Office of Poland was established in 1918 and those for the Czech Republic and Latvia existed since 1919 but were re-established under new laws in 1995 and 1997 respectively. The statistical offices of Malta, Cyprus and Turkey date back to 1947, 1950 and 1962 respectively. By their nature, command economies require statistics, but all CCs with such economies in the past have since the 1990s revised their national statistical laws similar to those found in western Europe.Today, in the majority of the CCs, the National Statistical Institute (NSI) falls under the responsibility of the head of government (Prime Minister). It generally has the leading and co-ordinating role in producing official statistics but works in close collaboration with the Central Bank and government ministries, especially the Reviewing the process 12 Enlarging the EU Statistical Network Reviewing the process Enlarging the EU Statistical Network 13 Ministry of Finance and the Ministry of Agriculture. In some countries such as Malta, the NSI is the key element of the Malta Statistics Authority, which itself comes under the Ministry of Economic Services. On the other hand, in Cyprus and Estonia, the NSI is within the Ministry of Finance. In some countries the statistical law defines those involved as ‘authorised performers’ in the case of Slovenia or as the ‘bodies of statistics’ in Bulgaria. These normally refer to the key bodies who are entrusted with the right to carry out official statistical activities under the law. Advisory statistical councils Almost every CC has a council to guide and advise the statistical body on plans and programmes for surveys. Estonia and Latvia are currently exceptions. Estonia works with ad hoc advisory committees on different statistical areas, whereas the Central Statistics Bureau of Latvia reports directly to the Ministry of Economy. Malta has a statistics authority that fulfils the role of Statistical Council and also has the national statistical office as its sole executive body. Bulgaria has both a high Statistical Council, which is technically oriented, as well as a national Statistical Council for policy-making. In general, the advisory bodies comprise both providers and users of statistics.They have a key role in recommending the programmes of surveys and other activities conducted each year. In some countries, the council members are appointed by the head of the National Statistical Institute (Bulgaria, Czech Republic). In other countries such as Cyprus, Lithuania, Poland, Romania and Turkey, the appointment is made by the government (Prime Minister).There are other cases where the council members are appointed by the respective organisations which they represent. Most of the council mandates are between four to five years, except for two years in the case of Romania. Leadership of the NSI The independence of a statistical service necessitates the independence of its head. In most cases the appointment of such individuals as director-generals or presidents of their services is made by the head of government or sometimes even the head of state. Often the tenure of the position is for a fixed period, such as Bulgaria for 7 years, Poland for 6 years with a maximum of two consecutive terms, Hungary for 6 years with two renewals at most. In Lithuania the term of office of the Director-General is not defined whereas Estonia has a statistical institute whose Director General is appointed by the Minister of Finance until retirement. The National Statistical Office of Malta has a Director General appointed by the board of the Maltese Statistics Authority. It is obviously important to avoid any suggestion that such a key role is a political appointment and fixed terms of office in most cases promoted stability regardless of the changes in fortunes of political parties. The major divisions within the internal structure of the NSIs are usually headed by senior officials with titles such as vice- president or assistant director general. Annex 3 gives an overview of management and statistical councils for each CC. Organisation and level of centralisation Most of the statistical institutes consist of a central office based in the capital city together with a network of regional offices. Some of these offices relate directly to the regional structure of the countries. Annex 4 shows the variation in the level of decentralisation in the CCs. The central office is usually divided into broad departments responsible for administrative and various statistical domains. The head of the NSI is usually assisted by one or more directors who oversee the central departments or who may be responsible for the co-ordination of the regional offices, as is the case in Turkey. Many regional offices come under the direct responsibility of the NSI head, for instance in Hungary, Poland and Romania. Poland, by far the largest statistical institute employs over 7300 employees with 89% of its staff in 16 regional offices. The Czech Republic is divided into 14 regions, each with Reviewing the process 14 Enlarging the EU Statistical Network its own directly elected government, and the Czech Statistical Office is represented in each region with 63% of its staff who assist regional authorities with statistical work. Regional networks are strongly involved in conducting surveys and data gathering at the local level. Some, such as in Bulgaria and Lithuania, even conduct local surveys for local clients. On the other hand, Slovenia has one central office in Ljubljana and nations such as Cyprus and Malta, with a total of only 95 and 131 NSI staff respectively, obviously do not require a regional network. A few NSIs also have a special centre attached to the statistical office. In the Slovak Republic for example, the SO SR has the INFOSTAT Research Centre which conducts methodological research, design and tests, carries out econometric modelling and develops the office’s automatic data processing systems. Statistics Lithuania has two public companies: the Statistical Centre which is the main provider of the IT services to the office and Statistical Surveys which deals with all the surveys. Human resources Continuity in human resources helps to ensure the achievement of longer-term goals. Most of the NSIs have impressive records in stability of staff, although it is commonly difficult to retain the best skilled staff, such as IT technicians and economists, because there is high market demand and a difficulty for government and administrative salary structures to adapt to market fluctuations.The only negative effect of this continuity was an occasional lack of staff rotation. Stability of staff also resulted in an older age profile generally. In the majority of countries, a large proportion of staff is over 45 years old. Malta is an exception with over half of its NSI staff being under 30 years. The CCs owe their impressive gathering, analysis and dissemination of data overwhelmingly to female staff. Women account for as much as 80 - 90 % of the staff of many NSIs, with a high level of university education reported. Annexes 6 - 7 provide a detailed breakdown of the NSI staff by gender, age and level of education. In some countries the staff of the NSI have the status of government employee – or civil servant. A transition of employment to grant this status was considered important in the Slovak Republic. Many NSIs have their own training centres, for example in Lithuania, Romania and Turkey. Although staff training varies in style and content from country to country, the recurrent themes are methodologies and the application of software and other IT techniques. Training programmes also include economic and statistical modules as well as language courses. In the Czech Republic, special attention is paid to the training of managers in quality control and managerial skills. All the CCs except Malta and Turkey, are involved in EU traineeships which take place at Eurostat or an NSI of a Member State for a period of 5.5 months. For example, between January 1999 and August 2002, the NSIs have benefited from around 140 traineeships. Some staff of the NSIs also attend training organised by the Training for European Statisticians (TES) Institute. Many NSIs suffer from lower than market salary levels, making it more difficult to attract young people seeking rapid salary advancement opportunities. As mentioned above, this is often felt to be a problem particularly in the IT field. Smaller countries, such as Cyprus and Malta, naturally employ fewer people in national statistics. However, this presents additional problems, because sometimes the work involved is just as great as that required in larger countries. Budgets and statistical programmes Transforming a statistical system to comply with the EU regulations requires a considerable financial investment. As all of the services are mainly totally funded by central government, not surprisingly some constraints are clearly obvious and repeatedly raise questions of feasibility to achieve objectives within the deadlines set. The main co- operation funding is from the Phare Programme and EU- Medstat programme as well as specific European Commission financial agreements for the Cyprus, Malta and Turkey. Among other funding sources are UNICEF, UNDP, World Bank, IMF and OECD. All CCs fund population and other censuses and often this can be an opportunity to upgrade methodologies and equipment. A special budget is drawn up to cover such large activities. Budget approval in the CCs are usually based on the submission of the annual statistical programme which details the statistical activities to be undertaken by the NSIs and other bodies in the statistical system. This programme is usually drawn up in collaboration with the relevant statistical authorities and a wide variety of users, approved by the Statistical Council and endorsed by Parliament. Variations exist in this procedure from one country to another. In Romania, the approved statistical programme is further endorsed through a government decision, while in the Slovak Republic the programme approved by the Statistical Council is published in the form of a decree. Once the NSI budgets are approved, the flexibility with which funds may be re-allocated from the central to the regional offices differs from one extreme to another. In Cyprus and Latvia, funds may be freely shifted between regional offices and headquarters. However in Latvia, excess funds are transferred to the budget of the Ministry of Finance at year-end. In Lithuania and Poland for example, there are strong public expenditure controls and initial allocation of budget items require government approval. The NSI budgets and statistical programs are briefly described for each country in Annexes 5 and 8. Data output Major classifications and key methodologies that are fundamental to the European Statistical System are generally at an advanced state of implementation by the CCs. A lot is owed to the extensive and efficient co- operation with Eurostat and Member States who have transferred know-how in a systematic manner over a long period of time in various ways including study visits, consultation seminars and training courses. Knowledge of the acquis communautaire in statistics is widespread in CCs and impressive progress in the harmonisation has been made over the past years. This is partly due to the system for monitoring compliance supervised by Eurostat. The NSI policy in all the CCs has been to adopt European classifications and nomenclatures. Many conform or are close to conforming already, including NACE Rev.1, CPA, PRODCOM, CN, COICOP, COFOG, GEONOM, NUTS, ISCED 97 and ISCO 88. Where the classifications do not yet conform, plans exist to bring them in line before EU accession. All the CCs have carried out a population and housing census in the last three years (except Malta who conducted one in 1995) and the majority have also conducted the agriculture census. Normally the responsibility for agricultural statistics is shared between the NSI and the Ministry of Agriculture. In all CCs, monetary statistics and balance of payments data are the responsibility of the Central Bank and the Ministry of Finance. Annual accounts are compiled on the basis of ESA 95 but in many CCs a number of details need to be addressed before full compliance is achieved. For monetary statistics, the central banks aim at complying with the requirements of the European Central Bank and IMF guidelines by improving data sources and classifications and close co-operation with other institutions compiling financial accounts. In the area of macro-economic statistics, there is a common need to reduce the time lag in producing the GDP estimates. For quarterly accounts, these need to be released 70 days after the data reference period. The compilation of the Harmonised Index of Consumer Prices (HICP) and regular participation in the project on purchasing power parity is done in the CCs. All the CCs carry out labour force survey (LFS) according to the international standards and in some countries ad hoc supplementary surveys to the LFS requested by Eurostat have been done and results transmitted accordingly. Another basic condition to be fulfilled by the CCs upon accession is the implementation of the INTRASTAT system Reviewing the process Enlarging the EU Statistical Network 15 for trading of goods between Member States. However this will require significant additional resources such as funding from Phare national funds. Quite understandably there are considerable variations among the 13 countries in terms of meeting statistical needs in the various different domains. These differences are covered in the individual country chapters that follow. A common problem which is observed in all the countries lies in the quality of the business register; this is elaborated upon below. Overall, however, it should be stated that where there are gaps, it is not through lack of initiative or desire. Many of the NSIs suffer from the need to bring about radical changes and significant updates in a comparatively short space of time and without major additional resources. Clearly evident, however, are the positive attitude, the planning and the will to succeed among the individuals themselves. Registers Registers are a tool of considerable importance in maintaining and updating statistical information. All the CCs, except Turkey, have a common business register which identifies the country’s legal economic enterprises. However, the major weaknesses lie in the implementation of the requirements relating to the coverage and types of statistical units and the lack of some variables such as local kind-of-activity units that are required in EU Member States. Some definitions and some variables do not meet all the requirements of the Structure of Business Statistics Regulation. Other problems include the existence of dead or inactive units not removed from the registers and the slow updating of the register.These lead to incomplete registers that then need to be updated from exhaustive statistical surveys and sampling surveys. The Slovenian register-based system is an outstanding model.All legal business entities and individuals are assigned a unique business or personal identification number. It is clear that countries where accurate and up-to-date registers, particularly business registers, are still to be completed are at a disadvantage. Considerable improvement can be achieved through good co-operation between statisticians and the authorities dealing with the business register as well as access to VAT and trade tax files. A central population register is an important element in any statistical system as it serves both administrative and statistical purposes, especially in conducting exhaustive and sampling surveys. However, such a register is not available in all the countries. In this case, administrative registers are commonly used for survey purposes to obtain information on the population between two censuses. Very often the NSIs also access the registers maintained by the civil registration body or Ministry of Interior. Many CCs also rely on vital events information to update existing population registers. The latest population census is used to resolve discrepancies between demographic statistics and the national population register. Statistical farm registers exist in many CCs and these are updated using the agriculture census. Annex 9 lists the registers available by country. Censuses and surveys All CCs have conducted population censuses following international recommendations and European requirements approximately within the last two years, except Malta, which carried out a census in 1995 and will conduct another in 2005. Many of the countries have carried out additional censuses of agricultural activities. Hungary carried out a vineyard and fruit tree survey in 2001. As stated previously, almost all statistical surveys are included in the NSI annual statistical programme. However in some countries (Bulgaria, Czech Republic, Slovenia) many surveys are done outside the programme by ministries and agencies. In the majority of countries, over 100 surveys are conducted annually. Given that many surveys are repeated monthly or quarterly, the number of statistical occurrences is therefore much higher. As an example, Poland carried out around 240 surveys in 2002, but including the frequencies the number of surveys reached a total of over 900. Reviewing the process 16 Enlarging the EU Statistical Network Reviewing the process Enlarging the EU Statistical Network 17 Dissemination of statistics The single dominant theme regarding dissemination across all the CCs is the rapidly growing importance of the Internet. Even those with less developed websites currently, recognise the need to give this avenue of communication great attention in the near future. Dissemination databases on-line, such as that developed recently in Hungary, offer a clear signpost to the most efficient use of Internet technology. It is also highly commendable that all websites are at least in the national language and English. Some are even in more than two languages. The CCs also recognise the need to make information available to all users at the same time and to provide publication and press release calendars. A publication catalogue is available in all NSIs and a calendar of release of publications is very common. Normally the release of latest information is announced well in advance. The number of publications published by the NSIs varies significantly. Simply relying on the total number of publications produced by the NSI can be misleading when making comparisons. One publication containing multiple themes and released monthly may be equivalent to several single theme publications released with the same frequency. Many CCs now make electronic copies of their publications available on Internet. Co-operation Most NSIs collaborate with various universities and research institutes with close links to the statistical service, several of whom are represented on the Statistical Council. For example, in Estonia the SOE obtains scientific advice from Tallinn Technical University and Tartu University. With the latter, there is permanent co-operation on methodological issues. In 2000, the SOE with the International and Social Survey Institute of the Tallinn Pedagogical University completed an analytical collection on adult training. In Malta, the NSO works with the University of Malta on joint projects such as in compiling analytical publications, lectures on official statistics by NSO experts and also in receiving tuition from the professors. Co-operation between individual researchers of different government departments and the NSO typically covers joint research projects. In Poland, the main scientific support comes from the Economic and Statistical Research Centre of GUS and Polish Academy of Science which carry out methodological research and development covering both economic and social areas. International statistical co-operation involves various projects, which have been conducted under the multi- beneficiary and national Phare programmes. The former ones are of a horizontal nature and facilitate participation in adoption of new methodology by means of pilot projects, participation in Eurostat meetings, working groups, TES courses, etc. The latter constitute a useful supplement and are geared towards more specific needs of the individual countries. In both types of Phare programmes the NSIs also benefit from extensive bilateral and multilateral co-operation with the NSIs of the Member States. This has contributed to the rapid adoption of new methodology to meet the statistical acquis and the other international standards. Compliance Tremendous progress has been achieved in many statistical fields at various levels and the countries will arrive at a point of compliance with European Union requirements at different times over the next few years. Areas currently needing further strengthening include the quality and timeliness of statistical data among others. However the achievements are not an end in themselves; the development of the statistical system towards compliance is an ongoing process. The outstanding effort by the team of statistics experts in monitoring and assessing readiness among the 13 countries is only exceeded by this historic bringing together of systems across the continent. From the Baltic to the Mediterranean, never has Europe known such a giant movement towards harmonisation. Bulgaria National Statistical Institute 2, P. Volov Str. BG-1038 Sofia Tel: +359 2 9842 835 Fax: +359 2 9842 851 E-mail: info@nsi.bg Web site: http://www.nsi.bg Country Profile Bulgaria Balgarija Pre-Accession Milestones 1998 Bulgaria starts general multilateral screening process in April 1999 Bulgaria submits a revised version of the National Program for Adoption of the Acquis in May 2000 Official opening of accession negotiations in February 2002 20 out of 31 chapters of the acquis provisionally closed by June 2007 Target year for EU accession ˘ Geographic co-ordinates 43 00 N, 25 00 E Area 110 994 km2 Climate Temperate with cold, snowy winters and warm summers Administrative Divisions 28 districts (oblasti) Capital City Sofia (1.2 million inhabitants) Population and Growth Rate 7.9 million, - 0.8 % (March 2001) Nationality Bulgarian Ethnic Profile Bulgarian 84 %,Turk 9 %, Roma 5 %, others 2 % Religion Bulgarian Orthodox 84 %, Muslim 12 %, Roman Catholic, others Official language Bulgarian National Currency 1 lev = 100 stotinky Exchange Rate against Euro 1€ = 1.9 lev (Quarter 1, 2002, New Cronos) System of Government Parliamentary Democracy a Executive Power President & Council of Ministers (elected by National Assembly) Head of State President elected for five-year term by popular vote Head of Government Prime Minister, elected by the National Assembly Legislative Power Unicameral National Assembly (Narodno Sobranie); 240 seats and members are elected by popular vote to serve four-year term Judicial Power Supreme Courts & Constitutional Court National Holiday Independence Day: 3 March (1878) 1988 Establishment of diplomatic relations between Bulgaria and EU 1990 Bulgaria joins Phare Programme 1993 Bulgaria signs Europe Agreement 1995 Bulgaria’s Europe Agreement of Association enters into effect in February 1995 Bulgaria submits an official membership application for EU membership on 14 December 1998 Official negotiations for EU membership were launched in March Enlarging the EU Statistical Network 19 Official Statistics in Bulgaria Bulgaria has an extensive system of statistics but much needs to be done to bring it fully in line with EU standards Overview Bulgarian National Statistical Institute (NSI) Legal framework harmonised with EU requirements in 2001 High Statistical Council and National Statistical Council President of NSI appointed by Prime Minister Over 1 900 staff in central and 28 regional offices About 120 surveys and 40 publications each year Census of population and housing in 2001 The Statistical System of Bulgaria Other bodies • Structural units within Ministries, institutions and other administrative bodies - Produces sectoral statistics (public finances and government expenditure, agriculture, health, education etc) - Conducts 9 surveys (included in Statistical Programme 2001) • National Bank of Bulgaria - Balance of payments; monetary and banking statistics - Co-ordinates Bulgaria’s involvement in General Data Dissemination System with IMF National Statistical Council • Established in 1999 as a body of policy makers, attached to the President NSI • Is responsible for drafting and implementation of National Programme for Statistical Surveys and approves a list roll of structural units in the Bodies of Statistics and Rules on their activity. • Consists of 16 members from Ministries, National Bank, Council of Ministers, other governmental bodies • Members are nominated and dismissed by the Heads of the respective Institutions • Chaired by President NSI High Statistical Council • Established in 1991 and is a consultative body to President NSI • It is technically-oriented and advisory by nature, consisting of 20-35 statistical experts • Members nominated by President NSI from Ministries, universities, and other Bulgarian organisations and public-legal institutions • Elects its own Chairperson among its members • • • • • • • 20 Enlarging the EU Statistical Network National Statistical Institute of Bulgaria (NSI Bulgaria) • First established in 25 June 1880 as a division within the Ministry of Finance, but became National Statistical Institute in 1991 • Based on Law on Statistics – Official Gazette N° 57 dated 25 June 1999, and amended in April 2001 • Employed over 1900 staff in 2001 in central and 28 regional offices • Is responsible for the major part of economic and social statistics in Bulgaria - Conducts about 120 surveys each year - Disseminates about 40 publications each year - NSI Bulgaria website provides statistics and information President of NSI Bulgaria • Nominated by the Prime Minister for a fixed seven-year term on the basis of the Decision of the Council of Ministers • Heads the NSI and chairs the Statistical Council • Prepares draft of the statistical programme NSI co-ordinating Councils consisting entirely of NSI experts and have advisory and co-ordinating roles within NSI. • Methodology Council with five members from NSI and other bodies, discusses statistics issues, chaired by President of NSI • Methodology and Training Council • Technical and Technological Council agrees on IT policy (in relation to both systems and software) and proposals for development of applications • Editing Council is responsible for the NSIs dissemination policy �� � �� � Prime Minister � The organisation The National Statistical Institute (NSI) is the principal organisation in the Bulgarian statistical system. It functions under the Law on Statistics 1999, amended 2001, as a state agency, within the general administration. Other main sources of data are the Bulgarian National Bank, Customs, the Ministry of Finance and various other ministries. The legal basis The 1999 Law on Statistics was amended in 2001 to bring its provisions in line with EU standards.The law provides a general framework for the duties of the NSI and other “Bodies of Statistics” in Bulgaria, without actually specifying who the other bodies are. The identity of the “Bodies” and the rules of their activity are to be determined by the National Statistical Council. The Law on Statistics includes: • Duties of the NSI • National Statistical Council and the national programme of surveys • Provision of data for surveys and the provision of statistical information • Data confidentiality • Maintenance of the unified register for identification of economic and other subjects in Bulgaria,known as the Bulstat register • Relations between the Bulstat register and other registers and systems • Administrative and general provisions The act also covers the Unified Information System for Reaction Against Crime (UISAC). This is a purely administrative system with no direct links to statistical activities. However as the responsibility of the system in relation to crime may undermine the confidence that the data are used solely for statistical purposes, there are plans to relocate the UISAC outside the NSI and other Bodies of Statistics. The management of the National Statistical Institute The President and two deputy presidents of the NSI are appointed for a period of seven years by the Prime Minister upon nomination by the Council of Ministers, who also approves the organisational rules of the NSI and the duties of the principal officers. Under the Law on Statistics, there is a High Statistical Council and a National Statistical Council.The former is technically oriented and advisory by nature. It consists of 35 recognised statistical experts or main users of information, who are nominated by the President of the NSI. The Law on Statistics states that the National Statistical Council consists of 16 members.Ten of them are nominated by various ministries,one by the Bulgarian National Bank,one by the Council of Ministers and two by other governmental bodies.The President of the NSI takes the chair. The roles of the National Statistical Council include: • Adoption of a long-term strategy for the development of statistical activity • Discussion and decisions on proposals from the bodies of statistics for including surveys of inter-institutional significance in the draft of the national programme • Drafting the national programme for statistical surveys and presenting its financial provisions to the Minister of Finance • Eventual approval of a list of units in the government administration to be considered as “Bodies of Statistics” The President of the NSI has set up a Methodology Council, consisting of five members from the NSI and ten representatives from various universities, government and business institutions. In addition, the NSI has a Methodology and Training Council, a Technical and Technological Council and an Editing Council. These are internal groups of staff of the NSI. Official Statistics in Bulgaria Enlarging the EU Statistical Network 21 Each year the NSI must prepare a programme of surveys and other activities. The NSI’s own plans must first be put to the High Statistical Council. The wider plan, including also work of other bodies of statistics, is put to the National Statistical Council. Once approved, it is presented to the Ministry of Finance, together with the budget for each survey. Once budget allocations have been agreed, any adjustments necessary are made and the final programme goes to the Council of Ministers for approval. Unless a survey is included in the approved programme the “Bodies of Statistics” cannot make use of the powers of mandatory participation provided by the Law on Statistics. In addition to its other activities, the NSI has the administrative responsibility for maintaining the Bulstat register. The NSI may also carry out surveys,or provide other services for payment, for private customers. Structure and staffing of the NSI With its head office in Sofia, the NSI has regional offices in each of the 28 oblasti or administrative divisions. These offices are responsible for data collection and initial processing in the oblasti except for data from central government bodies.They also help in the dissemination of results.The head office includes a training centre near Lovetch. The directors of the regional offices are appointed by the NSI President.They manage their own staff and budgets allocated to their office from the total NSI budget. Recent cuts in funding have created problems for these offices. The 1 900 staff of the NSI are 13 % male and 87 % female, of whom over two-thirds work in the regional offices.About 60 % have higher educational qualifications. In recent years, however, the NSI lost many well-qualified staff, particularly statisticians and IT specialists, to other employers, both in the public and private sectors where remuneration is relatively higher. The NSI training plan 2001 to 2006 has the objective of “providing high-qualified, motivated and stable personnel able to conduct statistical activity effectively in compliance with the European Statistical System requirements”.The training, internal and external, covers statistics, IT, marketing and information dissemination, the organisation and management of statistical projects and European integration. Funding When the programme of surveys is presented to the Ministry of Finance, it is used to bid for funds for the NSI and the other “Bodies of Statistics”.This means that funds for other activities (e.g. compilation of national accounts) have to be covered, as well as the overheads related to each project.Consequently, the costs allocated to surveys include substantial amounts for overheads.A budget for the year is allocated accompanied by an upper limit on the average salary for the year. It also indicates how much of the budgeted expenditure is to be financed by incomes to the NSI.This budget is then allocated within the NSI to the central and regional offices. In 2001, the basic budget was around € 6.8 million.The NSI President is obliged to submit to the Council of Ministers an annual report on the activities of the NSI and on the implementation of the national programme of statistical surveys. Co-operation There are strong links between the NSI and universities and research institutes in Bulgaria. They are represented on the High Statistical Council, the Methodological Council and within other groups. At present, the Law on Statistics does not cover access to individual data purely for the purposes of research, thus the full benefits of data collection cannot be realised. It is clear that the NSI has good relations with users in the government sector. However, private sector bodies are not directly represented in the councils responsible for approving the national programme. Improvements suggested by users from the private sector for users include faster publication of Official Statistics in Bulgaria 22 Enlarging the EU Statistical Network economic statistics, more regional data (eg on business climate), and better access to unpublished results available in the NSI. Regarding data suppliers, a start has been made on finding out more about the burden involved in completing NSI questionnaires. A survey in 2000 sought the opinions of directors of regional offices on the problem and their suggestions for reducing respondents’ burden.A similar survey to respondents (enterprises) was held in 2001. More information will be sought in the framework of the National Phare Programme 2002 within the data quality project. Bulgaria has participated in the many multi-national pilot projects in statistics that have been conducted under the Phare Programme. The NSI is also receiving support under the Phare national programmes. Information technology and methodology The NSI has a fixed network linking the regional offices with servers and local area networks in the central office. Data are processed by the regional offices before being transferred to central databases. Internal technical divisions are responsible for hardware, networks and communications, however there is a serious shortage of PCs. Links between the offices will improve as the national telephone system is enhanced during the next two or three years. In 2000, Eurostat’s basic methodological documents on quality in statistics were studied as part of a review of quality problems in statistical data collection and processing. The improvements planned to the quality of the register of businesses used for statistical purposes are particularly necessary and important. In contrast with many other such institutes, the NSI in Bulgaria does not have a central unit providing methodological assistance to those conducting sample surveys.To this effect, a new section will be established to provide at least guidance on sample design, selection and on estimation. The output Classifications For some time, NSI policy has been to adopt European classifications and nomenclatures whenever possible. Many conform or are close to conforming already, including NACE Rev.1, CPA, PRODCOM, CN, COICOP, COFOG, ISCED 97 and ISCO 88. Registers The NSI is required to maintain a Unified Register for Identification of Economic and Other Subjects, called the Bulstat register, with a unique identification known as the Bulstat code.The Bulstat register is purely administrative.All the information is accessible to the public and it is available on the Internet. The Statistical Business Register (SBR) is based primarily on the Bulstat register. It is updated only once a year. The quality of the Bulstat register is not sufficient as a framework for business surveys. The updating process is too slow and the information on kind of activity units is missing. Hence, the SBR has major weaknesses in the implementation of the requirements relating to the coverage and types of statistical units and lacks some variables such as secondary activities that are required in EU Member States.At present, the units responsible for surveys to businesses, mainly on structural and short-term business statistics, have been forced to maintain their own business registers.The improvement of the quality of the SBR is part of the Phare National Programme. Good co-operation between business statisticians and those responsible for the business register is needed. Such co-operation is also important with the tax administration in order to guarantee access to the VAT and trade tax files. This information can improve the quality of the business register and of business statistics. Official Statistics in Bulgaria Enlarging the EU Statistical Network 23 The NSI has started development on a unified register for identification of farmers, self-employed individuals and the other natural persons. The National Population Register is the responsibility of the Ministry of Regional Development and Public Works. Each individual has a unique personal identification number.The NSI receives information from the population register to prepare demographic statistics. The methodology complies with European requirements. Demographic and social statistics A population and housing census, in compliance with the UN, Eurostat and ILO recommendations, was carried out in 2001 and results are being published. It will also provide a register of agricultural holdings for use in the agricultural census in 2003. The NSI is working with ministries to create an information system on international migration. Education statistics follow the ISCED 97 classification.The NSI is able to provide most of the required data of the joint UNESCO/OECD/Eurostat questionnaire, except for the registers for students and teachers in higher education. The NSI will be participating in the Eurostat Continuing Vocational Training Survey. The NSI meets UNESCO requirements for culture statistics and conducts annual surveys on theatres, cinemas, museums, libraries,TV and radio, film production and publishing houses. The health interview survey of March 2001 was linked to the population census.The sample size was 10 000 inhabitants and response 90 %.The NSI plans to improve and systemise health statistics following changes as a result of the introduction of health insurance for hospital services. Declaration of work injuries is mandatory, development of data collection on occupational diseases is in progress and there are plans for statistics on home and leisure accidents according to international recommendations. There is an annual household budget survey of 6 000 households but response rates (only 60 %) and costs remain a problem. The expenditures are classified according to the COICOP.A time use survey was conducted in October 2001 in full compliance with Eurostat recommendations. In the absence of an official poverty line, household budget survey data are used as a basis for studies on poverty. A database on local authority social assistance covering mainly activities at the municipal level, is maintained and currently updated according to policy needs. At present there is no national database with information on the unemployed or on beneficiaries of social support. Housing statistics are based mainly on census data.There have been preliminary discussions about the possibility of creating a register of buildings and dwellings on the basis of data from the 2001 Census. A publication “Social Trends” has been prepared containing main social indicators. Further development of Bulgaria’s system of social indicators is in progress. It is planned to resolve discrepancies between demographic statistics and the national register of population by using the 2001 population census for selecting samples for household surveys. Regarding statistics on earnings and labour costs, there are problems related to the national accountancy standard of enterprises which does not contain analytical records for some labour cost elements needed for statistics. At present, there are no reliable, up-to-date sources for data on the distribution of employees by earnings or occupation. Macro-economic statistics The NSI is responsible for the full range of national accounts, including financial transactions accounts and balance sheets. Official Statistics in Bulgaria 24 Enlarging the EU Statistical Network Priority has been given to non-financial accounts, but work has now started on detailing proposals for the development of financial accounts, with the help of the Ministry of Finance and the Bulgarian National Bank. The NSI will need to devote sufficient resources to achieve the objectives. Annual accounts are compiled on the basis of ESA 95. Sources include NSI surveys, external trade statistics, data from the Ministry of Finance, tax revenues and data from the Bulgarian National Bank. Independent estimates of GDP are prepared based on expenditure and production approaches, at both current and the previous year’s prices. The first annual estimates become available four months after year-end; first final estimates are released a year later; actual final estimates after three years.Various improvements to the accounts have been introduced in the past few years. However arrangements need to be done to provide the NSI with access to individual VAT and other tax records. Quarterly accounts follow the same structure as the annual ones and are only released 80 days (instead of the recommended 70 days) after the end of the quarter. A new software system is being prepared for compiling the quarterly accounts – at present compiled using a sequence of unlinked spreadsheets. The Harmonised Index of Consumer Prices (HICP) has seen several improvements in recent years. A consistent monthly series for the years 1995-2000 is now available. COICOP was introduced in 1999, and with respect to problem areas, there are improved procedures for dealing with missing prices, for introducing adjustments for quality changes and on how to obtain a suitable detailed breakdown of tourism expenditure. The CPI is released around the tenth day of the month. Business statistics Although many improvements have been made during recent years, problems relate to the continuous changes within the business community, the accountancy law and the statistical business register. The compilation of structural business statistics is mainly based on information collected on the basis of accountancy law. Some definitions and some variables do not meet the requirements of the SBS regulation. The absence of some indicators and differences in their definitions are the main problems associated with the annual structural statistics. Deficiencies exist in the coverage of enterprises in the business register which lead to incomplete statistics, for instance for SMEs, communications and accommodation statistics. There are still some missing short-term indicators concerning sectors of industry and construction. Some improvements were made, the main one being the compilation of the industrial production index started in 1999. A survey in full compliance with the PRODCOM regulation is under development. The development and harmonisation of iron and steel statistics has not yet started. An aggregated energy balance has been developed in full compliance with Eurostat’s energy statistics methodology. Prices of electricity and natural gas in Bulgaria are state regulated. Compilation of price statistics will be started after deregulation. A quarterly short-term survey for the distributive trade is conducted on 4 000 enterprises, achieving very high EU compliance. Structural data of transport enterprises are produced annually and a survey on road freight transport is carried out quarterly. The national goods nomenclature for transport is compatible with NST/R. Statistics on transport of goods by rail are close to EU requirements. The Ministry of Transport and Communications is responsible for statistics on ports and air Official Statistics in Bulgaria Enlarging the EU Statistical Network 25 transport and reports regularly to the ICAO as requested.The NSI makes quarterly surveys of water transport and port enterprises that meet almost all EU requirements. Statistics on road accidents also comply with the EU requirements. Statistics on the information society and on audio-visual services are under development. The NSI conducts a quarterly survey on tourist accommodation and structural data on hotels, restaurants and travel agencies is produced annually. Coverage of accommodation statistics for small enterprises is not complete and the survey does not include all the variables required by the EU. A monthly border survey for Bulgarian residents and non-residents is being considered. Monetary, financial, external trade and balance of payments statistics The Bulgarian National Bank (BNB) is responsible for monetary statistics. Monetary and banking data are collected weekly and monthly.The monetary statistics required by the IMF and the ECB are compiled from the banks’ monthly returns.The BNB expects to be able to comply fully with EU requirements by 2006. The Ministry of Finance provides government finance statistics to the IMF. The new chart of accounts for budgetary enterprises takes account of the principles and definitions in ESA 95 and the new IMF manual.The main shortcoming is that data are available only on cash basis, but necessary measures have been taken for collecting data an accrual basis.At present, all government finance statistics are based on reports by the spending bodies, and a financial management information system is being established giving all operations. For trade in goods,data on each transaction is sent by customs to the Ministry of Finance.An electronic version of the records for each month is then passed to the NSI.Tables with foreign trade data are posted on the NSI Internet site about seven to eight weeks after the end of the month. NSI is studying the INTRASTAT system. It is important that customs and the VAT office are also involved. Balance of payments (BOP) accounts are compiled monthly by the Bulgaria National Bank eight weeks after the end of the month, following the IMF manual. Information on foreign direct investment is available from the foreign investment agency and a survey conducted by the NSI. Outward investment is covered by a BNB survey. The present arrangements do not provide adequately the detailed information on trade in services necessary to comply with EU requirements. This could be improved if the BNB receives details of each bank transaction and a highly developed processing system for the BOP accounts. Agriculture, forestry and fisheries statistics The main problems for agriculture statistics are the postponement of the agricultural census until 2003 and the lack of a farm register, complicated by the continuing process of land reform. The Ministry of Agriculture and Forestry (MAF) conducts annual land use surveys based on area frame sampling and conforming to LUCAS nomenclature. There is very little data available on farm structure and requirements for the EUROFARM database cannot be met. However, results from the pilot farm structure survey will be assessed by Eurostat. The development of data on viticulture is underway, covering the main topics in the EU vineyard survey.There is also work on wine production statistics. Full compliance is expected after 2003. Economic accounts for agriculture, prepared by the NSI comply as closely as possible with ESA 95. Income statistics of the agricultural household sector are planned for 2004.There are plans to conduct a monthly survey on agricultural prices according to EU requirements. Official Statistics in Bulgaria 26 Enlarging the EU Statistical Network MAF estimates crop production by interviewing farmers. Development of harvest forecasts (AGROMET) is planned to start in 2002. A number of pilot surveys on animal production have been carried out. Full compliance with the EU requirements is expected after the agricultural census in 2003. A forestry information system, with a large amount of information, has been functioning since 1992. Forestry statistics systems in line with EU requirements are being developed by the National Forestry Department and the NSI. A new unit dealing with fisheries statistics, created in the Agency on Fisheries and Aquaculture, will study EU legislation. Other statistics The Ministry of Environment monitors environmental quality and provides information on hazardous waste. The NSI has responsibility for statistics on emissions, waste, and expenditure on environmental protection. Bulgaria has agreed a framework of regional geography with the EU Commission for use in NUTS. The 28 NUTS 3 regions are the existing oblasti. The NSI has hardware and software for the analysis of geographically referenced data and for the production of maps. A regional database still needs to be developed. In the field of science and technology, Bulgaria has complied with the guidance in the Frascati Manual since 1996, and the NSI participates in the Eurostat working party on R&D statistics and in the pilot projects on a database of R&D and innovation activities. Statistics for Bulgaria are available at the NUTS 3 level. The information • NSI publishing policy is developed, discussed and updated for approval of top management by the Editing Council • About 40 publications both in Bulgarian and English in 2001 including Statistical Yearbook • Dissemination through reports, diskettes, CD-ROM and website www.nsi.bg • Press releases on a regular basis • Calendar of press releases published by week with day announced later • Release of information to all users at the same time • Data protection and confidentiality rules still need to be clarified • Improvements planned include further study of user needs, marketing strategy and continued development of website Conclusion As a result of considerable progress in the harmo- nisation of statistics with EU standards, Bulgaria is now able to supply many of the statistics required of Member States. This has been achieved partly as result of substantial programmes of assistance, whose continuation remains important. However, much remains to be done. The NSI has prepared a long-term strategy for the development of Bulgarian official statistics to meet national needs and to ensure full compliance with EU requirements by 2006. There are some major challenges to the institute: specifically, such as the establishment of a good quality statistical business register and generally, such as the provision of adequate funding. Official Statistics in Bulgaria Enlarging the EU Statistical Network 27 Cyprus Service of Cyprus 13, Andreas Araouzos Street CY-1444 Nicosia Tel: +357 22 309305 Fax: +357 22 374830 E-mail: cydsr@cytanet.com.cy Web site: http://www.pio.gov.cy/dsr Country Profile Cyprus Kypros Kibris Geographic co-ordinates 35 00 N, 33 00 E Area 9 251 km2 Climate Temperate, Mediterranean with hot, dry summers and cool winters Administrative Divisions 6 districts Capital City Nicosia (0.2 million inhabitants) Population and Growth Rate 0.76 million (with 0.68 million in the government-controlled area) +0.84 % (2001 estimate) Nationality Cypriot Ethnic Profile Greek-Cypriots 85 %,Turkish-Cypriots 12 %, others 3 % Religion Greek Orthodox 77 %, Muslim 18 %, other 5 % Official language Greek and Turkish National Currency 1 Cyprus Pound (CYP) = 100 cents Exchange Rate in Euro 1€ = 0.6 Cyprus Pound (Quarter 1, 2002, New Cronos) System of Government Parliamentary Democracy Executive Power President & Council of Ministers (appointed jointly by President & Vice President) Head of State President elected for five-year term by popular vote Head of Government President Legislative Power Unicameral Parliament; 80 seats: 56 assigned to Greek Cypriots and 24 to Turkish Cypriots; members are elected by popular vote to serve five-year term Judicial Power Supreme Court National Holiday Independence Day: 1 October (1960) Pre-Accession Milestones 1998 Accession negotiations with Cyprus started in March 2002 28 out of 31 chapters of the acquis have been provisionally 2004 Target year for EU accession 1972 Cyprus signs Association Agreement 1987 Association Agreement complemented by a Protocol 1990 Cyprus submits an official membership application for EU membership on 3 July 1995 Cyprus’s eligibility for membership was confirmed closed by June Enlarging the EU Statistical Network 29 The Statistical System of Cyprus 30 Enlarging the EU Statistical Network Official Statistics in Cyprus Cyprus has the basic infrastructure for meeting statistical needs, but some development is still required. Overview Statistical Service of Cyprus – CYSTAT Suitable legal framework under the Statistics Law 2000 Statistical Council to advise and evaluate 95 permanent staff, field workers employed on a casual basis as needed Over 30 regular publications, plus ad hoc research reports, census reports and classification information each year Census of population and housing in 2001, census of enterprises in 2000, census of agriculture in 2003, About 30 surveys each year Additional staff is required for the implementation of the CYSTAT programme of adopting the acquis communautaire in statistics • • • • • • • • Statistical Council • Members and President are appointed by the Cyprus Council of Ministers • The Director-General of the Ministry of Finance is the current President • Set up within statistical law which specifies the bodies to be represented on the Council (5 from the government sector and 5 from other bodies – 2 from employers’ associations, 2 from trade union associations, 1 academic representative) • Provides advice with regard to the preparation and implementation of the programme of statistical activities • Comprises a President and 10 members with a 5-year mandate (except ex-officio members) • Makes rules regulating its manner of operation CYSTAT works closely with • Central Bank of Cyprus - statistics on banking, monetary and balance of payments • Ministry of Finance – government expenditure and public finances • Ministry of Agriculture, Natural Resources and Environment • Planning Bureau – responsible for co-ordinating Cyprus’s preparation for EU membership • Other ministries and government bodies Statistical Service of Cyprus (CYSTAT) • First established in 1960 as a government office within Ministry of Finance, but maintains its autonomy in technical matters • Based on Statistical Law N° 15 (I) of 2000 (replacing the Statistical Law N° 47 of 1968) • Employed 95 staff in 2001 in the head office in Nicosia; it has no regional structure, but has small offices in Larnaca, Limassol and Pafos • Is the main producer of official statistics in Cyprus - Conducts about 30 different surveys each year - Disseminates about 30 publications each year - CYSTAT website contains information in Greek and English • Its staff are appointed by the Public Service Commission • Compiles its 5-year programme to be approved by Council of Ministers �� Ministry of Finance Director of CYSTAT • Appointed by the Public Service Commission for a permanent term and holds this post under the provisions of the Civil Service Act • Reports to the Director-General of the Ministry of Finance � � Enlarging the EU Statistical Network 31 The organisation The former Department of Statistics and Research, functioning since 1960 when the Republic of Cyprus was established, was recently renamed the Statistical Service of Cyprus (CYSTAT) under the Statistics Law of 2000. It is a government office within the Ministry of Finance. CYSTAT works closely with the Central Bank of Cyprus, the Ministry of Finance, the Ministry of Agriculture, Natural Resources and the Environment, the Planning Bureau and other elements of government. These are key members of the Cypriot statistical system and participate in various technical committees. The legal basis The Statistics Law No. 15(I) of 2000 takes account of the relevant EU statistical acquis and includes features typical of laws on statistics in the EU Member States. Participation in surveys carried under the Statistics Law is obligatory. The principal provisions include: • The implementation of statistical programmes and the conduct of surveys • The preparation of five-year and annual programmes of statistical activities • The establishment of a Statistical Council to provide advice on the preparation and implementation of the programmes • The dissemination of statistics • The promulgation of classification systems • The right of free access to the administrative records of ministries, government services and public corporations • The principles governing the statistical system CYSTAT maintains its autonomy in technical matters and has exclusive responsibility for the choice of methodology, technique, definitions and procedures for the realisation of surveys and for the time and manner of dissemination of the results. Provisions control the disclosure of confidential information and give the Director of the statistical service the right to grant access to confidential data for specific scientific research programmes. Section 15 of the Law concerning “Statistics of the European Union” will come into force under an order from the country’s Council of Ministers, when Cyprus becomes a Member State. It provides for the transmission of confidential data to Eurostat and also for the automatic incorporation in the statistical service’s five-year programme of any survey adopted under Council Regulation 322/97 on Community Statistics. Any Ministry or department of the government or public corporation may conduct surveys in matters specific to their areas of competence, provided they inform the Director and use classification systems adopted by CYSTAT. On the whole, the Statistics Law is concerned with how CYSTAT should work, rather than what it should be trying to achieve. Other relevant laws include the Civil Service Law of 1990, the Banking Law of 1997, and the Central Bank of Cyprus Law of 1963 (being amended). The management of CYSTAT The Director of CYSTAT reports to the Director-General of the Ministry of Finance. The organisational structure of CYSTAT comprises seven subject matter divisions, each headed by a senior officer. Appointed by the Council of Ministers for a period of five years is a Statistical Council. It comprises a President and ten other members, five from the government sector and five from other bodies: two each from employers’ associations and trade union associations and an academic representative from the University of Cyprus.The Statistical Council plays an advisory role which covers the following: Official Statistics in Cyprus 32 Enlarging the EU Statistical Network • Opinion and suggestions during the preparation of the programmes of statistical activities • Observation and evaluation of the manner of implementation of these programmes • Suggestions in relation to the further development of these programmes The Director-General of the Ministry of Finance has been appointed as the first President of the Statistical Council. CYSTAT is required by the Statistical Law to prepare a five-year work programme taking into account user views through various existing subject-specific committees. An annual statistical programme of statistical activities is also done by CYSTAT to implement the five-year programme. Both are then submitted to the Statistical Council and approved by the Council of Ministers. The structure and staffing of CYSTAT CYSTAT is an office within the Ministry of Finance. It is based in the capital, Nicosia. It does not have a regional structure, but does have small offices in Larnaca, Limassol and Pafos, which deal exclusively with fieldwork.With only 113 permanent posts (only 95 staff employed), CYSTAT is very small compared with the NSIs in most Member States and Candidate Countries. Each division has to deal with a wide range of subjects in relation to its size. 59 % of the staff are female and 60 % are over 45 years. Temporary staff are employed on a casual basis from the offices in the other towns and from the head office in Nicosia to carry out fieldwork, mainly by personal interview. For permanent appointments, posts are being advertised in the Official Gazette of the Republic. The process of selecting a short-list of candidates is handled by CYSTAT, but the final choice is made by the Public Service Commission, which is an independent body dealing with all recruitments of staff in the civil service. Unfortunately, delays in the procedures can mean that for some months CYSTAT may be without staff for whom funding is available. 62 % of the existing staff have secondary education. The staff are working very effectively; however, CYSTAT also needs urgently more qualified staff to meet the additional requirements that the acquis communautaire in statistics entails. In many respects, the work involved for National Statistical Institutes in smaller countries of the European Union is just as great as that required in larger countries. The numerous co-operation projects in which Cyprus is participating, particularly with Eurostat (Medstat Programme, pilot projects under the Phare Programme), provide valuable development opportunities for staff. CYSTAT is making use of the EU trainee scheme to a very limited extent. In-house training is also provided and there are study visits to Greek and other NSIs. Funding Budgets are allocated only for one year ahead. The total budget for 2001 was approximately € 6.6 million.This was a 52 % increase over the previous year but reflects extraordinary costs related to the census of population carried out in October 2001. There is considerable flexibility for moving funds between categories of expenditure when necessary. Capital expenditure on IT hardware, however, is handled centrally by the Department of Information Technology Services, which decides on requests and handles all procurement. Co-operation CYSTAT has close working relations with users in government. Contact with private users arises from statistics requests and some involvement in statistics Official Statistics in Cyprus Enlarging the EU Statistical Network 33 meetings. CYSTAT is developing stronger relations with the University of Cyprus, which is also represented on the Statistical Council. Information technology and methodology The Information Technology Unit of CYSTAT is responsible both for data entry and the processing of data for most surveys. It has a clear view of how IT services in CYSTAT should develop, in line with the practices in other European NSIs; the problem is how to make the transition. While the existing three IT staff and eight data entry operators might be adequate to maintain an existing configuration, additional expert help is needed to introduce new developments. There is no central database, which would be necessary for a more efficient dissemination policy. It is also necessary to set up a publications strategy. The collection of most of the data required from enterprises or households by very experienced interviewers employed as temporary staff is considered to be the key element in achieving high levels of response and, with extensive validity checking in CYSTAT, a high level of accuracy. Computer-assisted interviewing has also been introduced that provides immediate validation of responses. CYSTAT does not have a separate methodological section. Cross-cutting methodological issues are addressed through internal technical working groups. Particularly in a small office, this offers an appropriate way to develop office practice in relation to issues such as sample design and estimation, seasonal adjustment etc. CYSTAT has close working relations with users in the government sector, partly through various established technical committees.While there are contacts with users in the private sector, these mainly arise from requests for statistics. There is not the same emphasis on seeking the views of users outside government.The Statistical Council has the opportunity to better understand the needs of the private sector. Response rates from data suppliers are very high, though there have been some complaints from enterprises about the burden placed on them. Efforts are made to find the most suitable solution, for example using administrative sources more extensively. The output Classifications A number of statistical classifications and nomenclatures used in the EU have long been adopted, such as NACE, CN, GEONOM and CC (classification of types of construction). CYSTAT is implementing the Classification of Products by Activity (CPA) – initially for manufacturing – and plans completion in 2002.The adoption of NACE in other public administration offices is also in process, with the provision of technical assistance by CYSTAT. CYSTAT is also able to provide statistics on the basis of the international classifications relating to social issues. A proposal for the establishment and delineation of the five NUTS levels for Cyprus has been accepted by Eurostat, which provides for the Republic of Cyprus being classified as a single region at the NUTS 1, 2 and 3 levels. Business register CYSTAT’s business register is based primarily on the information collected in the five-yearly census of enterprises. The most recent one was carried out in 2000. Newly registered electricity consumers for Official Statistics in Cyprus 34 Enlarging the EU Statistical Network industrial or commercial purposes are additional sources of information used to keep the register up-to-date. It is planned also to use VAT information. This will help in identifying which businesses have ceased to operate. An identification of kind-of-activity units still needs to be implemented. Demographic and social statistics In October 2001 CYSTAT carried out a census of population and housing. The previous one was conducted in 1992. The 2001 Census included all the core topics specified in the UN/Eurostat recommendations and additional questions to meet Cypriot requirements. The Eurostat tabulation programme will be fully adopted. CYSTAT has been supplying demographic statistics to Eurostat since 1998, including information on migration and foreign workers in Cyprus. A labour force survey was introduced in Cyprus in 1999 on an annual basis, with one quarter of the survey sample being replaced each year. From the year 2003 onwards, the survey will start running on a quarterly basis. Other surveys include a quarterly establishments survey of employment, a labour costs survey, in line with EU requirements (carried out in 2001) and a structural earnings survey, following EU guidelines (to be carried out in 2003). The revised ISCED 97 classification has been adopted for the collection of information from educational establishments. The financial statistics on education are incomplete with respect to private educational establishments. Many Cypriot students are studying abroad and CYSTAT uses information regarding the applications for grants submitted to the Ministry of Finance, as a source for identifying them. Data are not available at present on adult vocational training. At present Cyprus is able to meet only a few of the requirements for culture statistics. CYSTAT is collaborating with the cultural services under the Ministry of Education to obtain the information recommended by the Eurostat Leadership Group on culture statistics. Information does not cover all that is required but includes the following: • Birth and death registration and causes of death • Data from government hospitals and clinics • A register of doctors, dentists and hospitals/clinics (carried out in 2000) • Accidents at work from records kept by the Ministry of Labour The information supplied by private hospitals is incomplete. A number of new initiatives are being explored by CYSTAT, including the launching of a health interview survey in 2003. A household budget survey conducted in 1996/97 complied fully with EU guidelines. However, this needs to be done on an annual basis.The next survey will be held in 2002/3, with a sample size of 3 600 households. CYSTAT can provide most of the social indicators required. Social protection is under study. Housing statistics need to be up-dated more frequently. Macro-economic statistics The change from SNA 68 to ESA 95 is ongoing. Concepts and definitions are being studied and adopted and relevant changes are made on survey data and estimation methods. Annual estimates of national accounts aggregates are compiled on data from CYSTAT and other sources, Official Statistics in Cyprus Enlarging the EU Statistical Network 35 including the Ministry of Finance, the Central Bank of Cyprus etc. A first set of accounts of the general government has been produced and work has started on a new input-output table. CYSTAT collects (and publishes quarterly) various monthly indicators of economic activity, as well as quarterly estimates of GDP. CYSTAT has been participating since 1997 in the Eurostat programme of work on purchasing power parities, as a member of the southern group of countries. All the necessary price surveys are conducted in accordance with the prescribed time-schedule. A Harmonised Index of Consumer Prices (HICP) for Cyprus has been produced monthly since January 1999 (with comparable figures back to 1996). It has been modified as necessary to comply with the changes required in coverage. Business statistics Business surveys are conducted by personal interview and achieve almost 100 % response rates. Survey questionnaires are gradually being extended and adapted to cover all the information required in the Structural Business Statistics Regulation. CYSTAT collects information annually on industry and construction, wholesale and retail trade, transport, finance and insurance, education, health and other services. CYSTAT is also modifying and extending its range of short-term inquiries to be able to meet the requirements of the Short-Term Statistics Regulation. Though Cyprus’s industrial production levels are below the relevant threshold, CYSTAT intends to introduce a PRODCOM survey in 2003. CYSTAT meets fully the requirements of the joint IEA/Eurostat energy questionnaires and has implemented the directive on the transparency of electricity prices. Statistics on transport and communications are compiled partly from CYSTAT surveys and partly from information from other agencies on road accidents, shipping, aviation, telecommunications and postal services.The annual survey has been brought into line with the Structural Business Statistics Regulation. Monthly indicators are also compiled and CYSTAT intends to harmonise this enquiry with the Short-term Statistics Regulation. The Department of Road Transport supplies information on registrations of motor vehicles, which is analysed by CYSTAT and published monthly, quarterly and annually. CYSTAT conducts an annual survey on road infrastructure and provides the results to Eurostat. As from 2002, a new survey on transport of goods by road was introduced. On maritime transport, the Cyprus Ports Authority has made arrangements with the Department of Customs and Excise for commodities to be classified as required and for the introduction and operation of a central computerised information system, facilitating the collection and storage of harmonised data for all ports. Cyprus’s aviation statistics are already harmonised with EU requirements. CYSTAT collects information on arrivals of all kinds of passengers in Cyprus at the ports of entry. Information on tourist accommodation is provided to CYSTAT by the Cyprus Tourism Organisation. Tourist flows and expenditure are covered through surveys at border points by CYSTAT. Monetary, financial, external trade and balance of payments statistics External trade statistics are compiled by CYSTAT on a monthly basis, based on the customs declarations made by use Official Statistics in Cyprus Official Statistics in Cyprus 36 Enlarging the EU Statistical Network of a Single Administrative Document.The Eurotrace software is used for dissemination purposes. Preparatory work has commenced for the introduction of the INTRASTAT mechanism, in collaboration with the VAT Service. Monetary statistics are the responsibility of the Central Bank of Cyprus, which is moving towards complying fully with the requirements of the ECB. The bank is also responsible for the balance of payments data, supplied quarterly and annually.A new balance of payments reporting framework has been implemented from 1st January 2002 to counter the loss of data associated with the abolition of controls on the flow of capital.This is an open settlements- based system, complemented by direct reporting and surveys. The fiscal deficit and debt accounts are compiled by the Ministry of Finance, based on the IMF’s concepts and definitions. Agriculture, forestry and fisheries statistics There is close collaboration between the Ministry of Agriculture and CYSTAT. Information on land use is derived from censuses of agriculture. CYSTAT carried out a census in 1994, and is planning another one, complying with EU guidelines, in 2003. Economic accounts for agriculture are prepared by CYSTAT and are being adapted to bring them into line with EU requirements, as are the mechanisms for collecting information on agricultural prices, including prices in the municipal markets, export prices and prices for own consumption. The Ministry collects statistics on viticulture and on fruit growing and has a register of vineyards. Information on cereals production is obtained from the Grain Commission and for other crops from CYSTAT. Changes to bring them into line with EU requirements have been made.There is almost full compliance in the collection of statistics on livestock and animal production. CYSTAT compiles supply balance sheets for crop and animal products produced in Cyprus and used for human consumption. The Department of Forests has only one outstanding difficulty in completing the joint FAO/ECE/Eurostat questionnaire: information on private forest holdings. However, none of these is active commercially. The Department of Fisheries has responsibility for the transmission of fisheries statistics to all international organisations and complies fully with EU requirements. Other statistics CYSTAT is co-ordinating statistics on environmental issues. New surveys on municipal waste generation and disposal and environmental protection expenditures in industry have been initiated in 2002. On topics such as air and water quality, CYSTAT is encouraging the agencies responsible to adapt or extend their information systems to produce the necessary data. A survey on research and development is carried out on an annual basis since 1999. An innovation survey is scheduled to be held in 2002 The information • Publications catalogue available with publications in Greek and English • About 40 publications each year including a yearbook entitled “Statistical Abstract” • Data also disseminated in bulletins, press releases or electronically • Press releases distributed by the government Press and Information Office • Release dates announced in advance for a number of key indicators • Much information available on the website www.pio.gov.cy/dsr Conclusion Overall, Cyprus is quite well advanced in the field of statistics. With the Statistics Law 2000 Cyprus now has in place a suitable legal framework and CYSTAT is working and planning to ensure that Cyprus complies with all the EU statistical acquis by the date of accession.To reach this challenging objective, CYSTAT is undertaking the following actions: • To increase its skilled staff in the fields where there are still gaps in the implementation of the acquis communautaire • To participate in pilot projects and working group meetings organised by Eurostat • To plan and implement the transition to more up-to-date and flexible IT systems • To give more emphasis to identifying the needs of business and users of statistics other than government bodies Official Statistics in Cyprus Enlarging the EU Statistical Network 37 Czech Republic Czech Statistical Office Sokolovská 142 CZ-186 04 Praha 8 Tel: +4202 7405 2421 Fax: +4202 8481 8103 E-mail: povolna@gw.czso.cz Web site: http://www.czso.cz Country Profile Pre-Accession Milestones 1988 Establishment of diplomatic relations between Czechoslovakia and EU 1990 Czechoslovakia joins Phare Programme 1993 Czech Republic signs Europe Agreement in October 1995 Czech Republic’s Europe Agreement of Association enters into force in February 1996 Czech Republic submits an official membership application for EU membership on 17 January 1998 Official negotiations for EU membership were launched in March 1999 Czech Republic submits revised version of the National Program for Adoption of the Acquis in May 2000 Official opening of accession negotiations in February 2002 25 out of 31 chapters of the acquis provisionally closed by June 2004 Target year for accession Czech Republic Ceská Republika˘ Geographic co-ordinates 49 45 N, 15 30 E Area 78 866 km2 Climate Temperate with cold, snowy winters and mild summer Administrative Divisions 77 administrative districts and 14 regions Capital City Prague (1.2 million inhabitants) Population and Growth Rate 10.2 million, - 0.25 % (2001 estimate) Nationality Czech Ethnic Profile Czech 94 %, Slovak 3 %, others Religion Roman Catholic 26 %,Atheist 58 %, others Official language Czech National Currency 1 koruna = 100 haleru Exchange Rate against Euro 1€ = 31.8 koruna (Quarter 1, 2002, New Cronos) System of Government Parliamentary Democracy Executive Power President & Cabinet (Cabinet appointed by President and approved by Chamber of Deputies) Head of State President elected for five-year term by both chambers of the parliament Head of Government Prime Minister, appointed by President Legislative Power Bicameral Parliament; Senate: 81 seats and serve six-year term; Chamber of Deputies: 200 seats and serve a four-year term; all members are elected by popular vote Judicial Power Supreme Courts & Constitutional Court National Holiday Czech Founding Day: 28 October (1918) Enlarging the EU Statistical Network 39 The State Statistical Service of the Czech Republic • • • • • • 40 Enlarging the EU Statistical Network Official Statistics in Czech Republic The Czech Republic has the structures and the skills to meet virtually all current and future statistics obligations Overview Czech Statistical Office (CZSO) Legal framework reinforced by amendment in 2001 Statistical Council chaired by President of CZSO President of CZSO appointed by President of the Republic Over 1 800 staff in central and 14 regional offices About 290 surveys and 230 regular publications each year Census of population and housing in 2001 Czech Statistical Office (CZSO) • First established on 8 January 1969 by Act No. 2/1969 Coll., on the establishment of ministries and other central authorities of the Czech Republic • Based on Act: No 89/1995, amended by Acts No 220/2000 Coll., 256/2000 Coll. and 411/2000 Coll. • Employed over 1800 staff in headquarters and 14 regional offices in 2001 • Responsible for major outputs of the statistical system - Conducts about 290 surveys in the statistical programme each year - Disseminates about 230 publications each year - CZSO website gives provides statistics and information Other bodies • Nine Ministries and the Czech Telecommunication Office (excluding Ministry of Finance & Czech National Bank which obtain data under different legislation) • Producing sectoral statistics (except banking and finance) in collaboration with CZSO or within the institution itself • Conducts around 160 surveys (included in Statistical Programme) each year • • • • • • • President of CZSO • Appointed by President of the Czech Republic upon the proposal of the government of the Czech Republic; the term of appointment is currently not defined, but a five-year term renewable twice is included in a draft amendment to the statistical act. • Heads the headquarters of the CZSO and chairs the Statistical Council • Attends Cabinet meetings and appoints CZSO staff Statistical Council • Consultative body of 11-21 members • Members appointed by President of the CZSO from experts in statistical theory and practice (ministries, academics, businesses) • Chaired by President CZSO and meets at least twice a year �� President of the Czech Republic � � Enlarging the EU Statistical Network 41 The organisation The principal body of the State Statistical Service of the Czech Republic is the Czech Statistical Office (CZSO). It is a central authority of the Republic with freedom from political interference, impartiality and the confidentiality of data guaranteed under the law. It is responsible for the methodology of statistical surveys. It collaborates and shares data with the Czech National Bank, the Ministry of Finance and other ministries. It is also required to process election results. The legal basis The State Statistical Service Act of 1995 provides the main legal basis for the statistical service and the CZSO. It covers: • Guarantees of impartiality • The Statistical Council • Limits on the compulsory collection of data • The use of administrative data sources • Confidentiality and individual data protection • Providing statistical information • Statistical classifications, nomenclatures and registers An amendment came into effect in 2001 to: • Strengthen the co-ordinating role of the CZSO • Widen access to data from tax, customs and social insurance administrations • Emphasise the protection of individual data • Permit the provision of individual data to international organisations, and Eurostat. Other legislation concerning statistics covers the population and housing census, banking, foreign exchange and environmental protection. The management of the CZSO The President of CZSO is appointed for five years (as per the amendment to draft statistical law) by the President of the Republic on a recommendation by the government and attends cabinet meetings of the government. There are four vice presidents for the main sectors: statistics, administration, systems development, regions and information output. One of the principal ways the CZSO co-ordinates the whole statistical service is through the preparation of the annual programme of surveys. This involves considerable meetings with ministries and discussions over methodologies. For 2002 the programme includes 124 surveys carried out by the CZSO and 157 by other bodies.There are also some surveys undertaken by ministries in addition to the programme. The Statistical Council is chaired by the President of the CZSO who also appoints the members from among experts in statistical theory and practice. They include representatives from ministries, the national bank, academic institutions and business organisations. The Council meets at least twice a year and its main roles are to consider the programme of surveys, proposals for regulations and questions related to the development of the state statistical service. However, the proposed annual programme is seen by the Statistical Council at a late stage, making it difficult to change decisions about additional requests for statistics. In the future, the role of the Statistical Council could be enhanced by seeking its views at a much earlier stage, giving the users outside government more influence in shaping the programme. Structure and staffing of CZSO The Czech Republic now comprises of 14 regions, each with its own directly elected government. The CZSO has been reorganised to have an office in each region. The work of Official Statistics in Czech Republic 42 Enlarging the EU Statistical Network these offices is focused on dissemination and statistical support to the regional authorities and on fieldwork. In addition, in 6 regions there are nationwide data processing departments, each with a responsibility for specific topics and reporting directly to the head office. The CZSO had over 1 800 staff in 2001, excluding a census unit. Of these, 37 % are in the central office in Prague, 75 % are female, 52 % are over the age of 45 and 36 % are graduates. Recruitment of staff is focused on lowering the average age and raising the proportion of graduates. The CZSO continues to give high priority to technical training in the EU statistical system and information technology. Foreign language and management skills are also included. The CZSO has made a start on the use of total quality management using the European Foundation Quality Model and has recently adopted an Ethical Code, which lays down principles on desired standards of behaviour and propriety of the CZSO employees. Funding The total annual budget for the CZSO has to be approved by parliament. In 2001, it was approximately € 64 million. This included provision for a population and housing census and for the realisation of the programme for the adoption of the acquis communautaire. Regular reports on the fulfilment of budget and the results produced are required.A very detailed monitoring of projects linked with EU requirements has been launched. The budget distinguishes between current and capital expenditure.The 2001 budget included approximately € 5.7 million for capital expenditures on the projects. Once the budget is agreed by Parliament, the CZSO has little freedom in the way it is spent. Moving funds between the allocations for capital expenditure, wages and other current expenditure requires special approval. However in recent years it has been possible to use savings from reducing staff numbers to improve the pay of remaining staff. The link between the annual programme and available resources still needs to be developed. At present the allocation of resources by the Ministry of Finance is determined according to the previous year’s figures and other elements beyond the control of the CZSO, such as the statutory limits on average wage levels. The first annual report, covering main CZSO activities in the year 2000, was published in 2001.This included priorities determined by the top management for the following year. Co-operation There are various universities and research institutes in the Czech Republic with close links to the statistical service and several are represented on the Statistical Council. The CZSO has formal agreements with academic institutes and universities. With regards to data supply, the CZSO also has agreements on mutual collaboration and data supply with most central government agencies, but not with local and regional governments. Regarding relationships with users, meeting the statistical needs of the EU has obviously been the driving force behind most of the developments in the statistical service. The arrangements for keeping in touch with users within the country relate mainly to specific subjects. The CZSO is, however, conducting a survey among users about their general requirements. There are also regular meetings with trade and business associations. Official Statistics in Czech Republic Enlarging the EU Statistical Network 43 Information technology and methodology The budget for the population census made it possible to upgrade significantly the information technology available to the CZSO. The CZSO now has a UNIX-based system with client-server architecture, a local area network in Prague and a wide area network covering the regional offices. At present approximately 1 400 PCs have been connected to the network so that practically all statisticians who are responsible for data evaluation, data capture or primary data processing have access to the network. Automated data capture has been introduced in the labour force survey and in price collection for the CPI. Optical scanning was used in the census of population, and is now used in processing data collected in surveys. Metadata on the network includes a description of statistical concepts, descriptions of codes/lists and regularly up-dated codes/lists as well as a set of metadata on the website describing various areas of statistical activities. The CZSO has introduced electronic versions of statistical questionnaires, particularly for economic surveys. They are available from the CZSO website or on diskette and have delivered improved response rates. Each year the CZSO arranges two one-day meetings with other bodies in the statistical service to discuss methodological issues. The output Classifications The CZSO has adopted all the major EU classifications such as COICOP, COFOG, ISCO-88, PRODCOM, ISCED, CC and GEONOM. National versions of several international classifications (such as CZ-NACE and CZ-CPA) are fully compliant with the corresponding EU classifications. The Czech Republic has agreed with Eurostat on the regional structure for the NUTS classification. Registers The business register is used for administrative and commercial as well as statistical purposes. The CZSO maintains both the public part of the business register for use by the other bodies in the state administration and the part available only for statistical use. The information on enterprises comes from the commercial courts, trade registers and professional registers. Each legal unit is identified by a number that is assigned at registration. The register has some 2.1 million units, including registered farmers and units in the government sector. The growth of units is about 140 000 annually. While all the large businesses and samples of the others are contacted in CZSO business surveys, there has been no contact since registration with the majority of the businesses. It is estimated that about half the apparently active units on the register are either inactive or wrongly classified. To rectify this, the CZSO has taken a number of steps, some as part of the national Phare Programme. Changes to the Statistical Service Act now make it possible to receive income tax information. Together with information on the self-employed from the social security system, this will help considerably in identifying which businesses have ceased to operate. Several special censuses and surveys have all yielded information that could be used to up-date the business register during 2002. In addition to the business register, there are also registers of census districts, farms and accommodation establishments. Legislation in business registration is Official Statistics in Czech Republic 44 Enlarging the EU Statistical Network included under numerous laws, allowing registrations from many different locations. A single law on business register would rationalise the procedure for registering businesses. The CZSO is also working with ministries in developing a system of basic interconnected registers for public administration, including population, business and real estate. Demographic and social statistics The population and housing census was conducted in 2001 and results are being released. Since January 2002, the content of the labour force survey has been fully harmonised with Eurostat standards. Information on unemployment is also available from the Ministry of Labour and Social Affairs. The latter also carries out surveys on social care and social security benefits including the ad hoc module set by Eurostat and maintains an Information System on Labour Costs. The Ministry obtains data on earnings from the administration of the social insurance scheme. To provide regional data comparisons, an annual survey by the CZSO collects information on employees and earnings in local units with 20 or more employees. Education statistics are compiled by the Institute for Information on Education. Data conforms to ISCED 97 and almost all information requested can be provided. All data are available at the NUTS 2 level. The CZSO is participating in the EU working group on the continuous vocational training survey provided to employees by employers. The Ministry of Culture and other bodies are able to supply indicators on most of the categories identified in the UNESCO classification of cultural activities. Data relating to audio-visual products is available from surveys carried out by the CZSO and by the Ministry of Culture. An extensive range of health statistics primarily from the Institute of Health Information and Statistics are available in line with WHO and other international guidelines. Information on accidents at work is available but some changes to the legislation under which this is collected are necessary to follow the standards used in “European Statistics on Accidents at Work”. The CZSO carries out regular annual surveys on incapacity for work due to illness or injury. The CZSO has had a continuous household budget survey since 1956, but it is based on quota sampling from the micro-census held every five years and has a small sample size. The CZSO has been experimenting with a survey based on random sampling, with a design suitable to produce regional estimates and questions aligned almost entirely with EU requirements. However, the response was only just over 30 % due to the heavy burden involved in record keeping. A re-design of this survey is necessary. The Ministry of Labour and Social Affairs is developing a system of social protection accounts (ESSPROS) for the beginning of 2003, with pilot results for the reference year 2000. Currently these accounts only cover the activities of the Ministry, but all relevant activities will eventually be included for accounts to be prepared by 2004. Macro-economic statistics Czech annual accounts at current prices follow ESA 95. Data including financial accounts and balance sheets for each sector are available at the three-digit level of NACE. So far, constant price figures are available from quarterly Official Statistics in Czech Republic accounts only, at a more aggregated level of activity. The improvements now being made to the business register are crucial to the reliability of the national accounts. Work is proceeding on supply and use tables with emphasis on achieving balances at current prices, rather than at both current and constant prices. Quarterly estimates of GDP are released ten to eleven weeks after the end of the quarter. The CZSO hopes to introduce flash estimates based on short-term output indicators.These would be released 45-50 days after the end of the quarter. The Ministry of Environment is currently preparing proposals for indicators of sustainable development. In 1998 the CZSO started preparing accounts on environmental protection expenditure in close consultation with the Ministry of the Environment and Eurostat. In the financial accounts, there are some adjustments that still have to be made to the government sector figures from the Ministry of Finance to bring them in line with ESA definitions. The CPI is published monthly, six working days after the end of the month using the current COICOP. The weights combine information from the household budget survey and the national accounts. The choice of outlets and the selection of items to be priced have been up-dated using results from the census of retail trade. The CZSO also introduced a Harmonised Index of Consumer Price (HICP), in parallel with the CPI. The CZSO participated fully in the Eurostat project to prepare 1999 purchasing power parity and has developed integrated sets of monthly producer, export and import prices. The PPIs are published at the three- digit level of NACE/CPA. The agricultural price indices are being developed by the CZSO as part of the Eurostat pilot project on agricultural statistics. Business statistics A review of the surveys used in compiling structural business statistics is underway. The compiling of short- term statistics is organised by sectors of the economy. The PRODCOM classification is used for the compilation of industrial production statistics. For the distributive trade, a full census of the legal units in retailing was carried out in 1999. Wholesale and retail trade provides the largest share of units on the register. A new survey for the year 2002 is under preparation. The sample will be designed to yield estimates at the three-digit level of NACE rev. 1. Road and inland waterways transport statistics from the Ministry of Transport and Communications already comply well with EU requirements. There is a regular monthly survey on rail transport with transport of goods between regions (NUTS 3) incorporated. It is expected that most of the data required by the draft council regulation on air transport will be provided for the year 2002. The Czech Republic is participating in the Eurostat working party on statistics on the information society and expects to propose new statistical surveys or additions in the programme for 2003. Information on tourist accommodation is available and the CZSO has introduced a monthly survey on Enlarging the EU Statistical Network 45 Official Statistics in Czech Republic utilisation. Monthly information is also available on border arrivals and departures. Information about tourism by Czech residents will be available from 2002. Service industries are included in structural business surveys. The planned new system of surveys will start in respect of 2002. Short-term statistics have been published regularly. In general, business statistics suffer from the deficiencies in the business register, complex questionnaires resulting in heavy burden on respondents, lack of response from small businesses and difficulties in assessing the accuracy of the data. Monetary, financial, external trade and balance of payments statistics The Czech National Bank (CNB) is responsible for monetary and balance of payments statistics and the recommendations of ESA 95 have already been adopted. Government finance statistics from the Ministry of Finance do not yet conform fully. Monthly statistics for external trade compiled by the CZSO on the basis of customs documents are published 16 working days after the end of the month. They conform fully to international guidelines. Plans are underway for the introduction of INTRASTAT.The CNB prepares quarterly balance of payments accounts following the IMF manual.The bank also has a programme in progress for the further development of statistics. Agriculture and forestry statistics A census of agriculture was carried out by the CZSO in 2000 with considerable help from the Ministry of Agriculture. The results are now in a suitable database and provide the first general overview of the structure and condition of the country’s agricultural sector. However, there are still no published statistics about household incomes in this sector. The CZSO has been involved in several Phare pilot projects organised by Eurostat to help Candidate Countries establish and implement different statistics covering agriculture. The CZSO reports almost full compliance in the area of crop production statistics. Regarding animal production, the Ministry is responsible for statistics on poultry and milk production while the CZSO is responsible for other animal products and balance sheets.These statistics are now in line with EU standards. Forestry statistics are fully compliant with the OECD/Eurostat/FAO questionnaire. Other statistics Between them, the Ministry of the Environment and the CZSO are able to supply most of the required environment statistics and indicators. Much regional and geographic information is available at NUTS levels 2 and 3. Some data down to level 4 is being added to the “KROK” regional database, which is accessible on the CZSO website. The CZSO has published regional accounts for the years 1996-2000. These include GDP at NUTS 3 level. It has not been possible to include regional estimates of household consumption expenditure because of the inadequate size of the sample in the household budget survey at present. The Czech Republic has developed its statistics on R&D and innovation following the methodology of the EU and the Frascati and Oslo manuals. It is represented at all Eurostat R&D working party meetings. There is a plan to launch a survey on innovation in 2002. Official Statistics in Czech Republic 46 Enlarging the EU Statistical Network The information • A full catalogue of publications can be found on the website www.czso.cz • Full range of information available in Czech, English, French and German • About 230 regular publications each year • Most publications in hard copy and electronic formats • Press releases according to a fixed calendar • Press briefings for major data releases • Sensitive data published at 09.00 a.m. on the date previously announced • CZSO Publications Shop and information centres in regional offices • Data protection and confidentiality rules in State Statistical Service Act Conclusion With the considerable development of its statistical services in recent years, the Czech Republic will comply with almost all the statistical acquis of the European Union by the end of 2003. Following the recent changes to the State Statistical Service Act, the institutional arrangements for the statistical system also compare well with EU standards. At the same time, opportunities exist to reduce the burden of reporting business data, improving the business register and perhaps giving the Statistical Council a larger role in contributing to the annual programme of surveys. Official Statistics in Czech Republic Enlarging the EU Statistical Network 47 Estonia Statistical Office of Estonia Endla 15 15174 Tallinn Tel: +372 6259 300 Fax: +372 6259 370 E-mail: stat@stat.ee Web site: http://www.stat.ee Pre-Accession Milestones Country Profile Estonia Eesti Geographic co-ordinates 58 00 N, 25 00 E Area 45 227 km2 Climate Temperate with cold, cloudy, humid winters and cool summers Administrative Divisions 15 counties (maakond), 42 cities (linn) and 205 rural municipalities (vald) Capital City Tallinn (0.4 million inhabitants) Population and Growth Rate 1.4 million, - 0.55 % (2001 estimate) Nationality Estonian Ethnic Profile Estonian 65 %, Russian 28 %, Ukranian, others Religion Evangelic Lutheran 78 %, Orthodox 19 %, others Official language Estonian National Currency 1 kroon = 100 cents Exchange Rate against Euro 1€ = 15.65 kroon (fixed exchange rate) System of Government Parliamentary Democracy Executive Power President & Council of Ministers Head of State President elected for five-year term by two-thirds majority, otherwise elected by electoral assembly Head of Government Prime Minister, nominated by President and appointed by Parliament Legislative Power Unicameral Parliament or Riigikogu; 101 seats; members are elected by popular vote and serve a four-year term Judicial Power National Court (chairman appointed by Parliament for life) National Holiday Independence Day: 24 February (1918) 1991 Establishment of official relations between Estonia and EU after its independence 1995 Estonia signs Europe Agreement 1995 Estonia submits an official membership application for EU on 28 November 1998 Estonia’s Europe Agreement of Association enters into force 1998 Official negotiations for EU membership were launched in March 2002 26 out of 31 chapters of the acquis provisionally closed by June 2004 Target year for EU accession Enlarging the EU Statistical Network 49 The Statistical System of Estonia Minister of Finance • • • • • • 50 Enlarging the EU Statistical Network Official Statistics in Estonia Estonia presents a very positive and competent approach to the collection and dissemination of official statistics Overview Statistical Office of Estonia (SOE) Legal framework harmonised with EU requirements and updated in 2000 No Statistical Council but permanent and ad hoc working groups with users Director General of SOE appointed by Minister of Finance About 360 staff in central office 160 surveys and 130 publications (65 titles) each year Population and housing census in 2000, agricultural census in 2001 • • • • • • • Other providers of official statistics • Bank of Estonia compiles balance of payments, and is responsible for monetary and financial institutions • Ministry of Finance partly responsible for Government Finance Statistics • Ministry of Agriculture for certain areas of agricultural statistics • Estonian Institute of Economic Research for consumer and industry sentiment surveys and provision of short-term analysis of Estonian economy Statistical Office of Estonia (SOE) • First established in 1921 as State Statistical Central Bureau • Based on the Official Statistics Act: RT*I 1997, 51, 822 of 11 June 1997 which came into force in July 1997, amended in June 2000: RT I 2000, 47, 289 • Employed about 360 staff in 2001, with central office in Tallinn (8 divisions and 3 departments) and two SOE sections in Viljandi; 10 supervisors working in 15 counties • Leading statistical institution in Estonia producing 80% of statistical indicators - Conducts about 160 different surveys each year - Disseminates about 130 publications (65 titles) per year in Estonian and English; high emphasis on electronic dissemination - SOE website provides statistics and information Management team and working groups • The management team consists of 17 persons (heads of divisions, departments, supporting units of SOE) • There are permanent and ad hoc working groups on different statistical areas (price indices, national accounts) with main user groups • There are no permanent working groups with providers of data Director General of SOE Nominated by Minister of Finance on permanent terms • Heads the SOE • Assisted by 2 Deputy Director Generals with a management team • Is responsible for the preparation of the annual list of official statistical surveys �� � Enlarging the EU Statistical Network 51 The organisation The Statistical Office of Estonia (SOE) is the main provider of statistics, with the Bank of Estonia contributing balance of payments, monetary and financial information. Additional data comes from other government ministries. Consumer and industry opinion surveys are carried out by the Estonian Institute of Economic Research. The SOE functions under the Official Statistics Act of 1997, amended in 2000. The legal basis The Official Statistics Act of 1997 was mainly in compliance with EU regulations at that time and stressed the importance of impartiality, reliability, relevance, cost-effectiveness, confidentiality and transparency. To reach full compliance a revised act came into force in June 2000. Among the provisions are: • Definition of official statistics • The conduct of official statistical surveys • The scope of the statistical bodies • Strengthening the co-ordination role of the SOE • The duties of agencies conducting official surveys • The requirement to inform respondents about the purposes of data collection • The obligations and liabilities of respondents • Data protection, transmission and dissemination Other laws cover censuses, population register, wages data, classifications and data protection.The statistical legislation is well in line with the EU statistics regulation. The SOE has now the right to communicate directly with all government agencies and to use data from administrative sources, whenever possible. However, the interpretation of the act and technical reasons are still hindering the fulfilment of this. Two working groups are currently addressing these issues. The management of the Statistical Office of Estonia The SOE is headed by the Director General, nominated by the Minister of Finance for an undefined period of time. He is assisted by two deputy directors general. One is in charge of planning, international affairs and EU-integration.The other one assists the Director General in administration and IT. The management team of the SOE consists of 17 persons (all heads of divisions, departments and supporting units). The mission of the Statistical Office is to provide society with objective and relevant official economic, population, social and environmental statistics. The SOE is responsible for the preparation of the annual programme of surveys. In planning this, the proposals and amendments of ministries, state agencies, county governments, research establishments and other institutions as well as international requirements are taken into consideration. The annual list of official statistical surveys is approved by the government. In 2001, there were about 160 surveys. A strategic plan 2001-2005 includes the following goals: • Provide society with an overview of the situation and trends in the country through official statistics • Enhance the managing and co-ordinating role of the SOE • Compliance with acquis communautaire and other international agreements • Minimise the burden on respondents • Use of modern information technology • Efficient organisational structure Currently the management of the SOE is improving internal efficiency by restructuring the organisation into more functional units. There is no standing Statistical Council which could have an advisory role in setting up priorities for Official Statistics in Estonia 52 Enlarging the EU Statistical Network statistical production. The importance of such a council has been discussed, but no definite decisions have been made. There are, however, both permanent and ad hoc working groups on different topics with main user groups, although not with providers of data. Structure and staffing of the SOE The organisation is divided into about 40 subject-matter units which form together eight line divisions and three horizontal departments. Every unit is in charge of one statistical domain. In one division a special section has been established for developing methodological solutions. The regional organisation was abolished and now there are two sections outside Tallinn. These are in charge of labour force statistics and regional statistics. Additionally there are 10 supervisors co-ordinating the work of the interviewers in all 15 counties. Of the 364 staff, 83 % are female, 53 % are aged over 45 years, and 76 % have university education. Staff turnover has been high (7 - 8 % per year over the past five years) particularly regarding IT, but the SOE has been able to offer better salaries recently. There are 100 interviewers who work on an hourly basis. In-house staff training has included courses on legislation, management issues, languages, IT, internal auditing and accountancy, with 136 people attending such programmes in 2001. There has also been participation in TES and other foreign training courses, plus long-term traineeships in Eurostat. Funding The SOE budget for 2002 is approximately € 3.5 million. The two previous years had unusually high budgets to cover census activities. Approximately 65 % of costs are for personnel. However, there are financial concerns related to the development of new systems such as INTRASTAT. Co-operation The SOE obtains scientific advice from Tallinn Technical University and Tartu University. With the latter, there is permanent co-operation on methodological issues. As an example of co-operation, in 2000, the SOE with the International and Social Survey Institute of the Tallinn Pedagogical University completed an analytical collection on adult training. Information technology and methodology The SOE’s IT budget is co-ordinated with other institutions in the Ministry of Finance, which has a special IT committee. The SOE is developing common databases for a user- oriented statistical information system to aid electronic data dissemination. It is a client-server system run on Oracle-based database server, containing a metadata system and a macro database. To improve overall response rate and quality, as well as improving productivity in processing, the SOE is working on electronic submission of primary statistical data. An electronic questionnaire, based on MS Excel, has been used in three statistical domains (comprehensive annual enterprise report, wages and salaries and agricultural statistics) in 2001. Although much has been achieved, exchange of knowledge between programmers at the SOE requires better co-ordination and improvement. An IT adviser was appointed in October 2001 to provide overall planning or IT strategy. The major issues for 2002 include IT security policy, development of common Official Statistics in Estonia Enlarging the EU Statistical Network 53 databases, electronic collection of primary data, development of INTRASTAT and tailor-made software. A special methodological unit has been established in the enterprise statistics division of the SOE for development of methodological solutions of the whole division. There is also permanent co-operation on the methodological issues with Tartu University. The output Classifications In January 2001, the State Classification Centre became part of the SOE. All of the most important international classifications and nomenclatures, such as NACE Rev.1, CPA, PRODCOM, COFOG, COICOP, ISCED, ISCO, and CN are already in use in Estonia. In addition, there are some local classifications of which the most frequently used is the classification of national administrative units and settlements for indicating territorial locations. The most important work in 2002 is the updating of NACE Rev.1 into a national version.The new versions of classifications NACE Rev.1.1 and CPA 2002 will be translated during this year for implementation from January 2003. The new version of COFOG (Classification of the Functions of Government) will be introduced in 2002. Business Register A database is used to create a sampling frame of business statistics known as the statistical profile. It is compiled in December every year and used for the production of structural business statistics of the same year and short-term statistics of the next year. The information is based on the legal registers and includes: • Enterprises and sole proprietors • Non-profit institutions and foundations • Central government and local government institutions and their subordinate establishments • Sole proprietors with very low turnover During the year, the database is updated with a special statistical register survey for new units and the data from the legal registers. Other data sources, such as statistical surveys, information from customs, tax, annual accounts and newspapers are also used in updating. From 2001, a new questionnaire includes information about LKAU. In 2001, the SOE conducted a special quality survey of the database. 3 000 enterprises were directly interviewed including questions on contact information, activities and size of enterprises. For the end of 2002, a new database is planned that will be fully in compliance with EU regulations on business registers. Demographic and social statistics Estonia carried out a population and housing census in 2000. The first final results were published in September 2001. They showed a population around 67 300 lower than the prevailing statistics based on the results of 1989 census. The differences are substantially unequal between regions due to migration. At present migration is underestimated and statistics are based on voluntary administrative data. In 2000 the Population Registration Act provided the legal basis to establish a register of the total population for the end of 2003. Data on immigration flows of non-nationals will be derived from a residence permits register and from a labour force survey. The labour force survey has an annual sample size of 8 800 households giving quarterly results for regions of Official Statistics in Estonia 54 Enlarging the EU Statistical Network Estonia (NUTS 3) and annual results for counties (NUTS 4). The multi-annual programme of ad hoc modules is implemented since 2001. Data are transmitted to Eurostat quarterly, three months after the end of the quarter. The survey corresponds now fully with EU regulations. Information on unemployment is different from that of registered job - seekers as there are no special incentives for unemployed people to register, poor availability of vacancies and a low level of unemployment benefits. Labour cost survey data for 2000 will be transmitted to Eurostat by end 2002. Data on pre-primary and higher education is collected and processed by the SOE, data on general and vocational education by the Ministry of Education. The register of educational programmes contains ISCED keys, allowing comparable basic educational data for UNESCO/OECD/Eurostat questionnaires. A continuing vocational training survey was carried out in 2000 according to international recommendations and the response rate was 72 %. The methodology of the continuous household budget survey corresponds with the Eurostat recommendations. The results are published mainly at the NUTS 2 level but some indicators are available at NUTS 3 and NUTS 4.The sample size is about 10163 households and the response rate at the household level 83 % and at the diary level 63 %. However, there is under-reporting in expenditure for certain products. The SOE has participated in an internationally harmonised time use survey and data will be used to review unpaid work in households. An integrated system of general living conditions statistics will include the household budget survey, the labour force survey, the time use survey, the adult education survey and a social survey (similar to ECHP and EU-SILC).A pilot survey was conducted in June 2002. The main survey will be conducted in 2003. Statistics on unemployment and social benefits and services are based on information delivered by the respective institutions. The system includes the same components as the ESSPROS but social protection expenditure is classified by type, not by functions. In 2002 the SOE will continue reclassification according to ESSPROS. Special attention will be paid to benefits in kind and expenditure on health. Macro-economic statistics The SOE publishes national accounts which in general conform to ESA 95, however they are only non-financial and capital accounts. The aim was to compile the general government accounts for 2000 including financial accounts in the annual publication “National Accounts of Estonia 2000”. One of the main targets is to improve accrual accounting principles. A number of details need to be addressed before full compliance with ESA 95 is achieved. The first supply and use tables are in place for 1997 but not yet integrated with the rest of the accounts. The SOE intends to reduce the delay so that the supply and use data becomes usable in current work. At the moment, the balance sheet of annual economic accounts is not included in the national accounts. Capital stock is estimated for non-financial and financial corporations and for general government institutions since 1993, but these estimates have not been published. Inventories are estimated according to the requirements using international methodology. The household sector presents the most difficulty, as very few data sources are available. More research work on possible information is planned. Official Statistics in Estonia Enlarging the EU Statistical Network 55 The SOE has made recently some improvements in the calculation and the publication of quarterly economic accounts, producing time-series with seasonal adjustment, which are greatly beneficial to users and economists. Data are provided to Eurostat within 90 days. The flash estimate method was developed with significant input from university scientists and experts. It is based on the use of monthly VAT statistics and is published 65 days after the reference period. The SOE has not published financial accounts yet but those for 1999 comply with main ESA 95 requirements. In co-operation with the Bank of Estonia and Ministry of Finance, the SOE plans to have improved data sources and close co-operation with other institutions for compiling financial accounts. During 2002, a systematic compilation process will be established. Since 1997, the SOE has been forwarding monthly data on the Harmonised Index of Consumer Prices to Eurostat. There were some problems in the interpretation and application of the regulations, for instance the adding of foreign visitors’ expenditures in Estonia, rents, package holidays and social protection. Discussions with the Estonian Tourist Agency, housing organisations and other experts have helped to resolve these issues. For the total industrial output price index, separately calculated output prices for domestic and for export markets have been introduced from January 2002, with the weights changed annually and a chain index used. In 2002, the construction price index has been re-based from 1997 to 2000. Export price data are collected directly from major exporters. During 2002, the calculations will be done using value of export according to NACE Rev.1 and additional enterprises and items will be added. The same will be done with regards to the import price index. Business statistics In 2001, EKOMAR (Comprehensive Annual Enterprise Report) was introduced for structural business statistics. It is a system of 31 different questionnaires adapted to enterprises, taking into account the economic activity and size by number of employees. All companies must supply information on general data, fixed assets, balance sheet total, export – import and LKAUs. Other modules vary according to the firm. EKOMAR is available through Internet with an electronic questionnaire including logic controls to help the enterprises. Public enterprises and private enterprises with more than 19 employees are surveyed totally. For smaller enterprises sampling is used. From the beginning of 2001, short-term business statistics are collected with new questionnaires meeting EU requirements that are sent out with the help of EKOMAR. These are monthly surveys of industry and distributive trade. For industrial production statistics the data for 2000 was collected in 2001 by a questionnaire based on the PRODCOM list. The PRODCOM list in Estonian and also questionnaires for respondents and other users are available on Internet. At present the SOE conducts two different surveys on road freight transport: one based on the vehicle register and the other on a road transport survey among Official Statistics in Estonia 56 Enlarging the EU Statistical Network enterprises. The data of national and international transport is broken down by NUTS classifications. The SOE surveys all 10 rail transport enterprises. Monthly surveyed variables are carriage of goods in tonnes and freight turnover in tonne-kilometres. The data on carriage of goods is fully in compliance with respective acquis. The SOE collects maritime transport data from ports and from sea transport companies. To comply with current EC regulations, additional data are required on type of cargo, port of loading and port of unloading, number of mobile units with cargo and without cargo and the size of vessels. Data about the country or the territory of registration of vessels are available, but not according to the correct nomenclature. The greatest problem of maritime statistics appears to be the lack of information at the port of the loading/unloading of goods. All the necessary variables on air transport statistics such as number of passengers, freight and mail by air are available in the Tallinn airport database. From April 2002, the airport has transmitted this international data in Excel format to the SOE, where it is coded for submission to Eurostat. The traffic security department of the Estonian national road administration maintains a database on road accidents and sends statistics to the SOE monthly. Main variables are the number of accidents, the number of killed and injured persons and the number of accidents caused by drunken drivers, in total and by county. Tourism accommodation statistics are collected from hotels, camping sites and other short-stay accommodation units. In addition data are also collected from enterprises with accommodation services as a secondary activity. A tourism demand survey is included in the labour force survey according to EU regulations. Monetary, financial, external trade and balance of payments statistics The main constraint for statistics on public deficit and debt is the availability of data. Since most of the information comes from the Ministry of Finance, closer co-operation is obviously essential. The weakest point is the calculation of local governments’ domestic debt. Using the guidelines, the SOE is planning to develop deficit and debt statistics based on ESA 95. As a result of closer co-operation with the Bank of Estonia over financial accounts, there have been already some discussions with the Ministry to improve local government debt figures on the basis of financial accounts by using counterpart sector information. The aim is to have comparable general government deficit and debt statistics in line with ESA 95. The successful implementation of the INTRASTAT system for trade in goods between Member States will greatly depend on funding from Phare national funds. It needs to be operational in January 2004. For trade in goods with third countries, the SOE has reconciled the methodology used with EU regulations as much as possible. In 2002, the SOE will pay more attention to the data quality and present foreign trade data on the Internet. Agricultural, forestry and fisheries statistics The agricultural census in 2001 provided complete information on the structure of agricultural holdings. Preliminary results were published in November 2001. General data, data on land use and number of animals were Official Statistics in Estonia Enlarging the EU Statistical Network 57 Official Statistics in Estonia published in June 2002. Data on machines, equipment, storage and animal husbandry facilities will be published in December 2002. Data on labour force, incomes, forestry and fishery will be published in 2003. The agriculture census 2001 data will be used for updating the statistical farm register. This currently lists 61 000 holdings of more than one hectare and will be the framework for a farm structure survey in 2003.The system for regular updating of the farm register has to be improved. Economic accounts for agriculture on the basis of Eurostat methodology (EAA 97) are compiled in co-operation with the Ministry of Agriculture and data are transmitted to Eurostat regularly. Economic data of agricultural households are estimated and together with adjusted macroeconomic data of agricultural holdings are used for compilation of GDP (ESA 95). More effort will be put into improving the method of calculation of consumption of fixed capital and to start the compilation of economic accounts for forestry in accordance with Eurostat standards. Land use, crop, livestock and produce information is generally harmonised with EU legislation. Fishery statistics are compiled from data sets of sea inspection and the Ministry of Environment.The list of all Estonian fishing vessels above 12 meters was prepared in 1999. A Phare project was launched to support the development of a fishing vessel register. The Estonian environmental inspectorate gathers monthly data on catches by fishing regions and species. Information is collected from the vessels fishing in the Baltic Sea and in the Northwest Atlantic area regulated by North Atlantic Fishery Organisation. Other statistics Environmental statistics are derived from the surveys in the annual programme. High-quality data are needed to be able to produce relevant environmental indicators, but this is not yet collected using harmonised methodologies. The current data collection does not allow the allocation of environmental pressures to NACE categories. In some of these surveys the SOE has not collected or processed the data. The target of the SOE is to have an impact on providers of basic data so that environmental classifications will be used in their data collection systems, otherwise the link (NACE breakdown) between economic data and environmental pressures cannot be established. Some environmental aspects of agriculture, transport and energy will be produced and made available via sustainability indicators published in 2002. The SOE started work on regional accounts in 1997. Eurostat technical assistance contributed a lot to the development of methodology of regional GDP estimation. By 2001, the SOE had provided all the necessary indicators in order to ensure compliance with the ESA 95 data delivery programme requirements for regional accounts. The methodology used to estimate regional GDP is generally in line with Eurostat guidelines. However, information on sole proprietors activities is insufficient as there is no special survey covering their economic activities. The absence of an adequate overview of the local units of economic entities is caused by the present legislation that does not require them to submit any information on their structural units. Development of register systems will contribute to an improvement of the quality of regional statistics. The SOE has regularly submitted data at the NUTS regional level 3 to the REGIO database. The Estonian regional development database was started in 1999. There are nearly 2000 tables on the main fields of statistics both in Estonian and in English. The regional breakdown of some data is at NUTS level 5. The SOE plans to submit the time-series corrected by new NUTS regional level 3 breakdown, to the REGIO database during 2002. To compile science and technology statistics, the government, higher education and private non-profit sectors have been covered by an annual R&D survey using internationally comparable definitions and methodology. The business enterprise sector is covered using the same sample as in the financial survey of enterprises. Official Statistics in Estonia 58 Enlarging the EU Statistical Network The information • Annual publication catalogue and calendar in advance and update of calendar every week on the website www.stat.ee • About 130 publications (65 titles) each year in Estonian with most of them in English • Statistical Yearbook of Estonia available in paper and CD-ROM versions • Publications priced according to market demand • Simultaneous release of statistical information at pre- announced dates via printed and electronic media • Focus on key clients (news media, governmental institutions, enterprises, foreign embassies) plus special products for target users • Attention to corporate image of the products and the office • Measurement of performance (news clippings, sales etc) • Continuous development of the Internet and other electronic products Conclusion The Estonian statistical system has the legal basis and the internal structure to meet current and future needs, although it is unusual not to find a Statistical Council. The financial basis of the SOE is good, but additional investment and special efforts will need to be made for INTRASTAT. The speed of implementation of new surveys and major revisions plus the flexibility and response rate from the staff sets a good example. This could be matched with deeper analysis and more thorough methodologies. The culture of continuous improvement and the client-service attitude towards dissemination of information are extremely positive. Among the problems that still need to be addressed include the updating of different registers, an improvement of the system for environmental statistics as well as that of government finance. However, complying with the requirements of the European Union seems to be within the capacity of the system. Official Statistics in Estonia Enlarging the EU Statistical Network 59 Hungary Hungarian Central Statistical Office Keleti Karoly 5-7 PO box 51 HU-1525 Budapest Tel: +361 345 6000 Fax: +361 345 6378 E-mail: kshintl@office.ksh.hu Web site: http://www.ksh.hu Pre-Accession Milestones 1988 Establishment of diplomatic relations between Hungary and EU 1990 Hungary joins Phare Programme 1991 Hungary signs Europe Agreement 1994 Hungary’s Europe Agreement of Association enters into force 1994 Hungary submits an official membership application for EU membership on 31 March Country Profile Hungary Magyarország Geographic co-ordinates 47 00 N, 20 00 E Area 93 030 km2 Climate Temperate with cold, snowy winters and hot summers Administrative Divisions 19 counties, 22 towns of county rank, 1 capital city Capital City Budapest (1.8 million inhabitants) Population and Growth Rate 10.2 million, - 0.3 % (2001 estimate) Nationality Hungarian Ethnic Profile Hungarian 90 %, others Religion Roman Catholic 52 %, Reformed 16 %, Lutherans 3 %, others Official language Hungarian National Currency 1 forint = 100 fillér Exchange Rate against Euro 1€ = 244 forint (Quarter 1, 2002, New Cronos) System of Government Parliamentary Democracy Executive Power The government headed by the Prime Minister Head of State President elected for five-year term by National Assembly Head of Government Prime Minister, elected by National Assembly on recommendation of the President Legislative Power Unicameral National Assembly (Orszaggy~ulés); 386 seats; members are elected by direct elections under a system of proportional and direct representation to serve a four-year term Judicial Power Supreme Court of the Republic of Hungary National Holiday Feast of St Stephen of Hungary, founder of the state: 20 August (1918) 1998 Official negotiations for EU membership were launched in March 1999 Hungary submits the first National Programme for Adoption of the Acquis in August 2002 24 out of 31 chapters of the acquis provisionally closed by June 2004 Target year for EU accession Enlarging the EU Statistical Network 61 The Statistical System of Hungary Prime Minister Other bodies • National Bank of Hungary provides statistics on balance of payments, foreign direct investment and financial accounts • Ministry of Finance prepares the Debt and Deficit Notification (KSH is involved) • Ministry of Economic Affairs is responsible for Structure of Earnings Survey • Other ministries conduct some surveys • • • • • • 62 Enlarging the EU Statistical Network Official Statistics in Hungary Hungary has the resources and expertise to fulfil its requirements in terms of official statistics Overview Central Statistical Office (KSH) Legal framework of 1993 revised in 1999 Statistical Council needing stronger strategic role President of the KSH appointed by Prime Minister for a six-year term About 1 800 staff and 19 regional county offices Over 200 surveys and 250 publications a year Population census in 2001, agricultural in 2000, vineyard and fruit tree survey in 2001 • • • • • • • Central Statistical Office (KSH) • First established in 1867 • Based on Act XLVI of 1993, amended by Act CVIII of 1999 • Employed about 1800 staff at the central office and the 19 county offices in 2001 • Responsible for major outputs of the statistical system - Conducts about 200 different surveys each year and acts as consultant for surveys conducted by other ministries - Disseminates about 250 publications each year - KSH website provides statistics and information National Statistical Council • Professional and advisory body of 33 members • Members appointed by the respective organisations represented in the Council (from official statistical service, employers, employees, chamber of commerce, social insurance, scientific community) for 3 years • Chair appointed from members by Prime Minister on the recommendation of the other members and submission of the President of KSH, for a defined period • Council meets as needed and the Commissioner for Data Protection is a permanent invited guest President of KSH • Appointed by Prime Minister for a 6-year term (renewable twice) and assisted by 4 Vice-Presidents • Heads the central office of KSH and is a permanent member of the Statistical Council • Compiles the annual statistical progamme �� � � Enlarging the EU Statistical Network 63 The organisation The Hungarian Central Statistical Office (KSH) undertakes the majority of the statistical activity. The National Bank of Hungary (NBH) provides statistics on balance of payments, foreign direct investment and financial accounts.The Ministry of Finance is responsible for preparing the debt and deficit notification and the Ministry of Economic Affairs provides labour costs statistics. Other ministries conduct some surveys. The responsibilities of the KSH include: • Planning of surveys and statistical processes • Harmonisation and professional direction of statistics • An annual statistical programme • Developing statistical methods, concepts, classifications • Supplying statistics to users • Maintaining a library and professional archive • Maintaining statistical registers The legal basis The Hungarian Statistics Law was enacted in 1993 and amended in 1999. It establishes the legal base for the work of the KSH as well as the statistical work of ministries and the national bank.There are separate acts for data protection, the census of population and the census of agriculture. Provisions in the law include: • Appointment and dismissal of the President and vice- presidents of KSH • Data collection on a compulsory and voluntary basis • Registration of businesses and other economic entities • Power to audit respondents’ information • An annual statistical programme • Data confidentiality and data transfer for statistical purposes • Conformity to international statistical requirements The management of KSH The President and four vice-presidents are appointed and may be dismissed by the Prime Minister. Appointments are for six years, renewable twice. The National Statistical Council operates as a professional advisory body to the President of the KSH. Members are drawn from the official statistical service (the main statistical producers), social insurance, employer and employee organisations, the Chamber of Commerce and local government. Representatives of the Hungarian scientific community are also included and the Data Protection Commissioner is invited to attend all meetings. The Chair is appointed from the membership by the Prime Minister on the recommendation of the President of the KSH. Currently the contribution of the Council to the strategic direction of the statistical system is limited.This may be improved by giving the Council a stronger and more authoritative role by involving more senior representative members with a wider strategic mandate. The whole statistical programme is covered in a single submission that is made by the President of KSH to the government, after consultation with the National Statistical Council. In October 2000 a five-year development strategy included the following objectives: • Adapt to the European Statistical System, with an urgency to develop the system of national accounts, introduce INTRASTAT, reorganise agricultural statistics and produce international migration statistics • Address in a more systematic way domestic needs for statistics • Improve methodologies in national accounts, social, environment and regional statistics • Establish a consistent data warehouse system including Official Statistics in Hungary 64 Enlarging the EU Statistical Network meta-data for internal users • Improve the dissemination system by monitoring user needs, increase electronic dissemination and develop more analysis and international comparisons • Focus on accuracy and reliability • Improve the register of business units • Reinforce the co-ordination of statistics produced outside the KSH • Be an active partner in relation to international co- operation and assistance Some of the ways the management has identified as conducive to attaining these objectives were: • Develop staff career programmes, job rotation, and training • Ensure a more balanced distribution of resources to different tasks and operations • Improve programming and project management • Develop the IT systems • Improve overall financial management • Assume a role in statistical training at post-secondary level • Create a code of ethics for statistics In 2001, the strengths and weaknesses of the KSH were reflected in a peer performance review. Based on these results, the KSH has established several task forces that are now actively addressing the more critical issues and several major steps have been taken to support the strategic plan. Structure and staffing of the KSH The KSH has about 1 800 staff, half located at the central office and the other half in 19 county offices. Of the total staff, 74 % are female, 56 % have university degrees and 52 % are aged over 45 years. The responsibilities of the four vice-presidents cover economic statistics, social statistics, co-ordination and administrative services, and finance and IT. There are 19 county directorates that are responsible for managing the field operations, data collection, data capture and some primary processing for their area. These offices are separate from other public administrative offices in the counties. They report directly to the President of the KSH who is also responsible for administering the budget allocated by the Parliament and for the staff employed. The primary focus of staff training is the civil service qualification examination, which is a requirement throughout public service but with no particular relevance to the statistical skills required by the KSH. An enhanced training budget would allow a clearer emphasis on the operational needs of the KSH. In 2002, the KSH launched a ‘Year of Quality’ with a quality management programme covering questionnaire design, field operations and monitoring other elements of quality. Funding The KSH has an annual budget of approximately € 43 million, with additional expenditure relating to censuses.There are very strong public expenditure controls in Hungary. The budget is imposed under a number of separate budget headings and sub- headings. Expenditure in each must be accounted separately and staff numbers are also controlled which leaves the President with less flexibility in management. There is no system for allocating costs to specific programmes although a system for time usage and budget accounting was being tried in some of the county offices. Co-operation Members of the Statistical Council are drawn from various institutions outside the KSH in order to promote the representation of the interests of society and data providers. The KSH also has agreements with various partners which require co-ordination of work programmes across the KSH and other parts of the system. Official Statistics in Hungary Enlarging the EU Statistical Network 65 Information technology and methodology The IT strategy is based on a homogeneous hardware and software structure in the head office and in the regional offices. Main commercial software components are used and there are also a centralised meta-database and database (production, data warehouse, dissemination). Advanced technologies are used for data collection (OCR, Internet based reporting) and publications are in electronic form (website, CD). A new in-house developed data warehouse opened in 2002 will provide Internet access to a dissemination database in the near future. A methodology unit reports to the President and has overall responsibility for survey design, weighting and time series methods. Other aspects of methodology are the responsibility of the specific central KSH department or section undertaking the survey. Data collection, capture and initial processing are carried out in the 19 county offices. Functions such as mail questionnaire despatch, computer based edit and imputation of raw data and all the statistical analysis and tabulation are undertaken by the IT Department. In each case, this work is under the control of the department or section undertaking the survey. The output Classifications Most Hungarian classifications already comply with EU standards. EU classification requirements are already met in relation to NACE, CPA and COICOP. Hungarian versions of NACE, CPA and PRODCOM have been in use for some time (since 1996, 1998 and 1999, respectively). In 2001 a PRODCOM database was set up and in April 2002 the first data transmission was submitted to EUROSTAT.The harmonisation of the Hungarian product classification will continue, but will only be finished in 2003. The Hungarian customs tariffs are fully harmonised and more detailed because of specific national statistics requirements.The KSH co-ordinates the development of a 6-digit classification for the tax office harmonised with NACE and CPA. The Hungarian version of the Classification of Types of Constructions (CC) is also harmonised. Business register The business register, containing over 750 000 units, is constantly reviewed.The sources for removing or adding units are the court of registration and the registration offices in the local governments. There is further co-ordination with tax authorities. The number of staff working on the business register at the KSH is sufficient to ensure a register of quality. In addition, a register co-ordination committee has been established with representatives of users. Work needs to be completed on separating legal units and enterprises. The priority given to business register development and the resources made available give a sound basis for achieving the necessary results. Demographic and social statistics Population estimates are available at all relevant NUTS levels and a new census of population was conducted in 2001.The total population counted in this census is consistent with demographic projections based on the previous census and with registration. In 2001, new legislation was passed on foreign citizens entering and staying in Hungary. This will lead to a significant improvement in coverage and reliability of international migration statistics. Moreover, from the beginning of 2002, a new data collection system was introduced for naturalised Official Statistics in Hungary 66 Enlarging the EU Statistical Network foreign citizens (about 5 000 per annum). Overall the level of compliance with EU requirements in migration statistics is high. The labour force survey is well-designed and close to full harmonisation and quarterly data have been transmitted regularly. Statistics on the level and structure of labour costs are almost fully harmonised; those on the structure and distribution of earnings are being further refined. In general a good level of quality assessment is undertaken. Statistics from the various aspects of the labour market are integrated and work is undertaken to assess the coherence and consistency of different sources. Some initial problems with the introduction of new education statistical system within the Ministry of Education affecting the production of education statistics have now been overcome. For causes of death and public health statistics, the main Eurostat requirements are being met.The KSH are considering the requirements of the programme of community action on health monitoring 2002 - 2007. Statistics on health and safety at work partly meet the Eurostat requirements in terms of definitions and methodology. Additional work is required on duration and severity of illnesses due to work accidents, occupational diseases and in commuting accidents. In 2000, a time-use survey was carried out in the framework of a comparative research programme organised by Eurostat. It is intended to publish several reports and studies on different topics.Among those that have already been published are material and non-material household transactions, social mobility, time-use of part-time farming, methodology of time use survey and relationship between income poverty and welfare benefits. Currently social protection statistics only partly meet the Eurostat requirements. The preparation work to establish a social protection database was launched in 2000 in co- ordination with the KSH by a working group of experts from the interested ministries. Financial data of certain elements of the social protection system is not available currently in accordance with ESSPROS methodology as it is difficult to get data from the private sector. In 2001, experimental statistics on state-financed social benefits given in 1999 were compiled according to ESSPROS methodology. In 2002, the KSH intends to extend this work to the non-profit sector, administrative costs and receipts. For distribution of income and living conditions statistics, the intention is to add questions to the existing household budget survey of more than 10 000 households. The KSH are concerned about response rates deteriorating with the additional data requirements. Macro-economic statistics The KSH has a relatively long history of compiling national accounts according to the SNA and, with some exceptions, the level of compliance is good. The responsibility for estimates of foreign trade was transferred from the Ministry of Economic Affairs to the KSH in May 2002, giving access to the customs documents and allowing much improved quality assurance procedures based on individual returns.There is close co-operation with the National Bank of Hungary to ensure the consistency between sector estimates and statistics based on financial flows. Input-output tables have now been produced for 1998 and a new table will be produced for 2000. Such tables will be constructed every five years in current and constant prices and used for both annual and quarterly accounts. Whilst a range of deflators exists, there is an intention to strengthen these over time. Official Statistics in Hungary Enlarging the EU Statistical Network 67 First estimates of quarterly GDP are currently produced at 60 days after the end of the quarter and more detailed estimates are produced a month later. There is a desire to shorten the deadline of second estimates by three weeks to 70 days after quarter-end. In 2002, work began on the seasonal adjustments of quarterly accounts. The NBH has the main responsibility for compiling financial accounts although the KSH contributes. There are plans to publish these accounts on a quarterly basis from 2003, after resolving some methodological and practical issues. The Hungarian Consumer Price Index is compliant with the EU requirements and the KSH is monitoring further requirements as the regulations are extended.The KSH produces and publishes a Harmonised Index of Consumer Prices (HICP) on a regular basis. Estimating expenditure by Hungarian residents elsewhere and by non-residents in Hungary requires a new survey based data collection. Further experimental work is planned with existing data and there are detailed plans for two surveys: foreign spending and Hungarian spending abroad. Business statistics An integrated questionnaire for the collection of structural business statistics within the industrial sector was developed for 2001. A new system for data processing and control was introduced. Participation in a pilot project launched by Eurostat for the Phare countries to develop SBS started in 2001 and by the end of 2002, the main requirements should be met. Short-term statistics are produced on enterprise (legal unit) level, except for retail trade turnover which is collected on LKAU level. Volume indices of industrial production, sales and producer price indices, as well as the retail trade turnover variable were supplied to Eurostat in 2000 and 2001.A regular survey concerning an output price index for construction, adopting regulations and recommendations of the EU, was introduced in 2000. Methods for the calculation of a cost index of dwelling construction have been elaborated adopting regulations and EU recommendations. In December 2001, data covering 22 variables related to industry, construction, retail trade and other services were sent to Eurostat.The degree of compliance with the STS regulation is already high and there are plans to further improve the situation. Data supply in GESMES format via STADIUM system is being tested now. Statistics on iron and steel are partly in compliance, awaiting some more precise specifications currently under development. Energy statistics are in a high state of compliance with EU and IEA requirements. Supply and demand statistics and energy balances are produced in accordance with IEA recommended methodology and there are long time series available. To implement the requirements for distributive trade, the KSH plans to enlarge the data collection on sales space for retail stores and introduce new data collection in the year 2002 asking for information on types of consumers and number of fixed market stands and/or stalls. In 2001, an analysis was performed of the necessary changes in the system of statistics on transport of goods by rail.The target is to include new harmonised data collection in this area into the statistical programme for 2003. There are plans to start a statistical survey on international road freight transport in 2002 and on carriage of dangerous goods in 2003. Data collection on transport on inland waterways was started in 2000 by the Ministry of Transport and Water Management. These data are not fully harmonised with the relevant directive. The KSH will work with the Ministry on proposals for harmonisation in 2002 with the aim to put a revised solution in the statistical programme for 2003. Official Statistics in Hungary Hungary takes part in the Phare pilot project on aviation statistics. Some results are expected by the end of 2002. An overall revision of the system of statistics on road traffic accidents was accomplished early in 2001. Definitions are fully adapted to EU and international standards. The information society has been surveyed through questionnaires for some years, new ones will be started in the near future. The supply side of tourism statistics is well covered and projects are in progress on mapping tourism demand and on non-profit accommodation establishments. Monetary, financial, external trade and balance of payments statistics The National Bank of Hungary provides statistics on balance of payments, foreign direct investment and financial accounts. In the field of general government expenditures, deficit and debt statistics do not yet fully comply with the relevant methodology. There is a general recognition to address the deficiencies that relate to the development of financial and non-financial accounts and various aspects of EDP. The production of external trade statistics has recently been transferred to the KSH, giving them direct access to the customs notifications and full responsibility for external trade statistics. Quality improvements and better harmonisation with balance of payments statistics is foreseen. The KSH will be looking further into the efficiency of the data control system and into issues related to the lack of full harmonisation between the Hungarian customs regulations and the EU customs code. Based on a feasibility study, a series of measures will be taken for the introduction of the INTRASTAT system in Hungary, identifying, amongst others, important partners, necessary legal changes and required budgets. As part of the preparation a committee is in the process of being established consisting of some ministries, lobby groups, chambers of commerce, future data providers and users. Extensive contacts and consultations with Eurostat and some EU member countries are ongoing, both related to external statistics in general and to INTRASTAT. Some important steps have been planned and taken in order to improve the quality and consistency of the external trade statistics. Further careful planning and follow up is necessary both for the process of the takeover by the KSH of the external trade statistics and for the development of the INTRASTAT system. Some data on trade in services and foreign direct investment has been sent to Eurostat.This will be developed considerably under the new agreement between the KSH and the NBH. Agricultural statistics The responsibility for agricultural statistics is shared between the KSH and the Ministry of Agriculture and Rural Development. During 2001, there has been substantial progress towards compliance where this had not already been achieved. In 2000 the census of agriculture was conducted. Fruit tree and vineyard surveys followed in 2001. Hungary also participates in the LUCAS Phare pilot project.The Ministry of Agriculture runs manifold activities in the field of crop monitoring, remote sensing, control of subsidies. This is under the responsibility of the Hungary Remote Sensing Centre within the Ministry of Agriculture. The development of agricultural statistics at the KSH is supported by the Phare Programme including a twinning component. There are nearly one million agricultural holdings, a substantial proportion of which does not work for economic purposes. 68 Enlarging the EU Statistical Network Official Statistics in Hungary This has a large effect on the data collection processes and the statistics produced. Other statistics Environmental statistics are compiled by the KSH with the Ministry of Environment and other ministries. This is well co- ordinated. Specific data collection in the field of environmental statistics are included as compulsory in the annual programme. Hungary is full member of the European Environmental Agency. The work on environmental statistics is well under way in Hungary and is expected to meet most of the EU requirements, although there are relatively few people allocated to this task. Hungary should be able to meet the regulations on waste statistics in its proposed form. The development of waste statistics is supported by a Phare multi-country project and by a Phare twinning project in 2002. For regional data in Hungary, there are seven NUTS 2 units and 20 NUTS 3 units, the latter corresponding to the county level.The NUTS 2 level is established on the basis of EU requirements and has so far no administrative function within the country. The framework for sub-national statistics is based upon 3 135 NUTS 5 units.The capital city Budapest consists of 23 districts. This is linked to a mapping tool. These have associated classification codes to allow aggregation for various purposes. Data from a range of administrative sources is available to support the aggregations. A multi-annual programme of statistics on R&D can supply most of the data required by the Eurostat questionnaires.There are some differences in the recording of R&D expenditures and some lack of detailed R&D data from the central budget. In 2002, there is planned further harmonisation of the regional methods; permanent adaptation of the manuals; continued participation in the R&D statistics working parties; contribution to the revision of the Frascati manual and a comprehensive use of new questionnaires. The pilot survey on innovation in the manufacturing sector was carried out in 2000 according to Eurostat requirements. The first national results were published in 2001.The work in 2002 will continue on the adaptation of definitions and methodology of Community Innovation Survey 3. The information • Extensive use of website www.ksh.hu • Dissemination database accessible on line • Approximately 250 publications each year • Press releases on a regular basis • Calendar of releases published in advance • Release of information to all users at the same time • Subject to protection of confidentiality, anonymous individual records made available for academic research and secondary analysis • Considerable amount of regional data available on CD- ROM or downloadable against payment Conclusion The statistical law in Hungary is modern and adequate. The basic culture within the KSH is strongly professional and the levels of technical and conceptual expertise are more than adequate to deal with emerging needs. The Hungarian statistical system is largely compliant with EU requirements. Where this is not yet the case, the staff of the KSH has a good appreciation of what is required and there are work programmes in place to resolve the issues. Enlarging the EU Statistical Network 69 Official Statistics in Hungary Latvia Central Statistical Bureau of Latvia Lacplesa Street 1 LV-1301 Riga Tel: +371 73 66 850 Fax: +371 78 30 137 E-mail: csb@csb.lv Web site: http://www.csb.lv Country Profile Latvia Latvija Pre-Accession Milestones 1992 Establishment of diplomatic relations between Latvia and EU 1992 Latvia joins Phare Programme 1995 Latvia signs Europe Agreement of Association 1995 Official application of Latvia for EU membership on 27 October 1996 Latvia establishes the European Integration Council 1996 Latvia adopts first version of the National Program for Adoption of the Acquis 1998 Europe Agreement enters into force 2000 Latvia begins accession negotiations with EU in February 2002 27 out of 31 chapters of the acquis provisionally closed by June 2004 Target date for EU accession Geographic co-ordinates 58 05 N, 28 14 E Area 64 589 km2 Climate Maritime; wet, moderate winters Administrative Divisions 26 districts (rajons) and 7 cities Capital City Riga with 0.8 million inhabitants Population and Growth Rate 2.4 million, - 0.78 % ( 2001 estimate) Nationality Latvian Ethnic Profile Latvian 58 %, Russian 30 %, Byelorussian 4 %, others Religion Lutheran 23 %, Roman Catholic 23 %, Russian Orthodox 11 % Official language Latvian National Currency 1 lats = 100 santims Exchange Rate against Euro 1€ = 0.6 lats (Quarter 1, 2002, New Cronos) System of Government Parliamentary Republic Executive Power Cabinet of Ministers headed by Prime Minister Head of State President elected for four-year term by Parliament Head of Government Prime Minister, appointed by President Legislative Power Unicameral Parliament (Saeima); 100 seats and members are elected by popular vote to serve four-year term Judicial Power Courts (Judges are confirmed by Parliament) National Holiday Proclamation of the Republic of Latvia: 18 November (1918) Enlarging the EU Statistical Network 71 Official Statistics in Latvia Latvia has successfully demonstrated its will and ability to meet the challenges in supplying statistical information Overview Central Statistical Bureau of Latvia (CSB) Legal framework amended in 1999 No Statistical Council President of the CSB appointed by cabinet on advice of Minister of Economy Over 600 staff and 27 regional offices About 110 surveys and 60 publications a year Census of population and housing in 2000 and agricultural census in 2001 The National Statistical System of Latvia Central Statistics Bureau of Latvia (CSB) • First established in September 1919. Renewal of independent Statistical Bureau in 1992. • Based on Law on State Statistics of the Republic of Latvia, 6 November 1997 and amended in January 1999 • Employed over 600 staff in headquarters and 27 regional offices in 2001 • Co-ordinates the national statistical system in Latvia - Conducts about 110 different surveys each year - Disseminates about 60 publications each year - CSB website provides statistics and information Other bodies • Bank of Latvia for financial and balance of payments statistics, • Ministries produce sectoral statistics • Financial and Capital Market Commission for analysis of data relating to financial and capital market • Others state and local government institutions for statistics specific to their fields Statistical work is at times outsourced to research institutes, such as: • Latvian Statistical Institute (affiliated to and supervised by CSB) for compiling business tendency statistics and analysis of regional statistics • Latvian State Institute of Agricultural Economics for agricultural economic accounts President of CSB • Appointed by Cabinet of Ministers upon the recommendation of Minister of Economy; the government of Latvia, for 5 years • Assisted by 2 Vice-Presidents whom (s)he appoints in compliance with the state civil service law • Responsible for 3 of the 7 CSB depart- ments in addition to horizontal duties as well as 4 independent divisions • • • • • • • 72 Enlarging the EU Statistical Network �� Ministry of Economy � � Official Statistics in Latvia Enlarging the EU Statistical Network 73 The organisation The Central Statistical Bureau of Latvia (CSB) is a state administrative institution under the supervision of the Ministry of Economy. The CSB is responsible for the execution and the methodology of all surveys conducted in the country. It co-operates with the Bank of Latvia, government ministries and other state and local government institutions in the production of official statistics. The legal basis The Law on State Statistics of the Republic of Latvia was adopted on 6 November 1997 and amended on 28 January 1999. It defines: • Tasks and responsibilities of statistical bodies • Types and means of data collection • Procedures for submitting state statistical information • Regulations for the users of state statistical information with regard to confidentiality • Liability for violating the law The law does not include any provisions concerning a National Statistical Council nor does it mention specific statistical areas. EU regulations are implemented by a decree issued annually to approve the statistical programme. Other laws concerning statistics cover the population and housing census, the Financial and Capital Market Commission and the protection of personal data. Some discrepancies related to data access exist between the Statistics Law and the Tax Law and these need to be reviewed. The management of the Central Statistical Bureau A special regulation approved by the Minister of Economy, stipulates the functions, structure and roles of the officials employed by the CSB. The President is an officer of the state civil service with a fixed term, and is confirmed by the cabinet on the recommendation of the Minister of Economy. The President appoints and dismisses his deputies. Under the law the CSB: • Informs data users on the issues covered by the statistical programme • Develops the annual programme within the limits of funding and disseminates information according to this programme • Establishes a uniform system of mandatory classification and coding of economic data aligned with international standards • Co-ordinates all national data flows and ensures the consistency of indicators in state registers and other information systems • Co-ordinates the activities of ministries, other state institutions and local governments in the area of statistics • Performs additional statistical tasks outside the state statistical information programme following agreements with third parties There is no Statistical Council that would normally include representation by experts and user groups, although a customer satisfaction survey was carried out in 2001. In the absence of a State Statistical Council, there are working groups that currently advise the CSB in some statistical domains on an ad-hoc basis. This involves them regularly and defines the working groups for all statistical domains. The CSB has its own Statistical Strategic Plan which is a revolving three-year programme determining the major directions of development of official statistics. Independent of this is the annual State Programme of Statistical Information under the responsibility of the CSB. This contains all statistical surveys conducted in 74 Enlarging the EU Statistical Network Latvia by the CSB and other bodies and is approved by the cabinet of ministers. Structure and staffing of the Central Statistical Bureau The network comprises the CSB headquarters and 27 regional offices. It currently employs 631 employees, including part time staff, 71 % of whom are in the headquarters in Riga. The regional offices collect and summarise survey data for transmission to the CSB headquarters. They have considerable capacity to maximise response rates contributing to the quality of data.The CSB management is currently preparing a major reorganisation of the whole network that will lead to the closure of most regional offices. 84 % of the staff are female, and 45 % of employees are over 45 years and 53 % have a university education (at the CSB headquarters alone, 70 % of the staff are university graduates). However, staff turnover is rising, partly due to salary levels that do not sufficiently attract young and highly skilled people. The Latvian Statistical Institute (LSI) is an affiliated research centre supervised by the CSB. It is financed by grants from the Latvian Council of Science comprising researchers from the Academy of Sciences, various universities and private research institutes and the Ministry of Education and Science. The LSI is contributing to the analysis of regional statistics and is conducting business tendency surveys. It is also using econometric methods in the investigation of consumption expenditure of Latvian residents. Training of the CSB staff generally takes place at the Latvian School of Public Administration according to the needs of government offices. Their courses cover management, law, economics, communication and foreign languages. Computer and information systems training have mainly been organised by IT professionals from the CSB. Many employees have also attended an introduction to European Union studies. Funding Activities of the Central Statistical Bureau are mainly financed by central government. The budget is approved annually by the cabinet. The CSB cannot build up reserves from efficiency gains as any excess funds have to be transferred to the budget of the Ministry of Economy at year-end. The annual budget increased by about 30 % from 2001 to 2002 to over € 3.5 million due to special funding made available for the population and agricultural censuses. Co-operation The statistical law lays the basic foundation for the co- operation between the CSB and the data producers, providers and users. It states the responsibility, rights, and duties of all those involved in the production of statistical information. Co-operation also exists with universities. Both bilateral and multilateral co-operation has contributed to the rapid adoption of new methodology to meet the statistical acquis communautaire and other international standards. The CSB is responsible for meeting the needs of Eurostat. A gradual decentralisation means that most of the experts and the senior statisticians have the responsibility to manage international co-operation activities concerning their own statistical domains. The Phare multi-country statistical co-operation programme facilitates participation in Eurostat meetings, working groups,TES courses and study visits etc. The CSB has also benefited from extensive co- operation with Sweden, Finland and Denmark. Information technology and methodology The core of the current system is a client-server approach supported by computer workstations, local area network and wide area networks and database Official Statistics in Latvia Enlarging the EU Statistical Network 75 servers. Communication with national and international computer networks is ensured by a connection of the CSB servers to the governmental data transmission network (VITA). It provides online connections with the local statistical offices as well as Internet and e-mail services. The software used on the network level is based on Novel NetWare and Windows NT. A modernisation project aims at the implementation of data warehouse technologies. It was started in response to a need for a harmonisation and standardisation of all statistical indicators to help to meet and perform Eurostat requirements in the field of statistical data preparation and provision. The Statistical Methodology and Organisation Division falls under the responsibility of the CSB President. It serves as a national methodological hub for all official statistical activities and prepares and co-ordinates in a centralised way the methods of statistical surveys organised by the CSB and other government institutions. The output Classifications Latvia has a very high level of compliance with Eurostat and other international organisations in classifications. In 2001 a wider use of recently translated and disseminated classifications in the daily statistical work was achieved by the application of CPA in enterprise structural business surveys and the collection of household budget survey data according to COICOP. The updating of NACE and CPA and the re-coding of statistical units in the statistical business register according to NACE Rev.1.1 are being finalised in 2002. PRODCOM statistics fully comply and since 1999 aggregated quarterly PRODCOM figures have been published in the CSB monthly bulletin and annual figures in the Statistical Yearbook. Latvia has introduced a regional breakdown for statistical purposes that has been accepted by Eurostat. According to this breakdown the state territory is divided into five statistical regions on NUTS 3 level. Registers All the legal units registered in the State Enterprise Register (SER) under the Ministry of Justice irrespective of their main business activity (all divisions of NACE classification) are included in the CSB business register. Currently the CSB receives monthly information about changes of names and addresses from the State Enterprise Register. The SER provides the CSB with data on newly created enterprises plus information on restructuring of existing enterprises. For updating the register, a special survey covers every enterprise, not included in regular survey, once in 18 months. The survey gives information about the status of activity, kind of activity, number of employees, turnover, capital, real ownership and entrepreneurial code. A list of LKAUs provides information about the location, kind of activity, the number of employees and any changes to previous listings. The register survey also gives information about real addresses, which in many cases are not the same as the legal addresses. The response rate of the survey is high and regional offices play a vital role in the collection of the questionnaires. The Residents’ Register (RER) in Latvia is located and managed by the Ministry of Interior. Since 2000, the CSB has been obtaining more and more of the data necessary for the compilation of demographic statistics from the RER. Official Statistics in Latvia 76 Enlarging the EU Statistical Network Demographic and social statistics The latest population and housing census was carried out in 2000. Essential efforts were made to prepare for the census using definitions and classifications adopted by UNECE and Eurostat. The CSB receives register information on birth, death and marriages from the registry offices on a monthly basis. The labour force survey in Latvia is now fully compliant with the requirements of the EU. Education statistics correspond to EU requirements. A full compliance will be ensured after the entire introduction of the ISCED 1997 version. The Ministry of Welfare and the Medical Technology Agency compile health statistics. There is information on medical staff and health care institutions, on the causes of death and by separate groups of diseases. In 2003, the CSB will conduct a full-scale health interview survey. A new monthly household budget survey (HBS) of 334 households was launched in May 2001. Non-response is relatively low at 26 %. Starting in 2002 the samples of the household budget and labour force survey have been co- ordinated using the same interviewer network. Data sources on social protection are the State Social Insurance Agency and the Social Assistance Fund of the Ministry of Welfare. The CSB receives detailed quarterly and annual accounts on pension, state social benefits and social assistance. Macro-economic statistics Macroeconomic statistics are compiled by the CSB according to ESA 95. Every year the complete system of national accounts is compiled. All data are presented in accordance with the Eurostat table formats. Currently quarterly calculations of GDP by the production and expenditure approaches are conducted, data are analysed and possible discrepancies eliminated. Quarterly data are revised after the presentation of annual results. Supply and use table data corresponds to the national accounts data. Calculations are conducted following ESA 95 requirements. Active work on monitoring own resources was started in 2001.The work on the preparation of the description for the checklist of administrative conditions in the area of the European Communities own resources has been started. Value added tax (VAT) calculations for national accounts and input-output calculations are based on treasury and state revenue service data and are conducted following ESA 95 requirements. In the area of price statistics, the CSB provides information on a regular basis in accordance with international methodology on consumer goods, investment goods and information on construction projects. The compilation of the Harmonised Index of Consumer Prices (HICP) and participation in the project on purchasing power parity on a regular basis has been launched. All of the main HICP reliability and compliance requirements have been met. The work of the CSB has been concentrated on the production of harmonised data taking into account the Eurostat recommendations. Business statistics The major part of the required indicators for structural business statistics is available. The CSB has taken measures for further improvement of the questionnaires Official Statistics in Latvia Enlarging the EU Statistical Network 77 in the course of the pilot project to collect data on variables that are missing. Nearly all required statistical indicators on the implementation and development of short-term statistics of enterprises are available except for new orders received in construction. The report on the iron and steel statistics includes the necessary indicators and it is available in good time. In the area of energy and raw materials statistics the CSB annually transmits to Eurostat and the International Energy Agency coal, oil, natural gas, heat and electricity and renewables questionnaires. All data are available and collected according to international methodology. Latvia provides data on about 80 % of the indicators related to transport in accordance with Eurostat, UN, ECTM and other international recommendations. Data on maritime transport statistics are compiled mainly in compliance with the requirements. As ships of Latvia navigate under foreign banners and may change them, it is not possible to register their activities. Statistics on passenger transport are compiled by the CSB in accordance with the existing structure of the database on rail, bus, urban electrical transport, sea and air passenger transport. A border survey is used as a source of information on international mobility. Data on road cargo transport is fully compliant with the EU requirements. Work aimed at introducing the requirements of the new regulations on rail transport is ongoing. Most of the required data will be available soon. The main data quality difficulties are caused by the difference in the Latvian rail system compared to the EU. A questionnaire for a survey on information technologies and e-commerce in enterprises has been designed according to the Eurostat proposal. This survey will be undertaken in 2002. By 2003, full compliance with the EU requirements will be achieved in tourism statistics due to measures improving data quality. Monetary, financial, external trade and balance of payments statistics The Bank of Latvia regularly submits monthly data on money supply, exchange rates, short-term and long-term interest rates and international reserves to Eurostat. The government provides information on the published government budget deficit and national accounts deficit, as well as links between the budget deficit and the state debt. The information received has been improved and corrected in line with ESA 95. For external trade statistics, the CSB has implemented international classifications and statistical methodology in accordance with the requirements of the acquis communautaire. Nevertheless, a new data collection system (INTRASTAT) will be necessary for monitoring trade between the Member States in conditions where there are no physical borders and taxes for goods circulating within the EU. The successful implementation of the INTRASTAT will greatly depend on funding from Phare national funds. It needs to be operational in January 2004. The Bank of Latvia has put in place the balance of payments compilation system, which ensures compliance with the Eurostat requirements for balance of payments and foreign direct investment statistics. Official Statistics in Latvia Official Statistics in Latvia 78 Enlarging the EU Statistical Network Agricultural, forestry and fishery statistics There are about 340 000 rural households in Latvia of which 230 000 are farms that have land as property or for usage.The first benefit of the agricultural census conducted in 2001 is an updating of the statistical farm register set up in 1999. Structural sample surveys of 13 000 peasant farms, household plots and private subsidiary farms are conducted twice a year. State farms and statutory companies are verified each year by exhaustive surveys. In addition, some financial indicators on expenses and incomes are collected. Forestry statistics partly comply with international methodology. Some distinctions regarding ownership and regional breakdown are not available. More work is required for the compilation of economic accounts for forestry in accordance with Eurostat standards. Fishery statistics are compiled taking into consideration international recommendations. The National Board of Fisheries is responsible for meeting the EU requirements. Other statistics Environmental statistics are produced in co-operation with the Ministry of Regional Development and Environmental Protection. The Latvian Environmental Agency collects data on air pollution, water, and hazardous and municipal waste and on chemicals and chemical products used. The CSB is collecting and summing up statistical data on investments, current costs, capital repairs of fixed assets for the protection of natural resources and on the operations of national parks and nature reserves. In general, statistical information at the regional level meets the demands of the EU. Using the population and agricultural censuses data, the level of compliance at the end of 2002 is expected to be close to 100 %. Latvian regional statistics are compiled in line with NUTS. The Latvian government has not yet defined territorial units on NUTS 3 level. For statistical purposes the CSB, however, has implemented a NUTS 3 territorial definition that has been accepted by Eurostat. Science and technology statistics partly comply with EU requirements. There is complete information on R&D in the public sector and in higher education. Since 2001 a survey has been conducted about R&D activities in the business sector. A sample innovation survey will be introduced in 2002 in both manufacturing and service sectors. Official Statistics in Latvia Enlarging the EU Statistical Network 79 The information • Publication Catalogue available with about 60 regular publications • Information Centre responsible for dissemination of all data and website www.csb.lv • Press Secretary reporting directly to the President of the CSB • Statistical Yearbook in paper and CD-Rom formats • Press releases on a regular basis announced also on website • Calendar of press releases published in advance in Latvian and English • Release of information to all users at the same time • Subject to protection of confidentiality, anonymous individual records made available for academic research and secondary analysis • Information Centre with an electronic database of publications • Customer satisfaction survey conducted in 2001 Conclusion The national statistical system has undergone major developments in the last few years.The main driving force behind this impressive and rapid progress is the powerful dedication of Latvia to become member of the European Union and to adopt and implement the acquis communautaire. The result of this effort is that high compliance has already been achieved in most areas. The Central Statistical Bureau is preparing for a far-sighted strategy to rely less on a large network of regional offices and more on the latest techniques for the collection of data. However, technical, financial and human resources need to be taken carefully into consideration at all stages in the transition. Lithuania Statistics Lithuania 29 Gedimino Ave. LT-2746 Vilnius Tel: +370 2 36 48 22 Fax: +370 2 36 48 45 E-mail: statistika@mail.std.lt Web site: http://www.std.lt Pre-Accession Milestones 1991 Establishment of diplomatic relations between Lithuania and EU in August 1995 Lithuania signs Europe Agreement in June 1995 Lithuania submits an official membership application for EU membership on 8 December 1996 Lithuania’s Europe Agreement of Association enters into force 1998 1st Accession Partnership for Lithuania was decided in March and updated in December 1999 2000 Official opening of accession negotiations in February 2002 28 out of 31 chapters of the acquis provisionally closed by June 2004 Target year for EU accession Country Profile Lithuania Lietuva Geographic co-ordinates 56 00 N, 24 00 E Area 65 300 km2 Climate Between maritime and continental; wet, moderate winters and summers Administrative Divisions 10 counties Capital City Vilnius (0.5 million inhabitants) Population and Growth Rate 3.5 million, - 0.27 % (2001 estimate) Nationality Lithuanian Ethnic Profile Lithuanian 83 %, Russian 6 %, Polish 7 %, Byelorussian 1 %, others Religion Roman Catholic 80 %, Evangelic Lutheran, Orthodox, Old Believer Official language Lithuanian (lietuviu) National Currency 1 litas = 100 centas Exchange Rate against Euro 1€ = 3.5 litas (Quarter 1, 2002, New Cronos) System of Government Parliamentary Democracy Executive Power President & Council of Ministers (elected by the Seimas) Head of State President elected for five-year term by popular vote Head of Government Prime Minister, appointed by President on Parliament’s approval Legislative Power Unicameral Parliament or Seimas; 141 seats and 71 members elected by popular vote, 70 by proportional representation for a four-year term. Judicial Power Supreme Courts & Court of Appeal National Holiday Independence Day: 16 February (1918) Enlarging the EU Statistical Network 81 The Statistical System of Lithuania • • • • • • 82 Enlarging the EU Statistical Network Official Statistics in Lithuania Lithuania has the positive spirit, the infrastructure and the skills to meet present and future statistical needs Overview Statistics Lithuania (SL) Legal framework harmonised with EU requirements in 1999 Eclectic mixture of members in Statistical Council Director General of Department appointed by Prime Minister About 600 staff and extensive regional offices Around 140 surveys and 100 publications a year Census of population and housing in 2001 • • • • • • • Department of Statistics or Statistics Lithuania (SL) • First established on 6 September 1919 • Based on Law on Statistics: No VIII-1511 of 23 Dec 1999 (revision of the1993 Law on Statistics) • Employed about 600 staff in 2001 in central and 10 County statistical offices and 38 statistical offices at municipality level • Leading statistical institution in Lithuania producing 80% of statistical indicators - Conducts about 140 different surveys each year - Disseminates about 100 publications per year in English & Lithuanian - SL website provides statistics and information Other agencies managing official statistics (included in work programme) • 10 Ministries producing sectoral statistics (eg public finances and government expenditure, agriculture, health, education etc) • Bank of Lithuania for balance of payments, monetary review, balance of credit institutions and foreign loans • National Stock Exchange for price indices and emission of stocks • State Property Fund and Lithuanian Patent Office Director General of SL • Appointed and dismissed by Prime Minister for a fixed five-year term. • Heads the central unit of SL and chairs the Statistical Council • Supported by 4 Deputy-Director Generals • Assigned the responsibility by government to approve work programme of the official statistics (which is first drafted by SL and submitted to the Statistical Council) • Chairman of the Statistical Council Statistical Council • Advisory institution of the SL since 1994 with 22 distinguished members • Members appointed by the government, who also approves its regulations • Chaired by Director General of SL and meets 2-3 times per year Statistical Surveys • Public company within SL dealing with surveys • Has about four experts (some of them having their main employment elsewhere) • Headed by one of the Deputy Director Generals of SL Statistical Centre • Public company within SL providing all its IT services • Has 100 employees under a renewable annual work contract approved by the government • Supervised by one of the Deputy Director Generals of SL �� Prime Minister � � � � Enlarging the EU Statistical Network 83 The organisation The Department of Statistics – also known as Statistics Lithuania (SL) - is the central statistical office and an institution of executive power of the Republic of Lithuania, financed from the national budget. It functions under the Law on Statistics 1999, which defines the organisation of national statistics as providing “objectivity and professional independence from political and other interest groups.” SL produces more than 80 % of the statistical indicators and co-ordinates data from other sources, mainly government ministries. The legal basis The 1999 Law on Statistics was a revision of the 1993 law to harmonise the legal structure with the European Union requirements. Other legal texts include laws on the population register and the population and housing census, plus resolutions and rules covering the European system of national accounts, confidentiality and the authorisation of Statistics Lithuania. The Law on Statistics includes: • General principles, including confidentiality, transparency, independence and concern for the response burden • Definitions of statistics agencies, principally SL but also central and local government bodies and the Bank of Lithuania • Provisions for administrative documents and registers to be sources of official statistics • Protection and use of official statistics (information must be widely available, not just to government and organisations, but to society at large) • The Statistical Council • An annual work programme • Protection of confidentiality of the individual respondent The management of Statistics Lithuania The Director General of SL is appointed and may be dismissed by the Prime Minister. The current holder of the post was appointed in 2001. There are also four deputy directors general. SL is responsible for preparing each year the work programme of official statistics, including work carried out by other institutions.This necessitates close collaboration.A first draft is discussed within the Senior Management Committee of SL, and after that the programme is submitted to the Statistical Council. The final programme has to be approved by the government or an institution authorised by it. The government has assigned to the Director General the responsibility for approving the programme. The Director General of SL is thus also in the position of being the chairman of the Statistical Council and responsible for approving the annual work programme. This is convenient at the moment as it provides the efficiency and the flexibility needed during the preparation of accession to EU membership. The composition and regulations of the Statistical Council are approved by the government.The Council should consist of “representatives of state and local self-government institutions, agencies managing statistics, education institutions, public organisations, enterprises, other respondents and the mass media”. This eclectic mixture is represented among the current 22 members. According to the Law on Statistics, the Statistical Council considers: • Methodological principles of censuses • Statistical registers and main surveys • The annual work programme Official Statistics in Lithuania 84 Enlarging the EU Statistical Network • Basic results of surveys • Issues related to data protection • Dissemination of statistical information • Conclusions and proposals to Statistics Lithuania The Council meets two to three times a year. Reports are made on these meetings and some documents are usually made public. It may invite non-members, such as specialists on a subject, to participate in a meeting. The Council has established several working groups. The current Director General of SL has discussed some changes, for example to enhance its role in the dialogue between producers and users of official statistics and to have more frequent meetings. There are a large number of advisory groups working for SL, providing specialist advice and feedback across most of the statistical areas. Structure and staffing of Statistics Lithuania SL has a central office in Vilnius, 10 offices at county level and 38 at municipal level. Of the total staff, about 50 % work in the head office, 25 % in the county offices and 25 % in the municipal offices.This network covers survey data collection including mailing and receiving business survey questionnaires, data capture and business registration. The county and municipal offices work in close co-operation with local or regional organisations. Some of the county offices also have considerable statistical activities of their own at the regional level.Taking into account that Lithuania is a relatively small country with a population of 3.5 million, it is unusual that half of the staff are in the regional offices. Reorganisation of the structure is addressed in a strategic paper. Of the total SL staff, 90 % are female and 65 % have a university degree. In the other offices, 90 % are female, with 50 % degree holders. The average age is 45. There is very high staff stability with a large number of employees having worked within the same field throughout their entire career. The management regards this mainly as an advantage implying enhanced professionalism. An unusual feature is the creation of two public companies of which SL is the sole owner.These create more flexibility in staffing issues. The Statistical Centre employs about 100 people and is the main provider of IT services to SL. Regulation is through an annual contract that is approved by the government, as there is no tendering process. The staff of this company are paid out of its revenues. The Statistical Surveys is a smaller company dealing with surveys and employing experts, some of whom are part time from universities. Each company has a director reporting to one of the deputy directors general of SL. This arrangement has created valuable flexibility with the rapid increase in IT use. The employment conditions of the staff are less regulated, allowing easier response to market rates. Intensive staff training programmes include courses in statistical methods, designing and programming of information systems, PC software and preparative training for the population census. Funding An average annual budget of about € 6.4 million has been allocated to Statistics Lithuania over the past three years.An additional expenditure in 2001 of approximately € 7.3 was related to the population census. There is strong control over public expenditure in Lithuania. The budget is imposed under a number of separate budget headings. It is also allocated to central and regional Official Statistics in Lithuania Enlarging the EU Statistical Network 85 expenditure. Staff numbers are also controlled. Hence the Director General has less scope to manage the office as flexibly as might be the case in many EU Member States. If a budget proves to be inadequate for the whole of a proposed annual statistical programme then users are involved in a process of review and reduction. Additional work may be undertaken if funded by other sources. Co-operation SL has close co-operation with a number of relevant university institutions and is aiming to formalise this with long-term agreements. It also has considerable co-operation with the NSIs of Sweden, Finland and Denmark and projects with neighbouring countries. A large technical co-operation programme within different fields is carried out with Phare finance. Information technology and methodology The company Statistical Centre provides IT services to SL and also handles printing with offset and digital equipment.A local area network exists between the central and main county offices while the other offices have dial-up connections into a wide area network. Development projects include data warehousing, OLAP technology, OCR (already used in processing the population census data), data collection through electronic questionnaires (using Internet particularly), an electronic data dissemination system and GIS technology. The methodological principles of censuses, statistical registers and main surveys are considered by the Statistical Council. The output Classifications Most Lithuanian classifications and nomenclatures are already consistent with EU and UN standards. The national versions of NACE and CN have been in use for some time and the implementation of CPA, PRODCOM, ISCO-88 and ISCED are in progress.The national versions of NACE, CPA and PRODCOM are to be brought in line with revisions made at the European level. Registers At present there are two business registers: legal and statistical.The registration of the legal register involves many local institutions, five ministries and the Bank of Lithuania. SL only registers budgetary institutions. Municipalities register most enterprises. The legal register has currently about 160 000 entries (of which more than 90 000 are individual enterprises without the status of a legal entity). SL will relinquish management after a law in 2002 establishes a new central register in the Ministry of Justice. The statistical register includes about 250 000 enterprises of which only about 67 000 are active.The main limitation of these registers is that only main enterprises are registered and they are not obliged to register their subsidiaries unless the latter have specifically been declared. As a result, it is difficult to take into account local units or kind-of-activity units and a statistical survey is the only way of obtaining information on these entities. A programme is underway to overcome these difficulties, including a project financed by the Danish government on the development of the database. Although some progress has been made, high priority must continue to be given, because many other statistical works depend on the quality of the business register, in particular design of survey samples and production of statistical information on businesses at national and local level. Since 2000, the population register has been maintained by the Ministry of Internal Affairs, which provides updated Official Statistics in Lithuania 86 Enlarging the EU Statistical Network information to SL. The registration began in 1992 when every person over 16 years was issued a National Passport and assigned a personal identification number. The register thus contains information on about 90 % of population (not including under 16s without passports). SL uses the data for demographic statistics and analysis, design of survey samples (household budget survey, labour force survey), internal migration statistics and to update the business register and farms register. The register of indicators is a metadata database, consisting of three parts.The first part covers 5 500 indicators.The second part includes approximately 7 000 time-series.The third part is under development with the aim of storing meta- information on surveys and questionnaires. Although for internal use only, it is planned to make it accessible on the Internet and it may be extended by linking it to data sets. The statistical analytical system is a database storing more than 10 000 time-series. It was created by the company Statistical Surveys and SL co-ordinates its development. It is regularly updated and used internally.Analysts in banking and finance may have access. Other external users are currently under consideration. Other databases for reference include the foreign trade database and the classification database.A regional database is also under construction, the objective being to feed the REGIO database of Eurostat. Demographic and social statistics The population and housing census was successfully completed in April 2001. Some results of the census have been released. A quality control also took place.The results will also be used to provide new bases for samples and the surveys. Priority users are the government, the parliament and other public authorities. However, SL plans to sell custom-tailored data extractions and will also develop GIS applications to analyse, present and disseminate the census results on a commercial basis. The labour force survey has been carried out twice a year but will be quarterly from 2002 onwards in compliance with EU requirements.This is possible with financial support from the Phare National Programme of 2000. A labour cost survey is under way although it will include only employers having the status of a legal person, as the business register does not include local units. A test on the survey on the structure of earnings was carried out in 2001 and in 2002 a pilot survey will be conducted. Compliance is expected for the 2002 study, to be carried out in 2003. No major difficulties are expected to establish a labour cost index for 2001, although financing is required. Macro-economic statistics Statistics Lithuania is responsible for national accounts.With the introduction of ESA 95, non-financial annual accounts have been compiled and published, at current and constant prices, and a number of revisions have been made to improve compliance with the acquis communautaire. Financial accounts have been compiled on the basis of ESA 95 methodology, but they have not been published. Quarterly national accounts are also compiled and published. Quarterly data of GDP and growth rate are published 90 days after the reference quarter. The data are available at current prices according to production, income and expenditure approach and GDP by kind of activity and expenditure approach at constant prices. Flash estimates of quarterly GDP and its growth rate are published 30 days after the reference quarter. Official Statistics in Lithuania Work is also in progress to compile input-output tables and supply-use tables. The work on improvement of methods continues. Main problems identified are the estimation of consumption of fixed capital and the change in the method of data reporting in central and local government from cash to accrual basis. Regarding dwelling services, SL follows Eurostat recommendations.To further improve the quality of national accounts data, a strategic plan on ESA 95 for the period 2002-2004 has been prepared with participation of the Bank of Lithuania, the Ministry of Finance and others. The price indices system contains CPI, interim HICP and PPI, as well as a construction cost index and unit value indices of exported and imported goods. Other developments include price indices for transport, storage, communications and imports. PPP is considered in the framework of the European Comparison Programme. The interim HICP does not comply with EU requirements concerning geographical and population coverage, the expenditure of institutional households, non-resident households and tourists. For the second stage, there are problems concerning health, education, social protection and insurance. It is important to establish close co-operation with ministries concerned with these sectors. Another problem concerns the methods of assessment of quality change in goods and services. For national CPI and HICP, the regional statistical offices collect prices in 19 territorial units. Each year a meeting is organised by the central office to compare practices and give instructions. Regarding the European Comparison Programme and calculation of PPP, Lithuania is included in the Northern Group, which covers ten countries under the leadership of Statistics Finland. Business statistics The structural business statistics produced by SL are mostly compliant with the SBS Regulation. The main sources are annual statistical questionnaires, balance sheets and the profit and loss accounts of companies, and income declarations of sole proprietorships, collected by means of an annual survey or through tax inspectorates. The reported unit is currently only the enterprise. A questionnaire has been prepared for 2002 for enterprises that have more than one KAU and more than one LKAU. The compliance with EU regulations on short-term statistics is already fairly good.The production index will be calculated at the beginning of 2002. A survey of industrial production is included in a project on further harmonisation. Domestic trade, construction, services, energy, transport and communications, and tourism indicators are calculated according to Eurostat recommendations. These surveys are also used to update the business register, although further improvements are very necessary. There is also a business tendency survey covering manufacturing, trade and construction. Tourism statistics are compiled monthly based on incoming persons by type of vehicle and citizenship. Information is collected from tourist enterprises.Accommodation statistics are compiled in compliance with the EU recommendation, however problems exist with private lodgings and rural tourism.A sample survey on outbound tourism needs to be conducted. Monetary, financial, external trade and balance of payments statistics Most monetary statistics are compiled from data from the Ministry of Finance and the Bank of Lithuania. Enlarging the EU Statistical Network 87 Official Statistics in Lithuania Official Statistics in Lithuania 88 Enlarging the EU Statistical Network Regarding state debt there is an issue over the quality of data. Financial accounts have been compiled (ESA 95 methodology) but publication is not planned until 2002 when further improvements have been made. Once customs have aligned their systems, foreign trade statistics will conform to EU standards.The introduction of INTRASTAT requires close co-operation and the experience of other states will be studied. EXTRASTAT is in compliance with the EU requirements. SL has also started work on an exporters/importers register. The Bank of Lithuania is responsible for the balance of payments statistics and special surveys are conducted. Quarterly information basically complies with the EU requirements. The Bank intends to implement monthly direct reporting of balances in 2002. Supporting EU information will be used for better compliance and higher quality. SL provides the Bank with data (on goods, services, foreign direct investment, transport, tourism, etc.) for the balance of payments. Agricultural statistics SL collects information from surveys, administrative files from the Ministry of Agriculture and other governmental institutions, e.g. the State Land Cadastre. The farm accounting data network under the responsibility of the Institute of Agrarian Economics is the basis for the calculation of value added from agriculture in the EU. A farm register has existed since 1997 with 1 500 agricultural partnerships and enterprises and about 70 000 farmer’s farms with over three hectares. By enlarging the scope to include farm land down to one hectare, the register will grow to include around 500 000 farms. There still remain some methodological issues in the supply balance sheets for crops and animal products. The estimation of employment in agriculture also needs to be improved. In the economic accounts for agriculture, there is a need to distinguish between the agricultural producers for market purposes and those who produce for own consumption only. The acquis communautaire in agricultural statistics will be implemented after the agricultural census is carried out. This began in June 2002 and the final results are expected to be published by 2003. The Lithuanian Government will take all necessary measures to ensure that the total agricultural census is completed in 2003. This census is crucial in improving information and methods in agricultural statistics, to update the farm register and to meet the EU requirements. Preparation of the census has already begun. Financing for starting the census is allocated in the state budget for 2002. A request for financing for completion of the census in 2003 has been filed by Statistics Lithuania and is in the process of budgetary formation at the Ministry of Finance. Other statistics Environmental statistics are compiled with the Ministry of Environment. Waste management statistics are based on the EU draft regulation. Statistics on water use and discharges to water have been delivered but are not entirely in compliance with EU standards. For environmental expenditure statistics and environmental accounts development work on methodology is still needed. Regional figures are available for employment, social, agricultural production, demography and energy statistics. SL has also made calculations of annual regional GDP at Official Statistics in Lithuania Enlarging the EU Statistical Network 89 current prices by ten counties comparable to the NUTS 3 level, and gross value added by the main broad economic categories. This has been carried out following the EU requirements. The inclusion of local KAUs in the business register is a prerequisite for further advances. There is now a government decree on territorial classification for statistical needs that is in compliance with the NUTS classification EU requirements. A centralised regional database is planned for authorised external users in the future. SL will also develop GIS concepts in statistics, both in the census context and elsewhere. The information • Statistical Information Bureau, under a deputy director general, responsible for relations with mass media, researchers, business and the public, plus the library and website www.std.lt • Sales and subscription of publications are handled by the public company “Statistical Centre.” • About 100 publications both in the Lithuanian and English languages each year • Press releases on a regular basis • Annual calendar of press releases published in advance • Release of information to all users at the same time • Subject to protection of confidentiality, anonymous individual records made available for academic research and secondary analysis • Strategic paper for 2002 - 2004 foresees procedure for informing respondents of survey results • User satisfaction survey questionnaire sent with publications and available on the web site Conclusion The Lithuanian statistical system has the environment, the infrastructure and the staff skills to meet both present and future needs for official statistics. In particular it has built the capability to support the statistical outputs required of a country aspiring to join the European Union. Lithuania has already reached a good level of compliance with the requirements of the acquis communautaire. Remaining problems have been identified and operational solutions sought. If efforts are maintained with the same intensity, Statistics Lithuania should be able to fulfil the requirements of the accession process. Malta National Statistics Office Lascaris Valletta CMR02 Malta Tel: +356 21 22 32 21-5 Fax: +356 21 24 84 83, +356 21 24 98 41 E-mail: nso@gov.mt Web site: http://www.nso.gov.mt Pre-Accession Milestones 1970 An association agreement is signed between Malta and the EEC 1976 First Financial Protocol between Malta and EEC 1986 Second Financial Protocol between Malta and the EEC 1989 Third Financial Protocol between Malta and the EEC 1990 Malta submits an official membership application for EU membership on 16 July 1996 Malta freezes its application for EU membership in November; starts working for a free trade area. Fourth Financial Protocol between Malta and the EU 1998 Malta reactivates its application for EU membership on 10 September 1999 Malta begins of screening process 2000 Official opening of accession negotiations with Malta 2002 22 out of 31 chapters of the acquis provisionally closed by June 2004 Target year for EU accession Country Profile Malta Malta Enlarging the EU Statistical Network 91 Geographic co-ordinates 36 00 N, 14 35 E Area 316 km2 Climate Mediterranean with mild rainy winters and hot dry summers Administrative Divisions none (administered directly from Valetta), but local councils are now established in over 60 localities in Malta and Gozo. Capital City Valetta (0.01 million inhabitants) Population and Growth Rate 0.39 million, + 0.74 % (2001 estimate) Nationality Maltese Ethnic Profile Maltese Religion Roman Catholic 91 % Official language Maltese and English National Currency 1 Malta Lira = 100 cents Exchange Rate against Euro 1€ = 0.4 Malta Lira (Quarter 1, 2002, NewCronos) System of Government Parliamentary Democracy Executive Power President & Cabinet (appointed by President on advice of Prime Minister) Head of State President elected for five-year term by House of Representatives. Head of Government Prime Minister, appointed by President for five years Legislative Power Unicameral House of Representatives; 65 seats with members elected on the basis of proportional representation for a five-year term. Judicial Power Constitutional Court and Court of Appeal National Holiday Independence Day: 21 September (1964) The Statistical System of Malta 92 Enlarging the EU Statistical Network Official Statistics in Malta Malta has seen a transition in its statistical system to meet EU requirements that has been rapid and efficient. Overview National Statistics Office (NSO) under supervision of Maltese Statistical Authority (MSA) Highly centralised system Strong legal framework harmonised with EU requirements Board of MSA fulfils role of Statistical Council Director General of NSO appointed by the board of the MSA Over 130 staff plus 150 part-time interviewers About 145 regular surveys and 13 regular publications plus over 100 news releases each year Census of population in 1995, the next one scheduled for 2005 • • • • • • • • National Statistics Office (NSO) of Malta • NSO’s predecessor was established in March 1947. NSO is the only executive body of the Malta Statistics Authority • Based on Malta Statistics Authority Act 2000 (Act No XXIV 2000) which came into force on 1 March 2001 and replaced the 1955 Act • Employed over 130 staff in 2001 and has no regional offices • Plays a central role in the production and services of the official statistics in Malta - About 145 surveys planned each year - Disseminates about 13 publications and 100 News Releases each year - NSO website provides statistical tables and information • NSO staff are not employees of the government Other bodies • Ministries - Treasury & the Ministry of Finance for government finance statistics - Ministry of Agriculture & Fisheries for statistics on fisheries • Central Bank of Malta - Balance of Payments - Monetary and banking statistics - Selected financial transactions Director General of NSO • Appointed by the Board of Authority following the consultation of the Minister for a period of three years with possibility of several terms • Heads the NSO and is an ‘ex-officio’ member of the Board • Draws up the Annual Business Plan of the NSO to be approved by the Board & Ministers �� � Minister for Economic Services Malta Statistics Authority • Nominated and appointed by the Minister for Economic Services and has a supervisory role over NSO • It liases between the NSO and other public bodies concerning the supply of statistical data and other statistical activities • Consists of Chairperson, Director General NSO (an ‘ex-officio’ member) and six other members with competence in statistics • Members are appointed from various sectors for a term of one to three years by the Minister and may be re-appointed on completing their term.The Chairperson is also appointed by the Minister • Except for the Director General NSO, members are precluded from access to confidential data and from analysing statistical data. � � Enlarging the EU Statistical Network 93 The organisation The National Statistics Office of Malta (NSO) centralises the production and service of official statistics. Other providers of official data include the Central Bank of Malta, the Treasury and various ministries. The NSO is the only executive body of the Malta Statistics Authority (MSA) that comes under the Minister for Economic Services. The MSA was created for regulating the collection, compilation and publishing of official statistics. Its board consists of a chairperson, the Director General of the National Statistics Office and six other members with technical competence from the Central Bank, the trade unions, the university, business plus two statistics professionals.The MSA is a supervisory body with no access to confidential data and no role in analysing data. The legal basis The Malta Statistics Authority Act of 2000 established the MSA. The separation between the government, the MSA and the NSO is clearly stated in the act. Under the act the MSA has the following roles: • Submission of the annual business plans and financial estimates to the Minister • Supervision of the production of statistics to international standards and requirements • Advice on statistical matters, including methodologies • Establishing priorities in the demand for official statistics • Liaison between the NSO and other bodies and co- ordination of statistical activities • Publication of the business plan after its approval • Dissemination of the knowledge of official statistics • Consider and comment on the annual report of the Director General of the NSO The NSO is mandated to compile statistics in all domains and has access to any administrative source. Confidentiality is clearly protected under the act. The NSO also has the right to retain revenues generated from products and services. The act also covers financial provisions, advisory committees and offences and penalties. Other legislation on statistics includes the Census Act and the Data Protection Act. The management of the NSO The central role of the NSO is emphasised by the new act, enabling it to improve the whole system by harmonising or reducing the data collection of other bodies. At the same time there is continuous liaison between the NSO and other key providers such as the Central Bank. Following consultations with the Minister responsible for statistics, the Director General of the NSO is appointed by the board of the MSA for three years, with a possibility of several terms. The Director General is also an ex- officio member of the board. A key role is the drawing up and implementation of the annual business plan. The plan is approved by the MSA and funding is negotiated with the Ministry of Finance. Parliament finally approves the budget. The Assistant Director is also appointed by the board of MSA. Both appointments are covered by regular performance evaluation under a Performance Management Programme introduced in the government service for all senior management staff. The MSA in its composition and advisory functions fulfils the role of the Statistical Council normally found in national statistical systems. The new law has provision for advisory committees to the board of the MSA. Official Statistics in Malta 94 Enlarging the EU Statistical Network Structure and staffing of the NSO The internal structure of the NSO will soon be organised into four divisions: social statistics, business statistics, economic statistics and corporate services. There are no regional offices. The NSO employs about 130 staff, of whom 61 % are female and 53 % are aged under 30. There is a growing level of tertiary qualifications. Currently, 71 % of the NSO staff have secondary education. There are also about 150 interviewers working part-time. It is likely that 20 to 30 new staff members will be added in the near future. All staff have participated in different IT courses. There have also been TES courses and internal programmes where foreign experts have provided training. To ensure the availability of skilled staff and to increase the number of statisticians in future, the NSO has made a proposal to the University of Malta for a Diploma in Official Statistics. Funding In May the NSO proposes its business and financial plans for the following three years to the Ministry of Finance. The strategic objectives and measurable results have to be defined and short-term goals indicated. If provisions are needed for investment purposes, tangible results have to be shown in the financial plan. In its National Programme for the Adoption of the acquis (NPAA), the NSO has convinced the government that compliance will mean increases in both one-off and continuous appropriations. The total budget of the NSO in 2001 was € 3.0 million, of which nearly one half was allocated to the adoption of the acquis. The total budget has increased by 42 % over the previous year and by 57 % since 1995. Running costs, including participation in Eurostat working groups and the acquisition of equipment, have to be covered by the normal budget. The technical assistance budget is only for the transfer of knowledge, such as consultancies and training. Most of the technical know-how gained has been funded by the NSO itself. The only external funds have been from the EU MEDSTAT programme and funds for training from the UNDP. The Director General has to submit an annual report on the work of the NSO to the MSA. No later than six weeks after the end of each financial year, the MSA must send a report to the Minister of Economic Planning and to the Minister of Finance dealing generally with the activities during the financial year. Co-operation The NSO works with the University of Malta on joint projects such as in compiling analytical publications, lectures on official statistics by the NSO experts and also in receiving tuition from the professors. Co-operation between individual researchers of different government departments and the NSO typically covers joint research projects. The NSO policy has been to consult its users on a regular basis. Meetings with parties concerned are arranged whenever matters of mutual interest need discussion. Feedback from users has also been sought in respect of all publications released by the NSO and via the NSO website. Media coverage has been regularly monitored. Under the new Statistics Act, the board of the MSA has a significant role in representing important user groups. The NSO has also adopted a Quality Service Charter that provides all users with indications on the level of service they should expect. During the last few years Eurostat has naturally been constantly consulted on the development in statistics. Official Statistics in Malta Enlarging the EU Statistical Network 95 Information technology and methodology The NSO is part of the total governmental IT infrastructure and receives technical support from a central service. All government departments are connected to this network which works on a contractual basis. The IT infrastructure of the NSO was renewed in 2000. Eight new applications were implemented, followed by a further ten in 2001. Some services, e.g. the Consumer Price Index and the use of the Eurotrace software, have their own servers for security purposes. Data transmission via GESMES and STADIUM are now working, in particular for the balance of payments, national accounts and trade statistics. Growing use is made of Internet as a basis for surveys.The NSO has a centralised database for production purposes, but a database for dissemination and user-access will require the introduction of metadata modules. A new library information management system will be defined and built up in the near future. All micro-data that is received from other administrations is validated and tested for quality by the NSO.This part of the statistics production process and the relevant methodologies and procedures are built into the systems of each thematic unit. Furthermore, increased attention is being given to proper documentation of all existing methodologies and statistical compilation procedures. The NSO has implemented new surveys and introduced new methodologies and statistical systems. There is room for further rationalisation in the data collection and for improved exploitation of the administrative records within the whole public sector. Co-ordination of data collections with tax authorities, the Employment and Training Corporation, social security authorities, the Malta Tourist Authority and the Department of Agriculture is highly recommendable in order to avoid overlapping collections. The output Classifications The NSO is well on the way to achieving compliance with the different statistical classifications. Full compliance with NACE and CPA is expected by the end of 2002 and the beginning of 2003 respectively. Clear priorities have been set. The need for a common classification database containing different versions of the classifications and their breakdowns has already been recognised and such a base will be developed in the future. Registers About 38 000 business units registered with the VAT office have already been coded and stored in the business register and a number of quality checks have been carried out.There is a joint working group of the VAT office and the NSO. For updating, the VAT authorities will inform the NSO on a regular basis of all new registrations and of any cancellations. There are administrative registers maintained by different authorities, such as the electoral register for population surveys and the common database for household surveys. Demographic and social statistics Annual statistics are compiled from data received from the common database for household surveys. Population projections are done regularly, currently up to 2050.The last census of population was done in 1995; the next will be in 2005. Constraints within the demographic sector are related to migration statistics. The introduction of the new tourism survey will, however, improve information. Emigration data are mainly collected from embassies. Information about Maltese immigration is received from the customs office. Official Statistics in Malta 96 Enlarging the EU Statistical Network The first labour force survey (LFS) was conducted in Malta in 2000. From March 2001 onwards the NSO started quarterly LFS data collection from 2 500 households. Transition to continuous data collection in the LFS is being explored and is expected to be implemented during 2003.A new set of surveys is required to collect data on the structure and distribution of earnings and labour costs and it is intended to introduce these surveys in the near future, with the aim of achieving compliance by the end of 2002. The NSO intends to work on this project in collaboration with the Social Security and the Inland Revenue Departments. A joint NSO-Ministry of Education working group will periodically review EU requirements regarding classifications, data collection and methodologies. The creation of new questionnaires on support staff and other personnel in education, new tabulations and guidelines for updates to the existing software have been put in place during 2001.The NSO has also carried out a continuous vocational training survey as recommended by Eurostat.The education statistics produced by the NSO meet Eurostat requirements. For cultural statistics, surveys have covered libraries, museums, musical groups, theatre and theatre groups, dance schools and dance groups, the cinema, the broadcasting media, music publishing and sales, book publishing and sales, music teaching and religious festivals. In addition to this, a cultural participation survey, “Kultura 2000”, was carried out. A benchmarking exercise took place among youth and sports organisations in 2001. The NSO produced an exhaustive cultural statistics publication in 2002. All the cultural statistics are compiled in accordance with Eurostat's methodology. The Department of Health Information of the Ministry of Health produces a large range of information about health status and government health services. It supplies the relevant data on an annual basis to the WHO.A pilot study for the first health interview survey started in 2001 while the actual survey began in 2002. Health and safety at work statistics are compiled by the Ministry of Social Policy, but there is no collection yet of data on home and leisure accidents. The NSO carries out a household budget survey every five years. The last survey was conducted in 2001 in accordance with Eurostat recommendations and methodologies. Coding of consumption expenditure is carried out in accordance with COICOP. A cross-reference between COICOP and CPA has also been developed. A time use survey is due to be carried out in 2002 using Eurostat’s methodology. Most of the requirements of income, poverty and social exclusion data in the EU context were met by the end of 2001. For social protection statistics, the NSO has established an inter-departmental working group with the aim of implementing the ESSPROS methodology. Most of the required data are available from administrative records. Data from non-government organisations is collected through a widened system of surveys. The first comprehensive social protection account is earmarked for publication during 2002. The first attempt at producing gender statistics was made in 1999 with the publication “Women & Men 1999”. Information on housing conditions is available from both the housing authority and the planning authority. Additionally, the household budget survey will also provide a benchmark on the housing situation in the Maltese Islands. Macro-economic statistics Malta is currently in the process of transferring its national accounts to a system that is consistent with ESA 95. The transition will be complemented with an overlap of two to Official Statistics in Malta Enlarging the EU Statistical Network 97 three years and bridge tables will be established between the old and the new systems. The achievement of compliance by the beginning of 2003 may require some new priorities in the statistics programme. New quarterly estimates as well as regional accounts according to the ESA 95 will not be produced until annual estimates are available. In 2000, the national accounts unit undertook further research on the non-observed economy in order to improve GDP estimates. Financial accounts have been given GFS and ESA codes to comply with Eurostat requirements. Work on the adoption of the Harmonised Index of Consumer Prices (HICP) has been largely completed. Purchasing power parities are required for the deflation of GDP for international comparison purposes. Malta is now fully in line with all the countries participating in the ECP and therefore fully compliant with Eurostat requirements in this area. Business statistics In Malta, the business unit is a legal unit and an enterprise and almost exclusively also a local unit. Given the size of the country, the regional importance of local unit information is almost negligible. The update of the business register is important for the coverage of the business population and for the workable business statistics data collection. The NSO conducts monthly surveys of the quarrying, manufacturing and construction sectors as well as in the wholesale and retail trades, with the possibility of also including some services sectors. Full compliance is expected to be achieved by the beginning of 2003. The energy sector in Malta is dominated by one large state energy provider and the NSO continues to work with them to compile the statistics required by this part of the acquis. The principal sources of data on maritime transport are the Department of Customs and the Malta Maritime Authority. Most of the statistics are compiled and reported by the NSO on an annual basis and generally conform to the requirements laid down by Eurostat. In the future, the NSO will use the data processed by the Malta Maritime Authority, ensuring that it is in the form required by the Eurostat database questionnaire. The Police Department is the primary source of road infrastructure and accident information. Most of the road data are now available in the form required by Eurostat. Details on road accidents, including the age and sex of accident victims, are available on a quarterly basis. Data from the Licensing and Testing Department is reliable with regards to the vehicle stock, including new registrations and scrapping, but not concerning the categories of goods vehicles. The current situation is being improved with the introduction of a vehicle roadworthiness test. The department has also established commercial vehicles by gross weight which will serve as a sample frame for a survey on goods transport by road. A pilot survey of road transport was conducted in 2001 to establish the volume of commercial road transport by weight/kilometres as required by Eurostat. Aviation statistics currently come from the Customs Department but in future the sources will be the Department of Civil Aviation and Malta International Airport. Conformity with the requirements of Eurostat will be achieved in 2002. Disembarkation cards have been the source of data on tourism. During 2000, the NSO launched a pilot survey of Official Statistics in Malta 98 Enlarging the EU Statistical Network inbound tourism (TOURSTAT) that provided much data. TOURSTAT was introduced on a regular basis at the airport in 2000. Interviewers have been recruited and trained.The survey has been tested for a period of about one year with a sample size of 72 000 passengers selected annually by a 2-stage sampling procedure. An accommodation survey (ACCOMSTAT) was started in January 2001. Accommodation establishments which have Internet access are able to submit the data via the NSO homepage. A pilot study was launched in 2001 to collect data on outbound and domestic tourism.A household survey (DOSTAT) will collect information on a monthly basis. Monetary, financial, external trade and balance of payments statistics The Central Bank of Malta is responsible for the compilation and dissemination of statistics on money and banking, selected financial transactions and Maltese lira exchange rates. The data comes from the local banking and financial institutions. The Central Bank will continue to upgrade its statistical reporting systems in line with the ECB recommendations. In the short term, it will focus on the harmonisation of the classification system to ensure that this meets EU standards. The NSO, the Malta Financial Services Centre, the Malta Stock Exchange and government departments are being informed of other ECB data requests that are not the direct responsibility of the Central Bank. These local agencies are now working closely together to review their compilation procedures and to bring them in line with EU requirements. The Central Bank is reviewing the present reporting forms and introducing specific feedback systems to enable the reporting institutions to provide data as required. This will ensure that the Central Bank will eventually meet the deadlines imposed by the ECB. The NSO is in a position to satisfy most of Eurostat’s requirements for foreign trade statistics. It is now transmitting such data on a monthly basis by means of the STADIUM-STATEL. The NSO has also linked up with the COMEXT trade statistics database. The NSO is also preparing for the eventual introduction of INTRASTAT and EXTRASTAT. The Central Bank is working closely with the NSO to produce monthly and quarterly balance of payments statistics consistent with the IMF recommendations. The complete current account is being compiled quarterly on a geographical basis with the EU and the rest of the world. From 2000, data on transactions with/from the USA, Canada and Japan has also been also collected. Agriculture and fisheries statistics The NSO is the central body in the production of agricultural statistics, collaborating with different services of the Ministry of Agriculture in data collection and other activities. The NSO has established an extensive register of all agricultural enterprises. Information from this register is now being stored into AGRISTAT, a GIS-based agricultural information system. A census of agriculture and a poultry census took place in 2001. Annual surveys are foreseen to update the database on pigs, bovines, sheep and goats. A pilot farm accountancy data network (FADN) survey was carried out. Full compliance with the acquis has already been achieved in viticulture statistics. Further surveys will update the available database. Concerning statistics on fruit growing, the NSO has carried out a full-scale census of all regions of fruit production. The collected data are stored into a GIS database. Full compliance has already been reached in this area. Official Statistics in Malta Official Statistics in Malta Enlarging the EU Statistical Network 99 There is still room for improvement in the data in economic accounts, as farmers are not used to keeping records of their expenditure. The NSO is close to satisfying all the EU requirements in crop production statistics. Good quality data are obtained from the agricultural enterprise register and from sector censuses. As far as livestock is concerned, censuses were taken in 2000 and these have permitted Malta to reach full compliance in porcine, bovine and goats/sheep statistics. Compliance with all the acquis relating to agricultural statistics is expected to be achieved by December 2002. In 2001, the Department of Fisheries and Aquaculture and the NSO jointly completed an inventory and register of fishing vessels and conducted a pilot study of the catch assessment scheme. The database and reporting facility of aquaculture statistics will be incorporated into the MALTASTAT system in accordance with the requirements of Eurostat by 2003. Other statistics A team has been established at the NSO to make an inventory of the different data sources for environmental statistics. It has already negotiated with many authorities and providers to get them to adopt some new classifications or to collect some new information. A joint working group has been set up for water and waste water statistics. A proposal to introduce a number of questions into the industry questionnaire will facilitate the collection of information related to energy, water consumption and waste water production. The information • Dissemination mainly through reports, news releases and website www.nso.gov.mt • In 2001, 13 major publications plus 4 publications on methodologies and sources • 100 news releases in 2001 • Many statistical tables on website • Currently small amount of revenue from publications or specialised information • Government bookshop sells MSA publications • Library and Information Unit/Data Shop in the NSO • Press conferences for major data releases • Data protection and confidentiality rules in the Malta Statistics Authority Act, 2000 Conclusion The Maltese statistical system is well advanced in its transition to a modern, comprehensive system in line with EU requirements. Not only has the renewal of data collection been underway but the whole process has also been under reconstruction. A significant legal change has taken place.This is an important step in the further modernisation of the system as a whole. It gives the Maltese Statistics Authority and its executive body - the National Statistics Office - better possibilities for streamlining and centralising the whole system of official statistics. The organisation has the necessary capacity to succeed in the implementation of all the changes, although this will demand a lot of effort from both management and staff. Poland Central Statistical Office Al. Niepodleglosci 208 PL-00925 Warszawa Tel: +48 22 608 30 00, +48 22 608 30 01 Fax: +48 22 608 38 63 E-mail: dissem@stat.gov.pl Web site: http://www.stat.gov.pl Pre-Accession Milestones 1988 Establishment of diplomatic relations between Poland and EU 1989 Poland joins Phare Programme 1991 Poland signs Europe Agreement (16 December) 1994 Europe Agreement of Association enters into force 1994 Poland submits an official membership application for EU membership on 8 April 1998 Official negotiations for EU membership launched and screening for EU accession began in March 2002 25 out of 31 chapters of the acquis provisionally closed by June 2004 Target year for EU accession Country Profile Poland Polska Geographic co-ordinates 52 00 N, 20 00 E Area 312 685 km2 Climate Temperate with moderately severe winters and wet summers Administrative Divisions 16 provinces (voivodships) Capital City Warsaw 1.6 million inhabitants (2000) Population and Growth Rate 38.6 million, + 0.02 % (2000) Nationality Polish Ethnic Profile Polish 98 %, others Religion Roman Catholic 95 %, Eastern Orthodox, Protestant Official language Polish National Currency 1 zloty = 100 groszy Exchange Rate against Euro 1€ = 3.6 zloty (Quarter 1, 2002, New Cronos) System of Government Parliamentary Republic Executive Power President & Council of Ministers Head of State President elected for five-year term of office in direct elections Head of Government Prime Minister, appointed by President and approved by the Sejm Legislative Power Bicameral Parliament; the Lower House (Sejm): 460 seats, elected under proportional representation and serve a four-year term; the Higher House (Senat): 100 seats, elected by majority vote on provincial basis and serve four-year term Judicial Power Courts and tribunals National Holiday Constitution Day: 3 May, Independence Day: 11 November Enlarging the EU Statistical Network 101 The Statistical System of Poland • • • • • • 102 Enlarging the EU Statistical Network Official Statistics in Poland Poland has the statistical environment, the operational capacity and the staff skills to respond to current and changing statistical needs. Overview Central Statistical 0ffice (GUS) Modern and sound legal framework Independent governing body with credibility and independence Employs over 7 300 people in 16 regional offices (825 at headquarters) Strict government control on costs but increased productivity Massive output – over 240 surveys and 300 publications each year (GUS and regional offices) Census of population and agriculture in 2002 • • • • • • • Prime Minister Central Statistical Office (GUS) • First established 13 July 1918 • Based on Polish Statistical Law of 29th June 1995 • Employed about 7300 staff in GUS and 16 regional offices in 2001 • Responsible for major outputs of the statistical system - Conducted about 240 surveys each year - Disseminates about 300 publications each year - GUS website provides statistics and information President of GUS • Nominated by Prime Minister for a fixed 6-year term upon the recommendation of the Statistical Council • Has a strong active and co-ordinating role in the preparation of the statistical programme which is submitted to the Statistical Council Scientific Statistical Council • Operates at the President of the GUS as an advisory and opinion-making body in the field of methodology of statistical surveys � � Other bodies • Ministries produce sectoral statistics - These are: Finance; Agriculture and Rural Development; Environment; Economy; Labour and Social Policy; Health; National Education and Sport; Internal Affairs and Administration; Justice • National Bank of Poland (NBP) - Produces statistics on balance of payments and foreign direct investment Statistical Council • Advisory body of 17 members (from state administration bodies and local, the Central Bank of Poland, organisations of employers, social organi- sations, professional and economic bodies, trade unions and experts from the field of social and economic sciences) • Appointed by Prime Minister for a 5-year term • Elects its own chair among members • Acts independently of GUS �� � � Enlarging the EU Statistical Network 103 The organisation The Central Statistical Office (GUS) has the responsibility for compiling and publishing most of the statistics. Some government ministries produce data on specific sectors such as public finances, government expenditure, justice, health, education and partially on agriculture. The National Bank of Poland provides monetary statistics, balance of payments details and data on foreign direct investment. The legal basis The legal basis for statistics in Poland is well established and covers the GUS and the other producers.The Statistical Law is modern. It was enacted in 1995 and amended in 1996, 1997, 1998 and 2001. Recognising the needs of a wide variety of users, the law provides a sound basis for producing statistics of good quality that will support public policy needs and command public confidence. The law requires compulsory responses for business surveys and censuses. Household and personal surveys are voluntary. Objectives covered under the law include the following: • Collection of data from entities and individuals • Confidentiality of information • Access to administrative records for statistical purposes • Access to statistical information for all users • A supervisory and advisory Statistical Council • An annual programme • The appointment of the President of the GUS • The maintenance of a business register Other legislation concerning statistics covers the censuses and are available in Polish. The management of the GUS The President of the GUS is appointed by the Prime Minister for a fixed six-year term upon recommendation of the Statistical Council. His role is defined in the Statistics Law and he has a strong co-ordinating role in the preparation of the annual statistical programme. Other areas under the responsibility of the President include: • Organising and conducting surveys and determining their methodology • Collecting, organising and analysing data • Conducting censuses • Developing classification standards and interpreting them • Disseminating statistical information • Demographic and economic forecasting • Maintaining registers • Making and announcing international comparisons • Conducting statistical research • Promoting knowledge about statistics The Statistical Council is appointed by the Prime Minister for a period of five years. It consists of 17 high-level representatives of the major ministries, the business community and other users or producers of statistics. It is an advisory body to the Prime Minister and has the status and independence to provide credibility and weight to the advice it offers. The Chair is elected by the members and the President of the GUS attends meetings of this Council. The Council’s functions include the following: • Determining annually a draft programme of surveys for the coming year, on the basis of the proposals prepared by the President of the GUS • Submission of this draft programme of surveys based on official data to the Prime Minister as President of the Council of Ministers • Recommendations on conducting new and periodic surveys and relating these to methods and preparations, planned for a period of ten years • Evaluating the implementation of the programme of surveys and giving opinions on significant issues concerning the development of official statistics Official Statistics in Poland 104 Enlarging the EU Statistical Network • Providing opinions on the appointment and recalling of the President of the GUS • Providing an opinion of the budgetary provision for statistical surveys The annual programme covers the statistical work of the GUS, the ministries and the National Bank of Poland. The President of the GUS consults with a wide range of contributors and users, including members of the Statistical Council, in order to prepare the draft of this programme. All mandatory surveys must be included as, once approved, the programme will be carried out under the Statistical Law. Structure and staffing of the GUS The President of the GUS is supported by four vice- presidents — for economic statistics, social statistics, administration and technical services — plus a civil service director. The central office is in Warsaw, but approximately 90 % of the 7 361 employees are in the 16 offices that correspond to the regional administrative division of Poland. The offices carry out local tasks, such as data collection, maintaining the business register and issuing publications. Most of them are also allocated central projects that may include developing new methods or launching new surveys that will subsequently be implemented nationally. The head of each regional office is accountable to the GUS President. The GUS has an extensive operational capacity and appropriate staff expertise to carry out its large range of activities. Of the total staff, 85 % are female, 42 % are over 45 years and nearly 60 % have secondary education. Increasingly, IT is being used to achieve greater efficiency in data collection and processing. This will enable staff to have higher value roles in analysis and conceptual development. About 3 500 staff are provided with some sort of training each year. Most of the staff training is in-house, although there is a training centre at Jachranka and an IT training centre at Radom. There is also an input from the Warsaw School of Economics. Funding Excluding censuses and the occasional funding from other sources, the basic annual budget for 2001 was approximately € 64 million.The budget is allocated under a number of separate headings and also separately allocated to central and regional expenditure. Public expenditure policy is tightly controlled in Poland, so increases in output are largely made possible through higher productivity. Co-operation The Statistical Law recognises the needs of a wide variety of users. The planning processes are thorough and involve both government and other users of the wider community, especially in the preparation of the statistical programme. The main scientific support comes from the Economic and Statistical Research Centre. This comprises of about 40 staff and is jointly funded by the GUS and the Polish Academy of Science. The centre carries out methodological research and development covering both economic and social areas. Within the GUS there is a scientific committee of university professors. It offers advice to the President on methodological issues. Information technology and methodology Compared to practice in many EU Member States, the GUS carries out a significant number of very large surveys employing exhaustive coverage of businesses.The result is that the statistics produced should be of very high quality and capable of providing the basis for finer Official Statistics in Poland Enlarging the EU Statistical Network 105 levels of analysis compared to systems that are more dependent on sampling. The GUS has benefited from a number of technical support programmes (especially Phare) that have permitted investment in information technology and communications. There is a wide area network connecting the regional offices, local area networks and over 200 file-servers. In general, data input and editing is carried out in the regional offices using software written by the GUS staff. Scanners are also used for data capture and this will increase considerably following the 2002 census. The output Classifications Great progress has been made and most Polish classifications (CPA, NACE, COICOP, NUTS, PRODCOM) are already in line with the European and United Nations standards. As EU and UN classification standards evolve in the future, this development will continue, at the same time ensuring that Polish user requirements are met. In a number of cases where no EU classification standards yet exist, the GUS has developed standards for national purposes while taking account of UN recommended practices. One issue to be addressed is the statistical burden on businesses and the need for harmonisation, particularly related to providing data for both production statistics and VAT purposes. Access to and harmonisation of government administrative records is currently under discussion. Business register The GUS has responsibility for the official national register of economic units (REGON) that covers all legal economic activities and non-profit organisations and their local units.While REGON is relatively complete there are some problems: • Units that cease to operate are consistently not identified • The use of tax records is not yet established • Lack of consistency within the various departments in the national administration Because REGON cannot be used as an extensive database for statistical design and estimation, the GUS is creating a new statistical business register (BSU – Base of Statistical Units) for implementation in 2002. This draws on the REGON information but will also contain additional data that are confidential under the Statistical Law. It will be useful for conducting statistical surveys and will be updated from administrative and statistical sources. Demographic and social statistics In 2002, the GUS will carry out a combined census of population and agriculture, using advanced IT. (The last population census was in 1988, the last agricultural census was in 1996.) Labour market surveys are conducted monthly, quarterly and annually. The monthly ones cover construction, energy, hotels and restaurants, manufacturing, mining, real estate, trade, transport and communications, plus other community, social and personal services. The labour force survey produces quarterly estimates consistent with the ILO definitions and meeting almost all European requirements. Macro-economic statistics Polish national accounts are essentially compliant with European Union requirements and appear to be a Official Statistics in Poland 106 Enlarging the EU Statistical Network particular strength of the GUS.There are plans for consistent development and improvements in methodology. Quarterly accounts are published 80 days after the end of the quarter compared to a European requirement for 70 days. To produce the figures earlier will require that the survey of non-financial corporations is accelerated. Public finances need further regulatory changes to ensure compliance with ESA95. The GUS produces a wide range of price indexes including Consumer Price Indices (CPIs), input and export price indexes, an agricultural price index and a producer price index. The Polish HICP is largely implemented. New products are reviewed twice each year with a view to introducing these into the index.There are plans to develop a regional CPI. Business statistics With regard to structural business statistics, the GUS provides complete coverage of activities, enterprise size and legal form, including data on local units. All legal units (around 140 000) are surveyed each year for details of foreign ownership. However only 10 % report such ownership and most limit information to the immediate owner. The new business register will allow a much more efficient survey to be designed. A comprehensive monthly production survey covers construction, manufacturing, trade and services for the value of production sold, employment and salaries. Since 2000 the survey has been enlarged to include PRODCOM questions on value and quantity of selected groups of products. It is supplemented by a specific survey for the retail sector. The index of production for manufacturing is derived from a monthly survey on turnover. The GUS also carries out business surveys. They are very important as a short-term indicator for users and the results are transmitted to the European Commission. The Ministry of Interior and Administration is harmonising vehicle registration between regions. This will improve statistics on transport of goods by road in compliance with EU requirements.As for maritime transport, it is hoped that all harbours will be fully equipped for data recording by 2003. Tourism data are collected and will be published with the frequency required by the EU directive by January 2003. Monetary, financial, external trade and balance of payments statistics Monetary statistics are the responsibility of National Bank of Poland (NBP) and work is in hand to improve the measuring of flow statistics. Yearly financial accounts are produced by the GUS, but currently with long delay after the year-end. There is the intention to reduce this delay to nine months. The lack of a suitable IT system in customs offices makes the collection of external trade data extremely labour intensive. The project to resolve this is due to be operational in 2003. For the last seven years, the GUS has conducted a survey of non-registered external trade. The intention is to continue this as a jointly funded exercise with the NBP. The NBP is starting to use foreign trade data in its quarterly balance of payments accounts. For geographical classifications, the NBP will treat the EU/rest of world split as the highest priority and will extend beyond this to major trading partners later. Official Statistics in Poland Enlarging the EU Statistical Network 107 Almost fully compliant with OECD/EU requirements, foreign direct investment data are the responsibility of the NBP and is now produced using the NAICS breakdown. Agricultural statistics The combined population and agricultural census in 2002, with annual structural surveys in 2005 and 2007, will obviously provide a major boost to data from this area.The census will also update the farm component of the business register. The GUS and the Ministry of Agriculture and Rural Development work together. For example, surveys of crop and animal production are carried out by the GUS, statistics on processed dairy products are the responsibility of the Ministry. Statistics on balance sheets for all products are not yet compliant with EU requirements, although some are very close. The Ministry of Agriculture and Rural Development is in the process of creating a multifunctional farm register and an animal register,which will have statistical potential for the GUS. Other statistics Poland has a long history of compiling environmental information. For the common 1998 OECD/EU questionnaire, 75 % of the data are available. A wide variety of regional statistics is compiled by the GUS. The GUS and the 16 regional office structure is an aid to obtaining and co-ordinating regional information. Work is clearly delegated regarding surveys, publications and analysis in regional profiles. The NUTS classification is now agreed. There are 16 NUTS 2 areas (voivodships), 44 NUTS 3 areas and much larger numbers of NUTS 4 and NUTS 5 areas. A time-series of regional statistics going back to 1995 for NUTS 2 ( and in some cases NUTS 3) is available. The information The GUS and regional offices have a good communication policy. • About 300 regular publications each year • Detailed release schedule announced in advance for key regular outputs • Well attended monthly press conferences carried out by the top management • Release of information made available to all users at the same time through paper, fax and electronic means • Anonymous individual data records are made available for academic research and secondary analysis subject to the protection of confidentiality • Key figures communicated through announcements by the President of the GUS, in the government journal “Monitor Polski” and in the journal of the GUS. • Wide range of information also available on CD and on the Internet www.stat.gov.pl Conclusion Poland has made substantial progress towards EU requirements and generally is either fully compliant or almost so. In the areas where compliance has not yet been achieved, staff members are well aware of the nature of the deficiency.There are detailed work schedules to correct this situation. The GUS has a well-developed statistical infrastruc- ture.The legal and administrative framework is sound. The survey programme is supported by a well-devel- oped communication policy. The Statistical Law allows the GUS to have access to administrative systems for statistical purposes. This will be essential to improve quality in certain areas, particularly as the new business register (BSU) is developed. Official Statistics in Poland Romania National Institute of Statistics 16, Libertatii Avenue, Sector 5 RO-70542 Bucharest Tel: +40 1 312 48 75, +40 1 311 33 09 Fax: +40 1 312.48.73, +40 1 335 73 73 E-mail: romstat@insse.ro Web site: http://www.insse.ro Pre-Accession Milestones 1974 Romania signs EC agreement on Generalised System of Preferences 1980 Romania signs Agreement on Industrial Products 1990 Establishment of diplomatic relations between Romania and EU 1993 Romania signs Europe Agreement of Association 1995 Europe Agreement comes into force 1995 Official application of Romania for EU membership on 22 June 2000 Romania begins accession negotiations with EU 2002 11 out of 31 chapters of the acquis provisionally closed by end of year 2007 Romania’s target date for EU accession Country Profile Romania România Geographic co-ordinates 45 00 N, 25 00 E Area 238 391 km2 Climate Temperate; cold, cloudy winters and sunny, dry summers Administrative Divisions 41 counties (judete) and Bucharest Municipality Capital City Bucharest (2.0 million inhabitants) Population and Growth Rate 22.4 million, - 0.18 % (1 July 2001) Nationality Romanian Ethnic Profile Romanian 90 %, Hungarian 7 %, Roma 2 %, others Religion Orthodox 87 %, Roman Catholic 5 %, Protestant 4 %, others Official language Romanian Currency 1 leu (Rol) = 100 bani Exchange Rate (1st Quarter 2002) 1€ = 28 344 rol (Quarter 1, 2002, New Cronos) System of Government Republic Executive Power Government (appointed by Prime Minister) Head of State President elected for four-year term by popular vote Head of Government Prime Minister, appointed by President Legislative Power Bicameral Parliament (Parlament); Senate 140 seats, Chamber of Deputies 327 seats; all members are voted by direct popular vote on proportional representation to serve four-year term Judicial Power Supreme Court of Justice National Holiday Unification Day (of Romania & Transylvania): 1 December (1918) Enlarging the EU Statistical Network 109 The National Statistical System of Romania • • • • • • 110 Enlarging the EU Statistical Network Official Statistics in Romania Romania has developed a national system of statistics that has constantly followed the need to meet international regulations Overview National Statistical Institute of Romania (NSI) Legal framework amended in 2001 Council for Co-ordinating Statistical Activity President of the NSI appointed by Prime Minister Over 1 800 staff and 8 regional offices and 34 county offices About 120 surveys and 30 publications each year Census of population and housing in March 2002 General agricultural census in December 2002 – January 2003 • • • • • • • • Government National Statistical Institute (NSI) • First official statistics in Romania set up in 1859. NSI was established in 1989 • Based on Government Ordinance: No 9/1992, amended by Government Ordinances No 83/1999, No 111/2000 and No 75/2001 • Employed over 1800 staff in headquarters, 8 regional offices and 34 county regional offices in 2001 • Responsible for very large part of statistical activities - Conducts about 120 different surveys each year - Disseminates about 30 publications each year - NSI website provides statistics and information Other bodies • National Bank of Romania • Ministries, public institutions and authorities • These bodies carry out surveys in the annual statistical programme co-ordinated (for methodology issues) by NSI President of NSI • Appointed by Prime Minister; term of appointment not defined • Heads the headquarters of NSI and is an ex-officio member • Is responsible for President’s control corps, audit office, juridical and contentious office Council for Co-ordinating the Statistical Activity (CCSA) • Consultative body of 25 members, including the President who is a council member by right • Members appointed by Prime Minister for 2 years from experts in statistical theory and practice (ministries, academics, businesses, trade unions) • The council approves its own functioning rules • Chaired by the President of the NSI �� �� Enlarging the EU Statistical Network 111 The organisation The National Statistical Institute (NSI) is the key organisation in the official statistical services of Romania. It is responsible for the national direction of statistical activity and activities at the county level. In addition, it co-ordinates the methodologies in the statistical divisions of the National Bank of Romania (NBR) and other public institutions and authorities. The NSI is subordinated to the government and financed by the state budget. In addition, there is a Council for Co- ordinating Statistical Activity that, under the law, has an important role in defining the general strategy of the whole national statistical service. The legal basis The organisation and the functioning of the official statistics system in Romania is regulated by a government ordinance of 1992, amended several times with the latest amendment being the Government Urgency Ordinance no. 75/2001 (published in the Official Journal of Romania no. 283/31.05.2001). The ordinance details the general duties of all bodies constituting the national statistical system including the National Bank of Romania. It also includes the basic principles regarding the official statistics organisation, protecting its professional independence. Under this ordinance, the organisation of the public statistics is based on the following principles: • Statistical autonomy; confidentiality; transparency • Relevance; proportionality; statistical ethics; cost–efficiency It should be noted that there are no provisions in the ordinance concerning the access to confidential data for scientific purposes. To conduct some of the most important surveys, such as the population and housing and the agricultural censuses, special legal acts are also required. The management of the National Statistical Institute The President of the NSI and the vice-presidents are appointed by the Prime Minister. There are no special provisions concerning appointment period, conduct or removal.The main responsibilities of the NSI include: • Providing information on the economic and social status of the country and providing to users the data from the surveys carried out • Creating systems of statistical indicators, methodologies, technologies and standards • Organising the collection of information through censuses and surveys • Co-ordinating statistical classifications and nomenclatures • Drawing up statistical studies on the economic and social aspects of development • Performing scientific research in the statistical field • Collaborating with ministries and the central public administration and services to ensure compatibility of official statistics with other information systems and to provide statistical staff training • Approval of methodologies developed by ministries, central bodies and other public services in order to ensure their suitability and reliability • Ensuring the compatibility of the national statistical system with the requirements of the UN, the EU and other international bodies The Council for Co-ordinating Statistical Activity includes the President of the NSI and representatives of the Romanian Academy, higher education, ministries, the Romanian National Bank, trade unions, employers’ Official Statistics in Romania 112 Enlarging the EU Statistical Network associations, media and professional associations. It is responsible for the approval and endorsing to the government of the general strategy for the development of the national statistical system and the annual programme of statistical surveys.The surveys are carried out by statistical departments within public administration and co-ordinated by the NSI. Structure and staffing of the National Statistical Institute The network comprises of the NSI headquarters and 34 regional offices that are county branches at municipal levels. In 2001, eight new regional divisions, at the level of NUTS 2, were created and located at the administrative centres.The total staff at the end of 2001 was 1 817, with 68 % in the regional and county offices. The structure of staff by age is well balanced between the younger staff (64 % under 45 years) and the statisticians with more experience. About 78% of the NSI staff are female and 60% have higher educational qualifications. There is a National Centre of Training in Statistics to guarantee the required continual training of the staff, with particular emphasis on IT training.The participation of the NSI staff in the TES courses is also very important especially in terms of development of statistical skills related to EU needs. As is often found in national statistical systems, salary levels in the Romanian NSI are lower than in the public administration generally. This has a negative effect on talented graduates. Funding Official statistics in Romania are financed by central government. For each survey the annual programmes include the estimated costs and the financing source.The annual budget proposal has to be submitted in some detail by the NSI and approved by government. In 2001, the basic budget was 8.6 million. The ministries producing official statistical data include in their own budgets the costs of their statistical activities. Extra expenditure was allocated to the budget for census operations. There is no flexibility in changing the initial allocation of the items of the budget. If, for some reason, there is a need to move funds from one item to another a special approval of the government is necessary. Control of the budget within the public administration is very strict but only in global terms and not in connection with the importance of each project. Co-operation The NBR and various ministries are both producers and users of official statistics.The relations between the NSI and the ministries are subject to protocols regulating the duties of each one concerning the exchange of data. There are 45 protocols concluded with the ministries. The tasks of the regional offices related to the national statistical work programme are, in practice, essentially the collection of statistical data and the production of some specific regional data and publications. Information technology and methodology Using funds from the Phare Programme, the NSI has proceeded to a first modernisation in its use of IT. The headquarters and the regional offices are connected, and the IT is up-to-date. Programmes for statistical data have been developed in-house to reduce dependency on external suppliers and costs. The NSI has the capability to design the surveys included in the annual programme and to develop the statistical Official Statistics in Romania Enlarging the EU Statistical Network 113 methodologies required. However, the NSI President has the possibility to contract out specialised services to achieve surveys, analyses and statistical studies. The output Classifications The Romanian statistical system has adopted all of the most important economic and social classifications in compliance with EU and international standards. Among these are NACE, CPA, PRODCOM and COICOP. Concerning the use of the structural nomenclatures, particularly in the economic field, there are no constraints in achieving compliance with EU standards before 2003. Registers The Romanian business register is called REGIS and has been built up over the 1990s. It includes: • All non-financial enterprises or pseudo-enterprises • All financial and insurance enterprises • The National Bank of Romania • Public administration entities • Social security institutions • Non-profit institutions • Physical persons and family associations carrying on economic activity The fiscal register is the main source of REGIS, but it is not exclusive. The trade register is also used as a control of quality source. Concerning the maintenance of REGIS, as there is a legal obligation to report to the fiscal and VAT registers about the new units from which the information is monthly transferred to REGIS, this statistical register is always sufficiently updated. Vital statistics are based on the register of civil registrations of the municipalities. Data on internal migration is obtained also from administrative records based on the obligation to register changes of residence. Data is transferred to the NSI on a monthly basis. Information on legal international migration is obtained from the Ministry of the Interior. There are no estimates of illegal migration. There is a population register under the responsibility of the Ministry of the Interior, but it is not used for statistical purposes. Demographic and social statistics Romania has detailed demographic and social statistics compiled partly by the NSI and partly by ministries or agencies. Some minor changes and extensions are needed to meet the acquis communautaire. The population and housing census was carried out in March 2002.The preliminary data will be available by July 2002; the definitive data in 2003. Since 1996 the NSI has carried out a quarterly continuous labour force survey. The methodology meets the ILO/Eurostat standards. The results are at the level of NUTS 2. The local agencies for employment and professional training transmit the data to the national agency for employment and professional training, which transfers it monthly to the NSI. Data about the structure of earnings, labour costs and working time use are obtained from a system of enterprise surveys. Data on monthly earnings are published as average gross and net monthly wages broken down by economic activity and by property type but not by region. Data on labour Official Statistics in RomaniaOfficial Statistics in Romania 114 Enlarging the EU Statistical Network costs are published as monthly labour costs per employee, broken down by economic activity and by form of property.There is no separate collection of data on full time and part time employees. Exhaustive surveys on all educational units from the public and private sectors provide information on the number and the endowment of schools and on the number of pupils and teachers. Statistical surveys on public and private health units, which are conducted yearly, provide data on the structure of health care by areas, personnel, number of hospitalised persons, etc. The Ministry of Labour and Social Solidarity provides the NSI with information on the number of casualties (broken down by persons killed and persons temporarily unable to work) and the number of collective accidents by branches of economic activity. The Ministry of Health supplies the NSI with data on occupational diseases. A household budget survey (HBS), living conditions survey (LCS), time use survey, health survey and life long training survey were carried out in 2000. From 2001, only HBS and LCS will be annual and other surveys will be carried out at longer intervals in accordance with international recommendations. Information on social state insurance and benefits for farmers is gathered from the Ministry of Labour and Social Solidarity. For others there is a quarterly statistical report for which the NSI collects and processes data together with that from the Central Office for Pensions. This office centralises all information on pensions (amount paid) and pensioners (number) regarding different kinds of pensions and provides the Ministry of Labour and Social Solidarity and the NSI with quarterly and annual detailed reports. Annual data on pensions, tickets for treatment in medical centres and state budget for social insurance are centralised at a general directorate of the Ministry of Labour and Social Solidarity and transmitted to the NSI. Information on social assistance is gathered by the Ministry of Labour and Social Solidarity and transmitted to the NSI. A yearly exhaustive survey on cultural units in the private and public sectors (libraries, theatres, and museums) delivers detailed specific information. Macro-economic statistics In Romania, 1999 is the most recent year that the final version of national accounts at current and previous year prices has been published. The semi-final version for 2000 and provisional for 2001 (GDP estimations) have also been published. Starting with 1998 the accounts follow ESA 95 both in terms of reference framework and detail of data. The NSI also publishes quarterly accounts. The Ministry of Public Finances supplies the basic data for government financial statistics.The NSI, NBR and Ministry of Public Finances are working together to ensure the use of the relevant international, and particularly EU, guidelines and standards. For price statistics, the existing CPI is based on weights derived from the last household budget survey. The NSI plans to revise the methodology to take into account new data available, especially in the household budget survey.The NSI also compiles producer prices and import and export unit value indices. Business statistics The business register is the main data source for the various surveys on business carried out by the NSI. Official Statistics in Romania The structural survey covers large enterprises (more than 20 employees), units of public and private administration, budgetary institutions, and banks and insurance companies. It does not include sole proprietorships or self-employed people. From 2001, the structural business survey has been fully compatible with EU regulations. There are quarterly surveys on manufacturing, construction and retail trade. Monthly surveys provide data for the index of industrial production. A monthly report provides data on production of electrical and thermal energy. Data on transport is gathered through statistical reports completed by autonomous bodies and companies from the public, private, mixed and co-operative sectors. Data refer to the volume of transport activity (goods and passengers) and to the indicators of fixed assets. Data on vehicles registered is collected from the Ministry of the Interior; for the sea and river vessels from the Ministry of Transport. Data on audio-visual services are collected from administrative sources and refer to film production, number of cinemas, audiences, number of performances, radio and television broadcasts. The Ministry of the Interior supplies information on international tourism. Foreign visitor arrivals by country of origin and Romanian visitor departures abroad are provided by transport used. A monthly survey is carried out on tourist arrivals and overnight stays by type of accommodation unit, by categories of comfort, by development regions and by tourism areas. To determine the amount of accommodation that exists, an exhaustive annual survey is carried out. A quarterly survey is carried out in travel agencies. This supplies information on the number of tourists booking through travel agencies, the tourism areas visited, the journey duration and foreign destinations. In 2002, a pilot survey will be carried out on tourism demand according to EU procedures. There is an exhaustive yearly survey on tourism accommodation units, providing data on arrivals and nights spent. Monetary, financial, external trade and balance of payments statistics Monetary and balance of payments statistics are the responsibility of the NBR and the most important statistics are in line with IMF standards. Financial accounts are a part of the annual national accounts compilation. Since 1998, the financial accounts have followed ESA 95 both in terms of reference framework and detail of data. The external trade statistics are compiled on a monthly basis as well as quarterly and annually.They are currently released 40 - 45 days after the reference months. They conform to international standards. The NBR uses this data in the compilation of the balance of payments. The NBR compiles quarterly balance of payments statistics following the fifth edition of the IMF Balance of Payments Manual. The data on foreign direct investment is collected from banks and from customs for investments in kind. The accession to the EU will require the adoption of the INTRASTAT system with the associated demands for quality and detail of data. Enlarging the EU Statistical Network 115 Official Statistics in Romania Agricultural statistics Since 1990, data on agriculture has been collected from the local administration, agricultural legal corporations and other economic agents involved in agricultural production. The existing system of agricultural statistics will be completely revised after the next agricultural census, which is expected to take place during December 2002 and January 2003.All the preparatory work is already concluded. As part of the organising of this census, a pilot survey will be carried out in August 2002. Other statistics Since 1993, data on expenditures for environmental protection have been collected by a yearly statistical structural survey of enterprises. Data on pollution are collected in co-operation with the Ministry of Water and Protection of the Environment. Information referring to the quality of environmental factors is gathered through statistical reports by activity and at the national level. The following indicators are computed: protected areas, biosphere reservations, national parks, water resources, quality of environment factors such as surface water, drinking water and air and expenditure for the protection of the environment. Romania has a regional development policy that will make the collection of regional statistics easier in the future.Additionally, the population census will make it possible to produce an important set of intra-regional social and economic data. Official Statistics in Romania 116 Enlarging the EU Statistical Network The information • Publication shop at headquarters and distribution to regional offices • Website www.insse.ro offers considerable range of general and key statistics • Statistical Yearbook and Demographic Yearbook • About 30 regular publications a year, some available in electronic version • Press releases on a regular basis also available on website • Calendar of press releases published in advance Conclusion The need to meet statistical requirements of the EU has been the main objective of the development of the Romanian national statistical system. Achieving full compliance with the EU statistical requirements strongly depends on the availability of sufficient resources. All of the most important national classifications compatible with the international ones have been adopted. The business register is a good tool for statistical production and quality control. Important progress has also been made in the macroeconomic field. The NSI of Romania has the potential to develop a modern statistical system. Official Statistics in Romania Enlarging the EU Statistical Network 117 Slovak Republic Statistical Office of the Slovak Republic Mileticova 3 SK-824 67 Bratislava Tel: +421 2 50 236 334 Fax: +421 2 55 424 587 E-mail: elena.stavova@statistics.sk Web site: http://www.statistics.sk Pre-Accession Milestones 1989 Establishment of diplomatic relations between Czechoslovakia and EU 1993 The Slovak Republic signs Europe Agreement 1995 Europe Agreement of Association enters into force 1995 The Slovak Republic submits an official membership application for EU membership on 27 June 1999 The Slovak Republic accepted by EU Member States as candidate in December 1999 The Slovak Republic submits a revised version of the National Program for Adoption of the Acquis in May 2000 Official opening of accession negotiations in 2000 2002 26 out of 31 chapters of the acquis provisionally closed by June 2004 Target year for EU accession Country Profile Slovak Republic Slovenská republica Geographic co-ordinates 48 40 N, 19 30 E Area 49 035 km2 Climate Temperate; cool summers; cold, cloudy, humid winters Administrative Divisions 8 regions (kraje) Capital City Bratislava (0.4 million inhabitants) Population and Growth Rate 5.4 million, + 0.03 % (2000) Nationality Slovak Ethnic Profile Slovak 86 %, Hungarian 10 %, other Religion Roman Catholic 69 %,Atheist 10 %, Protestant 6 %, others Official language Slovak National Currency 1 Slovak koruna = 100 haliers Exchange Rate against Euro 1€ = 42.2 Slovak koruna (Quarter 1, 2002, New Cronos) System of Government Parliamentary Democracy Executive Power President & Government Head of State President elected for five-year term by popular vote Head of Government Prime Minister, appointed by President Legislative Power Unicameral National Council (150 seats); members are elected on the basis of proportional representation for a four-year term Judicial Power Supreme & Constitutional Court, Regional and District Courts National Holiday Constitution Day: 1 September (1993) Enlarging the EU Statistical Network 119 The Statistical System of the Slovak Republic • • • • • • 120 Enlarging the EU Statistical Network Official Statistics in Slovak Republic Slovak Republic has both clear plans and the capacity for reaching the target of EU compliance upon accession. Overview Statistical Office of the Slovak Republic (SO SR) Legal framework harmonised with EU requirements under new law in 2002 Statistical Council of 23 chaired by President of the SO SR President of the SO SR has been appointed by President of the Republic Over 1 100 staff in central and 8 regional offices in 2001 110 surveys and over 120 publications each year INFOSTAT centre for R&D Public Opinion Institute for rapid polls • • • • • • • • President of the Slovak Republic Statistical Office of the Slovak Republic (SO SR) • Established 1st Jan 1993 • Based on the Act on State Statistics No 540/2001 • Employed over 1100 staff in central and 8 regional offices in 2001 • Responsible for co-ordination and major outputs of the statistical system - Conducts about 110 surveys each year - Publishes over 120 publications each year - The SO SR website provides statistics and information • Public Opinion Institute integrated as a unit of the SO SR Other bodies • Ministries - Produce sectoral statistics (eg public finances and government expenditure, agriculture, health, education etc) - Conduct about 230 surveys each year (included in Statistical Programme) • National Bank of Slovakia produces - Balance of payments; monetary and banking statistics President of SO SR • Appointed by the President of the Slovak Republic for a fixed 5-year term upon the proposal of the Government of the Slovak Republic. • Heads the headquarters and regional offices of the SO SR and chairs the Statistical Council • Prepares draft of the Programme of State Statistical Surveys Statistical Council • Advisory body of 26 members (no fixed term of appointment). Members appointed by President of the SO SR according to proposal of Ministries, state authorities and public-legal institutions • Chaired by the President of the SO SR �� INFOSTAT Research Centre • A contributory organisation of SO SR as its Research & Development Centre • A subsidised entity (financed partly by the SO SR and partly by profit gained from own activities) • Conducts methodological research, design and tests • Carries out econometric modeling and medium & long-term forecasting • Develops automatic data processing systems of the SO SR • Employs over 100 staff � ��� Enlarging the EU Statistical Network 121 The organisation The Statistical Office of the Slovak Republic (SO SR) is the principal element in the national statistical system. It has functioned as an autonomous organisation under laws of 1992 and 1993, which have been brought up-to-date by a new law introduced in January 2002. The SO SR does not carry out all surveys, but does determine which public body is to conduct a survey and monitors the design and methodology. Other organisations in the system include the ministries, four central bodies of state administration, the National Bank of Slovakia, the Institute for Informatics and Statistics (INFOSTAT - a research centre of the SO SR conducting R&D on methods and applications) and the Public Opinion Institute (public opinion polls). The legal basis A new Statistics Law of 2002 brings the following revisions: • Harmonisation with EU regulations • Integration of previous amendments into one act • Standardisation of reporting obligations among data suppliers • Harmonisation of terminology and definition of statistical terms • Precision regarding confidentiality • Clarification of the SO SR’s rights of access to administrative data • Duration of survey programme lengthened to three years • Change in the nomination procedure of the President of the SO SR and increase in independence Other legislation concerning statistics covers the population and housing census, the state information system, protection of personal data and a customs act. The management of the SO SR Until the year 2001 the President of the SO SR used to be appointed by the government. Under the new law, this appointment is made directly by the President of the Republic for up to two five-year terms. In addition, the President of the SO SR has more autonomy in deciding on the structure of the national network of statistical bodies. This network comprises the SO SR headquarters, eight regional statistical offices, INFOSTAT research centre and the Public Opinion Institute. Currently the Director of Infostat is appointed by the President of the SO SR and its operations are assessed by the SO SR management. Its major tasks are methodological research, design and tests, econometric modelling and long and medium term forecasting and developing automatic data processing systems. The Public Opinion Institute produces quick information based on 1 200-unit sample surveys, 80 % of which are financed by the SO SR. To assist the SO SR, a Statistical Council has an independent advisory role. It is chaired by the President of the SO SR and has 23 members representing various ministries, the Slovak Academy of Sciences, trade unions and employers’ associations and the National Bank of Slovakia. The Council holds a plenary session twice a year. It discusses the strategy of state statistics, principals of statistical activity of ministries and other institutions and the proposal for the statistical survey programme. It also comments on legislative questions in the statistics field. The SO SR is in charge of the programme of state statistical surveys. Requirements are submitted by various authorities and bodies. Based on these, the SO SR designs a programme which is then discussed by the Statistical Council and approved by the legislative body. The programme will include surveys by the SO SR and by Official Statistics in Slovak Republic 122 Enlarging the EU Statistical Network others in central administration and it must cover all the surveys that are mandatory. Questionnaires are published on the SO SR website. Structure and staffing of the SO SR The network currently employs 1 100 staff, of which 41 % have university degrees, 48 % are over 45 years, about 80 % are female and only 30 % work at the headquarters. There are eight regional offices that collect local or specific category data and service local customers. They are all similar in size with about 100 staff. Staff turnover has been quite high and salary levels may not be sufficiently attractive for young, skilled staff. However, changes in legislation establishing the status of civil servant may help in this respect. Training opportunities include new legislation, statistical methodology, IT, foreign language and general management. Part-time courses to upgrade education levels are also supported. Funding The SO SR budget has remained almost constant over the last few years, imposing continuous reductions in costs. The basic budget for 2001 was 7.1 million, 66 % being wages and social security. Operations such as the population census 2001, processing the results of parliamentary and communal elections and EU co- operation projects are financed by special budgets. There have also been externally funded projects. By approval of the state budget the Parliament determines individual financial limits for salaries, running expenses and capital expenses. The President of the SO SR decides on use of budgetary resources within these limits. The Programme of State Statistical Surveys has to respect the constraints of budget, which are already known before approval of the Programme. Co-operation The SO SR co-operates with many universities, scientific and research institutions and the Academy of Science. They are represented on the Statistical Council and the Statistical and Demographic Society. They also participate in a journal “Slovak Statistics and Demography”, published by the SO SR. The SO SR can release anonymous datasets to scientific institutes for research or scientific purposes. The universities are important for recruiting new staff and the SO SR gets about 10 -15 trainees a year. Regarding co-operation with users, a prime objective has been the need to meet the statistical requirements of the European Commission as the country prepares for EU membership. The Statistical Council ensures that the views of users within the country are taken into account and there is also an information service department. Information technology and methodology There are eleven local area networks within the central and regional offices.These in turn are linked to a wide area network called STATNET. Other connections to STATNET include INFOSTAT, the government network GOVNET and an Internet connection for approved users. ASIS is the automated statistical information system for effective collecting, exchanging, and dissemination of statistical data for both internal and external users (e.g. Eurostat,OECD, IMF). The Methodology and Informatics Section (MIS) of the SO SR has the following activities: • Conducting methodological co-ordination and project documentation in preparing the survey programme • Planning and implementing automated processing of statistical surveys • Processing survey data Official Statistics in Slovak Republic Enlarging the EU Statistical Network 123 • Conducting technical administration of statistical databases • Preparing computer aided presentations of survey results MIS has the opportunity to act as a catalyst for the harmonisation of the national statistical system. This is of crucial importance, given the large number of administrative surveys and data, which in the future ought to be accessed and used intensely by the SO SR. The output Classifications All national versions of statistical classifications (NACE, CPA, PRODCOM, CC, COICOP, GEONOM, COFOG) are fully in line with the acquis communautaire. The new law on statistics regulates a nation-wide harmonised use of classifications, which is a crucial step towards an effective use of administrative data. Registers Existing registers include the business register for corporations and the trade register for freelance business people.The small and medium enterprises census is providing additional data.A register of local units and KAU, essential for regional statistics, is still being prepared. All legal units are assigned an identification number that is issued by the SO SR. The registers are continuously updated by the regional offices and there is an annual central updating. The use of administrative sources in statistics is reduced by the lack of harmonisation of links between information kept in administrative registers and information kept in the business register. The new law on statistics expands the mutual co- operation and acquiring of additional information from administrative sources.This will be vital to bring the quality of the registers to the required level. Demographic and social statistics Statistics are available on marriages, births, deaths, divorces, internal migration and abortions. A future task will be the completion and harmonisation of the methodology and output according to EU standards. The first population and housing census in the independent Slovak Republic was conducted in 2001, enabling creation of an extensive database. The SO SR managed the methodology and co- ordinated the census preparation. It ensures data processing and publishing, data dissemination to the public and provides data for international comparison. A labour force survey has been conducted according to ILO standards for several years. Ad hoc supplementary surveys to the LFS requested by Eurostat have been carried out and the results transmitted accordingly. The structure of wages survey has been carried out for several years and a new questionnaire will be used in 2002 follows EU requirements. The labour cost survey carried out in 2001 was designed to achieve full compliance with EU regulations and covered all economic activities by NACE, all size classes of enterprise and whole country at NUTS 3 level. Education statistics are compiled by the Institute for Information on Education. Data are available in conformity with ISCED 97 and almost all the data requested can be provided. The Ministry of Culture and other bodies can supply indicators on most of the categories in the UNESCO classification of cultural activities. From the year 2001 on, statistical information on audio and audio-visual products have been provided to Eurostat. Health statistics are primarily the responsibility of the Institute of Health Information and Statistics.The tenth ICD classification is used. A range of statistics (including causes Official Statistics in Slovak Republic 124 Enlarging the EU Statistical Network on death and occupational diseases) is available in line with WHO and other guidelines. Information on accidents at work is available at the SO SR. Some changes to the legislation are needed to provide statistics in full compliance with EU definitions and methods. Home and leisure accident statistics started in 2001 covering data on first aid services and hospitals. Household budget statistics are produced based on a regular monthly sampling of about 1 800 households. In expenditure, the adjusted COICOP structure is used. Concurrently selected household characteristics are being monitored, with results published quarterly and yearly. Preparations are also being done for a time use survey. Data on social security has been provided to Eurostat according to the ESSPROS 96 methodology. The SO SR collaborates with the Ministry of Labour, Social Affairs and Family.A continuous adaptation of ESSPROS statistics will be necessary in view of planned changes in the social security system. Macro-economic statistics National accounts are complied by the SO SR according to ESA 95, with large supply/use tables forming an integral part. To calculate GDP, production, expenditure and income approaches are used based on constant prices. Sources of data include surveys, accounting statements, plus data from the Ministry of Finance, the National Bank of Slovakia Republic and others. Since the delay in delivering results does not conform to EU regulations, preliminary and revised results need to be produced. The ongoing progress in the business register will improve the quality of data in this area. Although quarterly accounts have been extremely well developed, there is a lack of consistency with annual national accounts. New procedures, research and greater co-operation are underway to resolve this. Financial accounts by sector are complied by the SO SR as part of the annual national accounts. The Consumer Price Index (CPI) is compiled by the SO SR as a fixed weights index according to COICOP. The CPI covers an estimated 90 % of households. In addition separate indices are compiled for seven population groups: pensioners, employees, farmers, employees with one child, employees with two children, employees with three children, low-income households. The CPI basket comprises over of 700 items classified by COICOP. CPI data are published within 15 days of the end of each month. Compliance with the most of the requirements for HICP has been achieved after a comprehensive revision of the CPI and a review of the preliminary HICP. The SO SR produces a range of Producer Price Indices (PPIs) covering agriculture and forestry, primary industry and construction. It does not compile a composite PPI for these activities in total. Business statistics Constant improvement in structural business statistics and short-term statistics has resulted in high levels of compliance. However, LKAU are not used in compiling the data. The PRODCOM classification is used for the compilation of industrial production statistics. Business trends surveys have been conducted for some time and from 2002 are fully compatible with the EU framework and extended to include the services sector. The energy statistics harmonisation project of the SO SR has questionnaires that take both the requirements of Official Statistics in Slovak Republic Enlarging the EU Statistical Network 125 international organisations and the needs of domestic users into account. The goal of the project is to compile energy balance data according to Eurostat methodology during 2002. Statistics on distributive trades were introduced in conjunction with unified structural business statistics and short-term statistics.The non-response rate is a problem. In transport statistics, road freight is surveyed weekly and the results evaluated quarterly at NUTS 1 level.The SO SR is aiming to realise fulfilment of regulations on road freight transport in the medium term. Fulfilment of regulations concerning infrastructure, combined transport, bilateral agreements and the law on railways is ensured by the Ministry of Transport, Posts and Telecommunications through the sector statistical surveys. For the regional statistics needs and for decisions on transport policy, a survey on inland transport of goods on the level of NUTS 3 has been conducted in 2001. For tourism statistics, the accommodation survey was fully harmonised from 2001. Problems exist with the response rate of private accommodation, with data collection for domestic tourism and with updating the register of accommodation establishments. Other services statistics include: real estate, rent of machines and equipment without operator, computer and related activities, trade services, personal services, education, other public, social and personal services and recreational, cultural and sporting activities. The main problem is data monitoring for KAU and LKAU which require the creation of a register of local units and the inclusion of KAU into the whole statistical system. Monetary, financial, external trade and balance of payments statistics The National Bank of Slovakia (NBS) surveys the 24 commercial banks and 2 foreign banks to compile money and finance statistics. Non-banking institutions and enterprises are surveyed on the basis of the Foreign Exchange Act that requests NBS to measure changes in foreign exchange assets and liabilities. FDI is measured in geographical and sector breakdown, with surveys of banks being used as checks of business corporations’ statements on foreign direct investment activities. Foreign trade statistics are in line with the international standards (UN), but do not fully conform to ESA 95. Since 2001 the classification GEONOM is used. The INTRASTAT system is a basic condition of the European Commission before accession of the Slovak Republic. The SO SR is planning the realisation of this project in the period 2002 to 2004. However, significant additional resources are necessary for its implementation. Agriculture and forestry statistics The SO SR is currently implementing a farm structure census covering all regional agricultural units, private farms and households (excluding big cities). This is a sample of 804 000 units. Final results of the census are expected to be available before the end of 2002. In compliance with EU regulations, the SO SR is conducting a harvest estimate three times a year during summer and autumn. Fruit and wine statistics are being harmonised with EU regulations in a joint initiative of the SO SR and the Ministry of Agriculture. In 2002 the compilation of the fruit statistics will be completed and wine statistics are expected. Official Statistics in Slovak Republic Forestry plays an important role in the Slovak Republic’s economy.About 60 % of the forest is privately owned, often in very small lots and the owners not identified. The first forestry accounts compiled according to the EU manual are expected by the end of 2002. Other statistics The SO SR is conducting two pilot projects on environmental expenditure and municipal waste management for completion in 2002 with data harmonised with EU definitions and methodology. Procedures of the compilation of regional accounts statistics are gradually being harmonised with the ESA 95. The classification for industries corresponds to NACE. Allocation of reporting units into regions is based on NUTS. The new Law on Statistics clarifies the SO SR’s right to access administrative data. This will be an important aid in the compilation of regional accounts. For other indicators, improvement is under way for compliance with the acquis. The R&D survey conforms to international standards and the methodology is based on the OECD Frascati manual. It covers business, government, higher education and private non-profit organisations. Data on R&D personnel and expenditure are available on national and regional level.The first innovation survey in 2000 had a sample size of 500 from the manufacturing sector covering the NACE sections 15 to 37. It had a response rate of 61 %.The next survey will be carried out in 2002, implementing the methodology of CIS-3 and modifying the questionnaire according to the Eurostat requirements. In addition, the SO SR provides information on licence agreements, patents, trademarks and indicators on certified products and certified quality management systems in industrial organisations. A HRST survey according to the OECD Canberra manual is under preparation. Official Statistics in Slovak Republic 126 Enlarging the EU Statistical Network The information • Dissemination mainly through reports and website www.statistics.sk • The SO SR publishes annual catalogue of publications. • Over 120 publications each year • Half of the publications bilingual (Slovak and English). • No revenue for the SO SR from publication sales • Calendar of first data releases, with timetable for four months • Release of information to all users at the same time via Internet • Press conferences for major data releases • Information Services Department accessible by phone, fax or email • Project for public database service via the Internet • Data protection and confidentiality rules in Law on Statistics Conclusion The Slovak statistical system has undergone major revisions and developments during the last few years. Reasons for the changes are, first, the transformation of the economy and, second, the determination in the process of accession to the EU that has been the driving force behind rapid progress in the adoption and practical implementation of the acquis communautaire. The results in statistical development are impressive and show clearly that the SO SR has both the financial and the human capacity to fulfil the current tasks. However, in order to meet future challenges related to the harmonization process with the acquis communautaire further strengthening will be needed. Special attention still has to be directed to developing further the business register, to improving the speed of annual national accounts and to starting the INTRASTAT system. The target of full compliance with the methodology of the acquis communautaire upon accession should not be a major problem.The SO SR has both clear plans and the capacity for reaching the target. Official Statistics in Slovak Republic Enlarging the EU Statistical Network 127 Slovenia Statistical Office of the Republic of Slovenia Vozarski pot 12 SI-1000 Ljubljana Slovenija Tel: +386 1 241 51 04 Fax: +386 1 241 53 44 E-mail: indok.surs@gov.si Web site: http://www.sigov.si/zrs Pre-Accession Milestones 1991 Independence of Slovenia 1992 Establishment of diplomatic relations between Slovenia and EU 1993 Europe Agreement of Association enters into force 1996 Slovenia signs Europe Agreement 1996 Slovenia submits an official membership application for EU membership on 10 June 1998 Official negotiations for EU membership were launched in March 2001 Slovenia submits revised version of the National Programme for Adoption of the Acquis 2002 27 out of 31 chapters of the acquis provisionally closed by June 2004 Target year for EU accession Country Profile Slovenia Slovenija Geographic co-ordinates 46 00 N, 15 00 E Area 20 253 km2 Climate Mediterranean climate on coast, continental in plateau to the east Administrative Divisions 136 municipalities (obcine), and 11 urban municipalities (obcine mestne) Capital City Ljubljana (0.3 million inhabitants) Population and Growth Rate 1.9 million, + 0.12 % (2000 estimate) Nationality Slovenian Ethnic Profile Slovenian 88 %, Serbo-Croatian 7 %, Religion Roman Catholic 72 %,Atheist, Eastern Orthodox, Muslim, Protestant Official language Slovene National Currency 1 tolar = 100 stotins Exchange Rate against Euro 1€ = 221.9 tolar (Quarter 1, 2002, New Cronos) System of Government Parliamentary Democratic Republic Executive Power President & Council of Ministers (elected by National Assembly) Head of State President elected for renewable five-year term by popular vote Head of Government Prime Minister, elected by Parliament upon proposal by the President Legislative Power Unicameral National Assembly or Drzavni zbor; 90 seats with 40 directly elected and 50 elected by proportional representation to serve four-year term Judicial Power Supreme Courts & Constitutional Court National Holiday National Statehood Day: 25 June (1991) Enlarging the EU Statistical Network 129 The National Statistics in Slovenia • • • • • • 130 Enlarging the EU Statistical Network Official Statistics in Slovenia Slovenia has an impressive national statistics philosophy, but more resources need to be provided to achieve compliance by the due date. Overview Statistical Office of the Republic of Slovenia (SORS) Legal framework updated in 2001 A register-based statistical system Statistical Council with many advisory committees Employs over 360 people in a centralised office Over 600 surveys and 140 publications each year Currently no revenue for SORS from publications or services Census of population in 1996, agricultural census in 2000 • • • • • • • • Statistical Council • Set up within statistical law (1995) • Is a professional methodological-advisory body for strategic and developmental questions of national statistics • Consists of 14 members with a 4-year mandate and elects its own President (who cannot be the Director General of SORS). • Members are appointed by the institutions they represent (National Council, government, Bank of Slovenia, employers, employees, judiciary, statistical experts, SORS) • Operates according to Rules of Procedure adopted in its first session • Plays a major role in the co-operation between National Statistics and other parts of the societ Statistical Advisory Committees • 22 committees established by SORS and responsible for individual spheres of national statistics • 250 members from private, public and research sectors who discuss and advise on statistical programmes, the legal setting and standards of the statistical system • Members are appointed by the Heads of the institutions, upon the request of SORS Statistical Office of the Republic of Slovenia (SORS) • First established in 1944 • Based on 1995/2001 National Statistics Act (based on the 1990 Personal Data Protection Act) • Employed over 360 staff in 2001 in a centralised office in Ljubljana; it has no regional office • Has the co-ordinating role within the National Statistics which represents the Statistical Service of the Republic of Slovenia - Conducts about 600 different surveys each year - Disseminates about 140 publications each year - SORS website provides information in Slovene and English • Has a permanent representative in the Committee of the Government for the Economy Prime Minister Other ‘authorised performers’ of the National Statistics • Bank of Slovenia - statistics on balance of payments, foreign direct investment, finance and monetary statistics • Ministry of Finance – government expenditure and public finances • Agency of Payments – accounting statements and balance sheets of legal commercial entities • Institute of Public Health – different data on public health and health and safety at work • Pension & Disability Insurance Institute, Health Insurance Institute and National Employment Office – different data on employment, unemployment, earnings and wages Other agencies and administrations co-operate with SORS to provide data for statistical uses or to carry out statistical work. Director General of SORS • Nominated by the Government for a five-year term (renewable), upon the nomination by Government of Slovenia • Represents the SORS and may be a member (not the Chairman) of the Statistical Council • Prepares the annual programme of statistical surveys, with consent of the authorized performers of National Statistics � �� � � Enlarging the EU Statistical Network 131 The organisation The Statistical Office of the Republic of Slovenia (SORS) is defined as the principal authorised body in the National Statistics system in the original 1995 National Statistics Act. It works with the Central Bank, the Ministry of Finance, Employment Service of Slovenia, Institute of Public Health, Pension and Disability Insurance Institute who are referred to as the other ‘authorised performers’ of National Statistics. Great use is made of registers with uniform identifiers. The legal basis The 1995 National Statistics Act was amended in 2001 as part of the preparation to meet EU requirements. Other main laws are the 1995 Business Register of Slovenia Act and the 1998 Central Population Register Act. Protection of personal data is covered by the 1999 Act on Personal Data Protection, amended in 2001. A distinction is made between obligatory and voluntary response to surveys. The National Statistics act covers: • The SORS • The Statistical Council and its advisory committees • The mid-term programme of surveys to be issued by the SORS and approved by the Government. • The annual programme of surveys to be proposed by the SORS with consent of other partners of National Statistics (authorised performers) • The obligation to conform to national and international standards, classifications and statistical methods • The relations with the respondents called reporting units • The protection of personal data in conformity with the 1999 Act on Personal Data Protection • The right for the SORS to link individual data and to maintain registers The SORS is described in the act as a professionally independent government agency. Under the 1995 Business Register of Slovenia Act any business entity in Slovenia, including the subsidiaries of foreign organisations, must be in the business register. The use of the Business Identification Number (BIN) is obligatory. The 1998 Act on the Central Population Register has extended the use of the Personal Identification Number (PIN) to every permanent resident in Slovenia. The PIN is used by most government bodies dealing with personal records (employment, pension and health insurance, health care, tax records). The management of the SORS The SORS is under the supervision of the Prime Minister. The Director General and the Deputy Director General are appointed by the government for a five-year period. It has a permanent representative in the Committee of the Government for the Economy. The two main elements for planning are the mid-term programme (normally five years but the next programme will commence in 2003 to coincide with Eurostat dates) and the annual programme, introduced in 2001, which will be key in conforming to the acquis communautaire. Previously, an annual work plan had this role.The mid-term statistical programme is prepared by the SORS in co-operation with the ‘authorised performers’ and needs to be approved in the form of a government decision. The SORS have the leading role in the programme implementation. The mission of the SORS includes • Development of National Statistics • Analysing statistics and their interpretation Official Statistics in Slovenia 132 Enlarging the EU Statistical Network • Fulfilment of the international statistics obligations • Establishing public need for data - in co-operation with the Statistical Council and statistical advisory committees • Setting up survey methods that conform to international standards • Collecting, processing and maintenance of records of statistical data • Provision of expertise to reporting units • Storing and dissemination of the results of statistical surveys • Co-operating with other authorities and organisations of public administration • Giving initiatives and proposals for supplementing the contents of existing records • Preparing statistical projections and trends • Developing methods and techniques for data protection The Statistical Council acts as a professional methodological and advisory body for strategic and development questions of national statistics. It has 14 members with a mandate of four years and comprises representatives of the National Assembly, the National Council, government, the Bank of Slovenia, representatives of employers and employees, one member of the judiciary, two recognised statistical experts and two representatives of the SORS.They elect their own president who cannot be the Director General of the SORS. The Council has currently 22 advisory committees responsible for individual fields of statistics. Their findings are taken into account when discussing the programme of statistical surveys. Structure and staffing of the SORS Due to the size of the country, SORS has no regional branches and is centralised in Ljubljana. In 2001, SORS had 10 divisions and more than 45 departments. National accounts, economic statistics, demographic and social statistics, environment and natural resources statistics are specialised divisions.The SORS currently has 362 employees. Of the total staff 49 % are over 45 years, 67 % are female and 68 % have university degrees.There is a higher turnover among young staff due to wage disparities between the private sector and administration. Training includes statistical, IT and language courses, as well participation in international seminars. Based on their own systems and experience of other NSIs, administrative sources and registers are fundamental to the way the Slovenian national statistics system operates. The SORS’s co-ordination role is crucial in setting up and linking these sources on the basis of common identifications and standard classifications. The process of data input and control moves data back and forth between expert units and the input division. This needs to be better harmonised to reduce inefficiency for small surveys or the bottlenecks caused during seasonal peaks and yearly surveys. There are plans to add new IT infrastructure to enhance data capture and processing. Funding The basic budget for 2001 was approximately € 9.3 million, of which only € 5.6 million were wages, a small figure for such a centralised system. For publications, data or services provided to users, the SORS is only allowed to charge for printing costs. This seems to be common in Europe and removes not only a source of revenue but also important incentives for a NSI. Co-operation The Statistical Law establishes a sound legal framework for relations with the data providers.The SORS have developed a customer-oriented strategy through the statistical advisory committees and the Information Centre within the dissemination department. Official Statistics in Slovenia Enlarging the EU Statistical Network 133 The SORS also co-operates with universities and research institutes: • Two experts appointed to the Statistical Council by the Statistical Association of Slovenia to advise on needs priorities and methodology • Statistical expertise provided by university professors • The Statistical Law allows researchers to request anonymous data or, for opinion polls, some limited personal data Information technology and methodology The Electronic Data Processing division develops and maintains software, technical infrastructure, databases and various common network functions and services. It offers some help-desk functions and limited internal training. It provides the communication link with Eurostat. To support the use of new classifications, the SORS has developed a reference database. This is used to extract a copy of the relevant classification to check data in surveys. The classification database is available for external users via Internet.This will support the suppliers when they are making data available to the SORS. The Statistical Council undertakes the setting up of methodological bases and methodologies for statistical surveys as well as their harmonisation with international standards. The output Classifications The SORS has adopted the majority of standard classifications including CPA, NACE Rev.1 and ISCO-88.The classification of territorial units - breakdown at SKTE 2/NUTS 2 level - is still under negotiation with the European Commission. In education, the SORS is in the process of implementing the new ISCED-standard. Registers Within the last 25 years, the SORS has built comprehensive administrative registers on territory, population, and business subjects with unique identifiers for their respective units. Based on the practice and example of Nordic countries, the Slovenian register-based system is today a sustainable model. For statistical purposes the SORS is authorised to use and link any administrative data with these comprehensive registers. The Central Register of Population, now kept by the Ministry of the Interior, contains complete data on Slovenian residents including foreigners, each individual having a personal identification number. The Register of Territorial Units, now kept by the Surveying and Mapping Authority, includes not only various administrative divisions of the national territory, but also all streets and house numbers for the whole country. The Surveying and Mapping Authority assigns an identification number to every new building.The identifier consists of the address, parcel of land, other administrative units, and the geo code of the building. The Business Register of Slovenia (BRS) contains records on all business entities in Slovenia (including those without the status of a legal entity), each of which is assigned a unique business identification number. It is necessary to check regularly the information in the register to improve data quality. The BRS is produced by combining data of all the registration authorities. As the BRS is an administrative register, data on units will only be changed if a reporting unit confirms the correction or if there is a change in the primary evidence. According to the Act on Payment Transactions that has been passed in 2002, a new agency will be established before the end of 2002 to maintain the BRS. The SORS will then have to establish a special statistical business register. Official Statistics in Slovenia 134 Enlarging the EU Statistical Network Demography and social statistics In the area of social statistics Slovenia is mostly in compliance with Eurostat requirements. Most surveys are based on a sample from the population register and they are conducted as face-to-face and telephone interviews. The population census of April 2002 is in full compliance with UN recommendations and is based on both the register and questionnaires.The current population statistics are derived from the population register where foreigners are included. Data on migration is also produced from the population register. Slovenia conducts a quarterly labour force survey with a sample of 6 000 households. It is mostly in compliance with the EU requirements. The SORS produces the monthly Statistical Register of Employed Persons, using it for both social and economic statistics. The register is based on administrative information from the Health Insurance Office. The register of unemployment covering the data on job seekers was set up in 1992. From this monthly unemployment statistics are compiled. Monthly data on wages and employees are collected in enterprises through surveys and through the statistical register on employment, which is maintained by the SORS. A labour cost survey was conducted for 2000 and a structure of earning survey will be conducted in 2002. At the moment, the SORS does not have a labour cost index, but the plan is to base it mostly on administrative data. In the area of education statistics, Slovenia is mostly in compliance with Eurostat requirements. Schools, universities and the Ministry of Education provide data. The SORS is working jointly with the Ministry for the adoption of the new National Standard Classification on Education based on ISCED. The continuous household budget survey, with an annual sample of 1200 households, is adapted to EU recommendations. It covers the level and structure of consumption and income, socio-economic, demographic and housing variables. The household survey is used for poverty analysis, but tax records are essential to provide income data. The SORS hopes to have access to these soon. A time-use survey is harmonised with Eurostat recommendations. The National Institute of Public Health compiles health statistics. Information on births and deaths comes from the Ministry of Internal Affairs. Cause of death information is provided to the SORS. In addition, the institute produces health statistics through 9 regional offices collecting data from hospitals and the primary health system. Data on social welfare benefits are collected through the Ministry of Labour, Family and Social Affairs and other institutions. Pilot studies based on the ESSPROS manual have been completed and the results were transmitted to Eurostat. Harmonised databases are now the next step. Business statistics The unit for business statistics carries out between 85 and 90 surveys a year. Industrial production is monitored monthly among LKAU with ten and more employees and annually among LKAU with five and more employees.The sample size is 2 200.The monthly survey is published rapidly, but the annual survey suffers considerable delays. Since there are few enterprises in the energy sector, it is fully monitored monthly and annually. The results for monthly surveys are published two months later. Official Statistics in Slovenia Enlarging the EU Statistical Network 135 Construction activity and material is measured yearly by two surveys with full coverage.The value of work done inland is monitored monthly among enterprises with more than ten employees and the value of work done abroad is monitored among all enterprises concerned. Prices of new dwellings and construction cost indices are collected every six months from local municipalities and the largest construction firms. Monthly figures on the stock of dwellings and floor space are obtained from the local government units issuing building permits. Trends in manufacturing and retail trade are monitored monthly and investment in industry is measured twice a year. In the latter, turnover and employment are surveyed monthly and wholesale trade quarterly. Catering and tourism are covered by eight surveys, including an annual census of activity in hotels and restaurants. Domestic and outbound tourism are covered by a quarterly telephone sampling of 3 000 people; foreign tourism by sampling 3 000 foreign tourists every three years and a survey on road border crossings every six years. For transport statistics, 25 different surveys are carried out, one of which is weekly. Means of transport, number of kilometres covered, number of passengers and tonnes of goods carried are collected monthly by mode of transport. For road transport there is a very high non-response rate of about 40 %. In addition traffic of passengers and goods on border crossings are monitored. Yearly data on transport infrastructure is also collected. The communication sector is monitored monthly and yearly by six surveys for postal and telecommunication services. The short-term statistics are getting closer to compliance with EU requirements and the work plans seem realistic. One significant task to be achieved in this domain is the change of platform for the monthly industrial statistics from mainframe to LAN. The project for the development of structural business statistics started in spring 2000. It provides a good framework, but it is still in its initial phase.The time schedule looks very optimistic considering current resources. Technical assistance is being received from France and Finland but the SORS staff must be expanded to resolve problems and meet deadlines. Emphasis is placed on speed of data collection and high co-operation between the units involved.The database will provide a very important source for national accounts and the global information system of economic statistics. Macro-economic statistics The SORS has 20 people working on national accounts, using administrative and statistical data.They also produce quarterly accounts within 90 days of the reporting period consisting of estimates in constant prices and current prices by the expenditure approach and in constant prices by the production approach.The quarterly accounts will be expanded in coming years to compile data by income approach. The annual national accounts are producing GDP estimates by production, expenditure and income. Efforts should focus on both methodology and data – the latter especially in the government sector. Staffing of the SORS in this area needs to be strengthened. The SORS is fulfilling the EU requirements concerning the HICP and has many international contacts to ensure that further improvements will be accomplished. The CPI was revised in 2000 and harmonised in terms of coverage and classification, and the HICP was launched in 2001.The indices are based on a small number of 11 000 price quotations for 600 commodities. Official Statistics in Slovenia 136 Enlarging the EU Statistical Network Monetary, financial, external trade and balance of payments statistics The Ministry of Finance compiles government finance statistics following the international and European recommendations in producing government financial statistics. It is working with the SORS in establishing the national accounts for the governmental sector. The Bank of Slovenia calculates balance of payments and the financial accounts following the recommendations for the compilation of banking and monetary statistics. Data are published monthly. For the balance of payments figures, the Bank of Slovenia is compiling international investment statistics, in line with international guidelines, on an annual basis. The lifting of capital controls agreed in the negotiations with the EU required a change of reporting. The Bank of Slovenia is currently compiling the financial accounts for all sectors of the economy. The structure of financial instruments is not sufficiently detailed and the frequency has to be changed to quarterly. The Bank of Slovenia prepares monetary statistics in line with the IMF guidelines. Some minor differences, compared with the ECB will be adjusted. External trade statistics are the responsibility of the SORS. Each month, the customs administration sends cumulative data for imports and exports per trading partner and per type of product and also on the most detailed level of other classifications used in external trade statistics. Furthermore average price indices for foreign trade (unit value indices) are produced. Work is continuing towards the introduction of INTRASTAT. Agriculture statistics As a consequence of changes in agriculture policy, the EU accession process and the need to improve data collection and estimation, a new system for agricultural statistics has been developed. Agriculture enterprises, co-operatives and agriculture holdings are covered through 37 surveys and 4 censuses and estimations.The statistics meet the demand from the EU to a very high degree. The SORS successfully conducted the census of agriculture in June 2000 and a full set of data are available. The Ministry of Agriculture now has a total register of farmers. In 2003, Slovenia will carry out the sample farm structure survey. The EU requirements concerning livestock and slaughtering statistics are fully complied with. For milk most EU requirements are fulfilled except information on amount of protein, but the dairies will be able to report this information. Further development of the agriculture and food information system is expected to concentrate on using all available administrative data to reduce the burden of farmer response and create a statistical picture of agriculture in a single database. The Agriculture Institute prepares balance sheets on crop and animal production and the income account for agriculture is estimated in the national accounts.The input side is based on much estimation, but the output side is very well covered by data. Environment The environment statistics are collected by the SORS and other government bodies. Data published covers a large range of information on air quality, water usage, waste, and investment and expenditures for environmental protection on an annual basis. Official Statistics in Slovenia The information • About 140 publications a year, including rapid reports of the results of surveys • Information Centre in charge of meeting the demand, particularly with tailored products • Calendar of release dates and release to all users simultaneously • Press conferences take place at least once a month • Special statistical tables and comments are prepared for participants in press conferences • Special unit dealing with database and network dissemination • Specialised dissemination tools - automatic answering machine, GIS, Internet data bank (in Slovene and partly in English and German) • Strong professional relationships are built with important users • Participation in exhibitions and conferences The SORS website www.sigov.si/zrs contains information in Slovenian and English. Conclusion The European statistical requirements were applied in Slovenia less recently than in other Candidate Countries. One outstanding feature of the Slovenian national statistics is its register-based system in which a sound strategy has been developed. The SORS and other authorised bodies make use of identifiable individual data from various official and other administrative databases of the public and private sectors, records, registers, data bases, etc. These are kept and managed on the basis of law or written consent of the individual. The comprehensive registers and the development of appropriate household surveys have brought the main demographic and social statistics in line with the national and international standards. Slovenia will comply with almost all the statistical acquis of the European Union upon accession.At the same time efforts need to be deployed to improve some points of business statistics and national accounts. Enlarging the EU Statistical Network 137 Official Statistics in Slovenia Turkey State Institute of Statistics Necatibey Caddesi, N° 114 06100 Bakanliklar,Ankara Tel (President of NSI): +90 312 418 87 19, +90 312 425 35 14 Fax (President of NSI): +90 312 425 33 87 +90 312 418 11 82 E-mail (publicat.): yayin@die.gov.tr Web site: http://www.die.gov.tr Pre-Accession Milestones 1963 Ankara Agreement was signed between Turkey and European Communities 1973 Additional Protocol came into force, which sets up mutual responsibilities for the transition period designed in the Ankara Agreement 1987 Turkey submits an official membership application for EU membership on 17 April 1995 Turk Association Council Decision of 5 March for the establishment of the Customs Union between Turkey and the EU 1998 First “Progress Report” on Turkey. 1999 Turkey declared as a formal Candidate Country. 2001 The Accession Partnership was declared on 8 March 2001 Council of Ministers approved the National Plan for Adoption of the Acquis (NPAA) on 19 March Country Profile Turkey Türkiye Geographic co-ordinates 36 00 N, 26 00 E Area 779 452 km2 Climate Temperate, hot dry summers, mild wet winters Administrative Divisions 7 regions, 81 provinces and 850 sub-provinces Capital City Ankara (4.0 million inhabitants) Population and Growth Rate 67.8 million (provisional estimate census 2000), + 1.8 % Nationality Turkish Religion Muslim 99 %, other 1 % Official language Turkish National Currency Turkish Lira Exchange Rate against Euro 1€ = 1 195 410 Turkish Lira (Quarter 1, 2002, New Cronos) System of Government Parliamentary Democracy Executive Power President & Council of Ministers (appointed by President upon nomination by the Prime Minister).There is also a National Security Council that serves as an advisory body to the president and the cabinet Head of State President elected for seven-year term by National Assembly Head of Government Prime Minister, appointed by President Legislative Power Uni-chambered Parliament Turkish Grand National Assembly (TGNA): 550 seats and members are elected by popular vote for a five-year term Judicial Power Constitutional Court National Holiday Independence Day: 29 October (1923) Enlarging the EU Statistical Network 139 Prime Minister State Institute of Statistics (SIS) • First established in 1926, but re-organised as SIS in June 1962 • Based on Law No 53 of 1962, Statutory Decrees No 219 of 1984, No 357 and 367 of 1989. • Employed over 3300 staff in 2001 (about half of which are temporary staff) for one year • 68% work at the SIS headquarters, 32% in the 22 regional offices plus one division in Van • Responsible for about 85% of all official statistics in Turkey - Conducts about 40 different surveys each year and carries out studies and research projects requested by government and public organisations - Disseminates about 70 publications, 25 news bulletins, 200 press releases per year - SIS website provides selected statistics and information Other bodies • Central Bank of Turkey carries out Balance of Payment and Money & Banking Statistics • State Planning Organisation in co- operation with Ministry of Finance provide government finance and tax statistics to SIS • Ministry of Agriculture collects raw data and transmits to SIS for processing and tabulation • Other ministries and institutions collect their own statistics High Supreme Council • The Council is currently not operational; its composition and functions are to be defined in the new statistical law • Advisory body with members to be represented by 21 institutions for a five-year term • Under the new law, members are to be appointed by Prime Minister upon nomination by the respective organisations represented on the Council (from State Planning Organisation, public institutions, state economy enterprises, universities, commerce, industry and Stock Exchange members, trade unions, private sector) �� • • • • • • 140 Enlarging the EU Statistical Network Official Statistics in Turkey Turkey will be supported by highly professional statistics staff in the many tasks ahead to achieve harmonisation with the EU standard. Overview State Institute of Statistics (SIS) Legal framework being substantially revised Statistical Council to be strengthened under new law President of the SIS appointed by Prime Minister Over 3 300 staff (52 % temporary staff)and currently 22 regional offices (some to be closed) About 40 surveys and 70 regular publications each year Census of population in 2000 Turkey has started well on the path to EU compliance but much is still to be achieved • • • • • • • • The National Statistical System of Turkey1 1A new Statistical Law is in preparation and will increase the co-ordinating role of the SIS in the system and strengthen the role of the High Supreme Council President of SIS • Appointed jointly by President, Prime Minister and Minister of State responsible for SIS; the term of appointment is not specified • Heads SIS and assisted by three Vice-Presidents �� Enlarging the EU Statistical Network 141 The organisation The main body in the Turkish statistical system is the State Institute of Statistics (SIS) which is responsible for about 85 % of the output. Balance of payments, monetary and banking statistics are published by the Central Bank of Turkey. Government finance and tax statistics are currently provided by the State Planning Organisation in co-operation with the Ministry of Finance. In addition, there are other institutions, ministries, state economic enterprises and other governmental bodies, which conduct statistics, but only in their own field of activity. The legal basis The current legal basis for the activities of the SIS is a law of 1962, with subsequent amendments in 1984 and 1989. A new law is planned to replace the present one to conform to EU requirements. Principal elements in the new law will be: • Improving the autonomy and increasing the co-ordinating role of the SIS • Introducing multi-annual planning • Reshaping and strengthening the role of the High Supreme Council • Guaranteeing equal and easy access to all statistical information • Regulating all aspects of statistical confidentiality The Higher Supreme Council is an institutional advisory body to the SIS as set up under the present law. The management of the SIS As part of the office of the Prime Minister, the SIS is headed by a President, assisted by three vice presidents. Currently, the President of the SIS is appointed jointly for a five-year term by the Minister in charge of the SIS, Prime Minister, and President. The same rule will apply under the new law. The new law will also stipulate a specific duration for the President’s term of office. The current annual statistical programme includes censuses defined by law, surveys decided on by the SIS, surveys and other projects requested by the government and those demanded and paid for by other institutions and international organisations. The SIS intends to establish a five-year multi-annual programme under the new statistical law, with annual programmes to monitor the implementation of the multi-annual programme. Under the new law, the High Supreme Council will function as a Statistical Council with 21 institutions. The number of representatives for each institution, remuneration policies, and working procedures will be determined by a different status to be issued by the Council of Ministers. They will propose members who will be appointed by the Prime Minister for a five-year term and remunerated by the SIS. The Council will remain as the advisory body to the Turkish Statistical System but its influence will be strengthened. Structure and staffing of the SIS There are currently 22 regional SIS offices and one division in Van.The closure of seven regional offices is on the agenda at the moment, as it is a result of a general government decision to close various regional offices of the different public organisations. However, the SIS is looking at ways to set up new city offices.The general tasks of these offices are to conduct fieldwork and some data processing and first quality checks under the direction of the headquarters in Ankara. A unit of regional organisation in the headquarters administrates the system and co-ordinates relations. The main office employs 1 193 permanent and 1 057 temporary staff recruited for one year renewable. The Official Statistics in Turkey 142 Enlarging the EU Statistical Network regional offices employ 409 permanent and 673 temporary staff. Of the total staff, 70 % have education to graduate level and 30 % are technical staff. 70 % of permanent staff are aged between 31 to 45 years, and only 44 % are female. Newcomers receive in-house training on topics such as state organisation, law, and organisational structure of the SIS. There are English language courses and modular specialised training on econometrics, sampling techniques, data analysis, national accounts, business statistics, and IT. The Training Centre has a vocational programme provided by highly qualified specialists of the SIS and university professors. Recruitment has been limited over the past four years, for budgetary reasons.The salaries of qualified staff at the SIS are comparable to salaries of those in other governmental agencies. Funding The basic budget of the SIS in 2001 was Turkish lira 23.9 billion, equivalent to € 21.6 million. The budgetary process includes submission of programmes of activity and corresponding budgets, intra- governmental discussion of the draft budget in September and discussion by parliament. Statistical investments over one or more years have to be approved by the State Planning Organisation (SPO) and once adopted, their funding is guaranteed over the required number of years. The SIS generates resources through sales of publications and dissemination services, organisation of training etc. A part of these resources can be used for investment but not for salaries. The EU Commission has granted Turkey € 15.3 million under the 2001 budget to be used for the statistics programme over a period of three years starting at the end of 2002. The main objective of this programme is to assist Turkey in the implementation of a pre-accession strategy in the field of statistics and, thus, to upgrade the basis of the Turkish statistical system. Co-operation The SIS emphasises close co-operation with the scientific community including many universities, research institutions, and professional associations. A statistical research symposium of more than ten years standing regularly brings together official and academic statisticians.A new Journal of Statistics Research is designed to encourage statistical research, to create a discussion forum for the problems and solution proposals in the field of statistics, and, thus, to improve statistical work. Special committees that include statisticians from universities are created to give advice to the SIS in all aspects of statistical work. Information technology and methodology The Data Processing Department handles processing for statistical purposes, hardware, software, and communication infrastructure and software application development. The Information Systems Unit is in charge of Internet applications, GIS and remote sensing and office automation. Both of these are directly supervised by the President of the SIS. The IT equipment is of good quality and provides a satisfactory capacity to produce statistics using modern technologies. A number of interesting projects are ready to be launched, resources permitting. Since 1997, the SIS has developed a data entry system, based on optical character recognition. This hi-tech system was used for the data entry first in 1997 population register and then in the 2000 population census forms. Official Statistics in Turkey Enlarging the EU Statistical Network 143 The SIS is maintaining and developing some major statistical databases on foreign trade, price indices, national accounts, agriculture and livestock, addresses of buildings, population censuses, and general elections. There is a project for online access. The output Classifications The SIS has used UN international classifications for a long time. Therefore, the full adoption of European systems that are similar to the UN classifications is a realistic task. In 1997, the SIS established a new national nomenclature of activities and commodities US-97 (National Classification-97, Ulusal Siniflama-97), which is based on ISIC. The first step in adopting NACE is the transition of the existing data to NACE categories. Subsequently, with the census of all establishments in 2003, NACE will become the reference classification for all statistics with an activity classification. Census data should be available in August 2003 for the purpose of building up the business register for use in 2004. This data will also be used to implement CPA as the national product classification. From 2002, the SIS is producing monthly and quarterly production statistics using PRODCOM. Full compliance has already been achieved in combined nomenclature for external trade statistics. The NUTS classification for Turkey also complies. An important project in the harmonisation of classifications will be the development and use of a classification server with links and transition keys in order to harmonise classifications within the public agencies in Turkey. Registers The SIS has no common register of reference. Every division in the SIS responsible for surveys maintains its own register of basic units and addresses using the 1992 census as a starting reference. The SIS has launched a project to create a new unique statistical business register based on the 2003 general census of industry and business establishments, with annual updates based on statistical surveys and administrative information coming mainly from the Ministry of Finance. It will improve the reliability of business statistics and will implement a system of statistical units and economic classifications in full compliance with EU standards. Similarly, there is a farm register based on the 2001 general agricultural census and updated on a regular basis. It will significantly improve the statistical infrastructure in the field of agriculture by providing up- to-date lists of agricultural holdings on which sampling surveys would be based. Demographic and social statistics The SIS has a long tradition of organising population censuses, the most recent having been conducted in October 2000. New technologies have been used for the control and processing of data and publication of complete results is planned for end-2002. In addition, a system is under design for inter-census population surveys that will also be completed by the end of 2002. Given the key role played by the population census as a benchmark, these surveys will be decisive in the future development of demographic and social statistics in Turkey. In order to produce comparable and continuous data on the national level, a new series of household labour force Official Statistics in Turkey 144 Enlarging the EU Statistical Network survey was conducted between 1988 and 1999, using the international standards of ILO. Recently some amendments have been made to minimise differences between ILO and Eurostat variables. Plans to increase the sample size in 2003 will make it possible to produce data on a NUTS 2 level, but additional financial resources are needed. Beginning in 2002, the SIS is conducting a continuous household income and consumption expenditure survey that will provide yearly results. The sample size is approximately 800 households per month. Classifications used are COICOP, NACE Rev 1 and ISCO-88. Yearly data will be published for the whole of Turkey and for urban and rural areas. It is planned to breakdown figures for 7 geographical regions and 19 selected city centres by using three years’ survey data. Macro-economic statistics The SIS compiles and disseminates national accounts on a quarterly basis according to the production and the expenditure approach. The income approach aggregates are compiled annually over the four quarters of the past calendar year. However, major revisions to methodology are being planned to implement the EU standard ESA 95 and to use new statistical sources such as the 2003 general census of industry and business establishments and the new household income and consumption expenditure survey. Currently several different institutions are involved in the compiling of government finance statistics. However, there are plans to create a new system under the responsibility of the Ministry of Finance. The SIS and the Ministry of Finance will co-operate very closely on the new system to provide a complete data basis for the implementation of general government accounts according to ESA 95. The development will also provide an opportunity for the SIS to get access to all data necessary for the establishment of general government accounts in its system of national accounts. The household income and consumption expenditure survey will enable a new Consumer Price Index to be prepared in compliance with EU requirements. This will be an important step in the SIS wish to comply with EU HICP regulations. Monetary, financial, external trade and balance of payments The Central Bank of Turkey compiles and disseminates balance of payments statistics on a monthly basis covering all transactions between resident and non- resident units and complying with the most recent IMF standards.The main sources are: • Bank statements for the amounts and nature of all transactions made with non-resident units on behalf of resident units • The SIS monthly data on imports and exports derived from custom statistics • Data on foreign investment and related re-invested income provided by the Treasury • Specific surveys conducted by the Central Bank of Turkey on travel-related transactions in private enterprises and shuttle trade Business statistics Since early 2002, the SIS has been using new questionnaires for its industrial production surveys derived from LKAUs. Information is collected from all private units with 25 and more employees and from all public units so that a broad coverage of the whole industrial sector is guaranteed. Data are provided by NACE categories and also comply with PRODCOM. Official Statistics in Turkey The SIS collects annual structural business statistics in economic activities covering the following: • Manufacturing industries • Mining and energy • Construction • Transport (except storage and communication services) • Trade and hotel, restaurant including catering services • Financial and insurance services • Other services (real estates, renting and business activities, education, health, and social work, other community, social and personal service) The data have been defined according to UN recommendations. Since 2001, the SIS has started to prepare adjustments to its current questionnaires, in order to reach full compliance with EU standards for the general census on industry and business establishments in 2003. Agricultural statistics The 2001 general agricultural census will provide a wealth of information on the detailed structure of agriculture in Turkey. The related list of agricultural holdings and the project of a periodically updated farm register will make possible a significant improvement in the quality of agricultural statistics. This will allow moving from the current system of annual experts’ estimates to actual agricultural holding surveys, as recommended by European standards. Moreover, a number of projects are currently planned by the SIS and the Ministry of Agriculture to improve the compliance with European standards in specific areas. Forestry statistics are compiled by administrative units within the Ministry of Forestry for administrative purposes. Studies are underway on the standard classification used in the EU.The SIS is not involved. Fishery statistics are based on an annual survey conducted by the SIS in co-operation with the Ministry of Agriculture on professional fishermen fishing in the territorial waters of Turkey. This survey is conducted by interview. It is exhaustive for large-scale activities and based on a representative sample for small-scale fishing. Regional statistics A large amount of regional information exists but the SIS uses different regional groupings for different statistics, making comparisons and the creation of a regional database impossible.There are 7 geographical regions, 81 provinces, 850 sub-provinces and 37 360 villages. Currently, only the provincial level of administration is in accordance with NUTS 3 level. For the NUTS 1 and 2 levels, there is no compliant administrative structure. However, 19 out of 23 regional offices match with the NUTS 2 level. Recently, Turkey has presented a proposal for the classification of regional units for statistical purposes according to the NUTS criteria which has already been approved by the EU Commission. The SIS plans to publish its main statistical indicators at least on a NUTS 2 level in the future. This is scheduled to start from 2004. It is also planned to develop a database for regional statistics. Enlarging the EU Statistical Network 145 Official Statistics in Turkey Official Statistics in Turkey 146 Enlarging the EU Statistical Network The information • Publication Catalogue available • Statistical Yearbook of Turkey and monthly Statistical Bulletin • The list of the SIS publications can be found in English at http://www.die.gov.tr/yayin/list_publications.htm • Plans to enhance website www.die.gov.tr • Development of on-line dissemination databases • Approximately 70 regular publications each year and 100 publications on specific programmes • 25 news bulletins and approximately 200 press releases a year • Calendar of releases published in advance • Client management system and user satisfaction survey planned for 2003 • Balance of payments statistics on web site of Central Bank of Turkey www.tcmb.gov.tr Conclusion Turkey has already started processes that are designed to bring the official statistics in line with European Union requirements. The forthcoming new statistical law is fundamental to development of the overall system. The amount of work required, however, will mean careful planning, clear priorities and regular monitoring of progress. The State Institute of Statistics has an impressive depth of experience matched with high professionalism. It will need the maximum of human and financial resources to ensure the success of the large number of different projects planned to meet the demands of compliance with EU regulations. Enlarging the EU Statistical Network 147 Annex Tables The following tables provide summary information on the statistical systems of all the Candidate Countries. They are based on global assessment reports plus information provided by the NSIs.To be consistent, only facts available for all countries were selected. As statistical systems are complex and often different in certain respects from country to country, it is impossible to give full coverage of all aspects in just a few tables. In addition, the countries are continuously improving their systems to conform to European standards. For these reasons, caution is advised in basing conclusions solely on comparisons of the information in the tables. For fuller detail, it is advisable to contact the respective NSIs or Eurostat. Annex 1 Name and Legal Status of National Statistical Institutes in Candidate Countries Name of National Statistical Institute and year of first establishment National Statistical Institute of the Republic of Bulgaria (NSI Bulgaria) First established in 1880 Statistical Service of Cyprus (CYSTAT) First established in 1960 Czech Statistical Office (CZSO) First established in 1919 (Czechoslovakia) Statistical Office of Estonia (SOE) First established in 1921 Central Statistical Office (KSH) First established in 1867 Central Statistics Bureau (CSB) First established 1919 Statistics Lithuania (SL) First established in 1919 National Statistics Office (NSO) First established in 1947 Central Statistical office (GUS) First established in 1918 National Statistical Institute (NSI) First established in 1859 Statistical Office of the Slovak Republic (SO SR) First established in 1993 Statistical Office of the Republic of Slovenia (SORS) First established in 1944 State Institute of Statistics (SIS) First established in 1926 148 Enlarging the EU Statistical Network Other laws related to statistics Civil Service Act, 1999; Law for Administration, 1998 Banking Law 1997, Central Bank of Cyprus Law 1963, Fisheries Law Act on Protection of Personal Data,April 2000; Banking Act, Foreign Exchange Act, Environmental Protection Act 1992 as amended, Commercial Code, 1991 as amended, Trades Licencing Act, 1991 as amended Population register Act, 2000;Wages Act, 1994; Public Service Act, 1995; Personal Data Protection Act, 1996 Act on Data Protection Regulation on Central Statistics Bureau, Data Protection Law of Latvia, Law on Financial and Capital Market Commission Law on Legal Protection of Personal Data, 1996; Law on Population Register, 1992; Data Protection Act, 2001 Law on Protection of Personal Data, 1997; Law on the Methodology of Calculating the Value of the Annual Gross Domestic Product, 2000;Regulation of the Council of Ministries on the Register of National Economy Entities (REGON),1999 Romanian Government Decision, May 2001 Act on State Information System 1995;Act on Protection of Personal Data in Information Systems 1998; Customs Act 2001 Business Register Act, 1995; Central Population Register Act, 1998; Personal Data Protection Act, 1999 Law on Central Bank of Republic of Turkey 1970, amended in April 2001 Statistical Act and Year of Act Other amendments to the act Law on Statistics, June 1999 Amended in April 2001 Law available on website in Bulgarian and English Statistics Law, February 2000. Replaced Law of 1968. Law not available on website. Act on State Statistical Service,April 1995 Amended by Act, January 2001. Law available on website in Czech and English Official Statistics Act, July 1997.Amended by Act, June 2000. Law available on website in Estonian and English Hungarian Statistics Law, May 1993 Amended by Act, December 1999. Law available on website in Hungarian and English Law on Statistics, November 1997. Amended in January 1999. Law available on website in English Law on Statistics, December 1999 which is a revision of Law on Statistics 1993. Law available on website in Lithuanian and English. Malta Statistics Authority, October 2000 which replaced the 1955 Statistics Act. Law available on website in English Polish Statistical Law, June 1995 Amended in 1996, 1997, 1998. Law available on website in English. Government Ordinance No 9,August 1992. Amended by Government Ordinance No. 75, May 2001, approved by Law No. 311, May 2002. Law not available on website. Law on State Statistics, 2002; Law available on website in Slovak and English. National Statistics Act, 1995 / 2001. Law available on website in Slovene and English. Law, June 1962; Statutory Decree, June 1984; Amended by Statutory Decrees 1989.A new Statistical Law being revised. New law will be available on website, in Turkish and English CC BG CY CZ EE HU LV LT MT PL RO SK SI TR Enlarging the EU Statistical Network 149 Annex 2 National Statistical System of Candidate Countries by Functional Organisation The NSI The NSI produces major part of economic and social statistics. It co-ordinates the state’s statistical activity and prepares statistical programme. CYSTAT is the dominant producer of official statistics and the system is strongly centralised. CZSO co-ordinates the State Statistical Service, is responsible for official statistics, and carries out part of the Programme of Statistical Surveys. SOE is the main provider of official statistics; small role played by other central authorities KSH produces the large majority of outputs. The CSB is responsible for the organisation of statistical work in Latvia and carries out a uniform state policy in statistics.The CSB itself is responsible for two-thirds of the official statistics in Latvia. SL is responsible for the official statistics in Lithuania.The Statistical Centre and the Statistical Surveys are public companies of SL NSO is the only executive body of the Malta Statistics Authority, and has a central role in collection, compilation and release of statistics. The role of the other Maltese authorities is to provide the relevant data to the NSO. GUS produces a large majority of main outputs and prepares the annual programme of statistical surveys. NSI is responsible for very large part of statistical activities. SO SR plays a co-ordinating role in statistics, but is semi-centralised for the distribution of statistical activities. SO SR carries out a proportion of the survey programme. INFOSTAT Research Centre is a contributory organisation of the SO SR. SORS has the leading and coordinating role in the national statistics programme. SIS is a functionally and regionally centralised system producing the majority of Turkey’s official statistics. It carries out studies and research projects requested by government and other public organisations. Other bodies providing statistics in their field Customs, National Social Security Institute. Various government bodies as well as the Electricity Authority, the Telecommunications Authority, the Tourism Organisation, etc. Czech Telecommunications Office. Estonian Institute of Economic Research. Local governments, Customs and Finance Guard, Hungarian Tax and Finance Control Administration, social security institutions, chambers. Financial and Capital Market Commission, State Revenue Service and Riga Stock Exchange. National Stock Exchange, State Property Fund and Lithuanian Patent Office. Customs Department;Tax department; Public Registry and Department of Employment & Industrial Relations; Employment & Training Corporation; Department of Health Information. Other administrative bodies which carry out statistical activities in their fields General Customs Directorate, Central Office of Pensions, National Office of Trade Register Central Tax Office, National Labour Office. Agency of the Republic of Slovenia for Payments, Institute for Health Protection, Pension and Disability Insurance Institute, Health Insurance Institute, National Employment Office State Planning Organisation,Turkish Railways, Undersecretariat of Customs, Undersecretariat of Trade, General Directorate of Security Affairs. Ministries & National Bank The major ministries (Agriculture & Forestry,Transport & Communications, Finance) and Bulgarian National Bank are authorised to collect data. Rules regulating their activity are being prepared. The ministries and the Central Bank of Cyprus work closely with CYSTAT and may collect data in their field. They participate in statistical committees. The Ministries of Transport and Communications, Culture, Labour and Social Affairs, Industry and Trade, Education, Youth and Sport, Health,Agriculture and Environment also conduct surveys in the Programme of Statistical Surveys. The Ministries of Finance and Agriculture, and others; Bank of Estonia Important contributions come from National Bank of Hungary, Ministry of Finance, Ministry of Economic Affairs, other ministries. CSB works in close contact with the Ministry of Finance, the Bank of Latvia and other ministries to prepare statistical indicators. The ministries, Bank of Lithuania. Central Bank of Malta;Treasury & Ministry of Finance; Ministry of Agriculture & Fisheries. Ministries of Finance,Agriculture, Environment, Economy, Labour and Social Policy and other ministries; National Bank of Poland. Specific statistical surveys falling within the competence of ministries and other public institutions, require the respective authorities to transmit to NSI, the statistical data to be included in the data series and publications or that need to be transmitted to international bodies. Ministry of Finance; National Bank of Slovakia; over half of the survey programme is conducted outside SO SR by Ministries of Agriculture, Education, Health, etc. Ministry of Finance, Bank of Slovenia. Central Bank of Turkey, Ministries of Finance,Agriculture Health,Work and Social Security and Education. CC BG CY CZ EE HU LV LT MT PL RO SK SI TR Annex 3 Management of the National Statistical Institute & Statistical Council NSI Appointment of NSI Leadership and Term of Service Prime Minister, upon nomination by Decision of the Council of Ministers, appoints President of NSI and two Deputy Presidents for seven years. Public Service Commission appoints Director of CYSTAT on a permanent term. President of the Republic appoints CZSO President upon the proposal of the Government.Term of appointment is not defined. Minister of Finance nominates Director-General of SOE on permanent terms. Prime Minister appoints President KSH for six years, renewable twice maximum. Cabinet of Ministers, upon recommendation of Minister for Economy appoints President CSB in compliance with law on state civil service.The two Vice-Presidents are appointed by the President CSB Prime Minister appoints (and may dismiss) Director-General of SL for a fixed five-year term. The Malta Statistics Authority, itself under the Ministry for Economic Services appoints Director-General NSO after consultation with the Minister responsible for statistics, for three years, renewable for further terms. Present incumbent is appointed for five years. The Prime Minister appoints President of GUS for six years. The Prime Minister appoints President NSI2. The President of the Slovak Republic, upon nomination by government, appoints President of the SO SR for five years. The government appoints Director- General SORS for five years. Minister of State, Prime Minister, and President of the Republic jointly appoints President of NSI.The term of appointment is not specified. 150 Enlarging the EU Statistical Network Members appointed by / Council chaired by HSC members are nominated by President NSI; Chairperson is elected among its members. NSC members are nominated by heads of respective units they represent and chaired by President NSI. Council of Ministers Director-General of Ministry of Finance is the current President. Appointed and chaired by CZSO President. Not applicable. Members appointed by the respective organisations they represent.The Chairman appointed from membership by Prime Minister upon recommendation of the other members and submission of the KSH President. Not applicable. Appointed by the Government. Chaired by Director-General of SL. Members appointed by the Minister responsible for statistics.The Chairman of Malta Statistics Authority (corresponding to a Statistical Council) is appointed by the Minister responsible for statistics. Appointed by Prime Minister. Elects its own chair. Appointed by Prime Minister upon the proposal of the institutes they represent.Council approves its own functioning rules.Chaired by the President of the NSI. Appointed by the President of the SO SR according to proposals of the bodies they represent Chaired by the President of the SO SR. Appointed by institutions they represent Elect its own chair (who cannot be Director –General of the SORS). To be appointed by Prime Minister upon nomination by the respective organisations represented on the Council. Chair yet to be defined. Number of Members Term of Council High Statistical Council (HSC) with 20-35 members; term is not defined; National Statistical Council (NSC) with 16 members; term is not defined; Other Councils1 Statistical Council with President and 10 members (5 government & 5 from associations and the University) for a five-year mandate. Statistical council with 11-21 members.Term not defined. No Statistical Council.There are permanent and ad hoc working groups on different statistical areas. Statistical council with 33 members for a term of three years. No State Statistical Council. Regular working groups are established in the most important statistical domains. CSB reports direct to Ministry of Economy. Statistical council is an advisory body with 22 members who approve their own council composition and its regulations; term not defined. Board of Authority with six members (term one to three years), a Chairman plus the Director General of the NSO as an ex-officio member. Statistical council with 17 members for a five-year mandate. Council for Co-ordinating Statistical Activity (CCSA) with 34 members and the NSI President (who is a council member by right) for two years. Statistical Council is an advisory body of 26 members.The term of appointment is not defined. Statistical council is a professional methodological- advisory body of 14 members for a four-year mandate.There are 24 statistical advisory committees established by SORS under the umbrella of Statistical Council. High Supreme Council3 which is currently not operational. Members from 21 institutions to be appointed for a five-year term under the new law. CC BG CY CZ EE HU LV LT MT PL RO SK SI TR Statistical Council 1Other councils set up by the NSI in Bulgaria are the Methodology Council, the Methodology & Training Council, the Technical & Technological Council and an Editing Council 2In Romania, there are no special provisions regulating the appointment, conduct and removal of the NSI President. 3In Turkey, the High Supreme Council will be re-organised under new Statistical Law currently under reform Enlarging the EU Statistical Network 151 Annex 4 NSI Organisation and Regional Structure in Candidate Countries NSI Head President, assisted by two Vice-Presidents and the Chief Administrative Secretary Director assisted by seven Senior Officers President CZSO, assisted by four Vice- Presidents Director-General of SOE, assisted by two Deputy Director Generals President KSH assisted by four Vice Presidents President CSB assisted by two Vice-Presidents Director General SL assisted by four Deputy Director-Generals Director-General NSO President assisted by four Vice-Presidents President NSI assisted by two Vice-Presidents and a General Secretary SO SR President assisted by a Vice-President and five General Directors Director-General assisted by Deputy Director President assisted by three Vice-Presidents Regional offices and allocated tasks 28 regional statistical offices only for data collection in the ‘oblasti’ and initial data processing; headed by Directors who are appointed by NSI President. Three small regional offices which deal exclusively with fieldwork. 14 regional offices focussing on dissemination, statistical support and fieldwork. Six nation-wide processing departments for processing data related to specific topics No regional office, only two sections4 of SOE in Viljandi; ten supervisors assisting and supervising data collection in 15 counties. 19 county directorates undertake field operations, data collection, data capture, primary processing in their area, under the responsibility of the President KSH. 27 regional offices for distribution of statistical questionnaires, data collection, validation and checking of primary information in their regions for electronic transfer to headquarters. Ten county statistical offices and 38 statistical offices at municipality level; they collect survey data (questionnaires, data capture, business registration). No regional office.The system of local councils is new in Malta, but some data is collected from them. 16 regional offices with their respective directors under direct responsibility of GUS President. Eight regional directions of NSI (at NUTS 2 level) and 34 county statistical offices at municipality level. All are fully subordinated to the NSI President. Eight regional offices for data collection and processing related to specific fields and provision of information services to customers. No regional office.The collection of data is decentralised. 22 regional offices5 plus one division in Van (each covering one to four provinces) whose work are co-ordinated by headquarters.They mainly collect census & survey data using questionnaires and methodology defined by headquarters. Headquarters and its responsibilities Three broad responsibilities (production and development of statistics, statistical infrastructure, administrative issues) split into nine departments. Seven divisions dealing with statistical domains and two general services sections (administration and IT). Four sectors (statistics, administrative, statistical system development and regions & information outputs) including several departments reporting directly to a Vice President. Eight divisions and three departments further sub-divided into 35 subject-matter units each in charge of one statistical domain. Four broad units of statistics (economics, social, co-ordinating, economic affairs & information) sub-divided into departments, reporting directly to Vice-President. Seven departments of which four are directly involved in data collection, processing and calculations, reporting to President and Vice-President. Four main departments employing 50% of SL staff (business, macro-economics, economic & social, agriculture & environment), some with functional units. Four divisions (social, business, economic, corporate services) Four sectors (economic, social, administrative, technical) divided into 12 divisions: employing only 10% of NSI staff. Five general directions (further sub-divided into three to five directions responsible for specific statistical domains) and 8 independent directions, employing 23% of total staff. Five organisational sections of which three are for statistical domains (business, social and demographic, national accounts) and two for general services to the SO SR (administration, methodology and IT) Ten sectors for handling various statistical fields; centralised for processing, protecting and disseminating data; divided into 45 departments Three sectors: service units, advisory units, supporting units all subdivided into 20 departments CC BG CY CZ EE HU LV LT MT PL RO SK SI TR Level of Centralisation 4The regional offices were closed down in 2000. 5Seven regional offices will be closed between May 2002 and May 2003. Annex 5 The NSI Budget (in € million)6 of the National Statistical Institutes for 2001 Basic Budget (in € million) 6.8 4.2 19.6 6.9 43.2 2.9 6.7 1.7 63.5 8.6 7.1 9.3 21.6 152 Enlarging the EU Statistical Network Comments Budget allocations are accompanied by an upper limit on the average annual salary. The revenues and expenditures including pre-financing of EU pre-accession programs and state budget transfers are determined by Law for State Budget. Budget is approved by Ministry of Finance and submitted to House of Representatives. Considerable flexibility for moving funds within expenditure categories. Budget for IT hardware is controlled by the government IT Department. EU MEDSTAT programme and other special Commission financial agreements also offer assistance. Annual budget has to be approved by Parliament based upon submitted activity reports and results obtained. Special approval from Ministry of Finance is required to move funds between capital and current expenditure. Re-allocations between regional and headquarters are done exceptionally.The CZSO has little freedom in the way this is spent once it is approved by Parliament. Receipts go back to the Ministry of Finance. The conduct of official statistical surveys is financed from the State budget.The State budget is approved by Parliament. For 2001 there was no special breakdown of the budget for the 2001 Agricultural Census.The total budget for SOE, including the Agricultural Census was 6.9. Strong public expenditure controls with budget allocations for separate headings and accounted for accordingly.There is no system for allocating costs to specific programs, a system for time usage and budget accounting would be useful. Such a system is currently under elaboration. CBS budget is approved by Parliament and is under the responsibility of the CSB President. Funds can be freely shifted between regional offices and headquarters, but any excess funds are transferred to the Treasury. Strong control over public expenditure by government: budget is imposed under separate budget headings and separately allocated to central and regional expenditure.This leaves less budget flexibility for Director-General. Three-year Business and Financial Plans are submitted for approval annually to Ministry of Finance. Running costs of statistics projects, Eurostat working groups, and acquisition of equipment are covered by normal government budget and pre-accession funds. EU MEDSTAT programme and other special Commission financial agreements also offer assistance. Strong public expenditure policy in Poland. Separate expenditure allocation to central and regional offices. Official statistics are funded by State budgetary funds. Budget approval is based on NSI annual programme of statistical surveys submitted to the government. Ministries use their own budgets for their statistical activities. Initial allocation of budget items can only be changed by special government approval so caution is exercised in budget control. Budget for national statistical sector is approved by Parliament after submitting the Final Account of the SO SR for the previous year.The President of the SO SR is responsible for the entire SO SR budget (headquarters and regional offices). Receipts go back to the State Budget. Slovenian statistical budget is mainly state-funding (nearly 90%), with around 20% allocated to surveys. Receipts from sales of publications, data or services cover only material costs (printing cost, paper) SIS is a separate heading under the State budget. Budgetary requests to the Parliament are based on submission of SIS activity programs. SIS is represented by the Minister of State responsible for SIS within the government. Funding for long term statistical investments requires the approval of the State Planning Organisation before being included in budgetary request. Once approved, the funding is guaranteed over the period required. CC BG CY CZ EE HU LV LT MT PL RO SK SI TR 6All conversions from national currency to € were based on the annual average exchange rate for 2001 from New Cronos.The basic budget excludes special expenditure related to population and agriculture censuses. Enlarging the EU Statistical Network 153 Annex 6 Breakdown of NSI Staff by Gender and Location, 2001 Total NSI Staff Male Female % of total % of total Number % 358 19% 18% 82% 1545 81% 12% 88% 1903 100% 13% 87% 93 98% 40% 60% 2 2% 100% 0 95 100% 41% 59% 690 37% 36% 64% 1176 63% 18% 82% 1866 100% 25% 75% 364 100% 17% 83% 0 0% 0 0 364 100% 17% 83% 845 46% 30% 70% 959 54% 22% 78% 1804 100% 26% 74% 448 71% 22% 78% 183 29% 1% 99% 631 100% 16% 84% 290 51% 15% 85% 275 49% 4% 96% 565 100% 10% 90% 131 100% 39% 61% 0 0% 0 0 131 100% 39% 61% 825 11% 22% 78% 6536 89% 14% 86% 7361 100% 15% 85% 436 32% 22% 78% 1381 68% 22% 78% 1817 100% 22% 78% 332 30% 29% 71% 771 70% 17% 83% 1103 100% 20% 80% 362 100% 33% 67% 0 0% 0 0 362 100% 33% 67% 1193 74% 53% 47% 409 26% 65% 35% 1602 100% 56% 44% Staff movement in 2001 55 recruited, 75 left 7 recruited, 6 left 180 recruited, 197 left 29 recruited, 51 left 152 recruited, 82 left 42 recruited, 42 left 59 recruited = 91 left 22 recruited, 12 left 824 recruited, 416 left 171 recruited, 335 left 92 recruited, 81 left 17 recruited, 13 left 60 recruited, 65 left Number of NSI Statisticians per 100 000 Population 23.2 14.1 18.1 26.0 17.9 26.3 16.8 33.6 19.0 8.11 20.4 18.8 2.4 Country Bulgaria Head Office Regional Office Total NSI Cyprus Head Office Regional Office Total NSI Czech Republic Head Office Regional Office Total NSI Estonia Head Office Regional Office Total NSI Hungary Head Office Regional Office Total NSI Latvia Head Office Regional Office Total NSI Lithuania Head Office Regional Office Total NSI Malta Head Office Regional Office Total NSI Poland Head Office Regional Office Total NSI Romania Head Office Regional Office Total NSI7 Slovak Republic Head Office Regional Office Total NSI Slovenia Head Office Regional Office Total NSI Turkey Head Office Regional Office Total NSI8 7 NSI Romania employs only 1785 of the total posts available. 8 The total NSI staff for Turkey excludes 1730 temporary staff recruited for one year renewable. Annex 7 NSI Staff by Age Group, Level of Education and Training, 2001 % of Personnel by Age in Years Below 30 31 – 45 45 + 154 Enlarging the EU Statistical Network EU Traineeships during tfhe period Jan 1999-Aug 20029 41 59 62 38 6 58 36 16 76 44 56 47 53 24 11 65 5 71 24 6 57 37 40 60 5 54 41 17 68 5 25 70 % of Personnel by Level of Education Below Secondary University Secondary Graduate Graduate 9 The data refer to only the EU traineeships which are covered under the Phare 1997-1999 Multi-beneficiary Programme, and that take place at Eurostat or the National Statistical Institute of a Member State for a period of 5.5 months.They include trainees from the NSIs as well as representatives of the ministries and the Central Bank. 15 20 65 7 33 60 12 36 52 19 28 53 17 31 52 19 36 45 9 42 49 53 27 20 22 36 42 20 44 36 16 36 48 18 33 49 8 70 22 BG CY CZ EE HU LV LT MT PL RO SK SI TR 20 0 12 10 15 8 15 0 18 18 10 16 0 CC Enlarging the EU Statistical Network 155 Annex 8 Statistical Survey Programs in the NSIs, 2001 Comments on Survey Programme Annual national programme for statistical surveys prepared by NSI is put to the High Statistical Council (for NSI surveys) and to the National Statistical Council (for surveys of other bodies).This is then approved by Ministry of Finance and Council of Ministers.The NSI also carries out surveys for payment on the basis of contracts with customers. It does not include all surveys undertaken by the Bodies of Statistics. CYSTAT prepares a five-year work programme plus the annual plan of statistical activities, in collaboration with various users.This is submitted to the Statistical Council for opinion and suggestions and is approved by Council of Ministers. It does not include statistical activity undertaken by other services. Annual programme of statistical surveys prepared by CZSO and approved by Council. Surveys are carried out by CZSO and other ministries. Some surveys outside the programme are also done by ministries and agencies. Annual list of official statistical surveys prepared by SOE in collaboration with ministries, local and international agencies.This list is approved by government and the questionnaires used approved by the Minister of Finance. Annual statistical programme proposed by President KSH to the government after consultation with the statistical council. KSH also acts as consultant in surveys of other ministries. Compulsory surveys in Hungary are included in the decree and any change in these surveys requires revised legislation. Annual state programme of statistical information prepared by CSB in consultation with ministries and other agencies, and approved by Cabinet of Ministers. It comprises all surveys conducted in Latvia by CSB or other bodies. Annual work programme of official statistics is prepared by SL in collaboration with other institutions involved, submitted to the statistical council and approved by the Director-General of SL to whom the government has delegated this authority for approval. Annual work plan prepared by NSO,approved by Malta Statistics Authority. Annual statistical programme prepared by GUS in consultation with ministries and other users, approved by the statistical council, enacted by Council of Ministers. Changes to the programme occur several times a year with the approval of amendment legislation. Annual programme of statistical surveys drawn up by NSI, approved by the statistical council and submitted for approval by Government Decision. Annual state statistical survey programme prepared by the SO SR in co-operation with central authorities and territorial bodies.This is approved by the statistical council and published in the form of a SO SR decree. From 2002 the SO SR applies a three-year Programme of State Statistical Surveys. Medium-term programme of statistical surveys prepared by SORS and approved in form of Government Decision in collaboration with various users. The surveys are carried out by the NSI and authorised performers.Other surveys excluded from the programme are carried out with the consent of SORS.The annual plan is determined by Director General with the consent of authorised performers and is published in the Uradni list Republike Slovenije. Annual statistical programme includes censuses defined by the law, surveys paid for and carried out by SIS, surveys ordered by government and surveys normally paid for by other institutions. Year of Population Census 2001 2001 2001 2000 2001 2000 2001 1995 2002 2002 2001 2002 2000 Total surveys per year by frequency10 Total Annual Monthly Quarterly 119 33 20 13 28511 57 62 154 163 11112 21 31 20013 115 27 32 10914 53 17 26 13515 65 31 30 14516 71 16 57 242 150 47 45 119 75 17 24 111 48 20 43 602 469 100 33 36 CC BG CY CZ EE HU LV LT MT PL RO SK SI TR Year of Population Census 2003 2003 2000 2001 2000 2001 2003 2001 2002 Dec 2002 to Jan 2003 2001 2000 2001 10 A blank cell denotes that the survey breakdown by frequency is not available for the country 11 In Czech Republic, this total includes 12 ad hoc surveys and 5 bi-annual surveys. 12For Estonia, 111 includes the population and housing census, agricultural census, 1 continuous survey, 2 surveys quarterly and monthly, 8 surveys quarterly, 4 surveys also monthly, 2 surveys once a year, 1 survey twice a year and 1 survey October.The figure of monthly surveys includes also 1 weekly survey. 13For Hungary, this includes about 26 occasional and bi-annual surveys 14For Latvia, this concerns only surveys introduced by the CSB.The total number includes surveys conducted weekly (1), semi – annually (5), twice per year (2) or by uncertain frequency (5) 15For Lithuania, this also includes bi-annual, weekly and other occasional surveys 16For Malta, this figure includes the household budgetary survey held between 2000 and 2001 and which is held once every five years Annex 9 The Statistical Registers in the CCs Registers Bulstat Register is an administrative register which identifies all legal economic and other subjects by a unique business identification number; Statistical Business Register (SBR) based on Bulstat and includes legal units and trade registrations; National Population Register which assigns a unique personal identification number to each Bulgarian Citizen and updated with registration of vital events. Business Register based on information from Census of Establishments conducted every five years; CYSTAT uses a population register set up recently at Ministry of Interior, for extraction of births and deaths information. Business Register of legal units assigned with an identification number, maintained by CZSO; Register of Census Districts based on population census, maintained by CZSO and contains boundaries of districts and territorial identification of houses; Farm Register ; Register of Accommodation Establishments; Social Security Register; Register of VAT Payers;Tax Register Business Register; Commercial Register; State Register of Government & Local Institutions; Estonian Population Register since 2002 based on Population Registration Database and electoral registers. Business Register.A central national population register is maintained by the Ministry of Interior. Its use for statistical purposes is limited to the provision of anonymous samples. Business Register maintained by CSB containing all legal units from State Enterprise Register for all NACE sections under supervision of Ministry of Justice, as well as all public organisations and local kind-of-activity units (independence of size class). Residents’ Register (RER) is managed by Ministry of Interior and freely accessible to CSB. Statistical Farm Register was set up in 1999 and updated according to Agricultural Census 2001 data Two business registers: Legal Register which contains budgetary institutions and enterprises and Statistical Register managed by SL.A new central register in Ministry of Justice to be set up in 2002. Population register maintained by Ministry of Internal Affairs, containing persons 16 years and above. Business Register; Employment Register;Water & Electricity Services Register; Common Database contains a list of Malta residents used for household surveys owned by Civil Status Department;VAT Register; Electoral Register; Farm Register; Register of Accommodation Establishments; Register of NGOs; Register of Sporting Organisations; Register of Cultural Enterprises and NGOs. REGON business register maintained through the regional offices. It is based on Tax and VAT records and includes all local, legal and business entities.The population register is not used for statistical purposes. Statistical business register REGIS set up by the NSI with administrative files (mainly fiscal register) as main data source with legal units, enterprises and local units. Population register under the responsibility of the Ministry of Interior but not used for statistical purposes.Vital statistics are based on the civil registration. Business Register; Farm Register; Register of Accommodation Establishments; Register of Census Districts; Register of Local units is in preparation.The Population Register is in responsibility of the Ministry of Interior. Register of Territorial Units (includes administrative territorial units, streets and house numbers, building identification). Business Register (includes all legal business entities, active or not, each with a Business Identification Number and unit of activity) under responsibility of SORS. Central Population Register with PIN assigned to each individual. No unique business register, but a register in each division of the SIS in charge of economic survey activities. Intense work is being undertaken for the Population Register. 156 Enlarging the EU Statistical Network Comments Bulstat is continuously maintained by NSI Regional Office staff SBR is updated once a year. National Population Register is continuously maintained by the Ministry of Regional Development and Public Works. The Business Register contains enterprises and local units, and in most cases local kind-of-activity units It is kept up to date by a continuous survey. The Business Register is a full historical and multi- sourced register, which covers the whole populaton of legal units in the Czech Republic. A Statistical Profile database exists based on the legal registers stated and is used for drawing up business registers. Updating of new units and inactive units is reasonably good. Limited use of administrative sources or registers for statistical production possibly due to legislation on data protection. Information from State Enterprise Register is updated monthly. Special register survey for updating of Business Register is carried out. Information about every legal unit and local kind of activity unit is updated at least once in 18 months.The CSB has an access to the Taxpayers register. SL receives updates of the Population register from the Ministry.The business registers do not contain all local kind-of-activity units. 4 % of all enterprises have more then one local kind of activity unit The Common Database is not a population register, and contains no social security number allocated by Department of Social Security. The Business Register is updated regularly in collaboration with VAT authorities. GUS plans to create a statistical business register based on REGON and updated from exhaustive statistical surveys. REGIS is continuously updated. The registers contain all legal units and enterprises assigned an identification number by SOSR. Updated continuously from statistical surveys and administrative sources. The project for a unique statistical business register was launched in 2001. Currently the registers are updated annually from surveys and administrative sources to a certain extent. CC BG CY CZ EE HU LV LT MT PL RO SK SI TR Enlarging the EU Statistical Network 157 Annex 10 Dissemination of Statistics, 2001 Number of Publications by Frequency Total Annual Bi-Annual Quarterly Monthly 3717 29 0 5 2 + 1 bi-monthly 34 26 0 4 4 22918 120 6 44 30 6519 35 1 10 3 250 19220 2 38 18 56 41 13 2 97 65 6 20 6 1321 13 30322 243 15 22 23 3223 18 2 7 5 124 76 5 29 14 136 80 4 18 34 6824 61 4 3 Comments on Dissemination. (Note: IMF Special Data Dissemination Standard = IMF SDDS) Week of release announced on NSI website. Press conferences announced 2-3 days ahead.Website mainly in Bulgarian, but some parts in English. Bi-monthly press conferences. IMF DDS since May 2000. SDDS by 2006. Publication catalogue available. Release dates for a number of key indicators are announced in advance. Results are released when available via press releases and website. Publications are in Greek and English. Publication catalogue on website, in Czech English / French / German. Economic & financial statistics by IMF SDDS. Catalogue includes calendar of news and press releases of year ahead. Publication catalogue available. Publication calendar available well in advance and updated weekly on the web.Website and most publications are in Estonian and English. On-line orders and public statistical database provided on website. Detailed release schedule for key regular outputs which are pre-announced. Dissemination database to a data warehouse to be introduced from April 2002. Publication catalogue (available in Latvian and English) available on website and sent to all subscribers. Release dates are available 4 month in advance; preliminary news release dates are available 1 year in advance. National summary data page according to IMF SDDS. CSB runs an Information Centre with an electronic database of publications. Publication catalogue available. Catalogue announces time of release well in advance and on website. Publications are in Lithuanian and English.A calendar for press releases is compiled for every year and disseminated widely also available on the website. The publication catalogue is available and currently being updated. No advance calendar for news releases (100 releases in 2000). Press conferences whenever necessary. Publications and website in English. Library & Information Unit since 1996. Publications catalogue available. Detailed release schedule for key regular output which are pre-announced. Monthly press conferences.Wide range of statistics on GUS website. Publications catalogue available in Romanian and English. Key indicators are released at pre-announced dates by press releases. Key statistics available on website in Romanian and English. NSI has publication shop. Publication catalogue available.Time-table of first data releases 4 months in advance on web page. Press conferences on release of significant statistics and thematic conferences for bank analysts. Publications are in Slovak with 50% being also in English. Electronic database via internet being set up.The SO SR has a data shop and Information Service Department. IMF SDDS since 1996. No calendar of release dates. Most publications are in Slovene and English, both on paper and internet. Monthly press conferences with special statistical tables prepared in advance. Statistical databank in Slovene, partly in English and German.Adopted IMF SDDS. Publication catalogue available.Advance release calendars for some data categories. Press conferences from time to time.Automatic answering system for key indicators. Summary statistics on Internet. Labour Force database on internet. Subscribes to IMF SDDS. CC BG CY CZ EE HU LV LT MT PL RO SK SI TR 17Includes 8 electronic publications 18Almost all publications of CZSO are available in electronic form.The total includes 29 publications which are not regular 19There are 65 titles and 130 publications including 16 periodicals. 2048 of the annual publications are also available on Internet and 49 on CD-Rom 21Excludes 5 thematic publications between 2001 / 2002 and Labour Force Survey reports 22Includes 6 electronic publications (in Polish/English) and 185 annual regional office publications 234 annual and 1 monthly publications are also available in electronic version 24SIS also published 100 publications on specific projects 158 Enlarging the EU Statistical Network Bulgaria National Statistical Institute 2, Panayot Volov Str. BG-1038 Sofia Tel: +359 2 9842 835 Fax: +359 2 9842 851 E-mail: info@nsi.bg Web site: http://www.nsi.bg Cyprus Statistical Service of Cyprus 13,Andreas Araouzos Street CY-1444 Nicosia Tel: +357 22 309305 Fax: +357 22 374830 E-mail: cydsr@cytanet.com.cy Web site: http://www.pio.gov.cy/dsr Czech Republic Czech Statistical Office Sokolovská 142 CZ-186 04 Praha 8 Tel: +4202 7405 2421 Fax: +4202 8481 8103 E-mail: povolna@gw.czso.cz Web site: http://www.czso.cz Estonia Statistical Office of Estonia Endla 15 15174 Tallinn Tel: +372 6259 300 Fax: +372 6259 370 E-mail: stat@stat.ee Web site: http://www.stat.ee Hungary Hungarian Central Statistical Office Keleti Karoly 5-7 PO box 51 HU-1525 Budapest Tel: +361 345 6000 Fax: +361 345 6378 E-mail: kshintl@office.ksh.hu Web site: http://www.ksh.hu Lithuania Statistics Lithuania 29 Gedimino Ave. LT-2746 Vilnius Tel: +370 2 36 48 22 Fax: +370 2 36 48 45 E-mail: statistika@mail.std.lt Web site: http://www.std.lt Latvia Central Statistical Bureau of Latvia Lacplesa Street 1 LV-1301 Riga Tel: +371 73 66 850 Fax: +371 78 30 137 E-mail: csb@csb.lv Web site: http://www.csb.lv Malta National Statistics Office Lascaris Valletta CMR02 Malta Tel: +356 21 22 32 21-5 Fax: +356 21 24 84 83 +356 21 24 98 41 E-mail: nso@gov.mt Web site: http://www.nso.gov.mt Poland Central Statistical Office Al. Niepodleglosci 208 PL-00925 Warszawa Tel: +48 22 608 30 00 +48 22 608 30 01 Fax: +48 22 608 38 63 E-mail: dissem@stat.gov.pl Web site: http://www.stat.gov.pl Romania National Institute of Statistics 16, Libertatii Avenue, Sector 5 RO-70542 Bucharest Tel: +40 1 312 48 75, +40 1 311 33 09 Fax: +40 1 312.48.73, +40 1 335 73 73 E-mail: romstat@insse.ro Web site: http://www.insse.ro Slovenia Statistical Office of the Republic of Slovenia Vozarski pot 12 SI-1000 Ljubljana Slovenija Tel: +386 1 241 51 04 Fax: +386 1 241 53 44 E-mail: indok.surs@gov.si Web site: http://www.sigov.si/zrs Slovak Republic Statistical Office of the Slovak Republic Mileticova 3 SK-824 67 Bratislava Tel: +421 2 50 236 334 Fax: +421 2 55 424 587 E-mail: elena.stavova@statistics.sk Web site: http://www.statistics.sk Turkey State Institute of Statistics Necatibey Caddesi, No. 114 06100 Bakanliklar,Ankara Tel (President of NSI) +90 312 418 8719 +90 312 425 35 14 Fax (President of NSI) +90 312 425 33 87 +90 312 418 11 82 E-mail (publicat.): yayin@die.gov.tr Web site: http://www.die.gov.tr Eurostat Statistical Office of the European Communities Directorate A – Statistical information systems; research and data analysis; co-operation with Phare and Tacis Unit A5 – Technical co-operation with Phare and Tacis countries Mail address: Bâtiment Jean Monnet, rue Alcide de Gasperi L-2920 Luxembourg Offices address: Bâtiment Joseph Bech, 5 rue Alphonse Weicker Office Bech A3/169 L-Luxembourg Tel: +352 430133589 switchboard 4301 1 Fax: +352 4301 32 139 Web site: http://europa.eu.int/comm/eurostat Contact Addresses Enlarging the EU Statistical Network 159 A ACCOMSTAT Accommodation survey in Malta AGRISTAT Database on agriculture in Malta based on Geographical Information System AGROMET Model used for harvest forecasting ASIS Automated Statistical Information System of the Slovak statistics B BIN Business Identification Number in Slovenia BNB Bulgarian National Bank BOL Bank of Latvia BOP Balance of Payments BRS Business Register of Slovenia BSU New statistical business register (base of statistical Units) in Poland C Canberra Manual ECD manual for ‘Human Resources in Science and Technology’ CCs Candidate Countries CC Classification of Types of Construction CESTAT Central and Eastern Europe Statistical Association CIS-3 Community Innovation Survey - 3 CN Combined Nomenclature CNB Czech National Bank COFOG Classification of the Functions of Government COICOP Classification of Individual Consumption by Purpose COMEXT Eurostat reference database on external trade statistics CPA Classification of Products by Activity CPI Consumer Price Index CSB Central Statistical Bureau of Latvia CYSTAT Statistical Service of Cyprus CZSO Czech Statistical Office D DOSTAT Household survey in Malta E EAA 97 Economic Agricultural Accounts ECB European Central Bank ECE Economic Commission for Europe (UN) ECHP European Community Household Panel ECP European Comparison Programme ECTM European Conference of Transport Ministries EDP Electronic Data Processing EKOMAR Comprehensive Annual Enterprise Report in Estonia EUROFARM Eurostat database on agriculture ESA 95 European System of National Accounts ESSPROS European System of Integrated Social PROtection Statistics EU European Union Eurostat Statistical Office of the European Communities Eurotrace Interface software used for dissemination of information between customs and statistics for foreign trade statistics EU-SILC European Union Statistics on Income and Living Conditions EXTRASTAT A system for collecting statistics relation to trading of goods between Member States and the third countries F FADN Farm Accountancy Data Network FAO Food and Agricultural Organisation FDI Foreign Direct Investment Frascati Manual OECD manual on international comparability of national R&D activities G GDP Gross Domestic Product GEONOM Country Nomenclature for the External Trade Statistics of the Community and Statistics of Trade between Member States GESMES An Electronic Data Interchange message - GEneric Statistical MESsage - for the exchange of any type of multi-dimensional data or chronological series GFS Government Finance Statistics GIS Geographic Information System GOVNET Government network in the Slovak Republic GUS Central Statistical Office of Poland H HBS Household Budget Surveys HICP Harmonised Index of Consumer Prices HRST Human Resources in Science and Technology I ICAO International Civil Aviation Organisation ICD International Statistical Classification of Diseases and related health problems IEA International Energy Agency ILO International Labour Organisation IMF International Monetary Fund INFOSTAT Slovak Research and Development Centre for Statistics INTRASTAT A system for collecting statistics in relation to trading of goods between EU Member States (intra-community trade) ISCED 97 International Standard Classification on Education ISCO 88 International Standard Classification of Occupations IT Information Technology Glossary 160 Enlarging the EU Statistical Network K KAU Kind-of-Activity Unit KROK Regional Database in the Czech Republic KSH Central Statistical Office of Hungary L LAN Local Area Network LCS Living Conditions Survey LFS Labour Force Survey LKAU Local Kind-of-Activity Unit LSI Latvian Statistical Institute LUCAS Land use/cover area frame survey M MAF Ministry of Agriculture and Forestry in Bulgaria MALTASTAT Database of statistics in Malta MEDSTAT Mediterranean Statistical Co-operation Programme MIS Methodology and Informatics Section of the SOSR MSA Maltese Statistical Authority N NACE Rev.1 Statistical Classification of Economic Activities in the European Community NAFO North west Atlantic Fishery Organisation NAICS North American Industry Classification System NBH National Bank of Hungary NBP National Bank of Poland NBS National Bank of Slovakia NEC (n.e.c) Not elsewhere classified NPAA National Programme for the Adoption of the Acquis NSI National Statistical Institute NSO National Statistical Office of Malta NST/R Standard Goods Classification for Transport Statistics, revised NUTS Nomenclature of Territorial Units for Statistics O OECD Organisation for Economic Co-operation and Development Oslo Manual OECD Manual ‘The measurement of scientific and technological activities’ OCR Optical character recognition (reader) OLAP Online Analytical Processing Operation 2002 Eurostat project on implementation of new versions of major activity classifications (NACE, CPA and PRODCOM) P PHARE Poland and Hungary Action for Restructuring of the Economy. It is an action plan for coordinated aid to Poland and Hungary (subsequently extended to the remainder of the Central and East European countries). PIN Personal Identification Number PPI Producer Price Index PRODCOM Classification of Industrial Products of the European Community R R&D Research and Development REGIO Eurostat harmonised regional statistical database REGIS Business Register in Romania REGON National register of economic units in Poland RER Residents’ Register in Latvia S SBR Statistical Business Register in Bulgaria SBS Structural Business Statistics SER State Enterprise Register in Latvia SIS State Institute of Statistics of Turkey SKTE 2 Slovenian regional classification equivalent to NUTS 2 SL Statistics Lithuania (Department of Statistics) SNA 68 System of National Accounts SOE Statistical Office of Estonia SORS Centralised Statistical Office of Slovenia SO SR Statistical Office of the Slovak Republic SPO State Planning Organisation of Turkey STADIUM STAtistical Data Interchange Universal Monitoring STATNET Wide area network in the Slovak Republic STS Short-Term Statistics S&U Supply and Use T TES Training of European Statisticians TOURSTAT Pilot survey for inbound tourism in Malta U UISAC Unified Information System for Reaction Against Crime in Bulgaria UN United Nations UNECE United Nations Economic Commission for Europe UNESCO United Nations Educational, Scientific and Cultural Organisation V VAT Value Added Tax VITA Governmental data transmission network in Latvia W WAN Wide Area Network WHO World Health Organisation WTO World Trade Organisation Glossary Enlarging the EU Statistical System Foreword Introduction Acknowledgement Table of contents Reviewing the process Bulgaria Cyprus Czech Republic Estonia Hungary Latvia Lithuania Malta Poland Romania Slovak Republic Slovenia Turkey Annex Tables Contact Addresses Glossary

Statistical books , Product code: KS-31-10-555, published on 13-Dec-2010

ISBN 978-92-79-16351-7 Price (excluding VAT) in Luxembourg: EUR 20 Income and living conditions in Europe Edited by Anthony B. Atkinson and Eric Marlier This book is about the incomes and living standards of the people of Europe. It treats employment, income inequality and poverty, housing, health, education, deprivation and social exclusion. The reader will learn about many of the social issues confronting Europe. How much income poverty is there in Europe? Is inequality increasing? Does a job guarantee escape from income poverty? How is Europe’s welfare state coping with the economic crisis? The book is a timely contribution to the Europe 2020 Agenda as it explores ‘the new landscape of EU targets’ and the implications for monitoring at EU and national levels. Evidence about these important issues comes primarily from the EU Statistics on Income and Living Conditions (EU-SILC), which represents a powerful instrument for the comparative analysis of the economic and social state of the EU as well as a growing number of non-EU European countries. The book is the result of the Network for the analysis of EU-SILC (Net-SILC), which was funded by the Statistical Office of the EU (Eurostat). Net-SILC was an ambitious initiative that brought together official statisticians responsible for producing statistics and researchers who use these data. By analysing statistics to examine the living conditions of European citizens we can learn how to produce more and better figures, and also how to develop evidence-based policies for better achieving social objectives. http://ec.europa.eu/eurostat KS-31-10-555-EN -C Incom e and living conditions in Europe Ed ited by A nthony B. A tkinson and Eric M arlier S t a t i s t i c a l b o o k s Edited by Anthony B. Atkinson and Eric Marlier Income and living conditions in Europe S t a t i s t i c a l b o o k s Edited by Anthony B. Atkinson and Eric Marlier Income and living conditions in Europe logos essentiels EN - FR- DE Fichier indd et pdf dans : /tampon revue/office/AO_10023/repasse/logos/logos-EN-FR-DE EN FR DE Europe Direct is a service to help you find answers to your questions about the European Union. Freephone number (*): 00 800 6 7 8 9 10 11 (*) Certain mobile telephone operators do not allow access to 00 800 numbers or these calls may be billed. More information on the European Union is available on the Internet (http://europa.eu). Cataloguing data can be found at the end of this publication. Luxembourg: Publications Office of the European Union, 2010 ISBN 978-92-79-16351-7 doi:10.2785/53320 Cat. No KS-31-10- -EN-C Theme: Population and social conditions Collection: Statistical books © European Union, 2010 Reproduction is authorised provided the source is acknowledged. Cover picture: © Michèle Reniers-Marlier, 2008 Printed in Belgium Printed on elemental chlorine-free bleached PaPer (ECf) 555 Income and living conditions in Europeeurostat 3 foreword The European Commission attaches utmost importance to tackling problems of poverty and exclusion and developing policies targeting the most disadvantaged of our citizens. One of the headline targets in the Europe 2020 Strategy for Jobs and Growth is promoting social inclusion, in particular through the reduction of poverty, by aiming to reduce the number of people at risk of poverty and excluded from full participation in work and society. The “Platform Against Poverty” under the Europe 2020 Strategy will bring together European action for vulnerable groups such as children and old people. Last but not least, 2010 has been the European Year for Combating Poverty and Social Exclusion. We must make sure that the most vulnerable are not left behind. The publication that you have in front of you is an integral part of this political agenda. The social indicators are essential to monitor progress towards our common goals. They play a key role in shaping our economic and social policies. We need reliable data for a high quality statistical analysis. Given that social well-being has many dimensions and its measurement goes well beyond the level of GDP, the improvement of the quality of statistics and their coverage is even more important. The publication is a significant contribution as it explores ‘the new landscape of EU targets’ and the implications for monitoring at EU and national levels. The Europe 2020 agenda, in setting a social inclusion target, has highlighted three dimensions of poverty and exclusion. It is also essential, however, that Member States – and the EU as a whole – continue to monitor performance according to the full set of commonly agreed social indicators underpinning EU coordination and cooperation in the social field. “Income and Living Conditions in Europe” is the result of the work of a Network established by Eurostat of statisticians responsible for producing statistics and researchers who use these data, which focuses on the contribution of the EU Statistics on Income and Living Conditions (EU-SILC). The book, therefore, is not just for policy-makers, nor just for statisticians. It will interest all those concerned with the social dimension of Europe. The reader will learn about how the citizens of Europe earn their living, about their living arrangements, their social participation, and about the ways in which their incomes are affected by taxes and transfers. The book gives a clear picture of many social problems confronting Europe and of the distributional effects of social and labour policies. The success of Europe 2020 with a truly social dimension will depend on real ownership at the European, national and local levels. Fighting poverty is a shared responsibility – one where everyone has a role to play. Providing a better understanding of these issues is a concrete step that will help the Commission and Member State governments to achieve their objectives. José Manuel Barroso President of the European Commission Income and living conditions in Europe eurostat4 (1) http://www.stat.gov.pl/eusilc/index.htm. Eurostat is the Statistical Office of the European Communities. Its mission is to pro- vide the European Union with high-quality statistical information. For that purpose, it gathers and analyses figures from the national statistical offices across Europe and provides comparable and harmonised data for the European Union to use in the defi- nition, implementation and analysis of Community policies. Its statistical products and services are also of great value to Europe’s business community, professional organisations, academics, librarians, NGOs, the media and citizens. Eurostat's publications programme consists of several collections: t News releases provide recent information on the Euro-Indicators and on social, economic, regional, agricultural or environmental topics. t Statistical books are larger A4 publications with statistical data and analysis. t Pocketbooks are free of charge publications aiming to give users a set of basic fig- ures on a specific topic. t Statistics in focus provides updated summaries of the main results of surveys, stud- ies and statistical analysis. t Data in focus present the most recent statistics with methodological notes. t Methodologies and working papers are technical publications for statistical experts working in a particular field. Eurostat publications can be ordered via the EU Bookshop at http://bookshop. europa.eu. All publications are also downloadable free of charge in PDF format from the Eurostat website http://ec.europa.eu/eurostat. Furthermore, Eurostat’s databases are freely available there, as are tables with the most frequently used and demanded short- and long-term indicators. Eurostat has set up with the members of the ‘European statistical system’ (ESS) a network of user support centres which exist in nearly all Member States as well as in some EFTA countries. Their mission is to provide help and guidance to Internet users of European statistical data. Contact details for this support network can be found on Eurostat Internet site. EUROSTAT L-2920 Luxembourg — Tel. (352) 43 01-1 — website http://ec.europa.eu/eurostat Eurostat is the Statistical Office of the European Communities. Its mission is to provide the European Union with high-quality statistical information. For that purpose, it gathers and analyses figures from the national statistical offices across Europe and provides comparable and harmonised data for the European Union to use in the definition, implementation and analysis of Community policies. Its statistical products and services are also of great value to Europe’s business community, professional organisations, academics, librarians, NGOs, the media and citizens. Eurostat’s publications programme consists of several collections: • News releases provide recent information on the Euro-Indicators and on social, economic, regional, agricultural or environmental topics. • Statistical books are larger A4 publications with statistical data and analysis. • Pocketbooks are free of charge publications aiming to give users a set of basic figures on a specific topic. • Statistics in focus provides updated summaries of the main results of surveys, studies and statistical analysis. • Data in focus present the most recent statistics with methodological notes. • Methodologies and working papers are technical publications for statistical experts working in a particular field. Eurostat publications can be ordered via the EU Bookshop at http://bookshop.europa.eu. All publications are also downloadable free of charge in PDF format from the Eurostat website http://ec.europa.eu/eurostat. Furthermore, Eurostat’s databases are freely available there, as are tables with the most frequently used and demanded shortand long-term indicators. Eurostat has set up with the members of the ‘European statistical system’ (ESS) a network of user support centres which exist in nearly all Member States as well as in some EFTA countries. Their mission is to provide help and guidance to Internet users of European statistical data. Contact details for this support network can be found on Eurostat Internet site. Income and living conditions in Europeeurostat 5 Acknowledgements by editors The Network for the Analysis of EU-SILC (Net-SILC) was an ambitious 18-partner Network bringing together expertise from both data producers (directly involved in the collection of EU-SILC data) and data users. It was established in response to a call for applications by the Statistical Office of the European Union (Eurostat) in 2008. We would like to thank Eurostat not only for funding Net-SILC but also for their very active and efficient support throughout the project. We would also like to give a particular word of thanks to Gara Rojas González and Pascal Wolff for their important assistance in the final editing of this book. Their detailed comments and suggestions have been extremely useful. This book represents a major output from the Network for the Analysis of EU-SILC (Net-SILC). However, not all of the scientific work produced by the Network could be included in it. More technical material, and the output from the more methodological Net-SILC work packages, are available in the series ‘Eurostat methodologies and working papers’. We wish to thank all the Net-SILC members and the institutions they belong to for their contribution to the project (for a list of Net-SILC members, see Appendix 1). The initial Net-SILC findings were presented at the international conference on Comparative EU Statistics on Income and Living Conditions (Warsaw, 25-26 March 2010) (1) which was organised jointly by Eurostat and the Net-SILC network. We would like to thank the Central Statistical Office of Poland for kindly hosting the event. Special thanks also go to Olympia Bover, Conchita D’Ambrosio, André Decoster, Stephen Jenkins, John Micklewright and Brian Nolan for discussing so thoroughly the papers at the conference and also for guiding us in our editorial decisions related to this book. Both this book and the Net-SILC Working Papers published by Eurostat have benefited from their input. Isabelle Bouvy and Begoña Levices have provided invaluable secretarial and bibliographical help. It should be stressed that the book does not represent in any way the views of Eurostat, the European Commission or the European Union. It also does not represent in any way the views of the persons and bodies thanked above. All the authors have written in a strictly personal capacity, not as representatives of any Government or official body. Thus they have been free to express their own views and to take full responsibility for the judgments made about past and current policy and for the recommendations for future policy. A.B. Atkinson (Nuffield College and London School of Economics, United Kingdom) E. Marlier (CEPS/INSTEAD Research Institute, Luxembourg) Income and living conditions in Europeeurostat 7 Table of Contents Foreword ............................................................................................................................................................3 Acknowledgements by editors ........................................................................................................................5 Table of Contents ..............................................................................................................................................7 List of tables, figures and boxes ....................................................................................................................15 Living conditions in Europe and the 1. Europe 2020 agenda (Anthony B. Atkinson and Eric Marlier) ............................................................................................21 1.1 Introduction .....................................................................................................................................22 1.2 Outline of the contents ...................................................................................................................23 1.3 Summary of main lessons for EU-SILC .......................................................................................28 1.4 EU-SILC in the new landscape of EU targets ..............................................................................30 1.4.1 Implications for monitoring at EU level.........................................................................31 1.4.2 Implications for EU social indicators .............................................................................32 1.4.3 Implications for monitoring at Member State level ......................................................33 1.4.4 An EU minimum income for children ...........................................................................33 References ...............................................................................................................................................34 Investing in statistics: 2. EU-SILC (Pascal Wolff, Fabienne Montaigne and Gara Rojas González) ......................................................37 2.1 Introduction .....................................................................................................................................38 2.1.1 A brief history ....................................................................................................................38 2.1.2 Policy context .....................................................................................................................38 2.2 The EU-SILC instrument and its governance ..............................................................................40 2.2.1 Scope and geographical coverage ....................................................................................40 2.2.2 Main characteristics of EU-SILC ....................................................................................40 2.2.3 Legal basis ..........................................................................................................................41 2.2.4 Common guidelines ..........................................................................................................41 2.3 Methodological framework ............................................................................................................42 2.3.1 Contents of EU-SILC ........................................................................................................42 2.3.2 Income concept .................................................................................................................42 2.3.3 Sample requirements ........................................................................................................44 2.3.4 Tracing rules ......................................................................................................................46 2.4 Information on quality ....................................................................................................................46 2.4.1 Some comparability issues ...............................................................................................46 2.4.2 Quality reports ...................................................................................................................51 2.5 Data and indicators .........................................................................................................................52 2.5.1 Data access .........................................................................................................................52 2.5.2 Indicators computation ....................................................................................................52 2.6 The way forward ..............................................................................................................................53 2.6.1 Improvement of timeliness and geographical coverage ..............................................53 2.6.2 Methodological and data improvements .......................................................................53 2.6.3 Coherence with other sources .........................................................................................54 2.6.4 Data linking .......................................................................................................................54 2.6.5 Revision of the EU-SILC legal basis ..............................................................................55 References ...............................................................................................................................................55 Income and living conditions in Europe eurostat8 Data accuracy in 3. EU-SILC (Vijay Verma and Gianni Betti) ...........................................................57 3.1 Introduction: a description of errors in survey data ...................................................................58 3.1.1 A typology of errors ..........................................................................................................58 3.1.2 Errors in measurement .....................................................................................................58 3.1.3 Errors in estimation ..........................................................................................................59 3.1.4 Item non-response ...........................................................................................................61 3.1.5 Comparability ....................................................................................................................61 3.2 Conceptual and measurement errors ............................................................................................62 3.2.1 Reporting of negative and zero values for income components .................................62 3.2.2 Total household gross and disposable income (HY010, HY020) ...............................62 3.2.3 Total household disposable income before social transfers (HY022, HY023) .........................................................................64 3.2.4 The importance of uniform procedures for achieving comparability ........................65 3.3 Non-response in EU-SILC .............................................................................................................66 3.3.1 A framework ......................................................................................................................66 3.3.2 Unit non-response.............................................................................................................67 3.3.3 Within-household (‘partial unit’) non-response ...........................................................68 3.3.4 Item non-response ............................................................................................................69 3.4 Sampling error .................................................................................................................................71 3.4.1 Jackknife Repeated Replication (JRR) for variance estimation ...................................71 3.4.2 Defining sample structure: ‘computational’ strata and PSUs ......................................72 3.4.3 Analysis of design effects in EU-SILC ............................................................................73 3.4.4 Illustrative estimates of variance and of design effect and its components ...............75 3.5 Concluding remarks .......................................................................................................................75 3.5.1 Diverse sources of non-sampling errors in EU-SILC ...................................................75 3.5.2 Improving the potential for assessment of data quality in EU-SILC ..........................76 References ...............................................................................................................................................77 Household structure in the EU 4. (Maria Iacovou and Alexandra Skew) ..........................................79 4.1 Introduction .....................................................................................................................................80 4.1.1 Countries and groups of countries .................................................................................81 4.2 Methodology ....................................................................................................................................82 4.2.1 Defining relationships between individuals ..................................................................82 4.2.2 Statistical analysis ..............................................................................................................82 4.3 Household composition..................................................................................................................83 4.4 Children ............................................................................................................................................85 4.5 Young adults .....................................................................................................................................88 4.6 Partnerships: cohabitationand marriage ......................................................................................89 4.7 Older people .....................................................................................................................................92 4.8 Synthesising the differences: factor analysis ................................................................................94 4.9 Conclusions ......................................................................................................................................97 References ...............................................................................................................................................98 Income poverty and income inequality 5. (Anthony B. Atkinson, Eric Marlier, Fabienne Montaigne and Anne Reinstadler) ...................................................................................101 5.1 Introduction ...................................................................................................................................102 5.1.1 Aim of this chapter ..........................................................................................................102 5.1.2 Role of EU-SILC ..............................................................................................................102 Income and living conditions in Europeeurostat 9 5.2 Income poverty/inequality across countries and comparison with international sources..........................................................................................................104 5.2.1 Evidence from EU-SILC on the risk of poverty ..........................................................104 5.2.2 Evidence from EU-SILC on income inequality ...........................................................109 5.2.3 Comparison with other cross-country sources ...........................................................112 5.3 Changes in income poverty and inequality over time ..............................................................118 5.3.1 Monitoring trends in EU-SILC .....................................................................................118 5.3.2 Changes in poverty risk ..................................................................................................120 5.3.3 Changes in income inequality .......................................................................................120 5.3.4 Comparison with national sources: a case study ........................................................120 5.4 Monitoring progress ......................................................................................................................124 5.4.1 An at-risk-of-poverty target...........................................................................................124 5.4.2 Three indicators ..............................................................................................................126 5.5 Conclusions ....................................................................................................................................129 References .............................................................................................................................................130 Characterising the income poor6. and the materially deprived in European countries (Alessio Fusco, Anne-Catherine Guio and Eric Marlier) ...............................................................133 6.1 Introduction ...................................................................................................................................134 6.2 Concepts and data .........................................................................................................................134 6.3 Material deprivation and income poverty in the EU ................................................................138 6.4 Relationship between income poverty and material deprivation ...........................................139 6.4.1 Factors affecting the relationship between income poverty and material deprivation ................................................................................................139 6.4.2 Results from EU-SILC ...................................................................................................139 6.5 Characterisation of material deprivation and income poverty in the EU..............................144 6.5.1 Work intensity of the household ...................................................................................146 6.5.2 Most frequent activity status ..........................................................................................147 6.5.3 Household composition .................................................................................................147 6.5.4 Age, gender and education .............................................................................................148 6.5.5 Health problems ..............................................................................................................148 6.5.6 Housing tenure status .....................................................................................................148 6.6 Conclusions ....................................................................................................................................149 References .............................................................................................................................................150 The distributional impact of imputed rent 7. (Hannele Sauli and Veli-Matti Törmälehto) .....................................................................................155 7.1 Introduction ..................................................................................................................................156 7.2 Theoretical and operational considerations ..............................................................................156 7.2.1 Housing wealth, housing consumption and disposable income ...............................156 7.2.2 Measurement of imputed rentsas income ....................................................................157 7.2.3 The data and the potential beneficiaries .......................................................................158 7.3 Imputed rents and income inequality .........................................................................................159 7.3.1 Overall distributional effect ...........................................................................................159 7.4 Imputed rents and income poverty .............................................................................................163 7.4.1 Imputed rents of outright owners .................................................................................165 7.4.2 Imputed rents of tenants ................................................................................................165 7.5 Imputed rent and deprivation indicators ...................................................................................166 Income and living conditions in Europe eurostat10 7.5.1 The impact on non-monetary deprivation indicators ................................................166 7.5.2 House rich — cash poor .................................................................................................168 7.6 Imputed rents and alternative measures of the economic benefits of housing .....................170 7.7 Capping imputed rents? ................................................................................................................171 7.8 Summary and conclusions ...........................................................................................................173 References .............................................................................................................................................177 Income from own-consumption 8. (Merle Paats and Ene-Margit Tiit) ...........................................179 8.1 Introduction ...................................................................................................................................180 8.1.1 Common recommendations for collecting the income data from own-consumption ..................................................................................................180 8.1.2 Recommendations in EU-SILC .....................................................................................181 8.2 Collecting income from own-consumption in EU-SILC ........................................................182 8.2.1 Countries where income from own-consumption is not included ..........................182 8.2.2 Countries where the income from own-consumption is included ...........................182 8.3 Results .............................................................................................................................................184 8.3.1 Impact of type of questionnaire on value of income from own-consumption. Comparison of EU countries using UDB data.............................................................184 8.3.2 Impact of type of questionnaire on value of income from own-consumption. Comparison of Estonian data using different types of questionnaire .......................185 8.3.3 Impact of own-consumption on the income-based EU indicators for social inclusion ...........................................................................................................186 8.3.4 Influence of own-consumption on poverty indicators ...............................................189 8.3.5 Changes in poverty risk rates due to OPP in different household types..................191 8.4 Summary and conclusions ..........................................................................................................192 8.4.1 Data comparability ..........................................................................................................192 8.4.2 The impact of OPP on poverty reduction ....................................................................193 8.4.3 Analysis of working hypotheses ....................................................................................193 8.4.4 Recommendations ..........................................................................................................194 References .............................................................................................................................................194 Socio-economic determinants of health in Europe 9. (Cristina Hernández-Quevedo, Cristina Masseria and Elias Mossialos) .....................................195 9.1 Introduction ...................................................................................................................................196 9.2 Literature review ............................................................................................................................196 9.3 EU-SILC sample and variables ....................................................................................................199 9.3.1 Health variables ...............................................................................................................199 9.3.2 Explanatory variables ......................................................................................................200 9.4 Methods ..........................................................................................................................................200 9.4.1 Measuring inequality in health outcomes ....................................................................200 9.4.2 Long-term inequalities in health ...................................................................................201 9.4.3 Decomposition analysis..................................................................................................203 9.5 Results .............................................................................................................................................203 9.5.1 Descriptive analysis .........................................................................................................203 9.5.2 Evidence on socio-economic inequalities in health outcomes .................................203 9.5.3 Sources of inequalities ....................................................................................................208 9.6 Discussion.......................................................................................................................................208 References .............................................................................................................................................210 Income and living conditions in Europeeurostat 11 Social participation and social isolation 10. (Orsolya Lelkes) .............................................................217 10.1 Introduction .................................................................................................................................218 10.2 Data ...............................................................................................................................................219 10.3 Social participation .....................................................................................................................220 10.3.1 Friendly Europe: frequency of social contacts ..........................................................220 10.3.2 Social participation in voluntary activities ................................................................223 10.3.3 Robustness of the results: comparison with the European Social Survey .............225 10.3.4 Social participation makes people happy ...................................................................229 10.4 Social isolation .............................................................................................................................229 10.4.1 An overview ...................................................................................................................231 10.4.2 Social isolation by age: it tends to increase by age, although relatively good informal support of help ......................................................................235 10.4.3 Social isolation is greater among the poor and the unemployed, although causality is unclear ...........................................................................................................235 10.5 Conclusions ..................................................................................................................................236 References .............................................................................................................................................239 Progress of living conditions — a dynamic model 11. of material deprivation for a European society (Matthias Till and Franz Eiffe) .........................241 11.1 Introduction .................................................................................................................................242 11.2 Understanding social inclusion as a multidimensional process............................................242 11.3 The EU-SILC longitudinal component as a source for monitoring change .......................244 11.4 Pan-European progress of living conditions ............................................................................245 11.5 Evidence on gross and net change of material deprivation items .........................................248 11.6 Winners and losers in a model of multiple changes ...............................................................252 11.6.1 Predicting net multiple improvement in Europe ......................................................253 11.6.2 Predicting gross multiple change of material deprivation in Europe .....................259 11.7 Conclusions and recommendations .........................................................................................260 References .............................................................................................................................................261 The distribution of employees’ labour earnings in the European Union: 12. data, concepts and first results (Andrea Brandolini, Alfonso Rosolia and Roberto Torrini) ...........................................................................................................................265 12.1 Introduction .................................................................................................................................266 12.2 Earnings in EU-SILC ..................................................................................................................267 12.3 How does EU-SILC compare to other sources? ......................................................................269 12.4 Time units and conversion rates ................................................................................................273 12.5 Earnings distributions in EU countries ....................................................................................276 12.6 The EU-wide distribution of gross earnings ............................................................................282 12.7 Conclusions ..................................................................................................................................284 References .............................................................................................................................................285 Educational intensity of 13. employment in Europe and the US (Donald R. Williams) ..........................................................................................................................289 13.1 Introduction .................................................................................................................................290 13.2 The research context....................................................................................................................290 13.3 Methodology and data ...............................................................................................................291 13.3.1 Methodology ..................................................................................................................291 13.3.2 Data .................................................................................................................................294 Income and living conditions in Europe eurostat12 13.4 Employment shares by skill level ...............................................................................................294 13.5 Demographic differences ............................................................................................................299 13.6 Summary and conclusions .........................................................................................................301 References .............................................................................................................................................304 Assessing and analysing in-work poverty risk 14. (Sophie Ponthieux) ..............................................307 14.1 Introduction .................................................................................................................................308 14.2 Definitions of workers and subsequent analysis of a working poor-type phenomenon ......................................................................................308 14.2.1 Three definitions of workers: active, employed, in-work .........................................309 14.2.2 Impact on the ‘size of the problem’ .............................................................................309 14.2.3 Impact on the analysis of the problem .......................................................................310 14.3 Poverty risk at the individual level or working households: two other ways to look at work and poverty risk ....................................................................317 14.3.1 At the individual level: a complementary approach in terms of ‘poverty in earned income’ .............................................................................................317 14.3.2 At the household level: in-work households? ............................................................322 14.4 Conclusions ..................................................................................................................................324 References .............................................................................................................................................327 The impact of basic public services on the distribution 15. of income in European countries (Rolf Aaberge, Audun Langørgen and Petter Lindgren) ................................................................329 15.1 Introduction .................................................................................................................................330 15.2 Definition and measurement of extended income ..................................................................331 15.2.1 Cash income ..................................................................................................................331 15.2.2 The value of public services .........................................................................................332 15.2.3 Allocation of public services ........................................................................................332 15.2.4 Accounting for needs ....................................................................................................334 15.3 Cross-country comparison of income inequality and poverty..............................................337 15.3.1 Main results ...................................................................................................................339 15.3.2. Interaction between incomes and needs for public services ..................................339 15.4 Conclusion ...................................................................................................................................343 References .............................................................................................................................................343 Distributional effects of direct taxes and social transfers (cash benefits) 16. (Vaska Atta-Darkua and Andrew Barnard) .....................................................................................345 16.1 Introduction .................................................................................................................................346 16.2 Source, methodology and concepts ..........................................................................................346 16.2.1 Source .............................................................................................................................346 16.2.2 Methodology ..................................................................................................................346 16.2.3 Issues of income inequality ..........................................................................................348 16.3 Results ...........................................................................................................................................349 16.3.1 Overall effect ..................................................................................................................349 16.3.2 Results for retired households .....................................................................................357 16.3.3 Comparison of Gini coefficients .................................................................................360 16.4 Conclusions ..................................................................................................................................364 References .............................................................................................................................................367 Income and living conditions in Europeeurostat 13 Policy simulation across countries using EUROMOD: stress testing European welfare 17. systems for unemployment (Francesco Figari, Andrea Salvatori and Holly Sutherland) ..........369 17.1 Introduction .................................................................................................................................370 17.2 EUROMOD ................................................................................................................................370 17.2.1 Data .................................................................................................................................371 17.3 Methodological approach ...........................................................................................................372 17.3.1 Counterfactual scenarios .............................................................................................372 17.3.2 Sample of interest ..........................................................................................................373 17.3.3 Indicators........................................................................................................................373 17.4 Welfare systems for the unemployed in 2008 ..........................................................................374 17.5 Relative resilience .......................................................................................................................376 17.6 Protection against risk of poverty .............................................................................................378 17.7 Cost of protection ........................................................................................................................381 17.8 Conclusions ..................................................................................................................................382 References .............................................................................................................................................385 Beyond 18. GDP, measuring well-being and EU-SILC (Anthony B. Atkinson, Eric Marlier and Pascal Wolff) ................................................................. 387 18.1 Introduction .................................................................................................................................388 18.2 Conceptual issues ........................................................................................................................388 18.2.1 Drivers vs. outcomes .....................................................................................................389 18.2.2 Change in population vs. change in individual well-being .....................................389 18.2.3 Frequency and timeliness .............................................................................................389 18.2.4 Different needs for different sub-populations ...........................................................390 18.2.5 Household vs. individual well-being ..........................................................................390 18.2.6 Flow vs. stock .................................................................................................................390 18.3 Composite indices .......................................................................................................................390 18.4 EU-SILC and other household data sources ............................................................................391 18.5 Coherence among household surveys ......................................................................................393 18.6 Coherence of income data at an aggregate level ......................................................................394 18.6.1 Household income ........................................................................................................394 18.6.2 Imputed rent on owner-occupied housing ................................................................394 18.6.3 Individual consumption expenditure of general government ................................396 18.6.4 Pensions ..........................................................................................................................396 18.6.5 Sampling and non-sampling errors ............................................................................396 18.6.6 Reconciliation ................................................................................................................396 18.7 Conclusions ..................................................................................................................................397 References .............................................................................................................................................397 Appendices ..........................................................................................................................................399 Appendix 1: List of Net-SILC members ............................................................................................400 Appendix 2: Country official abbreviations and geographical aggregates ...................................401 Country official abbreviations ...............................................................................................401 Geographical aggregates ..........................................................................................................401 Appendix 3: Other abbreviations and acronyms .............................................................................402 Appendix 4: Author index ..................................................................................................................404 Appendix 5: Subject index ..................................................................................................................410 Income and living conditions in Europeeurostat 15 List of tables, figures and boxes Tables Table 2.1: Minimum effective sample size for the cross-sectional and longitudinal components by country ........................................................................................................ 45 Table 2.2: Basic concepts and definitions (Are the national definitions comparable with those of the standard EU-SILC?), 2008 ................................................................................................... 51 Table 3.1: Households receiving income from self-employment, 2007 ........................................................ 63 Table 3.2: Ratio of upper percentiles to the median, 2007 Total disposable household income (HY020).................................................................................................. 65 Table 3.3: Unit non-response (cross-sectional sample, 2007) ....................................................................... 67 Table 3.4: Item non-response: income from self-employment (PY050), 2007 ............................................ 70 Table 3.5: Estimates of standard errors and components of design effects, 2005–2006 ............................. 74 Table 4.1: Distribution of household types, 2007 ............................................................................................ 84 Table 4.2: Distribution of households by number of children, 2007 ........................................................... 86 Table 4.3: Household type in which children live, 2007 ................................................................................. 87 Table 4.4: Young people: transitions and percentages living alone, 2007 ..................................................... 90 Table 4.5: Percentage of partnerships which are cohabiting rather than marital partnerships fordifferent age groups of women, 2007 .................................................................................... 91 Table 4.6: The living arrangements of people aged 65 years and over, percentages, 2007 ......................... 93 Table 4.7: Factor loadings, 2007 ......................................................................................................................... 95 Table 5.1: National at-risk-of-poverty thresholds for a household consisting of 2 adults and 2 children below 14 in EU-27 countries (PPS), Survey Year 2008........................................105 Table 5.2: World Development Indicators in EU-27 countries as published in 2009...............................114 Table 5.3: National at-risk-of-poverty rates in EU-27, Survey Years 2003–2008 ......................................119 Table 5.4a: Income inequality in EU-27 countries: S80/S20 ratio, Survey Years 2003-2008 ...................121 Table 5.4b: Income inequality in EU-27 countries: Gini coefficients, Survey Years 2003-2008 ....................................................................................................................................122 Table 6.1: Joint distribution of income poverty and material deprivation, national distributions and EU-25 distributions by broad age groups (%), 2007 ......................................143 Table A.6.1 (1/2): Determinants of income poverty and material deprivation, 2007 ..............................152 Table A.6.1 (2/2): Determinants of income poverty and material deprivation, 2007 ..............................153 Table 7.1: Imputed rent as income: the estimation methods in EU-SILC, 2007 ......................................158 Table 7.2: Changes in income inequality when moving from cash incomes to incomes augmented with imputed rents, 2007..........................................................................160 Table 7.3: EU-wide income inequality indicators (Germany excluded), 2007 ..........................................162 Table 7.4: At risk of cash poverty rates, at risk of poverty rates after inclusion of imputed rents and at risk of poverty rates after inclusion of capped imputed rents (% of persons), 2007 .....................172 Table 8.1: Reason for not collecting PY070 ....................................................................................................183 Table 8.2: Differences between the poverty risk rates without and with OPP for different types of household (households, percentage points), 2008 ...................................................192 Table 9.1: Long-term concentration indices and mobility indices, 2005-2007 .........................................207 Income and living conditions in Europe eurostat16 Table 10.1: Participation in various types of informal activities during the last year, % of population per country, 2006 ..............................................................................224 Table 10.2: Ability to get help and frequency of getting together with relatives or friends (%), 2006 ...225 Table 10.3: Share of population meeting relatives of friends at least once a week (%). Comparison of EU-SILC 2006 data with those of the European Social Survey (ESS) 2004 and 2006 ..226 Table 10.4: Share of population helping others (outside own household) and those engaged in political actions during the last year, %. Comparison of EU-SILC 2006 data with those of the European Social Survey (ESS) 2004 and 2006 ........................................................................................228 Table 10.5: Correlation between measures of subjective well-being and social participation, 2006 ......229 Table 10.6: Alternative measures of social isolation across EU countries, share of population affected (%), 2006 ...........................................................................................................233 Table 11.1: Comparison of the longitudinal pattern of the material deprivation indicator and number of changed items (in % of the longitudinal population), 2006-2007 ...................252 Table 11.2: OLS Regression model for predicted net and gross multiple changes ....................................254 Table 12.1: Earnings in EU-SILC and in national accounts in 2006 (millions of euros and per cent) ......................................................................................................................270 Table.12.2: Statistics for the distribution of gross earnings in EU countries, 2006...................................278 Table 12.3: Statistics for the EU-wide distribution of gross earnings, 2006 ..............................................281 Table 12.4: Variance decomposition of the logarithm of monthly full-time equivalent earnings (absolute values and percentage shares in italics), 2006 ...........................................284 Table A.12.1: Alternative definitions of employee cash or near cash income in EU-SILC (%). Survey Year 2007 ...................................................................................................287 Table 13.1: Occupational classifications, by skill level, 2007........................................................................293 Table 13.2: Occupational distributions by gender, age and citizenship, selected EU-SILC countries combined (per cent in occupation), 2007 .....................................................300 Table 13.3: Skill Distributions by gender, age and citizenship, selected EU-SILC countries combined (per cent in skill category), 2007 .................................................301 Table A.13.1: Sample sizes ................................................................................................................................305 Table 14.1: Active, employed, in-work (%), 2007 ..........................................................................................311 Table 14.2: Poverty risk and workers at risk of poverty by employment status (%), 2007 .......................312 Table 14.3: Poverty risk within full year employment and activity profile of workers at risk of poverty (%), 2007 ...........................................................................................................313 Table 14.4: Workers at risk of poverty and concentration of poverty risk by household type (%), 2007 .................................................................................................316 Table 14.5: Poverty in earned income and poverty risk of ‘in-work’ workers, 2007 ................................320 Table 14.6: Poverty in earned income and poverty risk by gender, 2007 ...................................................321 Table 15.1: Alternative definitions of equivalent income .............................................................................337 Table 15.2: Equivalence scales by household type, 2006 ..............................................................................338 Table 15.3: Gini-coefficient for the distribution of income by income definition and country, 2006 .........................................................................................................340 Table 15.4: At-risk-of-poverty by income definition and country (%), 2006 ............................................340 Table 15.5: At-risk-of-poverty decomposed by subsets according to income definition and country (%), 2006 ..................................................................................................341 Income and living conditions in Europeeurostat 17 Table 15.6: At-risk-of-poverty for cash income measure (EU scale), by country and by quintiles of the needs index (%), 2006 ...........................................................................341 Table 15.7: At-risk-of-poverty for extended income measure (EU scale), by country and by quintiles of the needs index (%), 2006 ...........................................................................342 Table 15.8: At-risk-of-poverty for extended income measure (NA scale), by country and by quintiles of the needs index (%), 2006 ...........................................................................342 Table 16.1a: Income quintile share ratios (S80/S20) for ALL households, 2007 .......................................351 Table 16.1b: Gini and concentration coefficients for ALL households (%), 2007 .....................................352 Table 16.2: Summary of the size of cash benefits and direct taxes as a percentage of gross income for ALL households, 2007) .......................................................................353 Table 16.3: Cash benefits as the percentage of gross income by quintile groups for ALL households, 2007 ................................................................................................356 Table 16.4: Direct taxes as the percentage of gross income by quintile groups, 2007 ..............................358 Table 16.5: Gini and concentration coefficients for RETIRED households, 2007 ....................................361 Table 16.6: Summary of the size of cash benefits and direct taxes as a percentage of gross income for RETIRED households, 2007 ..........................................................................................362 Table 16.7: Cash benefits as the percentage of gross income by quintile groups for RETIRED households, 2007 ......................................................................................363 Table 16.8: Direct taxes as the percentage of gross income by quintile groups for RETIRED households, 2007 ......................................................................................365 Table 16.9: Comparing Gini coefficients for disposable income by country, 2007 ...................................366 Table 16.10: Gini coefficients for income — the United Kingdom .............................................................367 Table 17.1: Characteristics of the new unemployed ......................................................................................374 Table 17.2: Average Relative Welfare Resilience Indicator (RWRI) with and without unemployment benefits (UBs) ..........................................................................................376 Table 17.3: Proportion of the new unemployed protected from falling below the poverty threshold in unemployment, with and without unemployment benefits (UBs) .......................................382 figures Figure 2.1: EU-SILC implementation ............................................................................................................... 39 Figure 2.2: Fieldwork period, 2008 ................................................................................................................... 48 Figure 2.3: Average interview duration per individual, 2008 ........................................................................ 49 Figure 2.4: Overall personal non-response rates, 2008 .................................................................................. 50 Figure 3.1: Types of errors in surveys ............................................................................................................... 60 Figure 3.2: Components of non-response ........................................................................................................ 66 Figure 4.1: Example of a household grid ......................................................................................................... 82 Figure 4.2: Clusters arising from Principal Components Analysis, 2007 .................................................... 96 Figure 5.1: National at-risk-of-poverty rates in EU-27, Survey Year 2008 ................................................107 Figure 5.2: National at-risk-of-poverty rates for children and for overall population in EU-27, Survey Year 2008 ......................................................................................108 Figure 5.3: National at-risk-of-poverty rates and relative median at-risk-of-poverty gap in EU-27, Survey Year 2008 ......................................................................................110 Figure 5.4: Income inequality in EU-27 countries, Survey Year 2008 ........................................................111 Income and living conditions in Europe eurostat18 Figure 5.5: National at-risk-of-poverty rates and S80/S20 ratios, EU-27, Survey Year 2008 ..................113 Figure 5.6: National at-risk-of-poverty rates in various EU and non-EU countries: Estimates from OECD, EU-SILC and LIS, Survey Year 2008 .............................116 Figure 5.7: National Gini coefficients in various EU and non-EU countries: Estimates from OECD, EU-SILC and LIS, Survey Year 2008 ......................................................................117 Figure 5.8: Millions taken out of income poverty and number of EU countries that need to be involved, Survey Year 2008 ...........................................................................125 Figure 5.9: Multiple indicators for the Europe 2020 target, figures for EU-27 in million of persons, Survey Year 2008 .............................................................................................127 Figure 5.10: Extent of overlap according to the three indicators on which the Europe 2020 target on social inclusion is based, EU-27, Survey Year 2008 ..............................................128 Figure 6.1: National material deprivation rates and national and EU-wide at-risk-of-poverty rates (AROP), 2007 ..........................................................................................137 Figure 6.2a: Intensity of deprivation (from 0 to 9) and deprivation rate (%) according to the level of equivalised income (% median), EU-15 and Norway, 2007 .............................140 Figure 6.2b: Intensity of deprivation (from 0 to 9) and deprivation rate (%) according to the level of equivalised income (% median), NMS10 excluding Malta, 2007 ....................141 Figure 7.1: Changes in income inequality and average income (without imputed rent --> with imputed rent) , 2007 ..................................................................................161 Figure 7.2: Changes in the at-risk-of-poverty positions when imputed rents are added to income (population shares), 2007 ...................................................................163 Figure 7.3: Relative changes in the FGT income poverty measures: total population (%), 2007 ...........164 Figure 7.4: Contributions of owner households (bars) to the change in total poverty risk rates (line), 2007 .............................................................................................................164 Figure 7.5: Contributions of tenant households (bars) to the change in total poverty risk rates (line), 2007 ...................................................................................167 Figure 7.6: Material deprivation rates in populations exiting from, entering in and remaining in population at risk of poverty due to inclusion of imputed rents in income, 2007...................................................................................................167 Figure 7.7: Changes in material deprivation and overcrowding in the population at risk of poverty due to the inclusion of imputed rents in income, 2007 ....................................................................................................................169 Figure 7.8: Share of cash poor house rich persons with imputed rent values that at least double their cash disposable income, 2007 ...........................................................169 Figure 7.9: Change of the range of the income share of imputed rents by capping based on overcrowding criteria, persons with non-zero IR, 2007 ..........................................172 Figure 7.10: Net change (entries minus exits) in the number of persons at risk of poverty when full or capped value of IR is added to income (% of persons at risk of cash poverty), 2007 ....................................................................174 Figure 7.11: Entries to and exits from the population at risk of poverty when IR is added to income (% of persons at risk of cash poverty), 2007 ......................175 Figure 8.1: Recording income from own-consumption and importance of that in total income, 2007 ............................................................................................................................185 Figure 8.2: Recording income from own-consumption and importance of that in total income by data collection method in Estonia, 2000–2008 ......................................................................................................186 Income and living conditions in Europeeurostat 19 Figure 8.3: Percentage of households producing goods for own consumption, 2008 ..............................187 Figure 8.4: Share of income from own consumption in total disposable household income (%), 2008 ...........................................................................................187 Figure 8.5: At-risk-of-poverty rate without and with OPP, % of people, 2008 ..........................................188 Figure 8.6: Relative median poverty risk gap (%), 2008 ...............................................................................188 Figure 8.7: In-work poverty risk without and with OPP, % of people, 2008 ............................................190 Figure 8.8: Income inequality (S80/S20 ratio) with and without OPP, 2008 .............................................190 Figure 9.1: Concentration curve for an indicator of health limitations compared to the 45-degree line (diagonal) of perfect equality – The example of Cyprus in 2007 ................................202 Figure 9.2: Percentage of individuals reporting very good or good self-assessed health, 2005-2007 ......................................................................................................................204 Figure 9.3: Percentage of individuals reporting health limitations in their daily activity, 2005-2007 .....................................................................................................................204 Figure 9.4: Percentage of individuals reporting a long-standing chronic illness, 2005-2007 ..................205 Figure 9.5: Concentration indices for health limitations for waves 2005, 2006 and 2007 .......................206 Figure 9.6: Decomposition results for the 2007 health model ....................................................................209 Figure 10.1: Frequency of getting together with relatives (%), 2006 ..........................................................221 Figure 10.2: Percentage of population who have frequent personal contact with relatives and friends, 2006 ........................................................................................................221 Figure 10.3: ‘Cyber’ contacts versus personal meetings: ratio of those with cyber contact compared to those with personal meetings minimum once a week, 2006 ..........................................................................................222 Figure 10.4: Social isolation across EU countries: not able to ask any relative, friend or neighbour for help (%), 2006 .........................................................230 Figure 10.5: Social isolation at an EU level: share of population never meeting friends, relatives or either of these (%), 2006 ....................................232 Figure 10.6: Ratio of those with no friends by age groups compared to the total population, 2006 .........................................................................................................234 Figure 10.7: Ratio of those with no help by age groups compared to the total population, 2006 ............................................................................................................................236 Figure 10.8: Ratio of those with ‘no friends’ by poverty status (ratio between those at risk of poverty and those not at risk), 2006...........................................................237 Figure 10.9: Ratio of those with ‘no help’ by employment status (Ratio between particular groups and the total working age population), 2006 ......................................238 Figure 11.1: Analytic framework for social exclusion in Europe ................................................................243 Figure 11.2: Importance of non-market services in the disposable incomes of private households*), national accounts, 2008............................................246 Figure 11.3: Gross change for deprivation items (%), 2006-2007 ...............................................................249 Figure 11.4: Net change for deprivation items (%), 2006-2007 ...................................................................249 Figure 11.5: Gross change in deprivation items for working age individuals who increase or decrease labour market participation (%), 2006-2007 ................................250 Figure 11.6: Map of predicted net multiple changes in the reference group (%), 2006-2007 ................256 Figure 11.7: Map of predicted gross multiple changes in the reference group (%), 2006-2007 ..............258 Figure 12.1: Map of available net and gross employee cash or near cash income in EU-SILC, Survey Year 2007 ..................................................................................................269 Figure 12.2: Tax wedge on labour costs for low wage earners in 15 EU countries (%), Survey Year 2007 ..........................................................................................................272 Figure 12.3: Impact on measured real wages of the choice of the PPP index, 2006 .................................274 Figure 12.4: Distribution of real monthly full-time equivalent gross earnings in EU countries (thousands of euros in PPS-HFCE), 2006 ......................................................................................................275 Figure 12.5: Decile ratio of gross earnings in EU countries, 2006 ..............................................................277 Figure 12.6: Distribution of real monthly full-time equivalent earnings in selected EU countries by different definitions of earnings (PPS-HFCE), 2006 ...............................................................................280 Figure 13.1: Employment shares by skill level, EU and US, 2007 ...............................................................295 Figure 13.2: Employment shares by skill level, selected EU-SILC countries, 2007 ..................................297 Figure 13.3: Cumulative employment shares by skill level and demographic group, selected EU-SILC countries, 2007 ...................................................................................................................302 Figure 14.1: Employment problems (*) and poverty risk (ratio of % of women to % of men) ..............318 Figure 14.2: Poor in earned income/not poor in earned income (%), 2007 ..............................................321 Figure 14.3: Poverty in earned income in in-work poverty risk by gender (%), 2007 .............................322 Figure 16.1: Concentration curve for benefits (5 households) ....................................................................349 Figure 16.2: Summary of the effect of taxes and benefits on reducing income inequality of the Gini coefficient (percentage point reduction in Gini coefficient), 2007 .........................................355 Figure 16.3: Scatter plot of disproportionality of benefits (%) vs. their average rate (%), 2007 ..............359 Figure 16.4: Scatter plot of disproportionality of taxes (%) vs. their average rate (%), 2007 ...................359 Figure 17.1: Average Relative Welfare Resilience Indicator (RWRI) and post-unemployment household income composition, with unemployment benefits ...........................................................................................................................377 Figure 17.2: Average Relative Welfare Resilience Indicator (RWRI) and post-unemployment household income composition by household income quintile group, with unemployment benefits.................................................................................................379 Figure 17.3: The proportion of new unemployed at risk of falling below the poverty threshold, with unemployment benefits ........................................................................380 Figure 17.4: Average budgetary cost per unemployed person (as a proportion of per-capita national disposable income) .......................................................................383 Figure 18.1: Linking national income flows to household disposable income .........................................395 Boxes Box 14.1: Definitions of workers .....................................................................................................................310 Box 14.2: Activity profiles .................................................................................................................................314 Box 14.3: An indicator of poverty in earned income ....................................................................................319 1Living conditions in Europe and the Europe 2020 agenda Anthony B. Atkinson and Eric Marlier (1) (1) Anthony B. Atkinson is at Nuffield College, Oxford, and the London School of Economics (UK); Eric Marlier is with the CEPS/INSTEAD Research Institute (Luxembourg). The authors would like to thank the European Commission for their support of the Network. The European Commission bears no responsibility for the analyses and conclusions, which are solely those of the authors. Addresses for correspondence: tony.atkinson@nuffield.ox.ac. uk and eric.marlier@ceps.lu. Income and living conditions in Europe eurostat22 1 Living conditions in Europe and the Europe 2020 agenda 1.1 Introduction This book is about the incomes and living standards of the people of Europe. The reader will learn about employment, income inequality and poverty, housing, health, education, deprivation and social exclusion. The chapters tell about how the workers of Europe earn their living, about the living arrangements of Europeans, about their social participation, and about the ways in which their incomes are affected by taxes and transfers. The book addresses many of the social issues confronting Europe. How much income poverty is there in Europe? Is inequality increasing? Does a job guarantee escape from income poverty? How is Europe’s welfare state coping with the economic crisis? Evidence about these important dimensions of European society comes from a data source that has been progressively implemented since 2003: the EU Statistics on Income and Living Conditions (EU-SILC). EU-SILC represents a powerful instrument for the analysis of the economic and social state of the European Union (EU) as well as a growing number of non-EU European countries. It is a large investment, and requires substantial effort on the part of the European Statistical System (ESS), but it is already playing a major role in the provision of key socio-economic statistics. EU-SILC has boosted the possibilities of carrying out comparative analyses of income distribution and living conditions in Europe. It is therefore important to take stock of what has been achieved and to consider possible future applications and developments of EU- SILC. It was for this reason that the Net-SILC Network was established, in response to a call for applications by the Statistical Office of the European Union (Eurostat) in 2008. The Network, coordinated by Eric Marlier in close cooperation with Anthony B. Atkinson, consisted of eight teams from participating ESS bodies (seven National Statistical Institutes (Austria, the Czech Republic, Estonia, Finland, Italy, Norway, the United Kingdom) and the CEPS/ INSTEAD research institute in Luxembourg), eight teams from academic institutions (2), with the additional participation of the Bank of Italy and the French National Statistical Institute (INSEE). Net-SILC was thus an ambitious 18-partner Network bringing together expertise from both data producers (directly involved in the collection of EU-SILC data) and data users. The present book represents a major output from the Net-SILC Network, but not all of the scientific work produced in the context of Net- SILC could be covered. In the book, we have focused on the research findings that we believe are likely to be of interest to the general reader and to those concerned with policy. We asked the authors of individual chapters to make them as accessible as possible to the non-specialist. More technical material, and the output from the more methodological work packages, are available in the series Eurostat methodologies and working papers. Our emphasis in this book reflects the fact that EU-SILC plays a central role in the promotion of the Social Agenda of the EU. (3) In its list of the main users of EU-SILC data, Eurostat puts at the head ‘institutional users’ and in particular the EU Social Protection Committee (SPC), the body that has been in charge of coordinating and monitoring together with the European Commission the Open Method of Coordination on social protection and social inclusion (Social OMC) since it was launched back in 2000. (4) (2) Nuffield College (UK), Wissenschaftszentrum Berlin für Sozialforsc- hung (‘WZB-Berlin’, Germany), Institut Wallon de l’Evaluation, de la Prospective et de la Statistique (‘IWEPS’, Belgium), European Centre for Social Welfare Policy and Research (Austria), London School of Economics (UK), Institute for Social and Economic Research of the University of Essex (ISER, UK), University of Sienna (Italy), Kent State University (USA). (3) On the ‘Renewed Social Agenda’ adopted by the European Commission on 2 July 2008, see: http://ec.europa.eu/social/main.jsp?catId=547. (4) The SPC consists of officials from mainly Employment and Social Af- fairs Ministries in each Member State as well as representatives of the European Commission; it reports to the EU ‘Employment, Social Poli- cy, Health and Consumer Affairs’ (EPSCO) Council of Ministers. In the context of the Social OMC, all EU countries cooperate in the fields of social inclusion, pensions, and healthcare and long-term care. For more information on the SPC and the Social OMC, see European Commission website http://ec.europa.eu/social/main.jsp?catId=750&langId=en. Income and living conditions in Europeeurostat 23 1Living conditions in Europe and the Europe 2020 agenda During the life of the Network, EU-SILC took on particular significance with the adoption in June 2010 by the European Council of the Europe 2020 Headline Targets. (5) The fifth of these targets relates to poverty and social exclusion, and EU-SILC will be the reference source for the three indicators on which this new EU target is based (as discussed further below in Section 1.4 and in Chapter 5). The EU at the time of writing has 27 Member States. Its current coverage reflects the Enlargements that have taken place in recent years. As a result of the May 2004 Enlargement, the EU grew from 15 to 25 Member States. The 10 new EU countries were Cyprus, the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Slovenia and Slovakia. In January 2007 (the most recent Enlargement), Bulgaria and Romania joined. (6) Readers should note that in a number of chapters Bulgaria, Malta and Romania are not covered because data for these countries were not available from the EU-SILC Users’ database (UDB) to which the Network had access. Increasingly, EU-SILC is being recognised as a significant international statistical resource, not least because its coverage is not confined to the 27 EU countries (EU-27). The ‘framework’ approach adopted when establishing EU-SILC is an innovative experiment that may have lessons for other areas of EU statistics. It is hoped therefore that the Net-SILC findings will appeal to readers from outside the EU. In particular, it is relevant to the world-wide interest in moving Beyond GDP, and this is the subject of Chapter 18. Our focus in the book is on EU-SILC, but reference should be made to other important EU sources of evidence about incomes and living conditions. These (5) The European Council, which brings together the EU Heads of State and Government and the President of the European Commission, defines the general political direction and priorities of the EU. Every spring, it holds a meeting that is more particularly devoted to eco- nomic and social questions – the Spring European Council. With the entry into force of the Treaty of Lisbon on 1 December 2009, it has become an official institution and has a President. The Con- clusions of the June 2010 European Council are available from: http://ec.europa.eu/eu2020/pdf/council_conclusion_17_june_en.pdf. (6) See list of ‘Country official abbreviations and geographical aggregates’ (Appendix 2). EU sources include inter alia the Labour Force Surveys (used for example in Chapter 17), the Eurobarometer (used for example in Chapter 11), and the European Social Survey (used for example in Chapter 10). An important resource for the analysis of tax and benefits is the EUROMOD model, described in Chapter 17. A major reference point for a number of chapters is the data provided by the Luxembourg Income Study (LIS) and the analysis by the Organisation for Economic Cooperation and Development (OECD). In the remainder of this Introduction, we describe (Section 1.2) the contents of the book and the other outputs from the Net-SILC Network, summarise (Section 1.3) the main lessons for the future development of EU-SILC, and consider (Section 1.4) the role that EU-SILC may play in the new political context of formation of national targets and related social policies within the broader framework of EU targets. We also get back to the latter in Chapter 5. 1.2 Outline of the contents The book opens in Chapter 2 with a description of the EU-SILC data, provided by Eurostat. The description of statistical sources and methods may not strike the reader as the most gripping subject. Many universities have removed courses on statistical sources from their social science syllabi, replacing them by courses that are more eye-catching or more mathematical. But data are very important, and cannot be taken for granted. While data can today be downloaded from many sources and immediately turned into tables and graphs or used to estimate statistical models, they can only be reliably used on the basis of an appreciation of their strengths and weaknesses. Unless one knows something about the origins of the data, and the processing methods that have been applied, a data user – even the reader of tables and graphs in this book – can go seriously wrong. For the same reason, we urge readers to consult Chapter 3 by Verma and Betti on data accuracy in EU-SILC. The authors have summarised succinctly, and in a largely non- Income and living conditions in Europe eurostat24 1 Living conditions in Europe and the Europe 2020 agenda technical manner, the different dimensions of data quality. As a minimum, the reader should look at Table 3.1, which lists the manifold possible sources of error. The risk in the other direction is that, rather than ignoring the possible shortcomings of the data, the reader is overwhelmed by the catalogue of possible sources of error. How, the reader may ask, can any weight be attached to the outcome of such a process? Such a reaction goes too far. One of the aims of Chapters 2 and 3 is to describe the procedures applied to deal with the potential problems and the checks that are applied. Indeed, many of the chapters consider the validity of the EU-SILC data, including comparisons with other statistical sources. These checks reveal that there are issues that need to be addressed (see Section 1.3 below), and some of the results must be hedged by qualifications. The overall picture, however, is re-assuring, and, in our view, the EU- SILC data have survived well the demands placed on them in the research projects carried out as part of Net-SILC. The substantive contents start with Chapter 4, where Iacovou and Skew examine the evidence about household structure in Europe. The differences across Europe in household formation are one of the features obvious to any traveller, and the data confirm a number of these impressions. In the Nordic countries, for example, around a quarter of all households consist of a single adult aged under 65, whereas in Cyprus, Portugal and Spain the proportion is less than a tenth. What is less obvious is how to draw out common patterns, particularly with the Enlargement from EU-15 to EU-25, and a particular focus of the chapter is the integration of ‘New’ Member States who joined the EU in May 2004 into the analysis. Their statistical analysis highlights three factors: the importance of the extended family, the stability of the intimate relationship, and the level of fertility. The role of the first two factors is conveniently summarised in Figure 4.2. After asking who lives in the household, the next question may well be ‘what is their income?’ Income is an important variable for Europe’s households. People are naturally concerned with how much they receive each month in the form of earnings from work (employment or self-employment), from pensions, from other government transfers such as unemployment benefits, family benefits or sick pay, and from their savings. In Chapter 5, Atkinson, Marlier, Montaigne and Reinstadler examine the distribution of income in EU-27. Are there large differences? In which countries are the differences largest? Particular concern attaches to those households considered ‘at–risk-of- poverty’ according to the EU definition (7) and this is one of three indicators that form the basis for the newly adopted EU Headline Target for poverty and social exclusion (see Section 1.4). The findings show that 1 in 6 (or 16 per cent) citizens of the EU-27 are at risk of poverty, and they are to be found in all Member States. This overall poverty rate has varied little over the period covered by EU-SILC. In three-quarters of Member States, the proportion of children at risk of poverty exceeds the overall proportion; there are real grounds for concern about child poverty and the social inclusion of children in Europe. Success in reducing income poverty tends to go with success in reducing income inequality; there are no instances of countries pursuing a low poverty/high inequality strategy. We do not yet know the impact of the economic crisis, but the picture prior to 2008 was not a static one. Some countries achieved sustained reductions in the proportions at-risk-of-poverty, but in the EU as a whole this progress has been offset by reversals in other Member States. It is widely (7) In each country, the EU indicator of at-risk-of-poverty is calcu- lated with a threshold set at 60 per cent of the national household equivalised median income; it is thus a relative definition. The most recent list of indicators that have been commonly agreed by the EU for monitoring the Social OMC was adopted by the EU Social Pro- tection Committee in the second half of 2009. This list includes four portfolios of indicators and context information: one for the So- cial OMC as a whole (overarching portfolio) and one for each of the three social strands (social inclusion, pensions and health portfo- lios). For each indicator, it provides the agreed definition and socio- demographics breakdowns. The detailed and updated description of the ‘Portfolio of indicators for the monitoring of the European strategy for social protection and social inclusion’ is available from: http://ec.europa.eu/social/main.jsp?catId=756&langId=en. Income and living conditions in Europeeurostat 25 1Living conditions in Europe and the Europe 2020 agenda believed that income inequality was increasing globally prior to the economic crisis, but the EU- SILC data suggest that the EU picture is more nuanced, with some Member States exhibiting declining inequality. The at-risk-of-poverty indicator described above relates to income, and the poverty threshold is defined relative to the median income of the country in which a household resides. The indicators of material deprivation recently adopted by the EU, analysed in Chapter 6 by Fusco, Guio and Marlier, represent a significant departure in that they are not income-based and in that the same threshold is applied across the EU-27. The EU deprivation indicators are based on the enforced lack of items from a list of nine items (which include one week annual holiday away from home, adequate heating, having a washing machine, etc). The resulting picture of deprivation is, not surprisingly, different from that with the at-risk-of-poverty indicator. While some countries, such as the Netherlands, score well on both, other countries are found in different positions. Hungary and Slovakia for example have high levels of material deprivation but low income poverty rates. Not only countries, but also people, change positions. In any country, some people are income poor but not materially deprived, and vice versa. There is in this respect a divide between EU-15 and the New Member States, there being a greater degree of overlap in the former case. The next chapters probe further into the living conditions of Europe’s households. Housing is evidently a key concern, but we have to take account of the different forms of housing tenure. In Chapter 7, Sauli and Törmälehto examine the consequences of the fact that owner occupiers are advantaged by virtue of not having to pay rent. It is therefore not easy to compare their standards of living with those of tenants. (There are also some tenants who pay rents below the market rate or live rent-free.) After all, if two owners were to rent out their houses to each other, then the rent received would count as part of their income. The procedure examined in Chapter 7 involves ‘imputing’ a rent to owners, to take account of the benefit derived (with the actual housing costs being subtracted). The authors show that such an adjustment would affect the majority of households: overall, nearly 80 per cent of EU households owned their main residence or rented at a below-market rent. The lowest home ownership rates are found in Austria and Germany. Inclusion of imputed rent leads to a lower estimate of the degree of income inequality. The at-risk-of-poverty rate would fall by 5 percentage points in Ireland and the United Kingdom, by 4 percentage points in Estonia and Spain, and by more than 2 percentage points in Belgium, Greece, Latvia and Portugal. Smaller in scale, but a relatively important source of income in some Member States, is the consumption of goods and services produced by the household, the subject of Chapter 8 by Paats and Tiit. This information is not collected by all countries participating in EU-SILC, on the grounds that other sources show that own consumption does not represent a significant proportion of income. For other countries, particularly Bulgaria, Latvia, Lithuania, and Romania, the amounts are significant, in that their inclusion reduces the at-risk-of-poverty rate by more than 1 percentage point. The book then turns to other dimensions of life in Europe. Chapter 9, by Hernández-Quevedo, Masseria and Mossialos, is concerned with the socio-economic determinants of health. Although, puzzlingly, the Europe 2020 Headline Targets do not include a health dimension, the EU has become increasingly concerned about the growing disparities in the health of the European population. The EU-SILC data used relate to self-perceived health status, the presence of long- standing illness or disability, and the presence of limitations on daily activity. As they show, there is considerable cross-country variation. The highest proportion reporting their health as ‘very good’ or ‘good’ is three-quarters or more in Cyprus, the Netherlands, Sweden and the United Kingdom, while proportions less than a half are to be found in Latvia, Lithuania, Hungary and Portugal. The proportions reporting health limitations on activity are around one fifth in Cyprus, Poland, Income and living conditions in Europe eurostat26 1 Living conditions in Europe and the Europe 2020 agenda Sweden and the United Kingdom, but over a third in Estonia, Finland and Latvia. The particular aspect on which the authors focus is the variation of health by socio-economic status: the feature identified by the WHO Commission on the Social Determinants of Health (World Health Organisation, 2008). As they show by means of concentration curves, health limitations are in all countries concentrated among the households with lower income. A feature of EU-SILC is the inclusion of special modules that vary from year to year, allowing the range of information to be extended. Chapter 10 by Lelkes makes use of the special module in 2006 that dealt with social participation. The results show that differences in the extent of participation across Member States are significant, but that there is no evident geographic pattern. She finds that ‘cyber’ intimacy is on the rise, although this mostly affects relationships with relatives. The longitudinal (panel) nature of the EU-SILC data is exploited by Till and Eiffe in Chapter 11. They begin by stressing the importance of being able to track changes over time in the circumstances of individuals and households. As they note, the stability of the overall EU poverty rate around 16 per cent is consistent with the same one sixth of the EU population remaining permanently below the poverty threshold or with a continuously rotating poverty population where everyone spends one year in six in poverty. Only panel data, following the circumstances of the same people over time, can determine how much mobility there is within the poverty population. Till and Eiffe concentrate on the elements of one of the recently adopted EU indicators of material deprivation. Their results show considerable gross change for a number of the items that constitute the indicator. There is little change for the ownership of TV, telephone and washing machine, but more than 15 per cent change for the affordability of a holiday or unexpected expenses. The next three chapters turn to the labour market. In Chapter 12, Brandolini, Rosolia and Torrini start from the long-standing aim of the EU to create an integrated labour market, facilitating the free movement of workers. They use the EU-SILC data to analyse, for the first time, the distribution of labour earnings in the EU-25 as a whole – i.e. considering the EU-25 area (except for Malta) as one single country. For monthly full-time equivalent gross earnings in the ‘Euro area’ (8), when earnings in different countries are adjusted using purchasing power parities (9), they find a Gini coefficient (10) of 34 per cent, a figure that rises to 38 per cent for the EU-25 area. The higher inequality in the larger grouping is largely attributable to the differences across countries; this in turn is much more due to the differences in the rewards associated with worker characteristics (such as age and education) than to differences in the distribution of these characteristics. This finding has evident implications for labour market policy. Education and skill feature prominently in Chapter 13 by Williams, who investigates the educational intensity of employment in the EU and draws an interesting contrast with the United States. He assigns skill levels to individual occupations, which are then grouped (9 groups in the EU and 11 in the US), and computes employment shares by these groupings. The comparison suggests that, despite the differences between the EU and the US, the educational intensity of employment, that is the underlying distributions of jobs and skills is quite similar at the (supra) national level. Within the EU there are differences across countries, and the author identifies four sub-groups. A crucial issue for EU policy is the degree of complementarity between the employment objective and the fight against poverty and (8) See list of ‘Country official abbreviations and geographical aggregates’ (Appendix 2). (9) Purchasing Power Parities (PPP) convert amounts expressed in a na- tional currency to an artificial common currency that equalises the purchasing power of different national currencies (including those countries that share a common currency). (10) The Gini coefficient is an income inequality indicator based on the cumulative share of income accounted for by the cumulative percent- ages of the number of individuals, with values ranging from 0 per cent (complete equality) to 100 per cent (complete inequality). Income and living conditions in Europeeurostat 27 1Living conditions in Europe and the Europe 2020 agenda social exclusion. In Chapter 14, Ponthieux asks whether work is sufficient to escape poverty. She highlights the key problems with the existing EU indicator of ‘in-work poverty risk’. First, there is the definition of a ‘worker’; secondly, there is the dual level of analysis, since work is an individual phenomenon, whereas the poverty status is defined for the household as a whole. The results show that the choice of definition matters. Increasing selectivity in the definition of workers does not have a uniform impact across countries and tends to eliminate those with less stable employment, hence emphasising the role of the household situation in generating the risk of poverty. The author argues that the existing EU indicator of in-work poverty needs to be complemented with an individual-based indicator of ‘poverty in earned income’, where people earn less, after tax, than the amount required to reach the poverty threshold for a single person. Chapters 15, 16 and 17 are concerned with the role of the state in taxation, the payment of transfers, and the provision of public services. Chapter 15, by Aaberge, Langørgen and Lindgren, focuses on the benefits provided by public education and health care services. The calculations of income inequality and poverty described in earlier chapters subtract the direct taxes paid by people to finance public spending (although not the indirect taxes), but take no account of the benefits in kind they receive (although cash transfers are part of disposable income). The authors examine how the extension of the definition of income to include a valuation of these benefits affects estimates of income inequality and poverty. There are two steps in the calculation. First, an amount has to be allocated to each household, which in the chapter is based on the cost of provision and the characteristics of individual households. Second, the equivalence scales used to adjust household income for household composition have to be modified to allow for differential needs for education and health care. The results show that there is a significant reduction in estimated inequality and poverty when health and education benefits are taken into account. The Gini coefficient, for example, is typically reduced by some 4 to 6 percentage points. Chapter 16, by Atta-Darkua and Barnard, investigates the distributional impact of the direct taxes and cash benefits. The impact has been the subject of studies in individual countries, such as the long-running series on ‘The effects of taxes and benefits on household income’ in the United Kingdom, but their study is the first to apply the methodology across the EU. As the authors emphasise, the calculation is an arithmetic exercise, since no attempt is made to estimate the distribution in the absence of taxes and benefits (if, for example, there were no state pensions, then many more pensioners would have other income or would be living with relatives). As in Chapter 9, one of the tools of analysis is the concentration curve, showing the distribution of taxes and benefits by pre-tax pre-benefit income. For the EU as a whole, the payment of cash benefits is associated with a reduction in the Gini coefficient from 39.6 per cent to 35 per cent, and direct taxes reduce it further to 31 per cent. The extent of the reduction differs considerably across Member States, from 14.6 percentage points in Ireland to 3.4 percentage points in Cyprus. The analysis of Chapter 16 records the impact of taxes and benefits as actually paid. Chapter 17, by Figari, Salvatori and Sutherland, asks how the European tax and benefit systems would react to changed circumstances — notably the current economic downturn. They ‘stress test’ the European welfare state. For this purpose, a micro-simulation model is required. The model used, EUROMOD, starts from survey data (in most cases EU-SILC) but then estimates how taxes and benefits could change in response to changed circumstances. If, for example, people become unemployed, then they may receive income replacement in the form of unemployment benefit and other transfers such as housing benefit; they may no longer be paying income tax and social security contributions on their earnings. Their central finding is that the key factor in protecting a household from a drop in income is the presence of other Income and living conditions in Europe eurostat28 1 Living conditions in Europe and the Europe 2020 agenda people with earnings in the household. In the United Kingdom, for example, whereas two- thirds of the unemployed are protected against falling below the income poverty threshold, the proportion falls to a quarter where the person becoming unemployed was the sole earner in the household. As far as the budgetary cost is concerned, the bulk of the cost is not the payment of unemployment benefit but the revenue lost in income tax and social security contributions. The final Chapter 18 by Atkinson, Marlier and Wolff takes up the Beyond GDP agenda and considers the way in which EU-SILC can contribute to the fuller measurement of the economic and social dimensions of well-being. In order to translate into concrete action the declared intentions of the European Commission in its 2009 Communication GDP and beyond, a number of major issues need to be taken into account and warrant further discussion. These issues concern both concepts and the development of data sources. In the former case, the chapter provides a checklist of questions that need to be addressed; and it considers whether the end-product should be a composite index, like the Human Development Index. In considering data sources, it is argued that the net should be cast wide, but that there needs to be further investigation of the combination by means of statistical matching of different pan-European surveys, such as EU-SILC, the Labour Force Survey, the European Quality of Life Surveys and the European Social Survey. The chapter also highlights the question of coherence: across household surveys and between household and aggregate data. In this way, the final chapter builds a bridge between the statistical source used in the book — EU-SILC — and the wider agenda for statistical development. 1.3 Summary of main lessons for EU-SILC The book as a whole demonstrates the value of the EU-SILC data. The data situation in Europe is incomparably better than 20 years ago. First, the European Community Household Panel (ECHP), and now EU-SILC, have provided Europe with statistical instruments that span the EU-27 and beyond, and provide rich information about a wide variety of dimensions. At the same time, there are a number of respects in which the instrument, its implementation, and access to the data, fall short of what is needed to address the questions investigated in the different chapters. One of the purposes of the Net-SILC Network has indeed been to identify directions for further development of the EU-SILC data. We summarise below a number of the proposals for improvement. A number of the suggestions concerned the provision of information to EU-SILC users and the elaboration of responses to questions. In Chapter 3, Verma and Betti showed how the investigation of data reliability requires fuller information than currently available in the Users’ database (UDB). (Although they note that there are respects, such as item non-response, for which the information supplied is excellent.) Importantly, (a) the panel design means that the proper calculation of response rates requires that the households be identifiable at successive interviews, and (b) the UDB does not, in most cases, contain the information on sample structure, particularly concerning stratification, necessary to compute sampling errors. Till and Eiffe note that important variables such as the calendar of activities and housing costs are not currently available in the longitudinal UDB. Brandolini, Rosolia and Torrini suggest that more information needs to be provided about the ways in which different earnings variables are calculated, including the use of imputation, and ideally there should be accompanying documentation on institutional features of the labour market. A number of suggestions concern the scope and form of the survey questions. In Chapter 4, Iacov- ou and Skew point out that EU-SILC differs from a number of other household surveys in not pro- viding a ‘household grid’ or ‘relationship matrix’, which records the relationship between each of the household members. In Chapter 8, Paats and Income and living conditions in Europeeurostat 29 1Living conditions in Europe and the Europe 2020 agenda Tiit draw on Estonian experience to show the im- pact of the type of questionnaire on the amounts reported as self consumption. In Chapter 10, Lelkes draws attention to variations in the ques- tions asked regarding participation in the special module of 2006, and highlights the significance of framing effects (i.e. the way the question is formu- lated, where it appears in the questionnaire, etc.). Till and Eiffe in Chapter 11 note a number of vari- ables that had been covered in the earlier ECHP but which are not available in EU-SILC. They also suggest that, in the case of certain material depri- vation variables, dichotomous response categories be replaced by a more differentiated set of answer categories. To these proposals made in individual chapters, we add an important consideration that is not adequately reflected in the book: the need to cover the non-household population. EU-SILC, like most household surveys, covers only those liv- ing in private households. The data typically omit those living in institutions, such as old people’s homes, military camps or prisons. The data omit the homeless. We would attach high priority to the extension of coverage to take account of these groups, potentially containing a disproportionate number of poor and socially excluded individuals. The issue of timeliness recurred. This should be seen as part of the more general issue of the frequency with which the variables need to be measured in order to allow for changes to be satisfactorily monitored. The use of annual observations is largely a convention, and there are undoubtedly cases where less frequent observations are sufficient. For example, the fact that EU-SILC has only covered social participation in a special module (in 2006) may not necessarily be a handicap if the module can be repeated, say every five years. On the other hand, there are other variables, such as living standards, where we may find it useful to carefully watch half- yearly or even quarterly changes. In these cases, EU-SILC data are not appropriate in their present form. In part this is a matter of reducing time lags between data collection and data publication. But it is also a matter of the design of the survey and the nature of the questions being posed. This brings us to the well-known disjunction between two reference periods: that of the information relating to the personal and household information at the time of the survey interview, and that of the income information. (See for example the last paragraph of Chapter 14.) Income-based indicators (such as the at-risk-of-poverty rate) are assessed, in all but two countries, (11) on the basis of the household income in the preceding calendar year but the household composition is that at the time of the interview. Relying solely on annual income in the previous calendar year introduces errors where the household composition has changed, and means that the assessment is delayed. For reasons of both accuracy and timeliness, consideration needs to be given to the collection of information about current income. The German Deutsches Institut für Wirtschaftsforschung (DIW Berlin), for example, reports measures of income inequality and poverty from the German Socio- Economic Panel (GSOEP) on the basis both of last year’s income and of current income. Resolution of this problem appears to us to be of high priority. Other detailed issues surrounding the data include: a) Chapter 6 highlights the importance of a careful examination of the lower tail of the income distribution and suggests that a common methodology for the treatment of outliers (especially negative income components) should be used at national and EU level, and that a better understanding of the underreporting of some income components is needed; b) several chapters emphasise the need to improve income information for the self-employed; c) comparability in the operationalisation of ten- ure status and the differences in the estima- tion methods used to calculate imputed rent, as shown in Chapter 7 (see particularly Table 7.1); (11) The two exceptions are the United Kingdom (total annual household income calculated on the basis of current income) and Ireland (calcula- tion on the basis of a moving income reference period covering part of the year of the interview and part of the year prior to the survey). Income and living conditions in Europe eurostat30 1 Living conditions in Europe and the Europe 2020 agenda d) Chapter 10 draws attention to a number of problems in the processing of the responses regarding participation in the special module of 2006; e) in Chapter 12, the authors describe the different definitions of earnings available for different countries (see Figure 12.1) and conclude that the net wage is not available for some, and not fully comparable for others. Gross earnings are the only indicator available for all countries; f) in Chapter 17, the authors underline the substantial amount of imputation and approximation necessary in using the EU-SILC data for the EUROMOD micro- simulation model. The reference to other statistical sources raises the issue of the coherence between different sources. A number of the chapters include comparisons between EU-SILC and other sources. In Chapter 5, Atkinson, Marlier, Montaigne and Reinstadler cite the OECD report (OECD, 2008), which contained a most helpful comparison of the OECD estimates with EU-SILC (2005 data, income reference year 2004) and LIS (mostly relating to years around 2000). In almost all cases, the estimates of poverty risk in the three sources are close; the Gini coefficients of income inequality from the three sources also exhibit a similar general pattern. In Chapter 12, Brandolini, Rosolia and Torrini make comparisons with national accounts aggregates (the total paid in wages and salaries) and with the OECD calculations of tax wedges (the sum of taxes and social security contributions as a proportion of total compensation (total employer wage cost)). As they note, these exercises serve to identify areas that need further examination, and demonstrate that more work of validation is needed. But their overall conclusion is that these comparisons ‘provide some reassuring evidence on the quality of the EU-SILC information on earnings’. In Chapter 16, Atta-Darkua and Barnard investigate how the EU-SILC results for the United Kingdom relate to those from other surveys: the Family Resources Survey, and the Living Costs and Food Survey. When account is taken of differences in definitions (regarding for example the income concept and the equivalence scale), there appears to be a reasonable level of coherence between the datasets. The importance of the comparison of results with other surveys was recognised when EU-SILC was initiated, and such comparisons have formed part of the quality reports provided by the EU-SILC national data collection units. This requires, in some Member States, greater integration of EU-SILC into the national statistical systems. 1.4 EU-SILC in the new landscape of EU targets The agreement on the Europe 2020 Agenda at the June 2010 European Council represents a significant departure and a major challenge. The challenge is first and foremost to make substantive progress along the directions signalled by the five Headline Targets. The fifth Target concerns the promotion of social inclusion, or the combating of poverty and social exclusion, defined on the basis of three indicators: the number of people considered ‘at-risk-of-poverty’ according to the EU definition (i.e. the poverty risk threshold is set at 60% of the national household equivalised median income), the number of materially deprived persons (EU definition but stricter; see Chapter 6) and the number of people aged 0–59 living in ‘jobless’ households (defined, for the purpose of the EU target, as households where none of the members aged 18–59 are working or where members aged 18–59 have, on average, very limited work attachment). The target consists of lowering by 20 million the number of people who are at risk of poverty and/or deprived and/ or living in ‘jobless’ households. For the EU-27 as a whole, this number is currently around 120 million. (12) In ensuring that progress is made in this fight against poverty and social exclusion, a key role (12) For a discussion of some of the key challenges to be met by the new Strategy, see Frazer, Marlier and Nicaise (2010). Income and living conditions in Europeeurostat 31 1Living conditions in Europe and the Europe 2020 agenda will be played by the monitoring process, and it is on this that we concentrate in this section. However, to underline the ultimate purpose of the monitoring process, we end with one concrete proposal for an EU policy that we believe would make a substantial contribution to achieving a reduction in poverty and social exclusion. This proposal follows naturally from the emphasis placed on children mainstreaming in Marlier et al (2007). 1.4.1 Implications for monitoring at EU level From the experience with target-setting in the field of macro-economics, it seems evident that the setting of the Europe 2020 Headline Targets has to be accompanied from the outset by appropriate monitoring procedures. As already noted, the social inclusion Headline Target for the EU as a whole is defined on the basis of three indicators: the at-risk-of-poverty rate, the rate of material deprivation, and the proportion of ‘jobless’ households. Under the principle of subsidiarity, Member States are free to set their national (outcome) targets on the basis of what they consider the most appropriate indicators given their national circumstances and priorities. Setting targets is a difficult area for a combination of political and scientific reasons. (13) Indeed, to be truly meaningful these targets need to be evidence-based and they should be the result of a rigorous diagnosis of the causes of poverty and social exclusion in the country. It is also important that Member States be asked to explain — again on the basis of rigorous analytical evidence — how meeting their targets will contribute to the achievement of the EU level target. This is a first challenge. The June 2010 European Council (see above) indicates that ‘progress towards the Headline Targets will be regularly reviewed’. This means that once national targets have been established, the EU Social Protection Committee should set in place criteria by which progress is to be assessed. If the ambitions of the Europe 2020 Agenda are to be (13) For a detailed discussion of targets, see: Marlier et al, 2007, Sections 6.2–6.4. realised, then there have to be criteria that identify situations in which country performance is falling significantly short of the target path to 2020. This is a second challenge, again for a combination of political and scientific reasons. Consideration has to be given to the relation, if any, between measured performance and the allocation of EU funds. This relation works in both directions. The allocation of funds may affect country performance. And policy may develop towards linking allocations to measured performance. As is discussed at greater length in Chapter 5, the first step in the monitoring process is the establishment of a benchmark. What is the base year figure against which reductions in poverty and social exclusion are to be judged? In the present case, in contrast to the macro-economic targets, the establishment of the benchmark is complicated by (1) the greater delays in obtaining data than in the macro-economic field, and (2) the impact of the economic crisis. The EU-SILC national and EU data on the basis of which the Headline Target was framed were collected in 2008. The material deprivation figures relate to 2008 whereas both the at-risk-of-poverty and ‘joblessness’ figures relate for most cases to 2007. With these being taken as the base, the first years’ experience will reflect the recession induced by the financial crisis, and this will have to be factored into the mid-term (2015) assessment of the Europe 2020 Strategy. The monitoring process of the EU social inclusion target is undoubtedly complicated for the EU as a whole by the final decision of the June 2010 European Council in favour of a three-indicator target that allows discretion to Member States. It is not obvious how the decisions of individual Member States can be reconciled. Of particular concern is the possibility that a country may adopt policies that improve the situation according to one indicator but worsen the situation according to the other indicators. There is already evidence that fiscal pressures are leading countries to scale back income support for the unemployed. It is possible that this may lead some people to Income and living conditions in Europe eurostat32 1 Living conditions in Europe and the Europe 2020 agenda take jobs, and hence reduce the proportion of jobless households, but at the cost of reduced household incomes and higher risk of falling below the poverty threshold. (As we have seen, the issue of in-work poverty is discussed in Chapter 14.) The one conclusion that is clear is that the European Commission will need to monitor the three indicators for all Member States, regardless of national priorities. It is only in this way that coherence can be maintained at EU level. 1.4.2 Implications for EU social indicators The adoption of the social inclusion Headline Target puts the EU social indicators under the spotlight. (14) Our initial reaction is that the indi- cators have stood up well to the scrutiny, reflect- ing the substantial amount of work carried out by the EU Social Protection Committee and its Indicators Sub-Group. It is also clear that their work has moved to a new plane. The establish- ment of the social inclusion Headline Target means that the three indicators on which it is based now play a more prominent political role, and that any revision of these indicators over the next decade may then lead to charges of ‘moving the goalposts’ while the game is in process. What does this imply for the three ‘EU targeted’ indicators? Does this mean that they are ‘frozen’? If so, to what does the ‘freezing’ apply? Clearly, key parameters such as the 60 per cent of median income cannot be varied. Equally clearly, at the other extreme, there could be no reasonable objection to improvements in the operation of EU-SILC that improved survey response. In- between come possible changes in the definition of household income applied in EU-SILC, where there have been a number of proposals to extend the range of the definition. (It should be noted that such extensions are likely to increase incomes, but that the effect on the at-risk-of-poverty rate is unclear, since both individual household incomes (14) For more information on the EU commonly agreed social indicators and their (potential) use in the Social OMC, see Atkinson et al (2002) and Marlier et al (2007). and median income would increase, so that each household’s income would be compared with a higher poverty threshold.) Here the Commission together with the SPC and its Indicators Sub- Group will have to exercise judgment. In the first half of 2010, they have, for example, already decided to extend the income definition to include private pensions. On the other hand, the extensive discussion of the proposal to include an allowance for the imputed rent of owner- occupiers (the subject of Chapter 7 in this book) has led to the conclusion that this should be introduced in the form of complementary, rather than replacement, indicators. We have given the example of the definition of income, but the same may apply to the list of items in the measurement of material deprivation. Judgment will have to be exercised regarding any proposal for change, and, in our view, the presumption should be in favour of new items entering via complementary, rather than via replacement, indicators. The issue of revisions is particularly likely to arise since the process of drawing up plans to meet the social inclusion Headline Target will no doubt lead Member States to subject the indicators to greater scrutiny. Countries will ask how far the indicators reflect the impact of their existing (sub-)national policies; they will ask how the measures they consider implementing will impact on the Headline Target indicators. Measures targeted at child poverty, for example, may involve in-kind benefits that are not recorded as income. In view of this, it seems to us desirable that the SPC and its Indicators Sub-Group, in close consultation with Eurostat, should establish a set of principles against which proposals for changes in the way that the EU set of commonly agreed social indicators are calculated can be judged (in particular, though not solely, the three indicators on which the EU social inclusion target is based). An ex ante statement of principles may reduce the scope for special pleading and manipulation. Such a principled approach may help avoid later charges of ‘moving the goalposts’. Income and living conditions in Europeeurostat 33 1Living conditions in Europe and the Europe 2020 agenda 1.4.3 Implications for monitoring at Member State level Translating the overall EU target into national targets can be done in different ways, as suggested by Marlier et al (2007, p. 216). One approach, for example, is to require each country to achieve an improvement in performance proportionate to their present shortfall. Alternatively, Member States may be set the task of emulating the best performers. Here we simply stress that the process of translation should be based on a set of defensible principles. Otherwise the process risks loss of legitimacy. Once Member States have identified their national targets, or indeed before finalising these, they have to face the challenge of identifying policies that can be expected to yield the desired improvements in performance. In considering the link between policy and outcomes, it is necessary first to project the future impact of existing and announced policies, and then to consider the range of possible new policies. At both stages, a potentially important role can be played by micro-simulation models. These models have been developed at a national level, and at an EU-level are represented by EUROMOD described in Chapter 17. Micro- simulation models are designed to investigate the impact of changes in taxes and benefits on disposable household income for a representative sample of the population. Starting from the observed situation, the effect of changes in policy is modelled. From knowledge of the policies, and administrative practice, it can be calculated how the disposable income of a given household would be changed by a policy proposal and how this would affect the incentives faced by individual workers. The former of these calculations allows a direct prediction of the impact on the at-risk- of-poverty rate. For the other two indicators the links are only indirect. From studies of labour supply, predictions can be made as to how changes in financial incentives affect work decisions, and hence the rate of joblessness. This has been the subject of a large economics literature. On the other hand, the impact on the indicators of material deprivation has been less studied, and this is a subject requiring further research. The Europe 2020 Agenda has highlighted three indicators of poverty and social exclusion, but it is important that Member States — and the EU as a whole — should continue to monitor perform- ance according to the full set of commonly agreed indicators underpinning EU coordination and cooperation in the social field. As set out by Mar- lier et al (2007), there are four ways in which the commonly agreed indicators can be employed in this EU coordination/cooperation process. The first application is their use in a forensic manner to identify possible explanations of differences in Member State performance. Secondly, they can be used as a point of reference in the individual National Strategy Reports on Social Protection and Social Inclusion (NSRSPSIs). The expecta- tion is not that countries would rely solely on these common indicators in reporting on social inclusion; rather, it is that the national indicators they develop and use for these purposes should be linked back to the common indicators as far as possible, in order to facilitate mutual learning. The third application is to increase the degree of ‘joined-up Government’. The multi-dimensioned nature of the commonly agreed indicators under- lines the need for cooperation between different agencies of Government as well as, in a growing number of countries, between different agencies belonging to different levels of Government. Fi- nally, the fourth application is to target setting; national targets should draw as appropriate on these indicators. 1.4.4 An EU minimum income for children To this point, our discussion has been procedural and methodological. The challenge is however a substantive one, and we would like to end this Introduction with a concrete policy proposal. This is addressed at the issue of child poverty that has been stressed in a succession of statements by the European Council and by the Commission. In the March 2006 European Council conclusions, Member States were Income and living conditions in Europe eurostat34 1 Living conditions in Europe and the Europe 2020 agenda asked ‘to take necessary measures to rapidly and significantly reduce child poverty, giving all children equal opportunities, regardless of their social background’. Member States have indeed responded, and a number had already set in place national objectives. The problem however remains a pressing one. As is shown in Chapter 5, in the majority of Member States the proportion of children living in households at risk of poverty exceeds the proportion for the whole population. In eight Member States, the proportion is more than 5 percentage points higher for children. The problem was extensively discussed in the influential report of the Social Protection Committee (2008) on Child Poverty and Well- Being in the EU (see also Frazer and Marlier, 2007 as well as Chapter 2 of Frazer, Marlier and Nicaise, 2010). In our judgment, a significant advance in reducing poverty EU-wide requires concerted action. Under subsidiarity, such actions would be implemented by Member States but the EU as a whole can set the guidelines for the actions. The concrete proposal made here is that the EU introduce a Basic Income for Children. Each Member State would be required to guarantee unconditionally to every child a basic income, defined as a percentage of the Member State median equivalised income (and possibly age-related). The implications of such a proposal have been modelled by Levy, Lietz and Sutherland (2007) using the EU tax benefit model, EUROMOD. They show that a Child Basic Income set at 25% of national median income would halve child poverty in all EU-15 Member States except Italy and the United Kingdom. Implementation would be left to Member States, who could employ different instruments. The minimum could be provided via child benefit, via tax allowances, via tax credits, via benefits in kind, or via employer- mandated benefits. The only restriction is that the set of instruments selected must be capable of reaching the entire population. The paramount reason for proposing an EU basic income for children is concern about child poverty. But a second reason for proposing an EU basic income for children is that it would contribute positively to other EU headline objectives. The risks of poverty and social exclusion among children are important in their own right, but they also have implications for the future. As noted by the Conseil de l’Emploi, des Revenus et de la Cohésion sociale (CERC) in their June 2004 Report, poverty affects not only children’s well-being at the moment when resources are insufficient, but also the capacity of children to develop, to build the required capabilities, including knowledge capital, cultural capital, social capital, health capital. It would thus also be a social investment, contributing to the education and employment EU Headline Targets. References Atkinson, T., Cantillon, B., Marlier, E., and Nolan, B. (2002), Social Indicators: The EU and social inclusion, Oxford University Press, Oxford. Conseil de l’Emploi, des Revenus et de la Cohésion sociale (CERC) (2004), Les enfants pauvres en France, Rapport No 4, France. Available from: http://www.cerc.gouv.fr/rapports/rapport4/ rapport4cerc.pdf. European Council (2006), European Council 23/24 March 2006: Conclusions, European Council, Brussels. European Council (2010), European Council 17 June 2010: Conclusions, European Council, Brussels. Frazer, H. and Marlier, E. (2007), Tackling child poverty and promoting the social inclusion of children in the EU — Key lessons, Independent overview based on the 2007 first semester national reports of the national independent experts on social inclusion, European Commission, Brussels. Available from: http://www.peer-review-social-inclusion. eu/network-of-independent-experts/2007/ rep or t s / f i r s t - s emester-2007/sy nt hes i s - report-2007-1. Income and living conditions in Europeeurostat 35 1Living conditions in Europe and the Europe 2020 agenda Frazer, H., Marlier, E. and Nicaise, I. (2010), A social inclusion roadmap for Europe 2020, Garant, Antwerp/Apeldoorn. Levy, H., Lietz, C. and Sutherland, H. (2007), ‘A guaranteed income for Europe’s children?’ in Jenkins, S.P. and Micklewright, J. editors, Inequality and poverty re-examined, Oxford University Press, Oxford. Marlier, E., Atkinson A.B., Cantillon B., Nolan, B. (2007), The EU and Social Inclusion: Facing the Challenges, The Policy Press, Bristol. OECD (2008), Growing unequal? OECD, Paris. Social Protection Committee (2008), Child Poverty and Well-Being in the EU: Current status and way forward, Office for Official Publications of the European Communities, Luxembourg. Available from: http://ec.europa.eu/social/main.jsp?catId= 751&langId=en&pubId=74&type=2&furtherPubs= yes. World Health Organisation (2008), Closing the gap in a generation: Health equity through action on the social determinants of health. Final Report of the Commission on Social Determinants of Health, World Health Organisation, Geneva. 2Investing in statistics: Eu-SILC Pascal Wolff, Fabienne Montaigne and Gara Rojas González (1) (1) Pascal Wolff, Fabienne Montaigne and Gara Rojas González are all at the Statistical Office of the European Union (Eurostat). Address for correspond- ence: pascal.wolff@ec.europa.eu. Income and living conditions in Europe eurostat38 2 Investing in statistics: EU-SILC 2.1 Introduction This chapter introduces the EU-SILC instrument, which after only a few years of existence has become the reference source for comparative statistics on income distribution and social inclusion in the European Union (EU). Its aim is to provide the reader with a conceptual and a practical insight into the background of this instrument, its main characteristics and some of its shortcomings, before going on to discuss areas for further improvement. Reliable and timely statistics and indicators, re- flecting the multi-dimensional nature of poverty and social exclusion, are essential for monitoring the social protection and social inclusion proc- ess. The EU-SILC instrument was devised by the EU Member States and the European Commis- sion in response to this general need, while main- taining the necessary flexibility for each country to integrate the new instrument into its own na- tional system of social surveys. This integration process is still on-going in some countries, with the aim of delivering national data that are fully harmonised with the standards and definitions commonly agreed at European level. A sign of the rapid success of EU-SILC is that 31 countries in 2010 have already implemented it − the 27 EU countries as well as Iceland, Norway, Switzerland and Turkey – and tested in three further countries (Croatia, the former Yugoslav Republic of Macedonia (FYROM) and Serbia). 2.1.1 A brief history In a number of European countries, national surveys on income and living conditions existed before the 1990s when the first EU-scale survey — the European Community Household Panel (ECHP) — was launched. The ECHP ran from 1994 to 2001 in 14 of the then 15 Member States (the exception being Sweden). Despite a high level of overall harmonisation in most countries, the ECHP suffered from some comparability and timeliness issues. It was with the triple aim of solving the ECHP’s technical problems, conforming to the internationally agreed definition of income and extending the data collection to the enlarged EU (and beyond) that the decision was taken to stop the ECHP and launch EU-SILC. After starting on the basis of a gentlemen’s agreement in 2003 in seven countries (six EU countries plus Norway; see Figure 2.1), the EU-SILC project was then implemented by means of a legal basis which was gradually adopted as from 2003 and implemented from 2004 onwards. 2.1.2 Policy context Member States coordinate their policies for combating poverty and social exclusion on the basis of a process of policy exchanges and mutual learning, known as the ‘Open Method of Coordination’. Since 2006, the framework for this process has comprised three policy areas: - eradicating poverty and social exclusion - ensuring adequate and sustainable pensions - providing accessible, high quality and sustain- able health and long-term care. The Europe 2020 strategy (2) adopted by the European Council in June 2010 sets out a vision of Europe’s social market economy for the 21 century. It shows how the EU can emerge stronger from the crisis and how it can be turned into a smart, sustainable and inclusive economy, delivering high levels of employment, productivity and social cohesion. In particular, the strategy sets Member States and the European Commission the goal of ‘Promoting social inclusion, in particular through the reduction of poverty, by aiming to lift at least 20 million people out of the risk of poverty and exclusion’. The fact that this target is fully based on EU-SILC data (See Section 1.4) is definitely a confirmation of the need for a harmonised cross- cutting survey of this kind. (2) For further details see http://ec.europa.eu/eu2020/index_en.htm. See also Chapter 5 of present volume. Income and living conditions in Europeeurostat 39 2Investing in statistics: EU-SILC Figure 2.1: EU-SILC implementation Countries 2003 2004 2005 2006 2007 2008 2009 2010 2011 EU-27 Belgium Bulgaria Czech Republic Denmark Germany Estonia Ireland Greece Spain france Italy Cyprus Latvia Lithuania Luxembourg Hungary Malta Netherlands Austria Poland Portugal Romania Slovenia Slovakia finland Sweden United Kingdom Croatia fYROM Iceland Turkey Norway Switzerland Serbia Full implementation Test implementation Income and living conditions in Europe eurostat40 2 Investing in statistics: EU-SILC The set of politically agreed outcome indicators plays a central role in monitoring the performance of Member States in promoting social inclusion. The purpose of these indicators is to allow the Member States and the European Commission to monitor national and EU progress towards key EU objectives in the areas of social inclusion and social protection, and to support mutual learning and identification of good (and bad) practices in terms of policies and institutional processes (See Section 2.5.2). 2.2 The EU-SILC instrument and its governance 2.2.1 Scope and geographical coverage As with most household surveys, EU-SILC covers only people living in private households; this needs to be borne in mind when carrying out statistical analyses and when interpreting indicators, both within a given country and between countries. The target population does not include persons living in collective households and in institutions. This is because the impact of excluding old people living in institutions, people with disabilities and other vulnerable groups, such as the homeless, may be very different from country to country. Some vulnerable groups living in private households may also be under- represented because they are not easy to reach. EU-SILC was launched in 2003 in seven countries under a gentleman’s agreement and later was gradually extended to all EU countries and beyond. As described in Figure 2.1 below, in 2010 EU-SILC has been implemented in 31 countries, i.e. the 27 EU countries, Iceland, Norway, Switzerland and Turkey — and tested in three further countries (Croatia, the Former Yugoslav Republic of Macedonia and Serbia). Small areas of the national territory amounting to no more than 2% of the national population are excluded from EU-SILC as are the following national territories: the French Overseas Departments and territories, the Dutch West Frisian Islands with the exception of Texel, and lastly the Scilly Islands. 2.2.2 Main characteristics of EU-SILC All EU Member States are required to implement EU-SILC, which is based on the idea of a com- mon ‘framework’ as opposed to a common ‘sur- vey’. The common framework consists of com- mon procedures, concepts and classifications, including harmonised lists of target variables to be transmitted to Eurostat. Two types of annual data are collected through EU-SILC and provided to Eurostat: - cross-sectional data pertaining to a given time period, including variables on income, poverty, social exclusion and other living conditions. The data for the survey of Year N are to be transmit- ted to Eurostat by November of Year (N+1); - longitudinal data pertaining to changes over time at the individual level are observed peri- odically over a four-year period. Longitudinal data are confined to income information and a reduced set of critical qualitative, non-mon- etary variables of deprivation, designed to identify the incidence and dynamic process- es of persistent poverty and social exclusion among subgroups of the population. The lon- gitudinal data corresponding to the period be- tween Year (N-3) and Year N are to be trans- mitted to Eurostat by March of Year (N+2). The survey design is nevertheless flexible in order to allow countries to anchor EU-SILC within their national statistical systems. For instance, the cross-sectional and longitudinal components may come from separate sources, i.e. the longitudinal dataset does not have to be ‘linkable’ with the cross-sectional dataset. Depending on the country, microdata come from: - two or more national sources (surveys and/or registers); - one or more previously existing national sources, whether or not combined with a new survey; Income and living conditions in Europeeurostat 41 2Investing in statistics: EU-SILC - a new harmonised survey to meet all EU-SILC requirements. Eurostat proposed an integrated design with a four-year rotation to those countries that had launched a new survey (3). Rotational design refers to the sample selection based on a number of sub-samples or replications, each of them similar in size and design, and representative of the whole population. From year to year, some replications are maintained, while others are dropped and replaced by new replications. The fundamental characteristic of the integrated design is that the cross-sectional and longitudinal statistics are produced from essentially the same set of sample observations, thus avoiding the unnecessary duplications which would be involved if entirely separate cross-sectional and longitudinal surveys are used. 2.2.3 Legal basis One of the strengths of EU-SILC is the existence of a legal basis which is binding on Member States as well as a requirement for accession countries. The development of the common framework, including the conception of the annual ad-hoc modules, is discussed on a permanent basis with the main stakeholders, in particular within the Living Conditions Working Group. In order to take stock of the initial years of implementation and to improve the outcome of EU-SILC, a revision of the legal basis is due to take place in 2011–2013. Specifically the EU-SILC legal basis consists of three main components: - a Framework Regulation (4) which defines the scope, definitions, time reference, characteris- tics of the data, data required, sampling, sam- ple sizes, transmission of data, publication, (3) Most of the EU Member States have adopted the 4-year rotational de- sign recommended by Eurostat. France has a longer panel duration (9 years) and Luxembourg has a pure panel supplemented with a new sample each year. (4) Regulation (EC) No 1177/2003 of the European Parliament and of the Council of 16 June 2003 concerning Community statistics on income and living conditions (EU-SILC). access for scientific purposes, financing, re- ports and studies for the EU-SILC instrument. This Regulation was amended by Regulations N°1553/2005 (5) and 1791/2006 (6) in order to extend EU-SILC to the new Member States - five Commission Regulations which specify some technical aspects of EU-SILC: ‘Defi- nitions’ (7), ‘Fieldwork aspects and imputa- tion procedures’ (8), ‘Sampling and tracing rules’ (9), the ‘list of primary (annual) target variables’ (10) and the ‘Quality reports’ (11) - annual Commission Regulations on the list of secondary target variables, i.e. the ad-hoc modules which are introduced in EU-SILC with the possibility of repeating a topic every four years or less frequently. EU-SILC is also carried out in Norway, Iceland and Switzerland (on the basis of specific agreements). As for accession and candidate countries, the implementation of EU-SILC is not compulsory until they become a new Member State, but it is strongly encouraged if the specific situation of a given country so permits. 2.2.4 Common guidelines The way to implement the EU-SILC legal basis is agreed between Eurostat and the national statistical institutes — in particular in the Working (5) Regulation N°1553/2005 of the European Parliament and of the Coun- cil of 7 September 2005 amending Regulation (EC) No 1 177/2003 con- cerning Community statistics on income and living conditions (EU- SILC). (6) Council Regulation (EC) No 1791/2006 of 20 November 2006 adapting certain Regulations and Decisions by reason of the accession of Bul- garia and Romania. (7) Commission Regulation (EC) No 1980/2003 of 21 October 2003 - up- dated by Commission Regulation (EC) No 676/2006 - implementing Regulation (EC) No 1177/2003 as regards definitions and updated defi- nitions. (8) Commission Regulation (EC) No 1981/2003 of 21 October 2003 im- plementing Regulation (EC) No 1177/2003 as regards the fieldwork aspects and the imputation procedures. (9) Commission Regulation (EC) No 1982/2003 of 21 October 2003 imple- menting Regulation (EC) No 1 177/2003 as regards the sampling and tracing rules. (10) Commission Regulation (EC) No 1983/2003 of 7 November 2003 im- plementing Regulation (EC) No 1 177/2003 as regards the list of target primary variables. (11) Commission Regulation (EC) No 28/2004 of 5 January 2004 imple- menting Regulation (EC) No 1 177/2003 as regards the detailed content of intermediate and final quality reports. Income and living conditions in Europe eurostat42 2 Investing in statistics: EU-SILC Group for Statistics on Living Conditions, and the Task-Forces reporting to it. This includes common procedures and concepts, as well as an increasing number of recommendations on how to word the underlying questions. The full set of guidelines is available to the public (12). Some minor amendments to the legal framework are also implemented on the basis of a gentlemen’s agreement, although these are obviously not legally binding. Recently the framework was refined to incorpo- rate recommendations on particular topics (such as variables concerning household definition, la- bour, health, housing and material deprivation) or methodological issues (such as the treatment of negative income, the conversion between net and gross income, the treatment of outliers and lump sums in some income components and the imputed rent) in order to improve the compara- bility between countries. As soon as these recom- mendations are agreed by the Working Group, they are incorporated explicitly within the annu- al version of the overall guidelines and gradually implemented. Strategic issues regarding the development of EU-SILC are discussed in the meetings of the Directors of Social Statistics of the National Statistical Institutes and the European Statistical System Committee (ESSC). 2.3 Methodological framework 2.3.1 Contents of EU-SILC EU-SILC is a multi-dimensional dataset focused on income but at the same time covering housing, labour, health, demography, education and deprivation, to enable the multidimensional approach of social exclusion to be studied. It consists of primary (annual) and secondary (ad- hoc modules) target variables, all of which are forwarded to Eurostat. (12) See in particular the annual guidelines available at: http://circa.europa. eu/Public/irc/dsis/eusilc/library?l=/guidelines_questionnaire&vm=det ailed&sb=Title. Given the principle of flexibility of the implementation of EU-SILC at national level, the sequence of questions needed to construct one target variable may vary from country to country. Nevertheless, recommended wordings of questions are available mainly for the ad-hoc modules, although the countries are not obliged to follow these recommendations. The primary target variables relate to either household or individual (for persons aged 16 and more) information and are grouped into areas: - at household level, five areas are covered: (1) basic/core data, (2) income, (3) housing, (4) social exclusion and (5) labour information; - at the personal level, there are five areas: (1) basic/demographic data, (2) income, (3) edu- cation, (4) labour information and (5) health. The secondary target variables are introduced every four years or less frequently only in the cross-sectional component. One ad-hoc module per year has been included since 2005: - 2005: inter-generational transmission of poverty - 2006: social participation - 2007: housing conditions - 2008: over-indebtedness and financial exclusion - 2009: material deprivation - 2010: intra-household sharing of resources - 2011: inter-generational transmission of disadvantages - 2012: housing conditions - 2013: well-being. 2.3.2 Income concept An important objective for EU-SILC is to adhere as closely as possible to the recommendations of the international Canberra Group on the definition of household income (13). The income concept in the full sense of the Canberra recommendations has only been fully implemented since 2007. (13) See Expert Group on Household Income Statistics, 2001. Income and living conditions in Europeeurostat 43 2Investing in statistics: EU-SILC Two main aggregates are computed from EU- SILC: total gross household income (GI) and total disposable household income (DI), which are defined as: GI = EI + SEI + PP (14) + CTR + OI DI = GI – CTP Where: EI = employee income (cash or near-cash employee income and non-cash employee income); SEI = self-employment income (but not goods produced for own consumption); PP = Pensions received from individual private plans; CTR = current transfers received (social benefits and regular inter-household cash transfers received); OI = other sources of income received (such as other capital income); CTP = current transfers paid (tax on income and social insurance contributions, on wealth and regular inter-household cash transfers paid). Employee income In EU-SILC, employee income is covered thanks to the collection of information on ‘Gross cash or near-cash employee income’, ‘Gross non- cash employee income’ and ‘Employers’ social insurance contributions’. For non-cash employee income, only company cars have been recorded since the beginning of EU-SILC and included into the income concept. From 2007 onwards, additional information covering all other goods and services provided free of charge or at reduced price by employers to their employees is to be collected, but is not yet included into the main income aggregates. The compulsory component of employers’ social insurance contributions has been collected since 2007, but it is not part of the main income aggregates. (14) The decision to include the ‘Pensions received from private plans’ vari- able into the income concept was taken by the Social Protection Com- mittee Indicators Sub-Group in May 2010. Self-employment income Self-employment income is broken down into ‘Gross cash profits or losses from self- employment’ (including royalties) and the ‘Value of goods produced for own consumption’. Various alternative approaches to the measurement of income from self-employment are allowed. The value of goods produced for own consumption has been included since 2007 if it represents a significant component of the overall income at the national level or of the income of particular groups of households. It has been collected by some of the Member States which joined the EU as from 2004 (see Chapter 8), but is not currently included in the main income aggregates. Private pension plans Regular pensions from private plans — other than those covered within the ‘Current transfers’ item — refer to pensions and annuities received in the form of interest or dividend income from individual private insurance plans, i.e. fully organised schemes where contributions are at the discretion of the contributor independently of their employers or government. Since July 2010, this income component is included in the EU-SILC standard income concept (also for all the previous waves of EU- SILC, as the required data were available). In the data analysed in this book, this income component is not included. Current transfers received Current transfers received include social benefits and regular inter-household cash transfers received. Social benefits are broken down into family and children-related allowances, housing allowances, unemployment benefits, old-age benefits, survivors’ benefits, sickness benefits, disability benefits, education-related allowances and other benefits not elsewhere classified. Income and living conditions in Europe eurostat44 2 Investing in statistics: EU-SILC Other sources of income received Three sources of income are covered under this item: - income from rental of a property or land; - interest, dividends, profits from capital investment in unincorporated business; - income received by people aged under 16. Current transfers paid Current transfers paid are broken down into ‘Tax on income and social insurance contributions’, ‘Regular taxes on wealth’ and ‘Regular inter- household cash transfers paid’. The ‘Employers’ social insurance contributions’ variable is not included in the computation of the main income aggregates, even though it would be crucial for cross-country comparisons related to labour cost. Imputed rent The imputed rent has been added from 2007 onwards for all households that do not report that they pay full rent, either because they are owner- occupiers or because they live in accommodation rented at a lower price than the market price, or because the accommodation is provided rent- free (See Chapter 7). Its inclusion in the standard EU-SILC income concept would have a significant impact on all income-based indicators and would create a serious break in the time series as imputed rent could not be included in the indicators prior to 2007 due to the unavailability of the required data. At the time of writing, the SPC Indicators Sub-Group is still debating the possibility of including imputed rent (net of interests paid on mortgage) or a fraction of it within (some of) the income aggregates (15). (15) In May 2010 the SPC Indicators Sub-Group ‘agreed on the principle to include the imputed rent component in a small number of poverty indicators which would be listed in the in the social inclusion portfolio as secondary indicators or context information’ (minutes of the meet- ing of the Indicators Sub-Group). It also highlighted the lack of cross- country comparability of this component. Imputation The EU-SILC framework requires full imputation for income components. The level of imputation of income components is reported in microdata by means of a set of detailed flags. This requirement helps to make the information delivered by EU- SILC more homogeneous and complete. 2.3.3 Sample requirements Sampling design Data are to be based on a nationally representative probability sample of the population residing in private households within the country, irrespective of language, nationality or legal residence status. All private households and all persons aged 16 and over within the household are eligible for the operation. Representative probability samples must be achieved both for households and for individual persons in the target population. The sampling frame and methods of sample selection should ensure that every individual and household in the target population is assigned a known probability of selection that is not zero. Germany, which had previously used quota sampling methods, was granted a transition period until 2008 when it was required to introduce fully representative probability sampling. Sample size The Framework Regulation and its updates define the minimum effective sample sizes to be achieved. The reference is to the effective sample size, which is the size that would be required if the survey were based on simple random sampling (design effect in relation to the ‘at-risk-of- poverty rate’ indicator = 1.0). The actual sample sizes have to be larger to the extent that the design effect exceeds 1.0 in order to compensate for all kinds of non-response. The sample sizes for the longitudinal component refer, for any two consecutive years, to the number of households or individuals aged 16 and over that are successfully interviewed in both years. Income and living conditions in Europeeurostat 45 2Investing in statistics: EU-SILC Table 2.1: Minimum effective sample size for the cross-sectional and longitudinal components by country Countries Households Persons aged 16 or over Cross-sectional Longitudinal Cross-sectional Longitudinal Belgium 4 750 3 500 8 750 6 500 Bulgaria 4 500 3 500 10 000 7 500 Czech Republic 4 750 3 500 10 000 7 500 Denmark 4 250 3 250 7 250 5 500 Germany 8 250 6 000 14 500 10 500 Estonia 3 500 2 750 7 750 5 750 Greece 4 750 3 500 10 000 7 250 Spain 6 500 5 000 16 000 12 250 france 7 250 5 500 13 500 10 250 Ireland 3 750 2 750 8 000 6 000 Italy 7 250 5 500 15 500 11 750 Cyprus 3 250 2 500 7 500 5 500 Latvia 3 750 2 750 7 650 5 600 Lithuania 4 000 3 000 9 000 6 750 Luxembourg 3 250 2 500 6 500 5 000 Hungary 4 750 3 500 10 250 7 750 Malta 3 000 2 250 7 000 5 250 Netherlands 5 000 3 750 8 750 6 500 Austria 4 500 3 250 8 750 6 250 Poland 6 000 4 500 15 000 11 250 Portugal 4 500 3 250 10 500 7 500 Romania 5 250 4 000 12 750 9 500 Slovenia 3 750 2 750 9 000 6 750 Slovakia 4 250 3 250 11 000 8 250 finland 4 000 3 000 6 750 5 000 Sweden 4 500 3 500 7 500 5 750 United Kingdom 7 500 5 750 13 750 10 500 Total of Eu Member States 130 750 98 250 272 900 203 850 Iceland 2 250 1 700 3 750 2 800 Norway 3 750 2 750 6 250 4 650 Source: Regulations (EC) No 1553/2005 and No 1791/2006 of the European Parliament and of the Council. Income and living conditions in Europe eurostat46 2 Investing in statistics: EU-SILC For the cross-sectional component, a minimum effective sample size of around 131 000 households, or 273 000 individuals aged 16 and over in the EU as a whole, has to be achieved. As for the longitudinal component, the respective requirements are 98 000 households and 204 000 individuals. Table 2.1 gives the minimum effective sample sizes required for each EU Member State (plus Norway and Iceland) in terms of households and individuals aged 16 and over. 2.3.4 Tracing rules In order to ensure the best quality output, minimum requirements for implementation have been defined within the legal basis (16) in addition to the definition of the minimum sample size. These rules concern, for instance, the use of proxy rate, the use of substitutions, fieldwork duration, non-response procedures, and tracing rules. In each country the longitudinal component of EU-SILC consists of one or more panels or subsamples (four subsamples in the recommended four-year rotational design). For each panel/ subsample, the initial households representing the target population at the time of its selection are followed for a minimum period of four years on the basis of specific tracing rules. The objective of the tracing rules is to reflect any changes in the target population drawn in the initial sample and to follow up individuals over time. In order to study changes over time at the individual level, all sample persons (members of the panel/subsample at the time of its selection) should be followed up over time, despite the fact that they may move to a new location during the life of the panel/subsample. However, in the EU-SILC implementation some restrictions are applied owing to cost and other practical reasons. Only those persons staying in one private household or moving from one to another in (16) Commission Regulation N° 1981/2003 on the fieldwork aspects and imputation procedures. the national territory are followed up. Sample persons moving to a collective household or to an institution, moving to national territories not covered in the survey, or moving abroad (to a private household, collective household or institution, within or outside the EU), would normally not be traced. The only exception would be the continued tracing of those moving temporarily (for an actual or intended duration of less than six months) to a collective household or institution within the national territory covered, as they are still considered as household members. 2.4 Information on quality 2.4.1 Some comparability issues The flexibility of EU-SILC may be seen as both its main strength and its main weakness. Various powerful arguments have already been mentioned in this chapter, but the main one is certainly the possibility of embedding EU-SILC into the national systems of social surveys. On the other hand, such flexibility could create problems of harmonisation and comparability across countries. This section addresses some of these comparability issues. Different sampling designs Almost all countries have used the integrated design proposed by Eurostat. Modified designs have been used in only a few countries, primarily for the purpose of integrating EU-SILC into an existing survey (e.g. Sweden, Finland and Germany), and/or incorporating an existing sample into EU-SILC (e.g. Norway). The EU-SILC framework encourages the use of existing sources and/or administrative data. However, in practice, not all EU-SILC variables can be obtained from registers and administrative data. Hence, it is possible to establish two groups of countries on the basis of the data source used in EU-SILC: in the countries referred to as ‘register’ countries (Denmark, Finland, Iceland, Income and living conditions in Europeeurostat 47 2Investing in statistics: EU-SILC the Netherlands, Norway, Sweden and Slovenia) most income components and some items of demographic information are obtained through administrative registers. Other personal variables are obtained by means of interview. In all other countries except Ireland (17), the full information is obtained by means of a survey of households and interviews with household members. All the designs ensure strict cross-sectional representativeness and enable a significant number of individuals to be followed over a period of at least four years. In line with the legal requirements, all samples are probabilistic since the launching of EU-SILC (18): with updated sampling frames and stochastic algorithms used to select statistical units. The sampling designs used in 2007 and 2008 by country were the following: - sampling of dwellings or addresses: the Czech Republic, Germany, Spain, France, Hungary, Latvia, Luxembourg, Malta, the Netherlands, Austria, Poland, Portugal, Romania and the United Kingdom; - sampling of households: Belgium, Bulgaria, Cyprus, Greece, Ireland, Italy and Slovakia; - sampling of individuals: Denmark, Estonia, Lithuania, Slovenia, Sweden, Finland, Iceland and Norway (all these countries are ‘register’ countries except for Lithuania). In all cases, unbiased estimates can be produced on firm theoretical grounds. In almost all countries, the coverage bias is under control with frequent updates of the frame. Countries have designed their sample so as to achieve a good trade-off between reporting needs at sub-national level and the cost effectiveness of the data collection. Significant increases of the sample size, driven by sub-national reporting requirements, were recorded in Spain and Italy. (17) In Ireland, upon the explicit agreement of the household collected, the information is obtained from administrative information. (18) With the exception of Germany for which an existing quota sample component was used until 2008. Different fieldwork periods National surveys also differ in terms of the period of time during which the fieldwork is carried out. The Regulation recommends that the one-shot survey fieldwork should extend over less than four consecutive months and the lag between income reference period and fieldwork is limited to eight months. When continuous surveys are used, the sample allocation over time should be monitored and the weighting adapted to produce unbiased estimates of the annual average. Figure 2.2 below shows that in 2008 most countries adopted a survey in which the fieldwork was concentrated in a period of a few months, mainly in the first half of the year, although there were some notable exceptions: - Ireland and the United Kingdom conduct continuous surveys throughout the year; - in Belgium, Italy, Malta, the Netherlands, Austria and Sweden the fieldwork is carried out mostly in the second half of the year. The impact of different fieldwork periods might have a noticeable impact over time when comparing indicators that show a steady and seasonal pattern, but the impact as regards analysis of permanent income distribution is likely to be negligible. The one-shot surveys always use the previous calendar year as the income reference period, whereas a sliding reference period is used for the continuous survey (19). The greater degree of inconsistency between income related variables and socio-economic related variables when the fieldwork period is distant in time from the income reference period can be identified as a weakness in some instances of EU-SILC implementation. (19) Two countries, Ireland and the United Kingdom, use a sliding reference period for income and taxes on income and social insurance contribu- tions. In Ireland it refers to the 12 months prior to the interview date. As for the United Kingdom, it is centred on the interview date. In ad- dition, the respondents are asked to provide figures which relate most commonly to their current (and usual) incomes, i.e. which could relate to the last week, two weeks, or month. These figures are then annualised. Income and living conditions in Europe eurostat48 2 Investing in statistics: EU-SILC Differences in the method of data collection and in interview duration In most countries (i.e. the non-register countries), all members aged 16 or over in selected households are asked to fill in a personal questionnaire, whereas in the register countries only one selected respondent per household receives a personal questionnaire. These two different rules have different impacts on the tracing of individuals over time (longitudinal dimensions) depending on whether only one or all household members are interviewed over time. The selected respondent model needs some adaptation in order to avoid bias in the follow up of children. The two different rules lead to different weighting schemes. In particular when the selected respondent type is used, the weights of the household and of the selected respondent are obviously different. EU-SILC was designed to keep the respondent burden under control so as to avoid an excessively high non-response rate and to ensure that the in- formation collected is of good quality. Although detailed collection of income components can be cumbersome, the aim was to limit the total dura- tion of the interview with each household member to less than one hour on average. The mean inter- view duration among countries carrying out full surveys was about 30 minutes per individual in 2008, with a maximum of 59 minutes in the United Kingdom (20). A significant decrease in interview times is observed for the register countries, where the average length of interview was 24 minutes. (20) In the case of the United Kingdom, EU-SILC questions are included as part of the General Household Survey questionnaire and there is no information on the interview duration of EU-SILC alone. Figure 2.2: fieldwork period, 2008 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 IS NO January February March April May June July August September October November December Source: Eurostat (EU-SILC microdata). Income and living conditions in Europeeurostat 49 2Investing in statistics: EU-SILC Different non-response rates Non-response is measured in EU-SILC at the three stages, i.e. address contact, household interview and personal interview. Figure 2.4 below presents the overall non-response rates for individuals for the whole sample and for the subsample corresponding to the new entries broken down by country. Total non-response of the selected households and individuals had to be less than 40%, which was seen as a challenge for a non mandatory survey. The overall non-response rate in the personal interview for the whole sample was below 10% in 2008 in four countries: Romania (5%), Slovakia (8%), Cyprus (9%) and Portugal (9%). At the other extreme, non-response rates exceeded 30% in five countries and even 40% in Denmark (45%). The rates for the new entries were generally significantly higher than for the whole sample, with peaks in Belgium (58%) and the Czech Republic (52%). The creation of models using external variables in order to correct non-response is highly desirable. Most of the countries apply either a standard post-stratification, based on homogeneous response groups, or a more sophisticated logistic regression model. Figure 2.3: Average interview duration per individual, 2008 0 10 20 30 40 50 60 Register countries Survey countries EE SI NO I S SE LT LV NL ES MT CY IE BE FR EL AT SK PT LU HU RO IT BG PL CZ DE UK Source: Eurostat (EU-SILC microdata). Income and living conditions in Europe eurostat50 2 Investing in statistics: EU-SILC Deviation from common definitions In EU-SILC comparability is sought via the conceptual harmonisation of target variables obtained, on the one hand, through their detailed definition as provided in EU-SILC regulations and guidelines and, on the other hand, through the active role of Eurostat in coordinating and supporting the overall implementation. Explicit deviations from these commonly agreed standards were allowed to a limited extent and are monitored through the quality reports (See Section 2.4.2). One example of such deviations concerns the precise definition of a household (See Table 2.2) which might restrict comparability. The different methods used for the computation of imputed rent may also raise issues of cross-country comparability. Figure 2.4: Overall personal non-response rates, 2008 0 10 20 30 40 50 60 RO SK CY PT EL IT LT FR FI NL CZ HU PL ES EE MT IE SI LV DE SE IS UK AT LU BG BE NO DK Whole sample New entries Source: Eurostat (EU-SILC microdata). Income and living conditions in Europeeurostat 51 2Investing in statistics: EU-SILC 2.4.2 Quality reports Adopted in 2005, the European Statistics Code of Practice sets common standards for the independence, integrity and accountability of the national and EU statistical authorities. The EU statistical authorities have undertaken to adopt a comprehensive approach to high quality statistics which builds upon a common definition of quality in statistics, in which the following dimensions are addressed: • relevance: European Statistics must meet the needs of users • accuracy and reliability: European Statistics must accurately and reliably portray reality • timeliness and punctuality: European Statistics must be disseminated in a timely and punctual manner • coherence and comparability: European Statistics should be consistent internally, over time and comparable between regions and countries; it should be possible to combine and make joint use of related data from different sources • accessibility and clarity: European Statistics should be presented in a clear and understandable form, disseminated in a suitable and convenient manner, and be available and accessible on an impartial basis with supporting metadata and guidance. This European definition of quality is monitored in EU-SILC with annual intermediate and final quality reports (21) prepared by both the member countries and Eurostat for the EU level. While the intermediate quality reports refer only to the cross-sectional operation, the final quality reports also refer to the longitudinal operation. The national quality reports provide a useful insight into national implementation practice and represent substantive information from which to draw preliminary conclusions regarding the quality of EU-SILC data. This material is complemented by the information that Eurostat collects through its frequent contacts with (21) As for the detailed contents, see Commission Regulation (EC) No 28/2004 of 5 January 2004 implementing Regulation (EC) No 1177/2003 as regards the detailed content of intermediate and final quality reports. Table 2.2: Basic concepts and definitions (Are the national definitions comparable with those of the standard EU-SILC?), 2008 BE BG CZ DK DE EE IE EL ES fR Reference population f f f f f f f f f f Private household definition f f f f f f f f f f Household membership f f f f f f f f L f IT CY LV LT LU HU MT NL AT PL Reference population f f f f f f f f f f Private household definition L f f f f f f f f f Household membership L f f f f f f f f f PT RO SI SK fI SE UK IS NO Reference population f L f f f f f f f Private household definition f f f f f f L f f Household membership L f f f f f L f f Source: National Quality Reports 2008. f (fully comparable); L (largely comparable). NB: for more explanations on the ‘L’s in this table, Eurostat, 2010 may be consulted. Income and living conditions in Europe eurostat52 2 Investing in statistics: EU-SILC national statistical authorities, in particular as regards data validation. The purpose of the EU quality reports is to summarize the information contained in the national quality reports. Their objective is to evaluate the quality of EU-SILC data from a European perspective, i.e. by establishing cross-country comparisons of some of its key quality characteristics. The EU quality reports, as well as most of the national country reports, are publicly available on Eurostat website. (22) 2.5 Data and indicators 2.5.1 Data access EU-SILC data are disseminated either as aggregated data or as microdata sets. Individual EU-SILC records are considered as confidential data within the meaning of Article 23 of Council Regulation 223/2009 (Statistical Law) because they allow indirect identification of statistical units (individuals and households). In this context they should be used only for statistical purposes or for scientific research. Aggregated results relate to indicators and statistics on income distribution and monetary poverty, living conditions, material deprivation and childcare arrangements. They are presented as pre-defined tables or as multidimensional datasets and may be extracted in a variety of formats. Commission Regulation 831/2002 (23) granted the European Commission permission to release anonymised microdata to researchers. Anonymised microdata are defined as individual statistical records which have been modified in order to control, in accordance with best practices, the risk of identification of the (22) http://epp.eurostat.ec.europa.eu/portal/page/portal/income_social_in- clusion_living_conditions/quality/eu_quality_reports. (23) Commission Regulation No 831/2002 of 17 May 2002 implementing Council Regulation No 322/97 on Community Statistics, amended by Commission Regulation No 1 000/2007 of 29 August 2007, concerning access to confidential data for scientific purposes. statistical units to which they relate. Both EU and national rules are applied for anonymisation, and are described in full with each release. They concern variable suppression, global recoding or the randomisation of some variables. Twice a year, Eurostat releases anonymised microdata to researchers (encrypted CD-ROM with documentation). Each CD-ROM contains data from the latest available operation, as well as revisions from any previous datasets. A detailed description of the full procedure for accessing microdata is provided on the Eurostat website (24). It should be noted that the dissemination by Eurostat of national microdata must be accepted by each national authority. As an example, Eurostat was not allowed in 2010 to disseminate the whole set of microdata from Malta and France as well as the longitudinal microdata from Germany for confidentiality reasons. This unfortunate situation — which is currently being addressed with the relevant national authorities — creates important difficulties for the users. In particular, the successive versions of the Users’ database used by the Net-SILC members and the authors of this book did not contain the data for the above mentioned countries. 2.5.2 Indicators computation The Open Method of Coordination for Social Protection and Social Inclusion (Social OMC), which was set up at the Lisbon European Council of March 2000, provides a framework for political coordination. Member States agree to identify and promote their most effective policies in the fields of Social Protection and Social Inclusion, with the aim of learning from each other’s experiences. The use of commonly agreed indicators to monitor progress towards commonly agreed objectives is an essential component of the Social OMC. These indicators consist of four portfolios of indicators: an ‘overarching list’ and a list for each of the three (24) See http://epp.eurostat.ec.europa.eu/portal/page/portal/microdata/eu_silc. Income and living conditions in Europeeurostat 53 2Investing in statistics: EU-SILC main areas covered by the Social OMC (poverty and social exclusion; pensions; and healthcare and long-term care). The current set of common indicators was approved in 2009 (25). A large number of indicators are computed on the basis of EU-SILC, which has become the second pillar of household social survey statistics at EU level, complementing the EU Labour Force Survey which focuses on labour market information. The development of indicators, under the responsibility of the SPC and its Indicators Sub-Group, is a dynamic process. The work of the national delegations of experts, who make up the Group, and the secretariat provided by the European Commission’s Directorate- General for ‘Employment, Social Affairs and Equal Opportunities’ (in close cooperation with Eurostat), has enabled the set of indicators (and breakdowns of these) to be considerably enriched. The indicators are permanently updated and disseminated on the Eurostat website (26). 2.6 The way forward Even though EU-SILC has become the EU reference for data on income and living conditions, Eurostat and a number of stakeholders are still reflecting on possible ways to further improve the tool and its uses. This book, and more generally the Net-SILC network which prepared it, is part of an effort to improve EU-SILC and the analysis based on it. At an international conference (27) which was jointly organised in March 2010 by Eurostat and the Net- SILC network, and which was hosted by Statistics Poland, a wide-ranging debate on present and future perspectives was held in the context of the future revision of the EU-SILC legal basis. Some of these considerations are presented below. (25) See http://ec.europa.eu/social/main.jsp?catId=756&langId=en. (26) See http://epp.eurostat.ec.europa.eu/portal/page/portal/employment_ and_social_policy_indicators/omc_social_inclusion_and_social_pro- tection. (27) 2010 International Conference on Comparative EU Statistics on Income and Living Conditions, Warsaw, 25–26 March 2010 (http://www.stat. gov.pl/eusilc/index.htm). 2.6.1 Improvement of timeliness and geographical coverage In the current situation, cross-sectional data pertaining to Year N — and referring in most countries to the income and tax of Year (N-1) — are available in the best case at the end of Year (N+1). This weakness was clearly highlighted by the recent economic and financial crisis, when EU-SILC was unable to deliver data describing the impact of the crisis on poverty and social exclusion. The need for further synchronisation with other EU reporting processes is also an issue. The time between data collection and data dissemination definitely needs to be shortened. Despite the considerable improvement observed in terms of timeliness with the transition from ECHP to EU-SILC, there might be a need to design different estimation strategies and to further streamline national processes. Developing a system based on or outside EU- SILC for the short-term monitoring of living conditions is another possible option in order to improve timeliness. At the same time, it is necessary to improve the access to and documentation of EU-SILC micro- data. The research community is making a strong case for the access to the EU-SILC Users’ data- base to be extended to microdata from all coun- tries, when in fact it was recently restricted (28). 2.6.2 Methodological and data improvements In the future, improvements will be introduced in the areas of technology, methodology and implementation in order to produce better quality data. Improvements will mainly be in terms of comparability and better fulfilling the needs of the various users, i.e. the European Commission and individual Member States, the scientific community and various international organisations. An ongoing dialogue between these different users is the only way to really improve the overall quality. (28) This request concerns the absence of some countries in the Users’ data- base (as described in Section 2.5.1). Income and living conditions in Europe eurostat54 2 Investing in statistics: EU-SILC A number of improvements were suggested at the Warsaw Conference and some of them are reflected in various chapters of the present publication. Suggested improvements include for instance: • anonymisation procedures and the extent — and level of details — of microdata available for research (e.g. on sample design, on specific income components) (Chapters 3 and 17) • better information on the relationships between household members (Chapter 4) • further and more systematic investigation of the coherence of/ comparability between EU- SILC and other — EU-wide and national — data sources (Chapters 5 and 18) • further analysis of the lower tail of the income distribution and treatment of negative income components (Chapter 6) • refinement of common guidelines on self- employment income (Chapters 6 and 14), goods and services produced for own consumption (Chapter 8) • improvement of the identification of self- employment activities within employment activities and improvements of the information provided through the calendar of activities (Chapter 14) • improvement of the methods (including their documentation) used by countries in order to estimate ‘imputed rent’ (Chapter 7) and net-to-gross conversion models (Chapters 12 and 17) • reflection on the most appropriate level of data collection — individual vs. household level — for certain income variables (Chapter 17) • need to enlarge the scope of the longitudinal component of EU-SILC (Chapters 9 and 11) • discussion on the opportunity to expand the non-monetary information available from the core set of EU-SILC variables (Chapters 10, 11 and 18). 2.6.3 Coherence with other sources Some information concerning the checking of consistency between EU-SILC and other national microdata sources is available from the quality reports, but such information needs to be further developed. The consistency between aggregates computed from microdata (EU-SILC) and macrodata (national accounts) sources should also be improved. In conjunction with the recommendations of the Commission on the Measurement of Economic Performance and Social Progress (29), Eurostat has set up a Task-Force on the distributional aspects of household income, consumption and wealth, which are intended to shed some light on this connection. 2.6.4 Data linking Users frequently request statistical information cutting across several dimensions of the quality of life. Such requests concern both the coverage of the information collected (e.g. quality of life, subjective wellbeing, social participation, consumption, or wealth) and its use in terms of assessing inequalities. The social statistics infrastructure, on the other hand, is organised around specific surveys and administrative sources independently covering many aspects that are relevant to users’ requests. Currently, there is no single data source that is able to cover all the necessary aspects at the microdata level. In line with the Commission communication on the ‘Production method of EU statistics: a vision for the next decade’ (30), Eurostat has launched a new project aimed at testing new techniques, such as linking and statistical matching of data from different sources, in particular EU-SILC, the Labour Force Survey, the Household Budget Survey, the European Central Bank Survey on Households’ Finance and Consumption or the (29) See http://www.stiglitz-sen-fitoussi.fr/en/index.htm. (30) COM(2009) 404 final, Communication from the Commission to the European Parliament and the Council on the Production method of EU statistics: a vision for the next decade. Income and living conditions in Europeeurostat 55 2Investing in statistics: EU-SILC European Foundation’s Quality of Life Survey. (For a discussion of this topic, see also Chapter 18.) 2.6.5 Revision of the EU-SILC legal basis Against a general background of modernisation of the whole system of production of European social statistics, the challenge of summarising the expectations from various stakeholders with often diverging needs, while at the same time responding to new requirements is without doubt a risky enterprise, but one with which Eurostat and the European Statistical System have to contend. Currently there are plans to revise the legal EU- SILC framework during the period 2011–2013. An essential prerequisite will be an analysis of the cost-efficiency of the whole operation — in particular its longitudinal component and the annual ad-hoc modules — as well as the length and content of EU-SILC. The overarching objective of this revision will be to stabilise and foster the main components of EU-SILC, while considering some possible changes (both to include emerging topics of interest and to omit less fundamental aspects). Taking stock of the first years of implementation of EU-SILC, as well as the new needs and constraints which have emerged more recently, the need to move towards greater harmonisation of input (in drawing up common reference questionnaires, for instance) will have to be balanced by the flexibility needed by the implementing countries. References Clemenceau, A. and Museux, J.-M. (2007), EU- SILC (Community statistics on income and living conditions: general presentation of the instrument), European Commission, Luxembourg. European Commission (2003), Regulation (EC) No 1 177/2003 of the European Parliament and of the Council of 16 June 2003 concerning Community statistics on income and living conditions (EU-SILC), European Commission, Brussels. Eurostat (2010), 2008 Comparative EU intermediate quality report Version 2, European Commission, Luxembourg. Available from: http://epp.eurostat.ec.europa.eu/portal/page/ porta l/ income_socia l_inclusion_living_ conditions/quality/eu_quality_reports. Expert Group on Household Income Statistics (The Canberra Group) (2001), ‘Final Report and Recommendations’, The Canberra Group, Ottawa. Available from: www.lisproject.org/links/canberra/finalreport.pdf. 3Data accuracy in Eu-SILC Vijay Verma and Gianni Betti (1) (1) The authors are with the University of Siena, Italy. They would like to thank Francesca Gagliardi for assistance in the research presented in this chapter, Giulia Ciampalini for her help in the production of numerical results and Olympia Bover for her valuable comments and suggestions. Of course, these persons are not responsible in any way for the present contents. The European Commission also bears no responsibility for the analyses and conclusions, which are solely those of the authors. Addresses for correspondence: vijay.verma46@gmail.com and betti2@unisi.it. Income and living conditions in Europe eurostat58 3 Data accuracy in EU-SILC 3.1 Introduction: a description of errors in survey data This chapter analyses sampling and non-sampling errors in EU-SILC, examining the impact of these errors on comparability across countries and over time. This section provides a typology of survey errors to set the framework for subsequent discussion. In the following sections major components and sources of error in EU-SILC are examined in- depth and empirically. An important concern is to explore and expose the barriers which researchers, using the restricted information provided in UDB and EU-SILC documentation in the public domain, face in assessing quality of the data. This issue is important for proper use of the data and for the development and improvement of EU-SILC itself, and needs to be brought out prominently. 3.1.1 A typology of errors Knowledge about data quality is required for their proper use and interpretation. Also, measures of data quality are important for the evaluation and improvement of survey design and procedures. Continued monitoring and improvement of data quality is particularly important in major continuing surveys such as EU-SILC. There are diverse forms and many different sources of errors in surveys, and various frameworks have been proposed for their classification. Different frameworks emphasise different aspects of the problem. None may be considered as ‘the best’, though some frameworks are more illuminating than others. The following framework is drawn from Verma (1981), further elaborated in Hussmans et al (1990). This framework distinguishes between two groups of errors affecting the survey process: (a) Errors in measurement These arise from the fact that what is measured on the units included in the survey can depart from the actual (true) values for those units. These errors concern the accuracy of measurement at the level of individual units enumerated in the survey, and centre on substantive content of the survey: definition of the survey objectives and questions; ability and willingness of the respondent to provide the information sought; the quality of data collection, recording and processing. This group of errors can be studied in relation to various stages of the survey operation. (b) Errors in estimation These are errors in the process of extrapolation from the particular units enumerated in the survey to the entire study population for which estimates or inferences are required. These centre on the process of sample design and implementation, and include errors of coverage, sample selection and implementation, non-response, and also sampling errors and estimation bias. In Figure 3.1 a third category, namely item non- response, has been added as an intermediate category between measurement and estimation errors. Each group of errors may be further classified in more detail in order to identify specific sources of error, so as to facilitate their assessment and control. The above categorisation, in terms of errors in measurement and errors in estimation, is more fundamental than the distinction usually made between sampling and non-sampling errors. It is important to note that the various phases of a survey are closely related. While it is useful to classify the total survey error into components, errors cannot always be attributed to a particular type or source. The same or similar methods of assessment and control may be suited for measuring more than one type of error, and some of the indicators obtained may provide no more than a general or overall measure of data accuracy without being able to identify specific sources and types of error. 3.1.2 Errors in measurement As noted, the broad range of ‘errors in measurement’ may be classified by source, Income and living conditions in Europeeurostat 59 3Data accuracy in EU-SILC for example as conceptual, response (‘data collection’) and processing errors. Conceptual errors concern the scope, concepts, definitions and classifications adopted in relation to the survey objectives, and are the most fundamental ones. The distinction between response errors concerning the process of data collection, and processing errors concerning the subsequent process of transforming the information into a micro database, is a useful one from the point of survey operations and methods of assessing and controlling these errors. Despite this operational distinction, however, the two classes of error are conceptually quite similar. Various components of measurement error may be distinguished. Further operational classification within each category may be introduced. Each type of error may be decomposed into bias and variance components. These distinctions are useful as the components differ in nature and in methods of assessment and control. (a) Measurement bias A part of the error is common to the work of all interviewers (or coder, etc.); this gives rise to response bias, i.e. more or less systematic errors in obtaining the required information. Bias arises from shortcomings affecting the whole survey operation: basic conceptual errors in defining and implementing the survey content; incorrect instructions affecting all the survey workers; errors in the coding frame or programs for processing the data, etc. Errors also arise from inherent difficulties in collecting certain types of information, more or less independently of the specific technical design and procedures of the survey, given the general social situation and the type of respondents involved. The first step in identifying bias is through logical and substantive analysis of the internal consistency of the data. Beyond that, the assessment requires comparison with more accurate information: data from external sources and/or data collected with special, improved methods. (b) Measurement variance This refers to variable errors in data collection and processing. In addition to biases common to the whole operation, each interviewer has his/her own particular bias, which affects the interviewer’s whole workload. This gives rise to correlated response variance, which indicates a lack of uniformity and standardisation in the interviewers’ work. By contrast, simple response variance is random, not correlated with any particular interviewer. It is an indicator of the inherent instability of particular items in the questionnaire. Its measurement requires comparisons between independent repetitions of the survey under the same general conditions — there is no way, in a single survey, to distinguish between variation among the true values of units (which gives rise to sampling error), and the additional variability arising from random factors affecting individual responses. 3.1.3 Errors in estimation Coverage and related errors Coverage errors arise from discrepancies between the target and the frame populations, and also from errors in selecting the sample from the frame. The condition of ‘probability sampling’ is violated if: (a) the survey population is not fully and correctly represented in the sampling frame; (b) the selection of units from the frame into the sample is not random with known non- zero probabilities for all units; or (c) not all the units selected into the sample are successfully enumerated. Coverage error concerns primarily (a), but also (b); (c) concerns non-response. Non-response errors Non-response refers to the failure to obtain a measurement on one or more study variables for one or more sample units. When a whole unit is missed, we have unit non-response. When a unit is included but information on some items for it is missed, we have item non-response. Non- response causes an increase in variance due to Income and living conditions in Europe eurostat60 3 Data accuracy in EU-SILC Figure 3.1: Types of errors in surveys Errors in measurement 1 conceptual errors • errors in basic concepts, definitions and classifications • errors in putting them into practice (questionnaire design, preparation of survey manuals, training and supervision of interviewers and other survey workers) 2 response (or ‘data collection’) errors • response bias • simple response variance • correlated response variance 3 processing errors • recording, data entry and coding errors • editing errors • errors in constructing target variables • other programming errors Mixed category 4 item non-response • only approximate or partial information sought in the survey • respondents unable to provide the information sought (‘don’t knows’) • respondents not willing to provide the information (‘refusals’) • information suppressed (for confidentiality or whatever reason) Errors in estimation 5 coverage and related errors • under-coverage • over-coverage • sample selection errors 6 unit non-response • unit not found or inaccessible • not-at-home • unable to respond • refusal (potentially ‘convertible’) • ‘hard core’ refusal 7 sampling error • sampling variance • estimation bias Non-sampling errors = 1 to 6 + Comparability, underscoring all aspects of data accuracy Source: Adapted from Hussmans et al (1990) Income and living conditions in Europeeurostat 61 3Data accuracy in EU-SILC decreased effective sample size and/or due to weighting and imputation introduced to control its impact; more importantly, it causes bias in so far as non-respondents are selective with respect to the characteristic being measured. For instance, one might expect persons with higher incomes to be more reluctant to give information on their income; similarly, poorer, unemployed and socially excluded persons are more likely to be missed in surveys such as EU-SILC. Classification of unit non-response according to the reasons or circumstances giving rise to it can be very helpful for identifying and controlling the extent of non- response and assessing its impact. It is most useful when the categories are designed to capture the most important factors in the particular survey, are not too numerous, and are clear and non- overlapping (Kish, 1965, Section 13.4A). Examples are units not found or not accessible, not-at-home, unable or refusing to respond. In a repeat survey such as EU-SILC, it can be very useful to distinguish between ‘potentially convertible’ refusals and ‘hard core’ refusals which have to be dropped from future rounds. For composite units (e.g. a multi-adult household), any of the above reasons may result in partial unit non-response, in the sense described in Section 3.3.3. Sampling error Sampling error is a measure of the variability between estimates from different samples, disregarding any variable errors and biases resulting from the process of measurement and sample implementation. Of course, sampling error represents only one component of the total survey error. For estimates based on small samples, this component may be the dominant one. In other situations, non-sampling errors, in particular sample selection, non-response and measurement biases, may be much more important. However, even in these cases, sampling error increases progressively as the estimates are produced for smaller and smaller subgroups of the population, such as for social classes or regions of a country: in a small enough subgroup, sampling error may well outweigh non-sampling errors. This consideration is very important in a multi-purpose survey such as EU- SILC, an important objective of which is to study differentials and trends. 3.1.4 Item non-response Item non-response can be seen as an intermediate category between errors in measurement and errors in estimation. Like any other error in measurement, item non-response is subject-matter specific. At the same time, it can be viewed simply as an addition to the existing unit non-response in analysis involving the particular item affected, thereby amounting to an error in estimation. Item non-response is particularly important in EU- SILC and similar surveys collecting complex and detailed information on components of household and personal income. Some components such as income from self-employment and capital can be subject to extremely high levels of item non-response. Information on an item may be incomplete simply because it is not feasible to seek it exactly or in full detail in an interview survey; these errors are akin to ‘conceptual errors’. The impact on the results may differ depending on the respondent’s characteristics and circumstances. Often information is missing because the respondent is unable to provide it, or the respondent may be unwilling to provide information which is considered too sensitive or personal. There can be an added, special reason for item non-response in surveys providing microdata to researchers and other users: this is deliberate suppression of some information, presumably based on confidentiality and similar considerations. For composite items (e.g. an income target variable composed of several individual items), any of the above reasons may result in ‘partial item non-response’, in the sense described in Section 3.3.4. 3.1.5 Comparability Comparability is increasingly considered as the central requirement of data quality. This Income and living conditions in Europe eurostat62 3 Data accuracy in EU-SILC dimension of quality is particularly important in a multi-country undertaking such as EU- SILC, where issues relating to comparability underscore all aspects of data quality, especially data accuracy. It is not possible to assess the extent and impact of sampling and non-sampling errors in EU-SILC without evoking at the same time the extent to which the results can be considered comparable across countries, across time, and also in relation to other data sources. We may also note that indications of accuracy (unit and item non-response rates, sampling variance, etc.) need to be defined and computed following identical procedures. 3.2 Conceptual and measurement errors There is a great variety of errors arising from conceptual and measurement (collection and processing) sources and the patterns can differ across countries. Below we present a few selected aspects of such errors with reference to the measurement of income, which constitutes the main topic of EU-SILC. Often it is not possible to associate these errors with a single source: usually the observed patterns reflect the combined effect of different sources. 3.2.1 Reporting of negative and zero values for income components As an illustration, Table 3.1 shows variation across countries in the incidence of reporting negative, zero and positive values of income from self-employment. The incidence of reporting a negative amount varies across countries: roughly half the countries in EU-SILC permit the recording of negative values for income from self-employment, and the other half do not. This illustrates the influence of variations in measurement procedures on accuracy and comparability of the data. It is not possible to present here detailed results for other income components individually. A few observations concerning capital income should, however, be made. The impact of conceptual differences is seen markedly in the case of this component. No countries record negative values for the component, except for the striking example of Denmark where over half the reported amounts in the 2007 data were negative. Nevertheless, even here this component accounts for around 20% of total income of households, which is practically identical to the average value of this share over EU countries. The large number of negative values in Denmark is in fact made up of numerous small amounts. There are also some other conceptual differ- ences in the measurement of capital income. For instance all but 1–3% households report zero income from this source in countries such as Greece and Hungary, while all but 1–3% report a non-zero income from this source in register countries like Denmark and Norway. These dif- ferences are also reflected in the mean amount per recipient — the values being much higher among the fewer recipients in the former coun- tries compared to the latter. Such differences are likely to arise from, among other factors, dif- ferences in the methods of measurement — re- gisters tend to record small values exhaustively, while in personal interviews only larger amounts are likely to be recorded. 3.2.2 Total household gross and disposable income (HY010, HY020) A source of variation affecting comparability is the presence of negative, zero and extreme (very large) values in the distribution of total household income. Often these differences result from different data sources and survey conditions and procedures. Only a minority (around one-third) of the countries permit negative values for total gross income but most (though not all) seem to permit zero values, though the proportions of such cases are generally very small. The incidence of negative and zero values is somewhat higher for total household disposable income. One of the main uses of this variable is Income and living conditions in Europeeurostat 63 3Data accuracy in EU-SILC Table 3.1: Households receiving income from self-employment, 2007 recipients as % of all households % share of total income mean per recipient % zero* % negative % positive total % negative of recipients (1) (2) (3) (4)=(2)+(3) (5)=(2)/(4) (6) (7)=(6)/(4) BE 89.7 0.1 10.2 10.3 0.7 6.1 59.8 CZ 81.5 18.5 18.5 15.1 81.5 DK 69.2 3.5 27.3 30.8 11.5 5.7 18.4 DE 89.6 10.4 10.4 9.2 88.4 EE 90.6 0.5 9.0 9.4 4.8 2.3 24.4 IE 78.9 21.1 21.1 13.1 61.9 EL 67.9 32.1 32.1 24.3 75.5 ES 85.6 0.8 13.6 14.4 5.7 8.2 56.7 FR 92.3 7.7 7.7 7.0 90.6 IT 72.7 0.2 27.1 27.3 0.8 20.4 74.6 CY 75.3 24.7 24.7 11.2 45.4 LV 91.8 0.2 8.0 8.2 2.5 4.5 54.8 LT 81.3 18.7 18.7 6.4 34.0 LU 93.2 0.1 6.7 6.8 1.5 4.3 62.2 HU 84.4 0.4 15.3 15.6 2.4 8.3 53.2 NL 84.9 2.3 12.8 15.1 15.3 6.1 40.0 AT 83.8 0.4 15.8 16.2 2.2 8.6 53.2 PL 78.3 21.7 21.7 9.9 45.6 PT 79.2 20.8 20.8 12.0 57.4 SI 75.1 24.9 24.9 5.4 21.6 SK 89.2 0.2 10.6 10.8 1.7 7.7 71.6 FI 84.8 15.2 15.2 5.5 36.5 SE 81.2 4.3 14.5 18.8 22.9 2.8 14.8 UK 87.5 0.0 12.5 12.5 0.1 8.8 70.4 IS 82.6 17.4 17.4 3.5 19.9 NO 86.0 2.9 11.1 14.0 20.9 5.8 41.3 Average 17.1 8.5 52.1 cv (%) 40.7 60.1 42.0 Source: EU-SILC Users’ database. Data weighted by household cross-sectional weight (DB090). NB: Gross self-employment income (PY050) is aggregated to the household level. * This column for ‘zero’ values may contain small numbers of missing values on income. ‘Average’ refers to simple (unweighted) average over the 26 countries shown. ‘cv’ is the coefficient of variation of unweighted country values. Reading note: The table shows that. for example in Belgium. 0.1% of households report a negative amount for income from self-employ- ment and 10.2% a positive amount. giving a total of 10.3% ‘recipients’. Negative reports form 0.7% of these recipients. Of the total income received by all households. that from self-employment constitutes 6.1%. However. considering only households with non-zero income from self-employment. the average amount of income from self-employment received by such households amounts to nearly 60% of the total income averaged over all households in Belgium. Income and living conditions in Europe eurostat64 3 Data accuracy in EU-SILC to serve as a measure of economic well-being. However, negative or zero values of disposable income do not provide a useful measure of well-being which can serve as a proxy for living standards. The process of equivalisation of income — which adjusts household income to take into account economies of scale — also makes little sense when applied to negative quantities. In any case, some measures of poverty and inequality cannot be construc- ted with negative or zero amounts of net disposable income. The presence of a few large values at the upper end of the income distribution is also problematic in this respect, though not necessarily in the same way as negative or zero incomes. While not affecting measures of poverty, the presence of even a few very large values can markedly affect the computed indicators of inequality such as the Gini coefficient (2) and the S80/S20 ratio (3). The variance of the estimates may also be greatly inflated. These factors impart instability to the survey estimates and adversely affect their comparability across time and across countries. For instance, we find (using 2007 data) that, on the average across EU countries, the 99th percentile of total household disposable income is around four times the national median income, with a small coefficient of variation (cv) of 15% across countries. The diversity among countries increases as we move closer to the upper end of the distribution: the cv of the ratio to national median increasing to 20% at the 99.5th percentile, 25% at 99.8th percentile, and to 60% among the largest recorded values in the countries. See Table 3.2. (2) The Gini coefficient is an income inequality indicator based on the cumulative share of income accounted for by the cumulative percent- ages of the number of individuals, with values ranging from 0 per cent (complete equality) to 100 per cent (complete inequality). (3) The ratio of the share of income going to the top 20 per cent of the population (referred to as the top quintile share) to that going to the bottom 20 per cent (the bottom quintile share). 3.2.3 Total household disposable income before social transfers (HY022, HY023) A major limitation of these variables is the high incidence of zero and negative values. Variable HY022 is constructed from total net income (HY020) by deducting from it social transfers other than pensions, and HY023 is constructed by further deducting pensions as well. A characteristic feature of these variables is the large proportion of zero and negative values encountered: while generally there are only a small proportion of such values in HY010 or HY020, these proportions become quite significant in the case of HY022, and tend to become very large for HY023. In the 2007 data for instance, averaged over countries, 3% of the computed values of total household disposable income before social transfers other than pensions (HY022) are negative or zero. This average figure increases to 17% for total household disposable income before all social transfers (HY023). The last mentioned proportion reaches or exceeds 25% in almost one-fourth of the countries. The presence of large proportions of zero and especially of negative values diminishes the usefulness of these variables in providing explanatory or policy-relevant indicators. Different factors may be involved in different countries in determining the prevalence of negative and zero values in HY022 and HY023. It is likely that such values appear in large numbers as a result of deducting social transfers from the household’s actual disposable income without adequately considering that outgoings (already deducted from income) may be conditional on the availability to the household of the social transfer income component which is being removed. An obvious example is a voluntary private transfer paid out by a household, itself dependent on social transfers as the main source of its income. Another important issue concerns the (net/gross) form of social transfers which are deducted from HY020 in the construction of HY022/HY023. Obviously, the deduction Income and living conditions in Europeeurostat 65 3Data accuracy in EU-SILC has to be net of taxes and contributions, but in some cases gross amounts seem to have been deducted. An added disturbing aspect — likely to have an adverse effect on comparability — is the apparently arbitrary choice in recording non- positive values either as zeros or as negative. In some countries negative values predominate among these, while in some others zero values predominate. In relation to non-sampling errors in EU-SILC data and their comparability across countries, it is important to investigate how far these markedly differing patterns arise from conceptual and procedural differences among the national surveys. 3.2.4 The importance of uniform procedures for achieving comparability Often the presence of negative, zero and very large values of household income is the result of errors in the data introduced at the collection or processing stages. While it cannot be assumed automatically that any such extreme values are erroneous, there is a high chance of that being the case. Empirically we find that country surveys differ greatly in the Table 3.2: Ratio of upper percentiles to the median, 2007 Total disposable household income (HY020) P= Ratio of Pth percentile to median income 80 90 95 99 99.5 99.8 maximum BE 1.7 2.2 2.6 3.9 4.6 5.9 20.3 CZ 1.6 2.1 2.5 3.8 4.5 6.4 22.3 DK 1.8 2.2 2.5 3.7 4.9 8.3 60.9 DE 1.7 2.2 2.8 4.6 6.1 8.9 26.5 EE 1.9 2.5 3.2 5.1 6.1 7.2 40.1 IE 1.8 2.3 2.8 4.4 5.9 7.9 39.9 EL 1.8 2.4 3.0 5.1 6.3 8.8 21.3 ES 1.7 2.2 2.7 3.9 4.7 5.9 10.2 FR 1.6 2.0 2.5 3.8 4.4 5.6 41.9 IT 1.8 2.3 2.9 4.7 5.8 7.2 25.7 CY 1.6 2.1 2.5 4.4 7.1 11.9 22.8 LV 2.0 2.8 3.6 5.6 7.1 7.8 23.9 LT 1.9 2.6 3.2 5.0 5.8 6.7 14.9 LU 1.7 2.1 2.6 3.9 5.0 5.7 75.8 HU 1.6 2.0 2.4 3.6 4.8 6.3 20.8 NL 1.6 2.1 2.5 4.4 6.0 10.4 16.5 AT 1.6 2.1 2.5 4.2 5.1 6.4 10.2 PL 1.7 2.3 2.9 4.5 5.4 6.7 28.5 PT 1.8 2.5 3.5 5.7 7.4 9.5 20.0 SI 1.6 1.9 2.3 3.2 3.6 4.2 8.9 SK 1.6 2.1 2.5 3.8 4.5 5.2 8.6 FI 1.7 2.1 2.5 3.8 4.7 7.1 33.3 SE 1.7 2.0 2.4 3.3 4.0 5.2 16.5 UK 1.8 2.3 2.9 4.8 5.9 7.6 57.5 IS 1.6 2.1 2.6 4.6 7.4 11.0 23.4 NO 1.7 2.1 2.4 3.5 4.3 6.0 23.8 average 1.7 2.2 2.7 4.3 5.4 7.3 27.5 cv(%) 6.3 9.5 12.5 15.5 19.5 25.9 60.5 NB: See notes to the previous table. Income and living conditions in Europe eurostat66 3 Data accuracy in EU-SILC incidence and patterns of occurrence of extreme values. In part this may result from differences in data sources and national situations, but mostly it seems to be the result of differences in conditions and procedures of data collection, and especially in how the data are recorded and processed. These differences damage the international comparability of the results. EU-SILC data can be made more comparable through greater standardisation across countries of the manner in which negative, zero and very large values are treated. The use of standardised procedures can, of course, enhance the data quality of individual EU-SILC surveys. Even more important is the positive effect such standardisation can have on comparability across countries and over time. Improved comparability may be considered as the most important justification for adopting common procedures for treating extreme values in the income distribution. 3.3 Non-response in EU-SILC Non-response — both unit and item non- response — is a serious problem in EU-SILC surveys. It is clear from the available national and Eurostat quality reports that non-response of both types is high in many countries, and very high in some. Apart from cross-sectional non- response, panel attrition is particularly serious in some cases, affecting also the consistency between cross-sectional and longitudinal results. 3.3.1 A framework Though normally a distinction is made merely between unit non-response and item non- response, in the complex data structure and content involved in EU-SILC a more complete classification needs to be employed, such as that in Figure 3.2. It would be extremely useful to study these different aspects of non-response in empirical detail. However, a major practical difficulty is the lack of information on non-response available to researchers with access only to the UDB. Variables required for the computation and understanding of non-response have been removed from UDB — presumably because of confidentiality concerns. Figure 3.2: Components of non-response Problem Description Common solution (1) Unit non- response failure to obtain any information on a sample household, including the household interview and personal interviews in the household Weighting (2) Partial unit non-response failure to obtain a personal interview with a subset of the eligible adults in a household Weighting or full-case imputation (3) Item non- response failure to obtain some target variables in an otherwise completed interview. (This generally affects non-income variables in register countries and all — especially income — variables in survey countries.) Imputation for missing items (4) Partial item non-response Refers to the situation when some but not all the information is obtained on a target variable. The most important case is that of detailed income components: a part of the component may be missing, and/or conversion may be required from the collected net to the required gross amount Micro-simulation (net-gross conversion), in conjunction with imputation for the missing part Income and living conditions in Europeeurostat 67 3Data accuracy in EU-SILC 3.3.2 Unit non-response Each stage involved in obtaining the interview contributes to unit non-response: successfully contacting the sample address; interviewing the sample household once contacted; and detailed personal interviews with all adults (or, depending on the survey design, with one selected respondent) in the household. Table 3.3, columns (1)–(3) give the response rates at the above three stages for 2007 cross- sectional samples. The figures are confined to the panel newly introduced in 2007 in the rotational design. The overall response rate for the personal interview is the product of these rates. Its complement, the overall non-response for the personal interview, is shown in column (4). A number of points are worth noting. (1) Non-response rates are very high — exceeding 33% in 8 of the 26 countries, and exceeding 20% in all but 6 countries. Such high rates can be expected to have a significant effect on the representativeness of the results. (2) The potential impact of non-response is further increased because its incidence often varies across Table 3.3: Unit non-response (cross-sectional sample, 2007) New panel Overall non-reponse rate Response rate by stage for personal interviews address contact household interview personal interview New panel Total sample (1) (2) (3) (4) (5) BE 99 48 99 53 36 HU 100 52 100 48 29 DK 86 69 100 41 42 ES 98 63 99 38 24 CZ 96 65 100 38 18 AT 100 65 99 36 23 EE 84 77 99 36 20 PL 99 72 93 34 22 LT 99 68 99 32 17 SI 98 73 100 29 24 IE 100 72 100 28 30 FI 100 75 100 25 17 EL 100 76 100 25 16 NL 95 83 100 22 17 IT 99 81 100 20 15 PT 98 88 100 14 20 DE 91 96 100 13 19 FR 99 88 100 13 15 CY 100 91 100 9 8 SK 100 98 100 2 16 average 97 75 99 28 22 Source: Compiled from national EU-SILC Intermediate Quality Reports 2007. NB: Countries ordered by col. (4), the overall personal interview response rate for the new panel. Countries where information for the new panel has not been reported separately are not shown. Reading note: Overall response rate is the product of the response rates at each stage, cols. (1)–(3). Col. (4) is the complement of the overall response rate. Thus 0.53=1-(0.99*0.48*0.99) for Belgium. Cols. (1)–(4) are for the newly introduced panel in the rotational design. Col. (5) is the overall non-response rate for the whole sample as reported in national quality reports. As explained in Section 3.3.2, we believe that these last-mentioned figures have not been correctly computed, and generally grossly under-state the actual non-response rates. Income and living conditions in Europe eurostat68 3 Data accuracy in EU-SILC different parts of the population with differing characteristics — such as having higher rates of non-response among persons at either end of the income distribution. It is therefore important to analyse non-response rates for subpopulations. Unfortunately this cannot be done for EU-SILC on the basis of microdata available to researchers, since variables concerning the response status of households and individuals have been excluded from those data. The figures reported in the table are merely reproduced from national or Eurostat quality reports. (3) The table also quotes in column (5) the reported non-response rates for the cross- sectional sample as a whole. Normally these rates should be higher than the non-response rates in column (4) for the newly introduced panel, since the older parts of the sample have been subject to additional non-response at previous waves. The reported results are mostly inconsistent with this for the following reason. In a cross- sectional sample based on a rotational design (Verma and Betti, 2006), proper computation of the rate of non-response must take into account all the losses in the sample which have occurred since the concerned units were first selected into the rotational design. The reported non- response rates are gross underestimates since their computation has been based entirely on the units present in the current cross-sectional data set. Units which were selected at an earlier time and remain in-scope of the target population, but were dropped from the survey due to earlier non-response are not taken into account in the computation of the current cross-sectional non- response rates, in so far as such units do not appear in the current cross-sectional data files used as the basis for these computations. (4) Unit identification numbers in EU-SILC are randomised for confidentiality reasons. This randomisation seems to have been done independently between the cross-sectional and longitudinal data sets, even though in terms of actual units these data sets largely overlap. The problem of correctly computing cross-sectional non-response rates can be resolved only by retaining the identification of the link between the cross-sectional and longitudinal samples at the micro level, and using the information on longitudinal follow-up rates in the computation of achieved response rates for the cross-sectional sample. For a sample introduced into the survey at an earlier wave, the actual response rate of its contribution to the current cross-sectional sample is the product of: (a) the response rate achieved when it was first introduced into the survey, akin to the complement of column (4); and (b) the ‘wave response rate’ at each subsequent wave, similar to the complement of column (5) per wave. (4) 3.3.3 Within-household (‘partial unit’) non- response The overall personal interview response rates discussed above incorporate the effect of within-household non-response, i.e. of failures to obtain personal interview(s) in households otherwise successfully enumerated. In any case, the contribution of such within-household non-response is generally very small at the aggregate level. However, this is not at all the case as far as the individual households affected are concerned. The income of the household (and hence the equivalised income attributed to each of its members) cannot be properly measured without including the contribution of all its members. The reported incidence of within-household non- response is around 1% in most countries, but is higher in a few (for instance, in the 2007 survey, around 3% in Latvia and Slovakia, and notably 10% in Poland as a clear outlier). Countries have used quite different methods to deal with the problem, which are as follows: (1) Full-case imputation of missing personal interviews. This can be a convenient and satisfactory method when the incidence of within-household non-response is small. (Followed by Belgium, Estonia, France, Cyprus, Lithuania and Austria.) (4) Wave response rate is the percentage of sample units successfully inter- viewed in wave t, among in-scope units passed on from wave (t-1) or newly created or added during wave t. Income and living conditions in Europeeurostat 69 3Data accuracy in EU-SILC (2) Adjustment of total income of the affected household by a factor (UDB variable HY025) determined on the basis of characteristics of the household and of the non-interviewed persons. (Followed by Germany, Greece, Spain, Latvia, Portugal and Slovakia.) (3) Taking no action, i.e. making no imputation or weight adjustment for the missing personal interviews. (Poland, despite high incidence of within-household non-response.) (4) Deleting from the data all households with one or more missing personal interviews. This inflates the overall household non-response rate. It can be wasteful, and also hides the problem of within-household non-response. Yet, this is a widely used practice. (Followed by Czech Republic, Ireland, Italy, Luxembourg, Hungary and the United Kingdom.) (5) All the register countries present a situation similar to (4), but arising from a different mechanism. Here the information on income comes from administrative sources, not subject to non-response. Complex non-income or ‘social’ variables are collected through personal interview in all countries, including register countries which follow-up one selected respondent per household for the purpose. These interviews are, of course, subject to high rates of non-response. (5) Unfortunately, households where such an interview cannot be conducted are dropped from the survey, hence losing also the information on income for these households — even though it would have been possible to compile this information without non-response from registers for all sample households and their members. Frick et al (2010) have recently analysed the problem of within-household non-response using data from more than 20 waves of the German Socio-Economic Panel (GSOEP). They (5) In fact, the overall personal interview non-response rates in register counties tend to be higher than those in survey countries: the respec- tive average figures being 27% and 21% for the 2007 cross-sectional sample, for instance. As noted, these figures from the national quality reports are themselves under-estimates. It should also be mentioned that within each group there is a wide variation across counties around the above-mentioned averages. evaluate different strategies to deal with this phenomenon, and show how the choice of the technique affects the substantive results and their comparability. 3.3.4 Item non-response Unlike unit non-response for which there is a lack of information, EU-SILC is exceptionally good in providing detailed information on item non-response in the microdata files and also in survey documentation. There are few other social surveys which match the EU-SILC standards in this respect. For every income component, the data provide two ‘flags’ indicating the form and the degree of completeness of the collected information. Though all income components are recorded gross of taxes and social insurance contributions, the collected amount may be gross, or it may be net of taxes and/or of social insurance contributions. The first flag records the form of collection, which determines whether micro-simulation is required to obtain the target gross amounts. Micro-simulation is similar to imputation in that both involve some form of modelling; micro-simulation tends to be more dependent on external data and relationships, while imputation more on relationships between the variables observed within the dataset. The second flag records the ratio of the amount actually collected to the amount recorded for the component concerned. As explained in the notes to Table 3.4, value ‘0’ means full item non- response — the percentage of cases in which the item has been completely imputed. Value ‘1’ means the amount is recorded exactly as collected, with no imputation or net-to-gross conversion. The remaining cases involve partial item non-response. In this case, the flag gives the combined effect of imputation and net-to- gross conversion. However, in cases where the amounts collected were already in the gross form, no net-to-gross conversion is involved and the flag departs from 1.0 only because of imputation Income and living conditions in Europe eurostat70 3 Data accuracy in EU-SILC for the part which was missing. In other cases the flag indicates a mixture of imputation and net-to- gross conversion. For illustration, values of the two flags are shown in the table for income from self-employment (PY050). These figures underscore the richness of the information available on item non-response in EU-SILC microdata. It has to be admitted, however, that the quality of the available information on flags is not uniformly good and the information is missing in some countries. Having a large proportion of cases with low values of the imputation flag indicates poor quality of the data. Large variability in this index across countries also casts doubt on comparability of the information. Table 3.4: Item non-response: income from self-employment (PY050), 2007 % receiving % distribution of recipients by mode of collection % distribution according to imputation factor (= value collected / value recorded) Total -1 1 2 3 4 5 = 0 0-0.5 0.5-1.0 = 1 >1 BE 6.2 0.1 100 71 0 0 28 100 DK 23.2 100 100 DE 6.1 100 9 4 4 84 100 IE 10.4 100 56 1 6 37 100 ES 7.4 100 29 2 36 33 100 CY 11.3 100 1 0 99 100 LU 5.0 100 46 0.3 53 100 HU 10.2 0.1 100 2 98 0.1 100 NL 9.6 100 100 100 AT 9.7 100 94 4 0.4 2 100 SI 15.8 100 36 5 3 54 1.3 100 SK 4.9 100 100 100 FI 21.3 100 100 SE 13.4 100 0 100 100 UK 7.3 100 22 0.1 0.2 78 100 IS 10.9 100 100 NO 11.2 8 92 100 CZ 7.6 2 18 79 1 16 81 1.3 100 LT 9.4 25 74 0 1 1 14 83 0.8 100 EE 6.7 33 65 3 14 1 18 67 100 PT 10.4 63 19 14 4 2 85 14 100 EL 19.5 100 0 100 100 FR 4.3 100 0 1 94 5.3 100 IT 16.6 100 0 4 19 5 72 0.0 100 LV 4.3 100 0 6 1 9 83 100 PL 10.6 100 0 20 12 15 54 0.0 100 Source: EU-SILC Users’ database; unweighted values. NB: -1 missing; 1 net of tax and social contributions; 2 net only of tax; 3 net only of social contributions; 4 gross; 5 not stated. Reading note: ‘Imputation factor’ = ‘0’ means full item non-response — the percentage of cases in which the item has been completely imputed. Value = ‘1’ means the amount is recorded as collected. with no imputation or net-to-gross conversion. The remaining cases involve partial item non-response; if ‘mode of collection’ = ‘4’. this partial item non-response is entirely due to a part of the information being missing; in other cases it indicates a mixture of imputation and net-to-gross conversion. Income and living conditions in Europeeurostat 71 3Data accuracy in EU-SILC 3.4 Sampling error 3.4.1 Jackknife Repeated Replication (JRR) for variance estimation EU-SILC is a set of large-scale household surveys based on complex designs. The surveys are multi- purpose, involving many types of variables, estimates, units of analysis, levels of aggregation of the results, and diverse subpopulations for which estimates of levels, differences and other relationships are required. Practical procedures for estimating sampling errors for such a survey: (i) must take into account the actual, complex structure of the design; (ii) should be flexible enough to be applicable to diverse designs; (iii) should be suitable and convenient for large- scale application, producing results routinely for diverse statistics and subclasses; (iv) should be robust against departure of the actual sample design from the ideal model assumed in the computation method; (v) should have desirable statistical properties such as small mean-square error of the variance estimator; (vi) should be economical in terms of effort and cost; and (vii) suitable computer software should be available for application of the method (Verma, 1991). Two broad practical approaches to the computation of sampling errors are: 1. computation from comparisons among certain aggregates for primary selections or replicates within each stratum, also known as the linearisation method; 2. computation from comparisons among estimates for replications of the sample, each of which reflects the structure of the full sample. A major advantage of methods in (2) above is that they do not require an explicit expression for the variance of each particular statistic, and hence can more easily handle complex statistics and designs, including multi-wave and longitudinal situations. The variance estimates take into account the effect on variance of aspects of the estimation process which are repeated for each replication. In principle this can include complex effects such as those of imputation and weighting, though often full repetition of these procedures for each replication is not feasible. A particular method of class (2) is the Jackknife Repeated Replication (JRR) method. The basic model of the JRR may be summarised as follows. Consider a design in which two or more primary selection units (PSUs) have been selected independently from each stratum in the population. Within each PSU, sub-sampling of any complexity may be involved, including weighting of the ultimate units. In the standard version, each JRR replication can be formed by eliminating one PSU from a particular stratum at a time, and increasing the weight of the remaining PSUs in that stratum appropriately, so as to obtain an alternative but equally valid estimate to that obtained from the full sample. Let z be a full-sample estimate of any complexity, and z(hi) be the estimate produced using the same procedure after eliminating primary unit i in stratum h, compensating for that by increasing the sample weight of the remaining (ah-1) units in the stratum by an appropriate factor. Let z(h) be the simple average of the z(hi) values over the ah sample units in h. Variance of z is estimated as (6): � � � � � �� � » » ¼ º « « ¬ ª �6¸¸¹ · ¨¨© § �6 2.1var hhii h h h zza az (3.1) The same relatively simple variance estimation formula holds for z of any complexity. Furthermore, apart from variance estimation of ordinary cross-sectional measures, application of the JRR methodology can be readily extended to more complex indicators based on the EU- SILC rotational panel design. These include longitudinal poverty rates based on union and/or intersection of an individual’s poverty statuses at a series of cross-sections, as well as measures of net change and averages over two or more waves (Verma and Betti, 2007). (6) The ‘finite population correction’, trivial in a survey such as EU-SILC, is neglected in (3.1). Income and living conditions in Europe eurostat72 3 Data accuracy in EU-SILC 3.4.2 Defining sample structure: ‘computational’ strata and PSUs Practical variance estimation methods, including the JRR, need to make some basic assumptions about the sample design. These include the following: 1. the sample selection is independent between strata; 2. two or more primary selections are drawn from each stratum; 3. these primary selections are drawn at random, independently and with replacement; 4. the number of primary selections is large enough for valid use of the variance estimation procedure described above. Though these basic assumptions regarding the structure of the sample for application of the method are met reasonably well in most EU- SILC surveys, often the assumptions are not met exactly. In many practical situations some aspects of the sample structure need to be redefined to make variance computation possible, efficient and stable. Of course, any such redefinition is appropriate only if it does not introduce significant bias in the variance estimation. A very convenient approach in practice is to summarise the most essential information about the sampling design in the form of two variables, coded for each unit in the microdata file: the ‘computational stratum’ and the ‘computational PSU’ to which the unit belongs. This can be done in most cases for the type of sample designs used in EU-SILC. Obviously, these two variables must be defined so as to meet the basic requirements (1)– (4) listed above for the application of the variance computation procedure adopted. Normally, we may expect the new variable ‘computational stratum’ to be related (and sometimes identical) to UDB variable DB050; similarly for ‘computational PSU’ and DB060. However, very often the UDB variables require some redefinition before they can be used for the purpose of variance estimation. The computation stratum has to incorporate all information about the stratification of the PSUs, including both explicit stratification and, where applicable, implicit stratification resulting from systematic sampling of the PSUs. It has also to ensure that each computational stratum contains at least two computational PSUs (which are then assumed to have been selected at random with replacement). Starting from the actual PSUs, the variable computational PSU should seek to create units reasonably large and uniform in size, and small enough in number so as to avoid excessive computational burden. To do the above in a statistically valid way requires sampling expertise. Apart from codes of the existing sample structure in the microdata files, this requires additional information: (i) detailed description of the sample design, identifying features such as the presence of systematic selection, ‘self-representing’ PSUs (which are in fact strata), etc; and (ii) information connecting the sample structure codes in the microdata with sufficiently detailed and clear descriptions on the basis of which the sample structure at the level of individual units can be identified. It is not possible here to go into technical details of how the required computational strata and PSUs may be defined most appropriately in the case of each EU-SILC national sample design. An extensive discussion may be found in Verma, Betti and Gagliardi (2010). It is important, nevertheless, to emphasis a point of great practical relevance in relation to variance estimation by users of EU-SILC data. The major problem is the lack of sufficient information for the purpose: the UDB does not contain information on sample structure, in particular concerning stratification. Consequently, from UDB, variances can be computed only for the handful of countries which have employed simple (unstratified) samples of households or persons, or where such a simple structure can be assumed as a reasonable approximation. Generally, however, appropriate coding of the sample structure, in the survey microdata preferably, is an essential requirement in order to ensure that sampling errors can be computed properly, taking into account the actual sample design. Lack of Income and living conditions in Europeeurostat 73 3Data accuracy in EU-SILC information on the sample structure in survey data files is a long standing and surprisingly persistent problem in survey work, as for example Kish et al (1976) discovered in their attempts to compute sampling errors for achieved survey data sets in the United States. (7) 3.4.3 Analysis of design effects in EU-SILC Design effect (Kish, 1995) is the ratio of the variance (v) under the given sample design, to the variance (v0) under a simple random sample of the same size: d2 = v/v0, d = se/se0. (3.2) Computing design effects requires the additional step of estimating the error under simple random sampling (se0), apart from its estimate under the actual design (se). Why are design effects needed and useful? EU- SILC regulations require information on effective sample size, which can be estimated only with information on design effects. Proceeding from standard errors to design effects is also essential for understanding the patterns of variation and determinants of the magnitude of the error, for smoothing and extrapolating the results for diverse statistics and population subclasses, and for evaluating the performance of the sampling design. It is important to note in this context that values of the design effect can differ greatly across variables and subpopulations within the same survey, and it is important to estimate and analyse this variation. (See for instance, Kish et al 1976, Verma et al 1980, Verma and Lê 1996, as examples from multi-country multi-subject surveys.) Why is analysis of design effects into components needed and useful? The general reasons for analysing design effects into components include the following: to better understand from where inefficiencies of the sample arise; to identify patterns of variation; through that, to improve ‘portability’ of the results to other statistics, (7) We are fortunate in having received additional information on sample structure (in particular on explicit stratification, variable DB050) from Eurostat for illustrative computation of sampling errors for EU-SILC surveys. But this information still had some major limitations. designs, situations, etc. It may also be noted that with JRR (and other replication methods) the total design effect can only be estimated by estimating its components separately. In applications to EU-SILC, there is in addition a most important and special reason for having procedures for appropriate decomposition of the total design effect into its components. Because of the limited information on sample structure included in the microdata available to researchers, direct and complete computation of variances cannot be done in many cases. Decomposition of variances and design effects identifies more ‘portable’ components, which may be more easily imputed (carried over) from a situation where they can be computed with the given information, to another situation where such direct computations are not possible. On this basis valid estimates of variances can be produced for a wider range of statistics, thus overcoming at least partly the problem due to the lack of information on sample structure in EU-SILC microdata. Components of design effect We may decompose the design effect into components as follows: v = v0∙d2 = v0∙(dW∙dH∙dD∙dX)2. (3.3) Here v0 is the variance (for the statistic concerned) in an equivalent simple random sample of individual persons; dW is the effect of sample weights; if relevant, dH is the effect of clustering of individual persons into households and dD the effect of clustering of households into dwellings; and finally, dX is the effect of other complexities of the design, mainly clustering and stratification. The effect of weights dW does not depend on the sample structure, other than the presence of unequal sample weights for the elementary units of analysis. Weighting generally inflates variance (weighting is primarily introduced to reduce bias). With the complex weighting procedures of EU-SILC, variation in weights can become large, inflating the design effect. This effect needs to be evaluated and controlled. Income and living conditions in Europe eurostat74 3 Data accuracy in EU-SILC Table 3.5: Estimates of standard errors and components of design effects, 2005–2006 n (persons) estimate %se* %se* (rand) dx dW dH dD d %se* (srs) (1) (2) (3) (4) (5)= (3)/(4) (6) (7) (8) (9)= (5)*(6)*(7)*(8) (10)= (3)/(9) Mean equivalised disposable income PL 32 820 3 686 0.71 0.77 0.94 1.21 1.74 Y 1.98 0.36 UK 15 434 22 686 1.25 0.94 1.33 1.02 1.53 1.00 2.07 0.61 AT 9 516 19 888 0.82 0.82 1.00 1.11 1.58 X 1.75 0.47 BE 8 205 19 274 1.33 1.19 1.11 1.18 1.55 1.00 2.03 0.65 LT 8 036 3 062 1.59 1.61 0.99 1.25 1.64 1.00 2.03 0.79 At-risk-of-poverty rate PL 32 820 18.4 0.45 0.44 1.02 1.08 1.74 Y 1.91 0.24 UK 15 434 18.0 0.95 0.60 1.57 1.07 1.53 1.00 2.56 0.37 AT 9 516 12.0 0.68 0.68 1.00 1.19 1.58 X 1.88 0.36 BE 8 205 14.1 0.68 0.60 1.13 1.05 1.55 1.00 1.85 0.37 LT 8 036 20.0 1.00 0.98 1.02 1.20 1.64 1.00 2.01 0.50 At-risk-of-poverty rate for children (aged under 16) PL 5 798 25.2 0.79 0.80 0.99 1.07 1.27 Y 1.35 0.59 UK 2 995 21.9 1.53 1.42 1.08 1.08 1.31 1.00 1.53 1.00 AT 1 794 14.7 1.47 1.47 1.00 1.12 1.29 X 1.44 1.02 BE 1 617 13.1 1.31 1.12 1.17 1.04 1.31 1.00 1.59 0.83 LT 1 267 23.6 2.18 2.16 1.01 1.21 1.23 1.00 1.49 1.46 At-risk-of-persistent-poverty rate (two year longitudinal panel) PL 32 820 12.7 0.34 0.34 0.99 1.05 1.74 Y 1.82 0.19 UK 15 434 10.4 0.59 0.53 1.12 1.07 1.53 1.00 1.83 0.32 AT 9 516 6.7 0.57 0.57 1.00 1.14 1.58 X 1.80 0.32 BE 8 205 8.9 0.66 0.58 1.15 1.15 1.55 1.00 2.04 0.33 LT 8 036 15.4 0.87 0.89 0.97 1.25 1.64 1.00 1.99 0.44 %se* For mean statistics e.g. equivalised disposable income — expressed as percentage of the mean value.For proportions and rates (e.g. poverty rates) — given as absolute percentage points. d Overall design effect Components of design effect: dX design effect due to clustering and stratification of ultimate sampling units (dwellings or households) dW effect of unequal sample weights dH effect of clustering of persons within households dD effect of clustering of households within dwellings (if applicable) Y = effect cannot be separately estimated because of lack of information identifying dwellings but is automatically incorporated into the overall design effect d; X = effect cannot be estimated, and cannot be included in the overall design effect d. Source: EU-SILC Users’ database. The computations refer to 2006 data in the 2-year (2005–2006) panel. Reading note: In PL for example. standard error for mean equivalised disposable income is 0.71% of the mean value (euro 3.686). for at-risk-of-poverty rate of 18.4%. standard error is 0.45 in (absolute) percentage points (implying a 95% confidence interval of 17.5–19.3%. for instance). Col. (4) gives standard error computed by ignoring any clustering and stratification of the ultimate sampling units (dwellings or households). The ratio of the actual to this ‘randomised sample’ standard error. col. (5). isolates the effect of clustering and stratification of dwellings/households in the sample. Col. (10) is an estimate of standard error which would be obtained in a simple random sample of persons. of the same size as shown in col. (1). Income and living conditions in Europeeurostat 75 3Data accuracy in EU-SILC Factor dH applies if v0 refers to variance in a simple random sample of individuals, while v refers to variance of a variable measured at the household level. For example, this factor equals square-root of household size for variables relating to household income when the unit of analysis is an individual person and v0 is defined to refer to a SRS of individual persons. For variables constructed at the household level on the basis of separate but correlated observations on individual household members, dH will be lower than the above depending on the strength of the correlation. The effect of clustering of households within dwellings or addresses is absent (dD=1) when we have a direct sample households or persons, or when such units are selected directly within sample areas — as is the case in most of the EU-SILC surveys. This effect is present when the ultimate units are dwellings, some of which may contain multiple households, but it is small in so far as there is generally a one-to-one correspondence between addresses and households. The above components of the design effect can be estimated without reference to information on the sample structure, other than weighting and identifiers linking different types of units (e.g. persons with their households). By contrast, computation of dX, the effect of complexities of the design such as multiple stages and stratification, requires information on the sample structure linking elementary units to their strata and higher stage units. Normally this effect exceeds 1 because the loss in efficiency due to clustering tends to be larger than the gain from stratification. We can expect it to be less than 1 in stratified random samples of elements. Procedures for estimating the design effect and its components are described in Verma, Betti and Gagliardi (2010). 3.4.4 Illustrative estimates of variance and of design effect and its components On the basis of the additional information provided by Eurostat for the purpose of this research, sampling errors have been computed as an illustration for a few countries shown in the Table 3.5. The results are for the 2006 sample in the longitudinal data set for the year 2006. This data set covers the preceding 2 or 3 years depending on the country. (8) The computations illustrated cover three cross-sectional indicators for 2006, and one longitudinal indicator defined over the two years 2005–2006. In the table, column (3) is the computed standard error based on the actual structure of the sample, and column (4) is the same statistic computed by treating the sample as a un-clustered and un-stratified sample of households. The ratio of the two, column (5) gives dX, the effect due to clustering and stratification of households in the sample. Two practically important and convenient points may be noted in relation to these results. Firstly, the complexity of the sample design at stages above the selection of households is represented by factor dX only; all other components of the design effect are independent of this complexity, and hence can be estimated despite any lack of information on sample structure in EU-SILC data files, except for the identification of individual addresses, households and persons, and their sample weights. Secondly, in many (though not in all) EU-SILC samples with a multi-stage design, only a small number of households or persons have been selected per PSU. In these cases factor dX tends to be close to 1, thereby not having a major effect on the overall magnitude of the sampling error. 3.5 Concluding remarks 3.5.1 Diverse sources of non-sampling errors in EU-SILC Following an examination of particular sources of errors in the preceding sections, it is useful to conclude by listing and classifying areas of particular interest and concern on which detailed evaluation studies are needed. (8) The necessary sample structure information was not available to the authors for the full cross-sectional sample for any of the survey years. Income and living conditions in Europe eurostat76 3 Data accuracy in EU-SILC Income variables • Analysis of the comparability of income distribution by component, especially monetary income from self-employment and capital, and income-in-kind from imputed rent, own production, company car and other sources. • Assessment of the impact on comparability of the net-to-gross conversion procedures used in different countries, examining how the procedures used fit into the general micro- simulation model SM2 (Betti et al, 2010). • Analysis of outliers and of zero and negative amounts in reported income. • More detailed study of comparability of self-employment income, considering both the mode of collection and the pattern of resulting data. Non-monetary deprivation • Study of comparability of non-income items defining living conditions and deprivation; comparison of indicators used for multidi- mensional poverty analysis. Consistency between cross-sectional and longi- tudinal components • Examination of national variations in consistency (or lack of it) between longitudinal and cross-sectional components, and its effect on comparability. Methodological • Analysis of the impact on comparability of the differences in structure of the EU-SILC instru- ment between ‘register’ and ‘survey’ countries. • Comparability of basic concepts for data collection and analysis, such as definition of the household, reference person, sample person and tracing rules. • Comparability of the national questionnaires and modes of data collection. • Effect of national differences in the cross- sectional non-response and panel attrition rates. • Study of differences in the weighting procedures used, and an assessment of the effects of such differences on the comparability of the results. • Comparability of imputation procedures in national surveys. 3.5.2 Improving the potential for assessment of data quality in EU-SILC It is obvious from the above discussion of errors in EU-SILC data that the scope and quality of this evaluation would have been improved with better information on sample structure and sample outcome of the surveys. Little information is available in EU-SILC documentation or microdata for an assessment of different types of measurement errors, except perhaps within some individual countries for their own surveys. The microdata available for research do not contain sufficient information on response status for assessing non-response rates, nor do they contain information on sample structure for estimating sampling errors and design effects. Of course, some limitations on the available information result from genuine concerns about preserving confidentiality of the data on households and persons taking part in the surveys. In this connection, we would like to conclude by pointing out a common misinterpretation which has had a serious negative effect on availability of microdata for research and other legitimate purposes. It is very important to note a major difference between social data based on sample surveys of small and numerous units such as households and persons, and some other types of data, such as those involving complete enumeration or pertaining to a small number of large units (e.g. enterprises) where there is a danger of exposure at the level of the individual unit (Verma, 1998). ‘Problems of confidentiality should not arise in the case of micro databases concerning surveys where items of the data … have identified numbers which cannot be connected by the user to the corresponding Income and living conditions in Europeeurostat 77 3Data accuracy in EU-SILC names even if used to relate the information to that from a different source; the [proportionately small] size of the sample … and the fact that named files are considered classified … should [usually] guarantee … sufficient respect for the needs of confidentiality. Problems become more sensitive in the case of microdata based on administrative records that aim to cover … the universe of individuals, families, companies [etc.]. In this case [by contrast], concerns felt about confidentiality would normally be well- founded.’ (Frey, 1996). References Betti, G., Donatiello, G. and Verma, V. (2010), ‘The Siena Micro Simulation Model (SM2) for net- gross conversion of EU-SILC income variables’, International Journal of Microsimulation, 3(2). Frey, L. (1996), ‘The strategy for making the microdata base accessible to users: partnership with researchers’, in Future of European Social Statistics. Guidelines and Strategies, Series 0D, Eurostat, Luxembourg, pp. 155–159. Frick, J. R., Grabka, M. M. and Groh-Samberg, O. (2010), ‘Dealing with incomplete household panel data in inequality research’, SOEP paper 290, DIW, Berlin. Hussmans, R., Mehran, F. and Verma, V. (1990), Surveys of the Economically Active Population, Employment, Unemployment and Underemployment, International Labour Organisation, Geneva. Kish, L. (1965), Survey Sampling, Wiley. Kish, L. (1995), ‘Methods for design effects’, Journal of Official Statistics, 11, pp. 55–77. Kish, L., Groves, R. and Krotki, K. (1976), ‘Sampling errors for fertility surveys’, WFS Occasional Papers No 17, International Statistical Institute, The Hague. Verma, V. (1981), ‘Assessment of errors in household surveys’, Bulletin of the International Statistical Institute, 49(2), pp. 905–919. Verma, V. (1991), Sampling Methods: Training Handbook, Statistical Institute for Asia and the Pacific (SIAP), Tokyo. Verma, V. (1998), ‘Data sources and access for comparative analyses’, in R. Walker and M. Taylor (editors), Information Dissemination and Access in Russia and Eastern Europe, IOS Press, Amsterdam, pp. 44–54. Verma, V. and Betti, G. (2006), ‘EU Statistics on Income and Living Conditions (EU-SILC): Choosing the survey structure and sample design’, Statistics in Transition, 7(5), pp. 935–970. Verma, V. and Betti, G. (2007), ‘Cross-sectional and longitudinal measures of poverty and inequality: variance estimation using Jackknife Repeated Replication’, Conference 2007 ‘Statistics under one umbrella’, Bielefeld University. Verma, V. , Betti, G. and Gagliardi, F. (2010), ‘An assessment of survey errors in EU-SILC’, Eurostat methodologies and working papers, Eurostat, Luxembourg. Verma, V. and Lê, T. (1996), ‘An Analysis of Sampling Errors for the Demographic and Health Surveys’, International Statistical Review, 64(3), pp. 265–294. Verma, V., Scott, C. and O’Muircheartaigh, C. (1980), ‘Sample designs and sampling errors for the World Fertility Survey’, Journal of the Royal Statistical Society, A, 143(4), pp. 431–473. 4Household structure in the Eu Maria Iacovou and Alexandra Skew (1) (1) The authors are at the Institute for Social and Economic Research of the University of Essex (ISER). Work for this chapter forms part of the Analysis of Life Chances in Europe (ALICE) project, funded by the UK’s Economic and Social Research Council. The work has also been supported by the Net-SILC pro- gramme, funded by Eurostat. We have received many useful comments on this work and would like to thank colleagues at ISER and those who attended the 2010 Colloquium on cross-national methods for the analysis of incomes and inequalities. Thanks also to Conchita D’Ambrosio for discussing our chapter at the Net-SILC conference organised in March 2010 in Warsaw. Finally, particular thanks are due to Tony Atkinson and Eric Marlier who coordinated the Net-SILC network, and who provided detailed feedback on earlier versions of this chapter. Of course, these persons are not responsible in any way for the present contents. The European Commission also bears no responsibility for the analyses and conclusions, which are solely those of the authors. Address for correspondence: maria@essex.ac.uk. Income and living conditions in Europe eurostat80 4 Household structure in the EU 4.1 Introduction Household structure is an interesting area for cross-national study for several reasons. Cross-national differences in household structure reflect important differences between societies: in culture and norms; in the cost and availability of housing; in the economic means available to different groups in society; and in social policy, where differences in tax and benefit regimes may lead to radically different patterns of household structure. Household structure is also interesting in terms of its relationship to a number of important outcomes. Poverty, for example, is intimately related to household structure. In the EU, poverty rates are conventionally calculated on the basis of household equivalent income (the sum of the incomes of all household members, divided by a factor related to the number and ages of these same household members) and household composition is therefore liable to affect both the numerator and the denominator of this calculation. There is a large literature dealing with the relationship between household composition and the risk of poverty (Bane and Ellwood, 1986), particularly relating to vulnerable groups: families with children (Bradbury and Jantti, 1999); young adults (Aassve et al, 2007) and older people (Rendall, 1995). Of course, poverty is not the only outcome related to household composition: children’s later outcomes, in terms of educational achievement, future earnings and so on, are affected by the composition of the households in which they grow up (Boggess, 1998; Francesconi et al, 2005), even after accounting for the effects of poverty associated with certain household structures, while older people’s health status is also related to household composition (Hays, 2002). Household structures across the pre-enlargement EU-15 have been widely documented (Iacovou, 2004; Tomassini et al, 2004; Andersson, 2004; Robson and Berthoud, 2003; and many others). There are also several studies based on surveys such as the Family and Fertility survey and the Gender and Generations survey, which include a limited subset of the new EU Member States of Eastern Europe (Hantrais et al, 2006; Hoem et al, 2009; Gerber, 2009). A smaller number of newer studies have used data covering most or all of the countries of the enlarged European Union: Mandic (2008) deals with home-leaving, Liefbroer and Fokkema (2008) deal with fertility; while Saraceno (2008) provides an overview of household structure in a number of different age groups, as well as some statistics on labour market status and time use. This chapter is based on EU-SILC. Being a general-purpose data set, EU-SILC does not allow for such detailed investigation of family formation patterns as some other data sets. However, its strength lies in the scope of its coverage, which makes it possible to draw comparisons of many aspects of family structure, over almost the entire European Union (2). We believe that this chapter provides a unique resource in this respect. We present detailed figures on household structure separately for each country in the sample. However, we also consider whether there exist groups of countries which display similar sets of characteristics, and which may be thought of as forming clusters. Again, there is a well- developed literature in this area relating to the pre- enlargement EU-15, and our focus in this chapter lies in integrating the new Member States into this area. In particular, we are interested to uncover the extent to which the new Member States may be incorporated into existing typologies of family structure, or whether behaviour in some or all of these countries differs so far from behaviour elsewhere in Western Europe that it is necessary to think in terms of an expanded typology. The section which follows outlines the typologies which have been used to conceptualise cross- national variations in family structure; we then move on to a discussion of the data, before presenting our results in Sections 4.3 to 4.8. (2) Bulgaria, Malta and Romania are not covered here because data for these countries were not available from the EU-SILC Users’ database (UDB) to which Net-SILC members had access. Income and living conditions in Europeeurostat 81 4Household structure in the EU 4.1.1 Countries and groups of countries Attempts to classify family structure across 20th Century Europe began with Hajnal (1965, 1982), who suggested an East-West division of European marriage patterns: regions east of a line from St. Petersburg to Trieste characterised by relatively early and near-universal marriage, and those to the west by later marriage, with a higher proportion of individuals remaining unmarried. In the 1990s, and considering variation in the countries of Western Europe in more detail, Reher (1998) outlined a typology based on geography and the familialistic legacy of the Catholic Church to explain features of family structure across this region. He described a ‘Northern’ cluster (Scandinavia, the United Kingdom, the Low Countries (3) and [much of] Germany and Austria), characterised by ‘weak’ family ties, early home-leaving, and a sense of social rather than familial solidarity with elderly or weak members of society; and a ‘Southern’ cluster (the Mediterranean countries, including Portugal) characterised by ‘strong’ family ties, later home-leaving, and a more family-based sense of solidarity. He noted that Ireland is an indeterminate case, being geographically Northern, but having much more in common with the Mediterranean countries in terms of family structures. Iacovou (2004) explored the extent to which a welfare regime typology as proposed by Esping-Anderson (1990 and 1999) could be used to explain family patterns in Western Europe. In fact it was found that a typology based on religious affiliation or geography explained family structure as well, if not better, proposing a spectrum ranging from Northern/ Protestant to Southern/Catholic. At one end, the Scandinavian countries are characterised by small households (particularly single-adult and lone-parent households), early residential independence for young people and extended residential independence for elderly people; cohabitation as an alternative to marriage; and (3) Member States referred to as ‘Low Countries’ are the Netherlands, Bel- gium and Luxembourg. an almost complete absence of the extended family. At the other end, the Southern European countries are characterised by relatively low levels of non-marital cohabitation, by extended co-residence between parents and their adult children, and by elderly people with their adult offspring; this, together with a much lower incidence of lone-parent families, make for much larger household sizes. Building on the work of Iacovou (2004), we use the following fourfold grouping for the purposes of presenting our results. The first group is a ‘Nordic’ cluster consisting of the Scandinavian countries (Sweden, Denmark and Finland) plus the Netherlands. The second group consists of the pre-enlargement countries of North-Western Europe: the United Kingdom, France, Germany, Austria, Belgium, Luxembourg and Ireland. The third group consists of the Southern European countries: Italy, Spain, Portugal, Greece and Cyprus. The final group is an ‘Eastern’ group consisting of the other post-2004 members of the EU: the Czech Republic, Hungary, Estonia, Latvia, Lithuania, Slovenia, Slovakia and Poland. Of course, not all countries fall neatly into one or other of these groups. Where there are intermediate cases, we have positioned these on the edge of a group. The Netherlands, for example, is, empirically speaking, in some respects closer to our North-Western cluster than the Nordic cluster, and has been placed on the boundary between the Nordic and North-Western groups. Ireland has been placed on the boundary between the North-Western group (where it belongs geographically) and the Southern group (with which it displays a large number of common features). And Cyprus has been placed on the boundary between the Southern group (with which it has clear geographical and cultural commonalities) and the other new EU members. As will become clear, the Eastern European countries are very far from forming a homogeneous grouping. This group may be thought of as consisting of three subgroups: the Czech Republic and Hungary (which have a Income and living conditions in Europe eurostat82 4 Household structure in the EU good deal in common with the North-Western cluster); Slovenia, Slovakia and Poland (which are extremely similar to the Southern cluster; and the Baltic Republics (Estonia, Latvia and Lithuania), which are in some respects most different to any of the pre-enlargement countries. Figure 4.1: Example of a household grid TO….. Codes: RELATIONSHIP Person 01 Person 02 Person 03 Person 04 … 1 Spouse/partner Person 01 2 Own child Person 02 1 3 Step/adopted/foster child Person 03 2 2 4 Sibling Person 04 2 2 4 ... 4.2 Methodology 4.2.1 Defining relationships between individuals When analysing people’s living arrangements, it is necessary to establish the relationships between members of households. Many household-level data sets do this by means of a ‘household grid’ or ‘relationship matrix’, which records the nature of the relationship between each of the household members (see Figure 4.1 for an example of a household grid for a household containing two parents and two children). Unfortunately, not all countries in EU-SILC collect this type of information, recording instead only the personal identifiers of each individual’s spouse or partner, mother and father, where these are resident in the same household. Thus, in the harmonised output for all countries we only have available this more limited information identifying a spouse, mother or father. We believe this deficiency in the data would be relatively easy to rectify, and that this should be a priority in future development of EU- SILC. In the meantime, the lack of a household grid does mean we are unable to measure family relationships as accurately as we would like. In particular, while we are able to identify which people are living as part of a couple, and/or with their children or parents, and in some cases with siblings and grandparents, many relationships (e.g. co-resident cousins or aunts/uncles) cannot be identified. In addition, there is uncertainty relating to the specific nature of the parent/child relationship, namely that the role of step-parents is not always clear. It appears that the use of the ‘mother’ and ‘father’ identifiers has not been entirely consistent, so that in some cases they have been used exclusively to indicate natural parents, while in others they have been used to indicate step-parents as well. Given the increase in stepfamilies over recent decades, this is a particularly unfortunate limitation with the data. Nevertheless, EU-SILC does provide interesting, and in some respects unique, opportunities for the analysis of household structure. 4.2.2 Statistical analysis The analysis in this chapter is for the most part descriptive — the figures and tables present means over the populations of interest, and compare them between countries. All country means are weighted using the cross-sectional weights supplied with EU-SILC (4). For much of the analysis these are means over individuals (where the exact population is detailed in the (4) The results in this chapter were calculated using version 2007-2 of the cross-sectional EU-SILC UDB. Income and living conditions in Europeeurostat 83 4Household structure in the EU footnotes). However, in some cases, it is more appropriate to calculate means over households. Where we have done this, it is stated clearly in the text and footnotes. For most of the analysis, we also present the mean across the EU-15 ‘old’ Member States, the mean across the nine ‘new’ Member States represented in these data and the mean across all countries in the sample (where countries are weighted according to their populations). Though we have computed standard errors for all the figures, we do not present them since this would add further complication to our already very full tables. These standard errors are sufficiently small that wherever we note systematic differences between groups of countries, these differences are statistically significant; however, smaller differences between countries in the same group may not be statistically significant. Full tables, complete with standard errors, are available in Iacovou and Skew (2010). Sections 4.5 and 4.8 use different analytical approaches to the rest of the chapter: Section 4.5 uses non-parametric regression techniques to calculate the median age at which young people make a range of life transitions (moving out of the parental home, living with a partner and having children). Section 4.8 synthesises the results from the foregoing sections using principal components analysis. Both techniques are explained further in each respective section. 4.3 Household composition In this section, we discuss household composition at its broadest level. The first seven columns of Table 4.1 define seven categories of households, and show how the prevalence of these household types varies across the EU. For example, we can see that a quarter (25.6%) of households in Finland consist of a single adult under 65. Columns 1 and 3 relate to households where at least one adult is aged under 65. In general these households are least common in the Southern European countries, plus Slovenia, Slovakia and Poland (though to less of an extent with regard to couple households); rather higher in the rest of Eastern Europe (particularly the Czech Republic, Hungary and Estonia); higher still in the North- Western group of countries; and highest in the Nordic group. Columns 2 and 4 relate to households where at least one adult is aged 65 or over. The distribution of these households does not follow our country groupings neatly; but this is to be expected, since many factors contribute to household composition among older people, for example, differences in life expectancy between men and women, rates of divorce and separation and the decision as to whether to live with adult children or other relatives. Single-adult households among the 65+ age group (column 2) are most common in the Nordic and North-Western groups of countries (where divorce is relatively common and where it is relatively unusual for older people to live with children or other relatives) and least common in the Southern countries (where divorce rates remain low, and where it is common for older people to live with adult children). Couple-only households where at least one partner is aged 65 or over (column 4) are most common in the Southern European countries (low divorce rates) and least common in Eastern Europe (high divorce rates, and a high incidence of multigenerational households). Column 5 relates to all other households where children under 18 are not present. In all countries, the majority of these are households containing both parents and their adult children; however, in the Southern and Eastern European countries, a substantial minority of households are composed differently — for example, with a couple plus another adult of similar age, who may be a sibling. These households are most common in the Southern European countries plus Slovenia, Slovakia and Poland; they are less common in the North-Western countries, and much less common in the Nordic cluster, where they account for only 4% of households in Denmark. The remaining household types relate to house- holds with children under 18. Those with a single Income and living conditions in Europe eurostat84 4 Household structure in the EU Table 4.1: Distribution of household types, 2007 Household composition: percentage of households Household sizeNo children under 18 in household Children under 18 present in household Single adult under 65 (1) Single adult aged 65+ (2) Couple both under 65 (3) Couple, at least one 65+ (4) Other, no under- 18s (5) Single adult with children (6) 2+ adults with children (7) Mean over indivi- duals (8) Mean over house- holds (9) Sweden 24.0 15.6 16.6 11.8 5.7 4.2 22.0 2.8 2.1 Finland 25.6 13.0 19.7 10.1 7.6 3.4 20.8 2.9 2.1 Denmark 30.2 14.0 16.5 9.9 4.4 4.8 20.2 2.7 2.0 Netherlands 23.5 11.7 17.0 11.1 10.0 2.8 23.9 3.0 2.3 United Kingdom 16.7 13.6 16.6 10.3 12.8 5.4 24.7 3.1 2.4 France 20.0 14.2 15.9 11.2 11.0 3.5 24.2 3.0 2.3 Germany 24.4 14.0 14.7 14.2 11.5 3.1 18.1 2.7 2.1 Austria 21.7 13.4 12.5 10.2 15.8 3.5 23.0 3.1 2.3 Belgium 20.6 13.5 15.6 10.4 13.4 3.8 22.7 3.1 2.3 Luxembourg 18.0 10.9 13.7 10.4 14.9 2.4 29.7 3.1 2.5 Ireland 11.3 10.1 9.5 7.3 20.5 7.1 34.4 3.6 2.8 Italy 14.1 15.0 8.5 11.1 24.2 1.9 25.1 3.1 2.4 Spain 8.6 8.7 12.2 10.0 29.2 1.1 30.2 3.3 2.8 Portugal 6.4 10.6 9.5 12.1 26.5 2.0 33.0 3.3 2.8 Greece 10.4 9.7 8.8 12.3 29.9 1.0 28.0 3.3 2.7 Cyprus 8.9 7.2 9.6 11.9 25.3 1.9 35.4 3.6 2.9 Czech Republic 12.4 11.4 14.4 10.0 22.2 2.9 26.7 3.1 2.5 Hungary 11.5 12.8 12.8 8.6 22.6 3.2 28.6 3.3 2.6 Estonia 18.3 15.4 11.1 7.8 19.1 4.2 24.2 3.1 2.3 Latvia 12.8 12.4 8.6 6.5 25.7 4.0 30.1 3.4 2.6 Lithuania 12.1 14.9 9.6 7.9 21.9 3.8 29.8 3.3 2.6 Slovenia 9.0 11.8 7.8 8.8 30.8 2.0 29.9 3.5 2.8 Slovakia 11.4 13.1 8.0 7.9 30.1 1.3 28.2 3.7 2.8 Poland 11.3 13.4 10.0 6.6 24.6 1.8 32.4 3.8 2.8 EU-25 17.6 13.2 13.5 11.0 17.0 3.1 24.6 3.1 2.4 EU-15 18.5 13.3 14.0 11.6 15.8 3.2 23.7 3.0 2.3 NMS 11.6 13.0 10.8 7.7 24.3 2.3 30.3 3.6 2.7 Source: EU-SILC Users’ database. NB: In this table, bold type denotes the eight countries with the highest incidence, and italics denote the eight countries with the lowest incidence of each situation. EU-25: Population weighted average of the 25 countries that were members of the EU after the 2004 enlarge- ment, except Malta for which data were not available from the EU-SILC Users’ database. NMS: Population weighted average of the 10 ‘New Member States’ that joined the EU in 2004 (except Malta). Income and living conditions in Europeeurostat 85 4Household structure in the EU adult (i.e. lone parent households, column 6) are in a minority everywhere, being most common in Ireland and the United Kingdom (7% and 5% of households respectively), as well as in Sweden, Finland and the Baltic states and least common in Southern Europe plus Slovenia, Slovakia and Poland. For those where two or more adults are living with children (these are not necessarily two-parent families; some are one-parent fami- lies with adult children as well as minor chil- dren; or they may be extended families with children) we see the opposite pattern: these are most common in the Southern countries, plus parts of Eastern Europe, and least common in the Nordic countries. The final two columns in Table 4.1 are concerned with mean household size. Column 8 shows mean household size using the individual as the unit of analysis; Column 9 calculates the mean over households, and thus provides smaller means, because larger households are only counted once. Mean household sizes are lowest in the Scandinavian countries, and also low in the North-Western countries, with the exception of Ireland. The two different methods of calculating mean household sizes produce slightly different rankings for the largest household sizes. Taking the mean over households, the largest households are seen in the Southern European countries, plus Ireland, Slovenia, Slovakia and Poland. If the mean is taken over individuals, on the other hand, the Eastern European countries are those with the largest household sizes: this is because the Eastern European countries have more very large households than the Southern European countries. 4.4 Children Children’s living arrangements are of interest to social scientists because of their relationship to child poverty and to outcomes in later life. We begin this section by examining family size, after which we turn to investigate children’s living arrangements. For a discussion of childlessness and how this relates to fertility levels in each country please see Iacovou and Skew (2010). From Table 4.2 we see that the very largest families are found in Ireland, where 21% of families have three or more children, and where 5% of families have four or more children. The next largest families are found in Belgium and the Netherlands, followed by the rest of the Nordic cluster. The smallest families, based on the percentage of households with three or more children, are found in Spain, Portugal, Greece and Italy — in these countries, under 7% of households have three or more children. These countries, in common with a number of other Eastern European countries, also have a relatively large number of households with only one child. We turn now to a ‘child’s-eye’ view of living arrangements. Declining marriage rates, rising rates of cohabitation and high rates of union dissolution — trends which have all been a feature of recent decades — mean children may spend time growing up in a number of different household types (e.g. lone parent households, cohabiting couple households). Table 4.3 shows the proportions of children (i.e. those under age 18) living in four such situations: living with one parent; with two parents who are cohabiting but not married; and two parents who are married to each other (5). There are also a small number of children who are not living with either natural parent; we include these in the table for completeness. Examining Table 4.3, we firstly notice that few children are living with an adult not defined as their parent (6); Latvia has the highest percentage, where 3.3% of children are living with an adult not defined as their parent. In terms of those living with parents, we see a high proportion of children (5) The EU-SILC data do not allow us to distinguish fully between natural parents, ‘official’ step-parents, and other co-resident partners, thus the ‘two parents, cohabiting’ and ‘two parents married’ categories include children living with two parents who are cohabiting or married, as well as children living with one parent who is cohabiting with, or married to, a partner who is not defined as the child’s parent. Despite these limitations, our findings are similar to those of (e.g.) Perelli-Harris et al (2009), who cover fewer countries with better data. (6) Table 4.3 is based on a sample of all under 18s, and some of those re- corded as living with no natural parents will be teenagers who have moved out of their parents’ home. These account for about one quarter of those recorded in this column. Income and living conditions in Europe eurostat86 4 Household structure in the EU Table 4.2: Distribution of households by number of children, 2007 (2) Percentage of households where children are present with: 1 child 2 children 3 children 4+ children Sweden 43.3 40.6 12.8 3.3 Finland 42.7 39.2 13.5 4.6 Denmark 41.3 43.4 12.5 2.8 Netherlands 38.8 42.7 14.1 4.4 United Kingdom 46.0 39.6 10.7 3.7 France 45.3 39.9 11.7 3.2 Germany 48.6 39.5 9.0 3.0 Austria 50.1 37.2 10.2 2.4 Belgium 44.5 36.8 13.7 5.0 Luxembourg 44.8 46.0 8.1 1.2 Ireland 43.8 35.2 16.0 5.0 Italy 55.2 37.9 6.1 0.8 Spain 55.2 39.9 3.9 0.9 Portugal 61.4 33.7 4.0 1.0 Greece 46.4 47.9 4.3 1.3 Cyprus 42.5 46.8 8.5 2.2 Czech Republic 53.4 39.6 6.0 1.1 Hungary 49.5 36.9 10.5 3.1 Estonia 58.0 32.9 7.5 1.5 Latvia 62.8 29.5 5.8 1.9 Lithuania 59.7 31.4 6.8 2.1 Slovenia 49.7 41.5 7.2 1.6 Slovakia 53.7 36.0 8.3 2.0 Poland 53.5 35.2 8.6 2.7 EU-25 49.5 38.9 9.0 2.6 EU-15 48.7 39.5 9.2 2.6 NMS 53.5 36.0 8.2 2.4 Source, EU-25, NMS: See Table 4.1. NB: In this table, bold type denotes the eight countries with the highest incidence, and italics denotes the eight countries with the lowest incidence of each situation. (2) These are calculated using the sample of households where any child under 18 is present; it is important to remember (a) that these are means over households rather than individuals, and (b) that they do not include any offspring who are not currently resident in the household, or any offspring over age 18, even if they are resident in the household. Thus, these figures will tend to underestimate the proportions of larger families, particularly in those countries where home-leaving takes place earlier; however, they are indicative of cross-country variations in family size. Income and living conditions in Europeeurostat 87 4Household structure in the EU Table 4.3: Household type in which children live, 2007 Percentage of children living with: % of children in 0 parent 1 parent 2 parents, cohabiting 2 parents, married multigenerational households Sweden 1.3 17.6 30.5 50.6 0.3 Finland 0.9 14.4 15.8 68.9 0.6 Denmark 1.5 17.9 15.1 65.6 0.4 Netherlands 0.3 11.1 13.1 75.5 0.3 United Kingdom 1.4 21.5 12.6 64.5 3.4 France 0.9 13.5 21.0 64.5 1.8 Germany 1.3 15.0 5.5 78.2 0.9 Austria 2.2 14.3 7.4 76.1 7.5 Belgium 2.5 16.2 13.7 67.7 2.2 Luxembourg 0.3 10.2 6.9 82.6 2.8 Ireland 1.9 24.3 5.9 67.9 4.5 Italy 0.8 10.2 5.2 83.9 5.0 Spain 1.2 7.2 7.9 83.7 5.8 Portugal 2.9 11.9 9.7 75.5 11.6 Greece 1.2 5.3 1.2 92.3 6.5 Cyprus 0.7 7.2 0.6 91.5 3.0 Czech Republic 0.6 14.9 8.2 76.3 7.7 Hungary 0.8 15.4 9.9 73.9 11.6 Estonia 1.9 21.8 23.9 52.5 12.0 Latvia 3.3 27.1 14.1 55.5 24.4 Lithuania 2.0 18.1 6.1 73.8 14.5 Slovenia 0.6 10.4 19.5 69.4 13.7 Slovakia 1.1 10.6 3.7 84.7 17.6 Poland 0.8 11.0 9.2 79.0 22.0 EU-25 1.2 14.1 11.0 73.8 5.4 EU-15 1.2 14.3 11.3 73.2 3.1 NMS 0.9 13.1 9.2 76.7 17.4 Source, EU-25, NMS: See Table 4.1. NB: ‘Children’ are defined as all those under age 18. Bold type denotes the eight countries with the highest incidence, and italics denote the eight countries with the lowest incidence of each situation. Income and living conditions in Europe eurostat88 4 Household structure in the EU living with lone parents and cohabiting parents in Nordic and North-western Europe and the Baltic states, but low proportions living in these parental types in Southern Europe. As we might expect, the countries with the lowest proportion of children living with either lone parents or cohabiting parents (i.e. those in Southern Europe) are those with the highest proportions of children residing with married parents. As we have seen before, there is a high degree of heterogeneity within the Eastern European group: in the Baltic republics, the rates of lone parenthood and cohabitation are among the highest in Europe (and rates of marriage are the lowest), while in Slovenia, Slovakia and Poland, lone parenthood and cohabitation rates are among the lowest (and marriage rates are among the highest). The final column of Table 4.3 shows the percent- ages of children who live in multi -generational households (defined here as households where grandparent(s) as well as parent(s) are present). There is a clear regional gradient here. Well under 1% of children in the Nordic cluster live in multi-generational households; 1–5% of children live in multi-generational households in all other North-Western countries except for Austria (where the figure is higher); and around 6% of children live in multi-generational households in Southern European countries (except in Portugal, where the figure is 11.6%). However, in Eastern Europe, the figures are much higher: over 10% of children live in multi-generational households in all countries except the Czech Republic, and this rises to over 20% in Poland and Latvia. 4.5 Young adults The transition from childhood to adulthood is characterised by a number of transitions: from the parental home to living independently; from the single state to living with a partner; and from childlessness to parenthood. Not all young people make all these transitions, and some never make any; however, the majority do make some of these transitions in their twenties or thirties. These transitions have a direct relationship with young people’s wellbeing and life chances: making these transitions at an early age is associated with early independence, but may also (particularly in the case of early home- leaving or early childbearing) be associated with an increased risk of poverty and disadvantage (Aassve et al, 2007). By contrast, the very late transitions observed in the Southern European countries, while being protective against poverty, may delay independence and may also be burdensome for the parents of young people (Schizzerotto and Gasperoni, 2001). Because some of these transitions are reversible — young people may leave home and move back in again, or they may live with a partner for a short time before subsequently splitting up, it is difficult to calculate the mean or median ages at which these transitions are made by observing the transitions themselves. Instead (taking home-leaving as an example), we assume that young people who are currently observed living with their parents have not made the transition out of the parental home, and we assume that those currently observed as living independently have made the transition. Of course, we will count some young people who have left home and come back again as not having yet made the transition; and we will count some people who are living away from home but for whom the transition is not permanent as having made the transition. But these errors are likely to cancel each other out. We then use non-parametric regression techniques to calculate the age at which 50% of all young people are observed living away from home, or living with a partner or with children, and consider this analogous to the median age of making the transitions. Before discussing these figures further, it is worth pointing out that they are based only on young people living in private households — those living in institutional settings such as military barracks or university residences will not be sampled. We believe our results are reasonably robust to these issues: see Section 12 of Iacovou and Skew (2010). Income and living conditions in Europeeurostat 89 4Household structure in the EU The results of these calculations are shown in the first six columns of Table 4.4. Results are shown for men and women separately, because women tend to make all these transitions at an earlier age than men. The first four columns show the transitions out of the parental home and to living with a partner. There is a strong divide here between the region- al groupings we have defined: for both men and women, the transitions take place relatively early in the Nordic and North-Western countries, and relatively late across Southern and Eastern Eur- ope. For leaving the parental home, the range in ages across countries is very large (50% of women have left home by age 20 in Finland and Denmark, while the corresponding age in many Southern European countries is 27 or 28). For living with a partner, the differences are not so stark in terms of the ages at which the transitions are made. In the Nordic countries, the median age at partnering is several years higher than the median age at leaving home, indicating that a prolonged period of living alone is the norm in these countries; while in the Southern and Eastern European countries the mean ages at leaving home and partnering are much closer together, typically around only one year apart. In the case of Poland and Slovakia, partnership on average occurs earlier than home-leaving, indicating that it is common for young adults to remain living with their parents while they also live with a partner. The last two columns of Table 4.4 support these findings: where the gap between these two ages is small, the percentage of young people living alone is also small, and where the gap between the two ages is large, this is reflected in a high proportion of young people living alone. Finally, we look at the age at which young people live with their own children (columns 5 and 6 of Table 4.4). For women, this approximates well to the median age at first birth; for men, the approximation is less good, because some men father children they do not live with. For this transition we see the smallest range in ages across countries. We also see that here, the pattern of cross-national variation is different, with the earliest childbearing evident in Cyprus plus the Eastern European countries; childbearing is relatively late in the Nordic cluster plus some of the North-Western countries, but latest of all in Italy and Spain, where the median age for a first birth calculated in this way is 32 for women and 36.5 and 35.5 respectively for men. 4.6 Partnerships: cohabitation and marriage One area in which there are substantial differences between Northern and Southern European countries is in the prevalence of cohabitation as a substitute for marriage (Kiernan, 1999): non- marital cohabitation is far more common in Northern than in Southern European countries, particularly in the Nordic countries, where it is very much the norm among childless young people. Table 4.5 shows the percentage of opposite-sex partnerships which are reported as cohabiting rather than marital partnerships in each country, for four age groups: couples where the woman is in her twenties, her thirties, her forties and her fifties. For each age group, two sets of figures are reported: the first for partnerships where there are no co-resident children, and the second for partnerships where the children of one or both partners are resident in the household. It should be noted that this is not a perfect indicator of couples who have children — many couples in their fifties, and some in their forties, will have children who have moved away from the parental home, and will thus not be counted as having children in the data. It is clear that there is a substantial age gradient in all countries, with couples in their twenties substantially more likely to be cohabiting than couples in their forties and fifties. These figures do not allow us to separate out age effects (sample members in their twenties have not got married Income and living conditions in Europe eurostat90 4 Household structure in the EU Table 4.4: Young people: transitions and percentages living alone, 2007 Age by which 50% of young people are living: % of people aged 18–28 who live alone Away from parental home With a partner With a child Men Women Men Women Men Women Men Women (1) (2) (3) (4) (5) (6) (7) (8) Sweden 20.9 20.3 27.3 23.9 31.8 29.1 33.1 23.4 Finland 21.4 19.8 24.8 21.9 34.3 30.1 23.1 21.9 Denmark 20.6 19.8 26.5 24.1 34.4 29.9 37.2 31.5 Netherlands 24.1 24.1 28.0 25.4 33.1 30.8 16.5 19.5 United Kingdom 24.0 22.0 27.1 24.5 34.6 29.6 6.5 4.6 France 23.5 22.1 26.8 24.6 32.0 28.4 17.0 14.9 Germany 25.0 22.3 27.5 25.5 34.2 30.9 9.4 17.0 Austria 26.1 23.7 29.7 26.3 33.6 29.1 12.3 10.0 Belgium 24.4 23.3 27.3 25.1 34.2 29.1 12.1 7.4 Luxembourg 26.2 24.2 28.8 26.1 32.8 29.0 7.8 6.7 Ireland 26.5 24.1 29.8 28.4 32.9 28.0 3.0 2.4 Italy 30.1 28.0 33.1 29.4 36.5 32.0 3.9 4.2 Spain 28.5 27.0 31.1 27.9 35.5 32.0 3.5 1.6 Portugal 29.1 27.4 29.9 27.9 32.0 29.1 1.5 2.5 Greece 31.8 27.4 33.6 28.7 35.6 30.5 8.4 9.0 Cyprus 28.3 25.3 29.1 25.8 31.4 27.7 2.9 2.9 Czech Republic 27.7 25.1 28.9 25.9 31.8 27.9 4.8 3.1 Hungary 27.6 25.0 28.4 26.0 31.2 27.9 3.3 3.9 Estonia 25.1 23.0 26.9 24.6 31.0 26.1 11.4 8.0 Latvia 27.7 25.4 27.9 25.9 29.1 25.1 1.8 1.5 Lithuania 27.2 24.8 27.7 26.4 29.8 25.9 3.6 3.6 Slovenia 30.8 28.0 31.2 28.4 33.2 28.9 1.6 1.5 Slovakia 30.3 27.8 30.0 27.7 31.8 28.8 2.0 0.8 Poland 29.1 26.3 28.5 25.7 30.8 27.2 2.5 3.3 EU-25 26.0 23.7 29.0 26.1 33.8 29.8 8.6 9.0 EU-15 25.5 23.2 29.1 26.2 34.4 30.4 10.0 10.3 NMS 28.6 26.0 28.7 26.0 31.1 27.5 3.0 3.2 Source, EU-25, NMS: See Table 4.1. NB: Bold type denotes the eight highest numbers, and italic type denotes the eight lowest numbers, in each column. figures in columns 1–6 derived from entire age distribution (also for EU-25, EU-15 and NMS aggregates). Income and living conditions in Europeeurostat 91 4Household structure in the EU Table 4.5: Percentage of partnerships which are cohabiting rather than marital partnerships for different age groups of women, 2007 Twenties Thirties Forties Fifties No children Children No children Children No children Children No children Children Sweden 91.1 68.5 81.5 44.0 44.8 28.6 21.0 13.5 Finland 81.4 44.8 61.0 22.5 37.0 17.3 16.7 8.2 Denmark 81.5 52.0 61.9 21.9 29.4 13.5 10.6 8.4 Netherlands 85.5 34.2 59.5 24.3 38.2 9.1 12.0 4.9 United Kingdom 65.2 40.6 37.7 20.4 26.1 9.7 8.2 4.3 France 78.8 46.8 61.5 30.5 37.7 14.7 12.1 6.0 Germany 64.4 18.6 41.1 7.3 15.7 5.1 5.8 2.5 Austria 54.6 24.6 46.6 10.3 15.3 5.5 7.0 1.4 Belgium 67.5 45.2 45.0 18.5 27.5 10.1 8.7 4.3 Luxembourg 58.5 18.5 25.7 9.4 22.2 8.5 8.4 1.3 Ireland 67.2 50.8 37.0 8.6 9.4 3.9 4.8 1.0 Italy 22.4 16.8 23.1 7.2 16.5 4.1 3.7 2.5 Spain 51.7 29.6 27.4 9.2 20.4 3.9 4.3 2.0 Portugal 39.2 30.1 28.5 8.2 16.0 5.1 8.2 3.3 Greece 25.2 0.3 6.9 0.3 5.0 0.8 4.4 0.5 Cyprus 32.7 1.9 15.1 0.5 2.3 0.1 3.2 0.0 Czech Republic 58.4 21.7 42.0 8.7 17.9 5.8 6.9 2.1 Hungary 56.6 24.2 49.6 11.8 19.7 7.0 13.4 3.6 Estonia 76.5 53.9 74.5 35.8 29.3 16.0 16.8 12.4 Latvia 52.3 28.9 57.5 14.7 25.7 9.6 13.7 5.2 Lithuania 45.8 11.7 26.9 6.8 11.3 2.3 3.5 0.9 Slovenia 65.1 36.5 44.8 22.8 35.7 17.0 11.9 11.0 Slovakia 17.5 5.4 22.5 3.8 15.2 1.9 3.7 1.5 Poland 25.8 6.6 11.2 2.2 13.5 1.5 2.4 1.6 EU-25 62.9 28.4 38.4 13.8 22.5 7.1 8.2 3.1 EU-15 65.8 33.3 39.2 15.5 23.3 7.8 8.5 3.3 NMS 40.0 13.2 25.9 6.3 16.3 3.7 6.2 2.3 Source, EU-25, NMS: See Table 4.1. NB: The sample consists of partnerships where the woman is aged 20–59; couples with children are defined as couples where the off- spring of at least one member of the couple lives in the household. Bold type denotes the eight highest numbers, and italic type denotes the eight lowest numbers, in each column. Income and living conditions in Europe eurostat92 4 Household structure in the EU yet, but many will) from cohort effects (people born in the 1980s are less likely to get married, ever, than people born in the 1950s). However, some combination of these two effects is leading to a strong gradient: across the EU as a whole, 63% of childless partnerships among people in their twenties are cohabiting, compared with just 8% of childless partnerships among those in their fifties; for partnerships where children are present, the corresponding figures are 28% for those in their twenties, against 3% for those in their fifties. A steep north-south gradient is also evident from Table 4.5. In the Nordic countries, well over half of all childless couples in their twenties and thirties are cohabiting; in the other Northern European countries, the proportion cohabiting is lower, but still high, while it is much lower in Southern Europe ranging from 7% of childless couples in their thirties in Greece to 29% in Portugal. Levels of non-marital cohabitation in the Eastern European countries are rather heterogeneous, being as low as Southern European levels in Poland, Slovakia and Lithuania, and comparable with Nordic levels in Estonia. There are also strong differences between couples with and without children: in all countries, for all age groups, couples with children are less likely to cohabit than couples without children, and in nearly all cases these differences are large. This difference between couples with and without children does not follow predictable regional lines. The difference does tend to be smaller where cohabitation rates are higher (Sweden, Denmark, Estonia and Slovenia) — but the difference is large in the Netherlands, Germany and Austria (where cohabitation rates are high) and also in Cyprus, Greece and Slovakia (where cohabitation rates are low). 4.7 Older people Increasing life expectancy and declining fertility mean that the elderly are set to form a progressively larger proportion of our population over future decades. Older people’s living arrangements are of key interest to policy-makers: as well as being a key determinant of older people’s well-being, living arrangements are related to levels of social expenditure on elderly people. Table 4.6 shows the proportion of older people living in four situations: alone; without a partner but with other people; with just a spouse or partner; and with a spouse or partner plus other people. Before commenting on the table, it is worth noting that these figures relate to older people in private households: older people in institutions such as nursing homes are not sampled by EU-SILC and are not included in this analysis. Each set of figures is calculated separately for men and women, and the differences between the sexes are starker here than elsewhere in this report, because of differences in life expectancy between men and women, and the consequently higher proportion of elderly women who are widowed. As we mentioned in Section 4.3, the proportion of older people who are living with and without a partner is also related to the prevalence of divorce and separation in each country. Two ‘ideal types’ are visible. In the Scandinavian countries plus many Northern European countries, in particular Germany and France, the predominant living arrangement for older people is either with a spouse or partner, or alone. Typically, living in a household with anyone except a spouse or partner accounts for only 10% or less of older people. In the Southern European countries, by contrast, it is much more common for older people to live with people other than a partner: in Spain, 42% of older women and 40% of older men live with others. This type of living arrangement is also relatively common in the new Member States, particularly Latvia, Slovenia and Poland. Using EU-SILC data it is not possible to determine the relationships of older people with the others with whom they live in every case. However, in every country, the large majority of older people who are observed living with people other than a spouse or partner, are observed living with at least one of their adult children. Income and living conditions in Europeeurostat 93 4Household structure in the EU Table 4.6: The living arrangements of people aged 65 years and over, percentages, 2007 Living alone No partner, living with other people Living with just a partner Living with a partner, plus other people Men Women Men Women Men Women Men Women Sweden 28.3 52.8 1.4 1.8 67.9 44.7 2.4 0.8 Finland 21.6 48.0 4.2 9.2 66.1 39.2 8.1 3.7 Denmark 28.8 56.2 0.8 1.9 68.4 41.1 2.0 0.8 Netherlands 19.2 49.1 1.4 3.4 74.9 45.9 4.5 1.6 United Kingdom 26.3 45.3 3.1 9.1 60.4 40.2 10.1 5.4 France 21.4 48.6 3.9 7.5 64.7 40.3 10.1 3.6 Germany 21.8 44.2 1.8 3.8 71.4 49.8 4.9 2.2 Austria 19.0 44.5 6.5 13.7 58.4 33.7 16.2 8.1 Belgium 22.5 45.7 4.2 9.1 62.4 40.2 10.8 5.0 Luxembourg 18.4 42.0 3.5 8.5 65.8 43.4 12.3 6.2 Ireland 25.6 38.5 10.3 21.0 50.4 34.4 13.7 6.1 Italy 16.4 40.1 6.8 18.3 51.5 30.9 25.3 10.7 Spain 10.1 25.5 9.0 25.7 49.9 32.1 31.0 16.7 Portugal 10.9 29.8 8.6 24.6 57.5 34.9 23.0 10.8 Greece 7.9 28.7 4.1 21.7 53.6 33.4 34.4 16.3 Cyprus 10.3 28.1 4.8 18.0 64.6 44.0 20.3 9.9 Czech Republic 17.2 41.7 4.5 19.5 64.0 33.9 14.3 4.9 Hungary 17.1 42.3 7.3 26.7 57.8 25.4 17.9 5.6 Estonia 21.1 47.2 5.8 22.5 54.9 23.3 18.2 7.0 Latvia 15.1 34.5 14.1 36.5 43.2 18.0 27.7 10.9 Lithuania 19.4 44.5 8.0 24.1 51.4 23.3 21.2 8.1 Slovenia 10.8 38.8 8.4 22.5 52.2 26.0 28.6 12.7 Slovakia 14.7 45.3 4.7 21.2 54.1 23.8 26.5 9.7 Poland 20.8 44.0 9.3 25.5 43.9 20.7 26.1 9.8 EU-25 19.5 42.1 4.7 13.5 60.5 37.3 15.3 7.0 EU-15 19.6 42.0 4.3 11.7 61.7 39.6 14.3 6.8 NMS 18.6 43.0 7.9 24.7 50.8 23.8 22.8 8.5 Source, EU-25, NMS: See Table 4.1. NB: Bold type denotes the eight highest numbers, and italic type denotes the eight lowest numbers, in each column. Income and living conditions in Europe eurostat94 4 Household structure in the EU These are generally not the same households which form the group considered in Section 4.5, of young adults living with their parents; in most cases, the parents in these households would be too young to be included in the analysis in this section. The relationship between these groups is worthy of further analysis. In one sense, the groups are clearly related, in that they are both composed of adults in the same household as their parents; moreover, they both tend to be found in the same groups of countries. However, there is a conceptual difference between the two household types in terms of the direction and nature of support (financial/caring), i.e. whether it is the parents that are supporting the children or the children that are supporting the parents. 4.8 Synthesising the differences: factor analysis From the figures in the preceding sections, a number of patterns have emerged. One way in which these may be synthesised is via the use of factor analysis. Principal components analysis identifies three main factors, which together explain 83% of the variation between countries in the factors explored. Factor loadings are given in Table 4.7, with the most important loadings being highlighted via shaded cells. We identify the first factor as being related to the importance of the extended family: the variables contributing positively to this factor are young adults living at home, older people co-resident with their own children, household size, and multigenerational households. Negatively related to this first factor are young adults living alone and prime-aged people (i.e. adults aged 35–64) living alone. If the first factor relates to the importance of the extended family, the second factor may be thought of as relating to the stability of the intimate relationship. The only variables which are significantly related to this factor are babies living with a lone parent, children living with a lone parent, prime-aged people who are divorced or separated (and not living with another partner) and old people living alone. This variable does appear to be related to the stability of the intimate relationship rather than to notions of social liberalism, since cohabitation as an alternative to marriage makes no contribution to this factor at all. The third factor relates to fertility, with childless women making a negative contribution, and the number of children per woman making a positive contribution. Factors 1 and 2 are plotted on Figure 4.2. Six clusters of countries have been identified. Clearly, there is no unique way of identifying these clusters — clusters towards the centre of the graph could be combined, as could the two clusters in the north-east of the graph. First, we note that the ‘old’ EU-15 form the clusters which might have been expected based on previous research. The social-democratic countries (including the Netherlands) form one group, scoring low on the extended family and high on the relationship stability axis. The Southern European countries score high on both the extended family axis and the relationship stability axis, while the remaining countries of North-Western Europe occupy an intermediate position on the extended family axis, and score generally lower than the other two groups on the relationship stability axis. Ireland occupies a position slightly apart from this group, scoring almost as high on the extended family axis as the Southern European countries, and low on the relationship stability axis. The new Member States are rather heterogeneous. Cyprus falls very close to the other Southern European countries, which is to be expected given commonalities of geography, language and culture. Three of the Eastern European countries display similar, but more extreme, characteristics to the Southern European group, scoring even higher on the extended family axis and at similar very high levels on the relationship stability axis. These countries are Poland, Slovakia and Slovenia, all of which have maintained a Catholic tradition through the Communist years (see Table 12.1 in the Appendix of Iacovou and Skew, 2010). The remaining countries include the three Baltic states — Latvia, Lithuania and Estonia — and Income and living conditions in Europeeurostat 95 4Household structure in the EU Table 4.7: factor loadings, 2007 Factor 1 — the extended family Factor 2 — stability of the intimate relationship Factor 3 — childbearing Babies aged under 2 years living with lone parent 0.34 -0.79 0.06 Children aged under 18 living with lone parent 0.03 -0.95 0.03 Young adults (18–35) living at home 0.94 -0.02 -0.17 Young adults (18–35) living alone -0.89 0.14 0.01 Prime-aged people (35–64) cohabiting -0.64 0.02 0.49 Prime-aged people (35–64) divorced 0.19 -0.90 -0.06 Prime-aged people (35–64) living alone -0.80 -0.47 -0.21 Women aged 33–37 with no children -0.16 0.28 -0.87 Women aged 33–37: mean number of children -0.12 -0.09 0.93 Old people (65 and above) living with their own children 0.92 0.01 -0.17 Old people living alone -0.34 -0.72 -0.14 Household size 0.74 0.35 0.40 Multigenerational households 0.91 -0.26 0.09 Proportion of variance explained 0.40 0.26 0.17 Source: See Table 4.1. NB: Shaded cells indicate the most important factor loadings. Income and living conditions in Europe eurostat96 4 Household structure in the EU Figure 4.2: Clusters arising from Principal Components Analysis, 2007 SE DK NL DE UK FR FI BE CZ LU AT IE HU IT EL EE LT PT ES LV CY PL SK SI 0.0 0.2 0.4 0.6 0.8 1.0 1.2 2.0 2.5 3.0 3.5 4.0 Fa ct or 2 : S ta bi lit y of th e in ti m at e re la ti on sh ip Factor 1: Importance of the extended family Source: See Table 4.1. Income and living conditions in Europeeurostat 97 4Household structure in the EU the Czech Republic and Hungary. All these countries occupy a more ‘south-easterly’ position on the graph than the other countries, scoring high on the extended family axis, but low on the relationship stability axes. Ireland — previously an outlier in relation to the other North-Western countries — occupies a position close to the Czech Republic and Hungary. These results are fairly robust to the particular variables included in the analysis. In particular, we experimented with different formulations of the variables indicating divorce, since it was unexpected (to us at least) that the Scandinavian countries, which score rather low on the relationship instability axis, while they have some of the highest divorce rates in the world. In fact, it appears that this factor does not relate to divorce per se, but rather to the proportion of people living alone following divorce or separation (and similarly, to the proportion of children living with an unpartnered parent following divorce or separation). It seems that the Scandinavian countries, while having high divorce rates, also have relatively high rates of subsequent repartnering, and thus have a much lower proportion of divorced or separated adults still living alone. We also explored the phenomenon of cohabitation in some detail; we had been expecting this analysis to generate a factor indicating social liberalism, which would be explained by cohabitation as well as by divorce and lone parenthood. However, we were unable to formulate any indicator of cohabitation which contributed significantly to any such factor; the second factor remained stubbornly as an indicator of partnership breakdown without subsequent re-partnering. 4.9 Conclusions In this chapter, we have mapped a range of indicators of household structure across the European Union. One of our main aims has been to focus particularly on the newer Member States of the EU, and to assess the extent to which household structures in these countries display similarities and differences to household structures in the ‘old’ EU-15. Of the new Member States, we find that Cyprus is extremely similar to the Southern European countries, as might be expected with reference to cultural, geographic and religious factors. We also find that there is a great deal of heterogeneity among the Eastern European countries. One group of countries — Slovenia, Slovakia and Poland — are consistently very similar to the Southern European countries. In these three countries, the extended family is the norm: young adults leave home late, older people often live with their adult children, three-generational households are common, and lone-parent families are relatively uncommon. In terms of mapping onto a geographical/religious spectrum, Slovenia is the only one of these countries which is geographically Southern, but all three of these countries remain strongly Catholic or Orthodox. The Czech Republic and Hungary, by contrast, have more in common with the countries of the North-Western cluster. On a large number of indicators, these countries occupy an intermediate position between the Nordic cluster on the one hand, and the Southern/Catholic cluster on the other; and in the factor analysis, they occupy a position close to the other countries of the North- Western cluster — particularly Ireland. Of the Eastern European countries, it is in the Baltic countries where family patterns diverge most widely from the geographical/religious spectrum. These countries display a number of features in common with the Southern European countries; chiefly, a large number of large and multigenerational households. However, they also display a number of striking dissimilarities with the Southern European countries, particularly in terms of the very large numbers of lone-parent families, and other single-adult households. In many respects, the Baltic states are very heterogeneous: for example, non-marital cohabitation is much more common in Estonia, and very much less common in Lithuania; while lone parenthood and multi-generational Income and living conditions in Europe eurostat98 4 Household structure in the EU households are more common in Latvia than in the other two Baltic states. In this chapter we have answered a number of questions, but these in turn raise further questions. One question, which we raised in Section 4.7, relates to the nature of multi-generational households. We have shown that, in a swathe of countries across Southern and much of Eastern Europe, co-residence between generations is very common, particularly so in contrast to the Nordic group of countries, where it is extremely unusual. We have shown that this co-residence is manifested both by young adults remaining in the parental household, as well as by older people living with their adult children. However, the question we have not yet been able to answer, is whether the second household type is merely a persistent form of the first (i.e. that the young adults whom we see living with their parents become the same prime- age adults who live with their elderly parents) or whether the two household types are in fact drawn from different social groups. Two other questions also arise relating to multi- generational households. The first is the extent to which they arise as a result of social and cultural preferences (people actually like living with other family members and make a positive choice to do this) as opposed to arising as a result of economic constraints (young people who would like to leave the parental home but cannot afford to; or older people who cannot afford to live alone). The second is the degree to which individuals are supporting each other, both economically and in other ways, by living together, and the direction of this support (parent to child, versus child to parent). In terms of the first question, there is limited evidence to suggest that in Southern European countries, at least part of young people’s extended residence in the parental home arises from preferences (Manacorda and Moretti, 2006). However, neither question has been addressed in the context of the European Union. Finally, the picture we have presented has been essentially static: we have not addressed the important issue of how household structures are evolving (Billari et al, 2002). We are unable to answer this question definitively with the cross- sectional data we have at our disposal; however, we may make inferences based on evidence drawn from elsewhere. 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Handbook of Quality of Life in the Enlarged European Union. Routledge, London/ New York. Schizzerotto, A. and Gasperoni, G. (2001), ‘Study on the state of young people and youth policy in Europe’, IARD, Milan. Tomassini, C., Glaser, K., Wolf, D. A., van Groenou, M. I. B. and Grundy, E. (2004), ‘Living arrangements among older people: an overview of trends in Europe and the USA’, Population Trends, Volume 115, pp. 24–34. Van Kerm P. and Pi Alperin M.N. (2010), ‘Inequality, growth and mobility: The intertemporal distribution of income in European countries’, paper presented at the 2010 conference of the Network for the Analysis of EU-SILC (Net- SILC), Warsaw, 25–26 March 2010. 5Income poverty and income inequalityAnthony B. Atkinson, Eric Marlier, Fabienne Montaigne and Anne Reinstadler (1) (1) A. B. Atkinson is at Nuffield College, Oxford, and the London School of Economics; Eric Marlier and Anne Reinstadler are at the CEPS/INSTEAD Research Institute (Luxembourg); and Fabienne Montaigne is at the Statistical Office of the European Union (Eurostat). The authors would like to thank Anne-Catherine Guio, John Micklewright, Jean-Marc Museux and Brian Nolan for helpful comments. Of course, these persons are not responsible in any way for the present contents. The European Commission also bears no responsibility for the analyses and conclusions, which are solely those of the authors. Addresses for correspondence: tony.atkinson@nuffield.ox.ac.uk and eric.marlier@ceps.lu. Income and living conditions in Europe eurostat102 5 Income poverty and income inequality 5.1 Introduction 5.1.1 Aim of this chapter This chapter focuses on the financial dimensions of poverty and inequality. Income is an important variable for Europe’s households. People are naturally concerned with how much they receive each month in the form of earnings (from employment or self- employment), pensions, government transfers (such as unemployment benefits, family benefits or sick pay), and from their savings. In this chapter, we examine the distribution of income in the 27 Member States of the European Union (EU-27). Are there large differences within and across countries? In which countries are the differences largest? Particular concern attaches to those households which, according to the EU definition, are ‘at-risk-of-poverty’ as this is one of the three indicators that form the new EU Headline Target on social inclusion adopted by the June 2010 European Council in the context of the Europe 2020 Agenda (see Chapter 1). The chapter has four main aims: 1. to identify (in the remainder of Section 5.1) the particular role of the EU-SILC data as a source of evidence about income inequality and poverty 2. to analyse (Section 5.2) headline indicators for income poverty and inequality that have been agreed at EU level, with particular reference to the cross-country patterns 3. to examine (Section 5.3) changes over time in income inequality and poverty 4. to consider (Section 5.4) how the EU indicators based on the EU-SILC data can be used in monitoring the Europe 2020 Agenda. From the chapter, the reader will, we hope, learn about the income dimension of poverty and social exclusion in the EU-27, as shown in the EU-SILC data, and how this evidence relates to that from other sources. The chapter looks back in time, to see how (income) poverty and inequality have changed in recent years, and forward in time to consider the implications of the Europe 2020 Agenda. 5.1.2 Role of EU-SILC As described in Chapter 2, EU-SILC is not a common survey across countries. In this respect, it differs from its predecessor, the European Community Household Panel (ECHP), which was based on a standardised questionnaire (the ECHP ran from 1994 to 2001 in most of the then 15 EU countries, providing comparative data on income and living conditions for the years 1993 to 2000). EU-SILC is a harmonised data framework involving ex ante standardisation but allowing countries a large degree of flexibility in the underlying source(s) and some flexibility in the concepts and definitions. For example, while in the ECHP the income reference period was the previous year, the EU-SILC income reference period may be a fixed 12-month period (such as the previous calendar year or tax year) or a moving 12-month period (such as the 12 months preceding the interview) or be based on a comparable measure. (2) EU-SILC is not based on a common questionnaire used in all countries, but on a common ex ante framework that defines the harmonised ‘target variables’ to be collected/produced and provided to Eurostat by the national statistical institutions. The aim of this procedure was to facilitate EU-SILC being embedded within the national statistical systems, allowing the results to be produced at a lower additional cost in terms of resources, while serving a common EU purpose. The intention in allowing a degree of flexibility is to secure, not input harmonisation, but output harmonisation. Output harmonisation in EU-SILC is sought through the use of common guidelines and procedures, common concepts (e.g. that of ‘household’) and (2) In practice, except for Ireland and the United Kingdom, the income reference period is for all EU countries the calendar year prior to the Survey Year. In Ireland, the survey is continuous and the reference pe- riod is the last 12 months. In the UK, current income is collected and annualised with the aim of referring to the current (survey) year - i.e. weekly estimates are multiplied by 52, monthly estimates by 12, etc. (Eurostat, 2009; see also Chapter 2). Income and living conditions in Europeeurostat 103 5Income poverty and income inequality classifications aimed at maximising comparability of the information produced. In this respect, it may be contrasted with ex post standardisation, where data from different sources are processed to put them as far as possible on a common basis, as in the Luxembourg Income Study (LIS). In this case, the aim is again output harmonisation, but without an ex ante framework. The scope for ex post standardisation is limited by the constraints imposed by the original survey designs or other sources (such as data from administrative/ register records). Finally, EU-SILC may be contrasted with meta- analyses that take, not the microdata, but the results from different sources and seek to put them in a common framework. In the study of income inequality, this approach was particularly developed by Simon Kuznets (1963). In the case of both income inequality and poverty, a lead was taken by the OECD, who published the study by Sawyer (1976), assembling results from some dozen countries, and later Atkinson, Rainwater and Smeeding (1995) which covered 17 countries. The current OECD work involves ‘a regular data collection … (at around 5-year intervals) through a network of national consultants’ (2008, p. 47). The national experts ‘apply common conventions and definitions to unit record data from different national data sources and supply detailed cross-tabulations to the OECD’ (2008, p. 41). This procedure of ‘customising results’ may be seen as lying between that of LIS, which produces microdata, and that of Kuznets, where the results are pre-defined. It has the advantage over meta-analyses of pre-imposing a degree of standardisation but ‘its disadvantage is that it does not allow accessing the original microdata, which constrains the analysis that can be performed’ (OECD, 2008, p. 41); directly related to this disadvantage, it also seriously hampers the possibility of controlling the quality of the data received. In short, we have a ‘hierarchy’ of degrees of standardisation: 1. common survey instrument (ECHP); 2. ex ante harmonised framework (EU-SILC); 3. ex post standardised microdata (LIS); 4. ex post customised results (OECD); 5. meta-analyses of results (Kuznets). Presenting them in this rank order may seem to imply a quality ranking (with 1 at the top). However, it should be borne in mind that tighter requirements of standardisation may have a cost in terms of reduced accuracy in the final statistical outcomes. In particular, a common set of variables may have differing significance in different countries, and a degree of flexibility may allow national statistical institutions to provide data better suited to purpose. Input harmonisation does not necessarily ensure output harmonisation. Different sources may be appropriate in different countries. For example, the use of tax records may allow superior income data to be collected in some countries but may not be possible or reliable in other countries. The ultimate validity of the results may be greater where countries are allowed to make use of register data, and not constrained to take income data from survey interviews. The EU-SILC procedure may therefore be seen as a balance of considerations. There is a cost in that greater flexibility may lead to lower comparability, but this may allow data to be drawn from different sources including sources other than household surveys. It may also have been instrumental in allowing Member States to reach agreement that EU-SILC could be adopted on a continuing annual basis. In this respect, there is an important difference between EU- SILC, on the one hand, and the LIS and OECD data, on the other hand. The results in the OECD report Growing Unequal? (OECD, 2008) relate to the mid-80s, mid-90s, and mid-2000s. Such decadal observations are valuable but of limited use to policy-makers. LIS has more frequent observations, approximately semi-decadal: Waves I (around 1980), II (around 1985), III (around 1990), IV (around 1995), V (around 2000) and VI (around 2004). But the data are not annual. Income and living conditions in Europe eurostat104 5 Income poverty and income inequality The essential requirement of (timely) annual data is apparent from the recent economic and financial crisis. The occurrence of such events will only by chance correspond to the decadal or semi-decadal measurements. Data for 2004, the central year for Wave VI in LIS, and the year taken for 23 of the 30 observations analysed by the OECD in their 2008 report (2008, Table 1.A2.3), are too far distant to provide a benchmark for monitoring the impact of the crisis and the subsequent recession. (Indeed, even annual data may not always be sufficient for monitoring purposes — see the discussion on timeliness and frequency at the OECD March 2009 Roundtable on Monitoring the effects of the financial crisis on vulnerable groups of society (3) and Section 18.2.3 of Chapter 18.) EU-SILC has therefore a distinctive role on the international scene. At the same time, it is important to examine how the findings relate to those in other cross-country sources. The OECD in its 2008 report makes exactly such a comparative analysis, and the present chapter uses this analysis in Section 5.2 when comparing the EU-SILC evidence on income inequality and poverty with that in other international sources. 5.2 Income poverty/inequality across countries and comparison with international sources 5.2.1 Evidence from EU-SILC on the risk of poverty The chapter begins with the key income-based indicators from EU-SILC Survey Year 2008. ‘Income’ refers here to the total household disposable income; it includes cash transfers and is net of income taxes and social insurance (3) See: http://www.oecd.org/document/2/0,3343,en_2649_33933_425079 06_1_1_1_1,00.html. contributions. (4) In order to reflect differences in household size and composition, total household income is divided by an equivalence scale (called the modified OECD scale), which gives a weight of 1 to the first adult, 0.5 to other household members aged 14 and over and 0.3 to each child aged under 14. This means that, for a couple and 2 children, income is divided by 2.1 (1 + 0.5 + 0.3 + 0.3), so that an annual income of €10 500 becomes an equivalised income of €5 000 which is artificially assigned to each of the four household members (i.e. also to each of the two children). As explained above, the data in the 2008 Survey are based on the income reference year 2007 (except in Ireland and the United Kingdom). The reader should bear in mind that we are considering annual income in 2007 in relation to the household circumstances at the time of interview in 2008. There may have been changes in these circumstances, such as the arrival of a new baby. The EU headline indicator of (income) poverty/ inequality is the proportion of the population living ‘at-risk-of-poverty’, defined as those living in households whose total equivalised income is below 60 per cent of the median national equivalised household income. It is thus a relative concept. The equivalised income of €5 000 for the four members of the family described above is compared with 60 per cent of the median in the Member State in which they live. Table 5.1 provides the value of the national income poverty thresholds for each Member State for a family consisting of 2 adults and 2 children below 14. To make them more comparable, because the cost of living can vary a lot from one country to the next, these thresholds are expressed in Purchasing (4) The definition of income used here excludes imputed rent, i.e. the mon- ey that one saves on full (market) rent by living in one’s own accommo- dation or accommodation rented at below-market rent (see Chapter 7 for a discussion of imputed rent and its measurement). It also excludes non-cash transfers, such as education and healthcare provided free or subsidised by the government (see Chapter 15). Finally, as explained in Chapter 2, it also excludes pensions from private plans (which as from the second half of 2010 will be incorporated in the EU-SILC income definition for all – past and future – waves) and most non-monetary in- come components (on the latter, see Chapter 8 which discusses income from own consumption). Income is neither top-coded nor bottom- coded. Income and living conditions in Europeeurostat 105 5Income poverty and income inequality Table 5.1: National at-risk-of-poverty thresholds for a household consisting of 2 adults and 2 children below 14 in EU-27 countries (PPS), Survey Year 2008 Belgium 21 307 Bulgaria 5 882 Czech Republic 12 239 Denmark 22 111 Germany 22 317 Estonia 9 769 Ireland 22 993 Greece 15 223 Spain 17 621 France 20 441 Italy 18 969 Cyprus 23 804 Latvia 9 246 Lithuania 8 812 Luxembourg 34 661 Hungary 8 385 Malta 15 924 Netherlands 23 759 Austria 23 621 Poland 8 222 Portugal 12 113 Romania 4 005 Slovenia 17 630 Slovakia 8 484 Finland 20 227 Sweden 21 792 United Kingdom 24 436 Source: EU-SILC, Eurostat-CEPS/INSTEAD calculations (1 July 2010). The income reference year is the calendar year prior to the Survey Year except for the United Kingdom (Survey Year) and Ireland (12 months preceding the survey). NB: Purchasing Power Standards (PPS) convert amounts expressed in a national currency to an artificial common currency that equalises the purchasing power of different national currencies (including those countries that share a common currency). Reading note: In Bulgaria, a family of 2 adults and 2 children below 14 will be considered ‘at-risk-of-poverty’ if it has a total disposable income of less than PPS 5 882; in Sweden, the same family will be considered ‘at-risk-of-poverty’ if it has a total disposable income of less than PPS 21 792. Income and living conditions in Europe eurostat106 5 Income poverty and income inequality Power Standards. (5) So, if we take our example above and assume that this family has an income of 10 500 Purchasing Power Standards (rather than euros), then the four members of this family would not be considered at risk of poverty in eight EU countries (all of them are New Member States: Bulgaria, the three Baltic States, Hungary, Poland, Romania and Slovakia); in the remaining 19 EU countries, they would be considered income poor. Figure 5.1 shows the standard bar chart for the percentage of people living in households at risk of poverty. The countries covered are those in EU-27. The average for the EU-27 as a whole is 16.6 per cent, which means that 1 in every 6 of EU citizens are at risk of poverty, or around 80 million people. (6) The rate for the 12 ‘new’ Member States (NMS12) was 17.3 per cent, a little but not much higher than for EU-15 with a rate of 16.4 per cent. It is certainly not the case that those at risk of poverty on the EU definition are mostly to be found in the New Member States: of the 80+ million at risk of poverty in EU-27, 64 million are to be found in the EU-15. In Germany, alone, there are 12½ million; in the United Kingdom 11½ million; in Italy 11 million; and France and Spain together account for a further 17 million. In the largest New Member State, Poland, the number of people at risk of poverty is about 11½ million. On this relative poverty measure, New Member States are to be found at both ends of the national figures, which range from 9–11 per cent (in the Czech Republic, the Netherlands, and Slovakia) to 20 per cent or more in Lithuania, Greece, Bulgaria, Romania and Latvia. The picture shows that, in terms of cross-country variation, there is a relatively continuous gradation. It is not easy to draw sharp dividing lines on the basis (5) On the basis of Purchasing Power Parities (PPP), Purchasing Power Standards (PPS) convert amounts expressed in a national currency to an artificial common currency that equalises the purchasing power of different national currencies (including those countries that share a common currency). (6) This ‘EU-27 average’ is a weighted average of the 27 EU Member States’ percentages, in which each country percentage is weighted by the coun- try’s population size. EU-15, NMS10 and NMS12 averages presented in this chapter are calculated in the same way. For the countries included in the various geographical aggregates, see the list of ‘Country official abbreviations and geographical aggregates’ (Appendix 2). of income poverty performance. There are only four jumps from an adjacent country in excess of 1 percentage point: Finland/ Malta (1.1), Poland/ Portugal (1.6), Bulgaria/ Romania (2), and Romania/ Latvia (2.2). From Figure 5.1, we can assess the ambition of the Europe 2020 Agenda ‘to lift at least 20 million people out of the risk of poverty and social exclusion’ (European Council, 2010). Measured in terms of the at-risk-of-poverty rate, (7) it would mean reducing poverty and social exclusion by 4 percentage points. The EU-27 as a whole would have to match the performance of Austria. It is also clear that attainment of this ambition requires, as far as the at-risk-of-poverty indicator is concerned, action by the six largest Member States. France, Germany, Italy, Poland, Spain and the United Kingdom cannot stand aside. If they were to do so, then reaching the 20 million target would require the virtual elimination of income poverty in the other 21 Member States. Who is ‘at-risk-of-poverty’? EU-SILC allows income poverty rates to be calculated for many groups within the population. Here we focus on just one group which has (rightly) received a great deal of attention in recent years: the proportion of children living in households at risk of poverty. (8) This is referred to for short as ‘child poverty’, although it should be emphasised that what is being measured is the status of the household where the child lives (see above example). It should also be emphasised that no account is taken of the possibly unequal sharing of income within the household. Figure 5.2 shows the child poverty risk rate in each country compared with the overall poverty risk rate for Survey Year 2008. Countries lying on the heavy line have the same rate of child poverty risk as overall population poverty risk. The cause for concern about child poverty is that relatively few (only about a quarter of the 27 EU Member States) are below this line. For seven Member States, the child poverty rates are more than 5 percentage (7) This is in fact only one of three indicators (see Chapter 1 and see also below). (8) See, for instance: Frazer and Marlier (2007), Social Protection Commit- tee (2008), Tárki (2010), Frazer, Marlier and Nicaise (2010). Income and living conditions in Europeeurostat 107 5Income poverty and income inequality Figure 5.1: National at-risk-of-poverty rates in EU-27, Survey Year 2008 0 5 10 15 20 25 30 LV RO BG EL LT ES EE UK IT PT NM S1 2 PL EU -27 EU -15 CY IE DE BE MT F I FR LU AT HU S I SE DK SK NL CZ Source: EU-SILC, Eurostat-CEPS/INSTEAD calculations (28 April 2010). The income reference year is the calendar year prior to the Survey Year except for the United Kingdom (Survey Year) and Ireland (12 months preceding the survey). Reading note: The at-risk-of-poverty rate in Latvia is 25.6 per cent. Income and living conditions in Europe eurostat108 5 Income poverty and income inequality Figure 5.2: National at-risk-of-poverty rates for children and for overall population in EU-27, Survey Year 2008 5 10 15 20 25 30 35 5 10 15 20 25 30 Ch ild a t- ri sk -o f- po ve rt y ra te % Overall population at-risk-of poverty rate % MT SK HU LU PL IT RO NMS 12 Source: See figure 5.1. Reading note: Countries lying on the heavy line have the same rate of child poverty risk as overall population poverty risk. for 7 Member States and also for the (weighted) average of the 12 New Member States, child poverty risk rates are more than 5 percentage points above the overall rate — shown by those above the dashed line. So, in Romania for instance, the at-risk-of-poverty rate is 32.9 per cent for children whereas it is 23.4 per cent for the overall population. Income and living conditions in Europeeurostat 109 5Income poverty and income inequality points above the overall rate — shown by those above the dashed line in Figure 5.2. So that while in Hungary child poverty rate is slightly below the EU average (19.7 vs. 20.1 per cent), it is 7.3 per cent higher than the overall population poverty rate. Above the dashed line are Luxembourg and Italy, but the other 5 countries are New Member States. The overall child poverty rate for the 12 New Member States is indeed 4 percentage points higher than for EU-15 (23.1 vs. 19.3 per cent). So far, we have been counting the number of people, or the number of children, at risk of poverty. But how far do they fall below? The final EU indicator considered here is the total poverty risk gap. What is the total income shortfall? Figure 5.3 shows, in addition to the at-risk-of-poverty rate, the median percentage by which households fall below the income poverty line. For EU-27, the figure is 22 per cent, which means that half of the at-risk-of-poverty population are living on less than 78 per cent of the income poverty threshold. Since the threshold is 60 per cent of median income, this means that the shortfall is some 13 per cent of median income. What is of interest is that the graduation is now much less smooth as we move across countries. For half the Member States (those to the left of Germany in Figure 5.3), the shortfall is between 15 and 20 per cent, but for Germany and countries to its right the gaps range from 16.5 to 32.3 per cent. EU-SILC contains much further rich data about the risk of poverty, but the evidence presented above from the 2008 Survey (income year 2007) shows that the risk is pervasive, affecting all Member States. New Member States are not concentrated at the top of the scale. Looking to the future, achievement of a 20 million reduction requires action by the large Member States: the largest six account for nearly three-quarters of the total at risk of poverty. 5.2.2 Evidence from EU-SILC on income inequality To this juncture, we have focused on the bottom of the income distribution. What is the overall extent of inequality? Many are concerned that inequality was a factor contributing to the economic crisis; others are concerned that the crisis will exacerbate inequality. But just how unequal are incomes? The two main indicators of income inequality used at EU level are shown in Figure 5.4. The first is the ratio of the share of income going to the top 20 per cent of the population (referred to as the top quintile share) to that going to the bottom 20 per cent (the bottom quintile share). This ratio, also called S80/S20, varies from 3.4 to 7.3 across the EU Member States. There is an interesting geographical pattern. The lowest ratios are found in some of the New Member States (Slovenia, Slovakia, the Czech Republic and Hungary) as well as in Austria and the Nordic countries. Then come Malta, Benelux, Cyprus and France. In Southern Europe (except Cyprus and Malta), Poland, the United Kingdom and Lithuania, the ratios are between 5.1 and 6.1, and they are 6.5 or more in Bulgaria, Romania and Latvia. For the EU-27 as a whole, the S80/ S20 ratio is 5. It should be noted that the latter is the weighted average of the 27 national ratios, in which each country ratio is weighted by the country’s population size; it is thus not the same as the ratio of the top to bottom quintile shares in the EU-27 as a whole, which can be expected to be higher. The second indicator of income inequality shown in Figure 5.4 is the Gini coefficient, a summary measure, based on the cumulative share of income accounted for by the cumulative percentages of the number of individuals, with values ranging from 0 per cent (complete equality) to 100 per cent (complete inequality). The Gini coefficients vary a lot across countries, from 23 per cent in Slovenia to 38 per cent in Latvia. (9) For the EU-27 as a whole, the (weighted) averaged value is 31 per (9) The scales for the two inequality indicators in Figure 5.4 are different but the indicators move very closely together. There is no reason why this should necessarily be the case. A redistribution that affected only those between the bottom quintile and the top quintile would have no impact on the S80/S20 ratio but would affect the Gini coefficient as this indicator considers the entire income distribution and not just the top and bottom quintiles. Income and living conditions in Europe eurostat110 5 Income poverty and income inequality Figure 5.3: National at-risk-of-poverty rates and relative median at-risk-of-poverty gap in EU-27, Survey Year 2008 5 10 15 20 25 30 35 At-risk-of-poverty rate Relative median at-risk-of-poverty gap Source: See figure 5.1. Reading note: In the Czech Republic, the at-risk-of-poverty rate is 9.1 per cent and the median poverty gap is 18.5 per cent of the poverty threshold; the latter means that half of the at-risk-of-poverty population are living on less than 81.5 per cent of the poverty risk threshold. Income and living conditions in Europeeurostat 111 5Income poverty and income inequality Figure 5.4: Income inequality in EU-27 countries, Survey Year 2008 22 25 28 31 34 37 40 2 3 4 5 6 7 8 G in i c oe ffi ci en t % Ra ti o of s ha re o f t op 2 0 % to s ha re o f b ot to m 2 0 % S80/S20 left hand scale Gini coefficient right hand scale SI SK CZ SE HU DK AT FI MT NL BE LU CY FR IE DE EU -15 EU -27 E E PL IT NM S1 2 ES UK EL LT PT BG RO LV Source: See figure 5.1. NB: Countries are ranked first according to their S80/S20 ratio, and then according to their Gini coefficient. Reading note: In Slovenia, the S80/S20 ratio is 3.36 (left hand axis) and the Gini coefficient is 23.4 per cent (right hand axis). Income and living conditions in Europe eurostat112 5 Income poverty and income inequality cent. What do such values mean? The following hypothetical calculation may be helpful. Suppose that the tax and transfer system is approximately of the form of a uniform tax credit and a constant tax rate on all incomes, that the government spending on goods and services absorbs 20 per cent of tax revenue, and that the Gini coefficient for disposable income is 48 per cent in the absence of redistribution. Then, an increase in the tax rate of 5 percentage points would be needed to reduce the Gini coefficient by 3 percentage points. (10) Since a tax rise of 5 percentage points would be a challenge for any Finance Minister, this suggests that a 3 point difference would be salient. This means that moving across a vertical division in Figure 5.4 represents a significant — in economic terms — difference. Applying the criterion that 3 percentage points represents a ‘salient’ difference in the Gini coefficient, we obtain a partial ranking of Member States. We cannot say that inequality is different in France from that in Germany (in Survey Year 2008), but there is a salient difference between the Gini coefficients for France and the United Kingdom, as there is between those for Sweden and France. On this basis, income inequality is higher in Latvia than in any other country apart from Romania, Bulgaria and Portugal. Income inequality can be said to be lower to a salient degree in Slovenia than in all Member States apart from Slovakia, the Czech Republic, Austria, Hungary and the Nordic countries. How is inequality in income related to income poverty? Do the same countries have both low at-risk-of-poverty proportions and low income inequality? There is no reason why this should (10) See Atkinson (2003), p. 484. The Gini coefficient is equal to half the mean difference divided by the mean. Taxation with a constant mar- ginal tax rate implies that the mean difference is reduced by (1-mar- ginal tax rate); the mean is reduced by (1-average tax rate). 1 minus the average tax rate is what is left for households after paying for govern- ment goods and services: in this example, 80 per cent. With no redis- tribution, the tax rate would be 20 per cent. So that the Gini coefficient for disposable income would be the same as for pre-tax income. If the marginal tax rate is raised to 25 per cent to finance redistribution via a uniform tax credit, then (1-marginal tax rate) becomes 75 per cent, while the average tax rate (allowing for the credit) is unchanged. The Gini coefficient is therefore reduced to 75/80 of its previous value: i.e. from 48 per cent to 48 per cent times 75/80, which equals 45 per cent. necessarily be the case. The share of the bottom 20 per cent may reasonably be taken as closely linked to the incidence of income poverty, but this leaves considerable room for differences in the other quintile group shares. A country may for example have a share for the bottom 20 per cent of 11 per cent, which — if equally distributed — would ensure an income equal to 55 per cent of the mean. (11) Since the mean is typically higher than the median, this could well be above 60 per cent of the median and the poverty risk score could be zero. Such a (low poverty risk) bottom quintile share could however be combined with a relatively unequal distribution, such as 12, 13, 14 per cent for the second to fourth quintile groups and 50 per cent for the top 20 per cent. The S80/ S20 ratio would then be 4.55, which is not much lower than the EU-15 average (4.88). In fact, as may be seen from Figure 5.5, the at- risk-of-poverty rate is closely correlated with the degree of income inequality as measured by the S80/S20 ratio (the same is true with the Gini coefficient in place of the S80/S20 ratio, although this is not shown here). There do not appear to be countries with medium/high inequality and low poverty risk. A simple regression shows that the inequality ratio explains 85 per cent of the variance in the poverty rate, and that an increase in the ratio from 3.5 to 4.5 is associated with a 3.4 percentage point increase in the poverty rate. 5.2.3 Comparison with other cross-country sources There are now a variety of sources of internationally comparative data on income inequality and income poverty. The best known is perhaps the World Bank’s World Development Indicators (WDI), which shows in its 2009 edition estimates of the distribution of income or consumption for 136 countries in the form of the Gini coefficient and the shares of income quintile groups (World Bank, 2009, Table 2.9). The values for 24 out of the 27 EU countries (11) The figure of 55 per cent is obtained by dividing 11 per cent by the group’s proportionate share (20 per cent): 11/20 = 0.55. Income and living conditions in Europeeurostat 113 5Income poverty and income inequality Figure 5.5: National at-risk-of-poverty rates and S80/S20 ratios, EU-27, Survey Year 2008 AT EU-27 NMS12 BE BG CY CZ DE DK EE ES FI FR EL HU IE IT LT LU LV MT NL PL PT RO SE SI SK UK 5 10 15 20 25 30 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 A t- ri sk -o f- po ve rt y ra te % S80/S20 ratio Source: See figure 5.1. Reading note: Each point corresponds to a Member State in EU-27, showing on the horizontal axis the S80/S20 ratio and on the vertical axis the at-risk-of-poverty rate. Income and living conditions in Europe eurostat114 5 Income poverty and income inequality Table 5.2: World Development Indicators in EU-27 countries as published in 2009 Gini coefficient (in %) S80/S20 ratio Income reference year Source BE 33.0 4.87 2000 Income data from LIS BG 29.2 4.38 2003 Expenditure data CZ 25.8 3.55 1996 Income data from LIS DK 24.7 4.31 1997 Income data from LIS DE 28.3 4.34 2000 Income data from LIS EE 36.0 6.32 2004 Expenditure data IE 34.3 5.68 2000 Income data from LIS EL 34.3 6.19 2000 Income data from LIS ES 34.7 6.00 2000 Income data from LIS FR 32.7 5.58 1995 Income data from LIS IT 36.0 6.46 2000 Income data from LIS CY not included LV 35.7 6.28 2004 Expenditure data LT 35.8 6.29 2004 Expenditure data LU not included HU 30.0 4.50 2004 Expenditure data MT not included NL 30.9 5.09 1999 Income data from LIS AT 29.1 4.40 2000 Income data from LIS PL 34.9 5.81 2005 Expenditure data PT 38.5 4.17 1997 Income data from LIS RO 31.5 4.87 2005 Expenditure data SI 31.2 4.80 2004 Expenditure data SK 25.8 3.95 1996 Income data from LIS FI 26.9 3.82 2000 Income data from LIS SE 25.0 4.02 2000 Income data from LIS UK 36.0 7.21 1999 Income data from LIS Source: World Bank (2009). Income and living conditions in Europeeurostat 115 5Income poverty and income inequality (data for Cyprus, Luxembourg and Malta are not included in the WDI table) are shown in Table 5.2, together with the sources. There are two evident problems. The first is that the data come from two different sources. It is stated that data for ‘the high-income countries’ are income data taken from the LIS database, and this applies for 16 of the countries. But for eight countries, all New Member States, the data relate to expenditure and come from other sources. Secondly, as explained earlier, the LIS data are not annual, and those used in the 2009 WDI relate mostly to the year 2000 or, in seven cases, even earlier. This latter point reduces significantly the value of the WDI compilation. It certainly appears a little odd that the data in the 2009 WDI table for Liberia and Morocco relate to 2007, whereas the data for France are no more recent than 1995. The former problem limits the comparability within the EU, although the expenditure data may be more comparable with those for middle-income and develo- ping countries. The question naturally arises as to why the WDI does not employ the EU-SILC data, which would have the definite advantages of being more current and of not mixing income-based and expenditure-based estimates? The answer may depend on the comparison of this new source with the longer established LIS and with official sources such as the OECD. Here we may turn to the OECD report (OECD, 2008), which contained a most helpful comparison of the OECD estimates with EU-SILC (2005 data, income reference year 2004) and LIS (mostly relating to years around 2000). There is relatively little discussion of the findings of the comparison in the OECD report, perhaps because the results appear reassuring. Their figures for the at-risk- of-poverty definition based on 60 per cent of the median are reproduced in Figure 5.6. (12) The three bars show the estimates for each country for the OECD, EU-SILC and LIS (in some cases one of the latter two is missing). (12) The comparison also includes four non-EU countries: Iceland (IS), Norway (NO), Switzerland (CH) and Turkey (TR). In almost all cases, the estimates of poverty risk in the three sources are close. Only for 9 of the 57 possible comparisons is the difference equal to 3 percentage points or more (although the estimates are rounded to the nearest integer, so that some of the differences may be only 2.1). Three countries (Germany, the Netherlands and the United Kingdom) account for six of these discrepancies, and these differences are identified by the OECD as a matter for concern. The differences in the case of Germany are four (LIS/OECD) and five (EU-SILC/OECD) percentage points. These differences are among those discussed further in Section 5.3. It should also be noted that only one of the nine discrepancies (for Sweden) concerns the comparison of the EU-SILC and LIS estimates, which are generally closer. The Gini coefficients of income inequality from the three sources are compared in Figure 5.7. The general pattern is similar. It has to be borne in mind, and this applies to both the poverty risk figures (Figure 5.6) and the Gini coefficients (Figure 5.7), that the definitions are not identical. The EU-SILC estimates use the modified OECD equivalence scale described above, whereas, a little strangely, the OECD does not use the scale that bears its name, but uses a square root equivalence scale, as in the LIS data. Use of this latter scale means that income is divided by the square root of the household size (two in the case of the four- person household example), which means that the relative position of different households will be affected. This may well affect the comparison, as may the fact that the OECD and EU-SILC data refer mostly to 2004, whereas the LIS data refer to a variety of years around 2000. All in all, there appears to be a high level of coherence between the cross-country datasets. The data for certain countries needs to be examined, but data created by the EU-SILC framework approach do not seem to be out of line with those assembled by the LIS or OECD methods. Income and living conditions in Europe eurostat116 5 Income poverty and income inequality Figure 5.6: National at-risk-of-poverty rates in various EU and non-EU countries: Estimates from OECD, EU-SILC and LIS, Survey Year 2008 0 5 10 15 20 25 30 CZ SE DK HU IS NO CH AT LU FR NL SK FI BE UK DE EL IT PL PT ES IE TR OECD EUSILC LIS Source: OECD (2008, Table 5.A2.1). NB: Non-EU countries are Iceland (IS), Norway (NO), Switzerland (CH) and Turkey (TR). Depending on the country, the income reference year varies between 2000 and 2005. Reading note: for Ireland, the at-risk-of-poverty rate is 20 per cent according to the EU-SILC estimates, 22 per cent according to the LIS estimates, and 23 per cent according to the OECD estimates. Income and living conditions in Europeeurostat 117 5Income poverty and income inequality Figure 5.7: National Gini coefficients in various EU and non-EU countries: Estimates from OECD, EU-SILC and LIS, Survey Year 2008 0 5 10 15 20 25 30 35 40 45 50 SE DK IS CZ DE LU AT SK FI HU NL BE FR NO IE ES EL IT UK PL PT TR OECD EU-SILC LIS Source: OECD (2008, Table 1.A2.3). NB: Non-EU countries are Iceland (IS), Norway (NO), Switzerland (CH) and Turkey (TR). Depending on the country, the income reference year varies between 1999 and 2005. Reading note: for Sweden, the Gini coefficient is 23.0 per cent according to the EU-SILC estimates, 23.4 per cent according to the OECD estimates, and 25.2 per cent according to the LIS estimates. Income and living conditions in Europe eurostat118 5 Income poverty and income inequality 5.3 Changes in income poverty and inequality over time 5.3.1 Monitoring trends in EU-SILC In the previous section, we have described the situation in the EU in 2007 (the 2008 Survey Year related in nearly all countries to incomes in 2007). But much of the interest of the figures lies in how inequality and poverty are changing over time. In this respect, it is frustrating that we can say little about what has happened since 2007. At a time of economic crisis, everyone, citizens and politicians alike, wants to be able to monitor what is happening to living standards following the financial crisis and the subsequent world recession. Who is bearing the burden? It is also important, however, to understand what was happening before the economic crisis. How far had the EU been successful in its 2000 declared ambition of achieving a significant reduction in poverty and social exclusion? Was it the case that there had been rising inequality, a factor which some commentators have treated as a contributing to the crisis? Here too we are limited as to what we can say. As explained in Chapter 2, EU-SILC was launched in 2003, with income reference year 2002, on the basis of a ‘gentleman’s agreement’ in six Member States. The official starting date for EU-SILC was Survey Year 2004 for EU-15 (minus Germany, the Netherlands and the United Kingdom, plus Estonia), with income reference year 2003. The New Member States that joined the EU in 2004 (apart from Estonia) as well as Germany, Netherlands and the United Kingdom, started with respect to Survey Year 2005. Bulgaria entered in Survey Year 2006, and Romania in Survey Year 2007. This means that there are data for between 2 and 6 years — see Table 5.3. (As indicated previously, the income reference year is different for Ireland and the United Kingdom.) Can we identify from this short EU-SILC time series countries where income poverty and inequality are decreasing or increasing? In the case of year-to-year changes, sampling errors are clearly relevant. In the case of the at-risk- of-poverty rate, Lelkes et al (2009, Figure 1.10) show for Survey Years 2004–2006 10 countries where there were changes outside the 95 per cent confidence interval for the preceding year. (13) The countries are equally divided in their direction of movement. The ‘improvers’ were Estonia, Ireland, Netherlands, Poland and Slovakia. Those moving towards higher poverty risk were Finland, Italy, Latvia, Luxembourg and Sweden. Year-to-year variation on account of sampling error certainly means that we should not attach weight to modest changes in the at-risk-of-poverty rate over time. The sampling errors reported for the 2005 EU-SILC for the proportion at-risk-of- poverty imply a one-sided 95 per cent confidence interval of less than 1 percentage point for 11 of the 23 countries analysed and in all cases it is less than 2 percentage points (Eurostat, 2008). Account has also to be taken of non-sampling errors, as has been discussed in Chapter 3. These considerations refer to the ‘supply side’: the accuracy of the estimates supplied by EU- SILC (or other sources). It is indeed a pre- requisite that the observed performances are different. But we have also to ask about the ‘demand’ side. What differences are of interest to the user? Here the Europe 2020 targets provide a point of reference. The ambition of the EU is to reduce those at risk of poverty and social exclusion by 20 million. In terms of the at-risk- of-poverty rate, this would mean a reduction of approximately a quarter (20 million out of 80 million) or, put differently, a reduction of about 4 percentage points for the EU-27 as a whole. Applied at the level of individual countries, a reduction of a quarter would mean between 2½ and 6½ percentage points. Taking account of both supply and demand side considerations, we pay particular attention in what follows to changes of 2 percentage points or larger. (13) We have here excluded Hungary on the grounds explained by Lelkes et al, that there appear to be problems with the estimate for 2006 (Survey Year). Income and living conditions in Europeeurostat 119 5Income poverty and income inequality Table 5.3: National at-risk-of-poverty rates in EU-27, Survey Years 2003–2008 2003 2004 2005 2006 2007 2008 EU-27 : : 15.9 16.2 16.7 16.6 EU-15 : : 15.7 16.0 16.5 16.4 NMS12 : : : : 17.6 17.3 NMS10 : : 17.3 16.7 15.1 15.0 BE 15.3 14.3 14.8 14.7 15.1 14.7 BG : : 18.4 21.8 21.4 CZ : : 10.4 9.8 9.5 9.1 DK 11.7 10.9 11.8 11.7 11.7 11.8 DE : : 12.3 12.7 15.2 15.3 EE : 20.2 18.3 18.3 19.4 19.5 IE 20.1 20.9 19.7 18.5 17.3 15.5 EL 20.7 19.9 19.6 20.5 20.3 20.1 ES : 19.9 19.7 19.9 19.7 19.6 FR : 13.5 13.0 13.1 13.1 13.4 IT : 19.1 18.8 19.6 19.8 18.7 CY : : 16.2 15.8 15.5 16.3 LV : : 19.2 23.1 21.2 25.6 LT : : 20.5 20.0 19.1 20.0 LU 11.9 12.7 13.7 14.1 13.5 13.4 HU : : 13.4 15.9 12.3 12.4 MT : : 14.1 13.6 14.4 14.7 NL : : 10.8 9.9 10.2 10.6 AT 13.2 12.8 12.3 12.6 12.0 12.4 PL : : 20.6 19.1 17.3 16.9 PT : 20.5 19.4 18.5 18.1 18.5 RO : : : : 24.8 23.4 SI : : 12.2 11.7 11.5 12.3 SK : : 13.3 11.6 10.5 10.9 FI : 11.1 11.7 12.5 12.9 13.6 SE : 11.3 9.3 12.3 10.8 12.2 UK : : 19.1 19.2 19.1 19.0 Source: See figure 5.1. Income and living conditions in Europe eurostat120 5 Income poverty and income inequality 5.3.2 Changes in poverty risk What do we learn from Table 5.3 if we run our cursor over the figures identifying cases where the Survey Year 2008 data represent a change of 2 percentage points of more in the proportion at-risk-of-poverty relative to an earlier year? For six Member States, we have EU-SILC data covering six years. For only one — Ireland — did an earlier year have a proportion that differed by 2 percentage points or more. Between 2003 and 2008, Ireland moved from having an above EU- 27 average at-risk-of-poverty rate to one that is below it. In the other five countries there were falls, but these were smaller and in some cases reversed: for example, in Greece the proportion fell, then rose, and then fell. For the countries with five years of data, Finland saw an increase in the at-risk-of-poverty rate in each year and ended with a figure 2½ percentage points higher — an increase of nearly a quarter. In the opposite direction, Portugal, with an initially high at-risk-of-poverty poverty rate, showed a reduction of 2 percentage points. Sweden showed both falls and rises of at least 2 percentage points, but ended in 2008 with an at-risk-of-poverty rate less than 1 percentage point different from that in Survey Year 2004. There is some tendency for convergence, with high poverty risk countries tending to show reductions in income poverty rates (although not universally) and for there to be slippage in the opposite direction among the previous better- performers. This is illustrated by the fall between Survey Years 2005 and 2008 in the at-risk-of- poverty rate for the NMS10 group, i.e. the 10 countries that joined the EU in 2004, where the rise in Latvia was more than offset by the falls in Poland and Slovakia. In sum, the picture prior to 2008 was not a static one. Some countries have achieved sustained reductions in the proportions at-risk-of-poverty, but in the EU as a whole this progress has been offset by reversals in other Member States. 5.3.3 Changes in income inequality It is widely believed that income inequality has been on the increase. This belief is much influenced by the experience of the United States, but has the same happened in Europe? The EU-SILC data suggest that the EU picture is more nuanced. Tables 5.4a and 5.4b show the EU-SILC results for the two inequality indicators used in the previous section. Overall the weighted-average indicator for EU-27 hardly changed between Survey Years 2005 and 2008. (Again it has to be remembered that this is the average of national inequalities, not the overall EU inequality taking account of between-country differences.) This did not reflect stasis. There were country changes, and indeed some degree of convergence. The average for the 10 New Member States showed a reduction in inequality: the S80/S20 ratio went from 5.6 to 4.6, and the Gini coefficient fell by nearly the 3 percentage points that we described as a ‘salient’ change in the previous section. There were falls of more than 3 percentage points in the Gini coefficient in Estonia and Poland. If we look at EU-15, then among the larger countries there is little evidence of change in France, Italy, Spain and the United Kingdom. The most evident change in the EU-SILC data is the rise in the S80/S20 ratio (from 3.8 to 4.8) and in the Gini coefficient (from 26 to 30 per cent) in Germany. (During the same period, the at-risk- of-poverty rate measured on the basis of EU- SILC also increased sharply in Germany, from 12.3 per cent to 15.3 per cent; we come back to these estimates in Section 5.3.4.) These country differences underline the need to compare the EU-SILC findings with those from national sources, to which we now turn. 5.3.4 Comparison with national sources: a case study The provision of data on income inequality and poverty has a long history in individual Member States. Whereas in some countries the launching Income and living conditions in Europeeurostat 121 5Income poverty and income inequality Table 5.4a: Income inequality in EU-27 countries: S80/S20 ratio, Survey Years 2003-2008 2003 2004 2005 2006 2007 2008 EU-27 : : 4.88 4.80 5.01 4.95 EU-15 : : 4.75 4.72 4.88 4.88 NMS12 : : : : 5.51 5.24 NMS10 : : 5.55 5.22 4.67 4.59 BE 4.34 3.92 4.03 4.17 3.87 4.07 BG : : 5.12 6.92 6.48 CZ : : 3.67 3.52 3.51 3.42 DK 3.58 3.42 3.50 3.44 3.73 3.63 DE : : 3.80 4.08 4.96 4.78 EE : 7.23 5.93 5.51 5.54 4.99 IE 4.98 4.96 5.01 4.87 4.78 4.47 EL 6.38 5.95 5.79 6.05 6.01 5.89 ES : 5.13 5.43 5.28 5.27 5.43 FR : 4.17 4.03 3.97 3.83 4.17 IT : 5.74 5.55 5.49 5.49 5.13 CY : : 4.35 4.29 4.46 4.14 LV : : 6.66 7.88 6.33 7.34 LT : : 6.94 6.31 5.91 5.90 LU 4.06 3.92 3.87 4.18 4.02 4.07 HU : : 4.04 5.46 3.70 3.61 MT : : 3.93 4.03 3.84 4.00 NL : : 3.98 3.84 4.02 4.02 AT 4.05 3.77 3.77 3.65 3.76 3.72 PL : : 6.64 5.65 5.26 5.12 PT : 6.95 6.93 6.76 6.47 6.09 RO : : : : 7.84 7.04 SI : : 3.43 3.39 3.31 3.36 SK : : 3.92 4.05 3.47 3.36 FI : 3.53 3.63 3.63 3.72 3.75 SE : 3.28 3.30 3.53 3.36 3.52 UK : : 5.85 5.40 5.45 5.64 Source: See figure 5.1. Income and living conditions in Europe eurostat122 5 Income poverty and income inequality Table 5.4b: Income inequality in EU-27 countries: Gini coefficients, Survey Years 2003-2008 2003 2004 2005 2006 2007 2008 EU-27 : : 30.2 29.9 30.6 30.6 EU-15 : : 29.9 29.5 30.2 30.4 NMS12 : : : : 31.8 31.3 NMS10 : : 32.1 31.7 29.7 29.4 BE 28.3 26.1 27.9 27.8 26.3 27.5 BG : : : 31.2 35.1 35.9 CZ : : 26.0 25.3 25.2 24.7 DK 24.8 23.9 23.9 23.7 25.2 25.1 DE : : 26.1 26.9 30.5 30.3 EE : 37.4 34.1 33.1 33.4 30.9 IE 30.7 31.6 32.0 31.9 31.3 30.0 EL 34.7 33 33.2 34.3 34.3 33.4 ES : 30.7 31.8 31.1 31.3 31.2 FR : 28.3 27.8 27.3 26.4 28.1 IT : 33.2 32.8 32.1 32.2 31.0 CY : : 28.7 28.8 29.8 27.9 LV : : 36.1 39.2 35.4 37.7 LT : : 36.3 34.9 33.8 34.0 LU 27.6 26.4 26.5 27.8 27.4 27.6 HU : : 27.5 33.3 25.7 25.2 MT : : 27.0 27.3 26 26.9 NL : : 26.7 26.4 27.6 27.7 AT 27.3 25.8 26.1 25.3 26.1 26.1 PL : : 35.6 33.3 32.2 32.0 PT : 37.7 38.1 37.7 36.8 35.8 RO : : : : 37.8 36.0 SI : : 23.8 23.8 23.2 23.4 SK : : 26.2 28 24.5 23.6 FI : 25.4 25.9 25.8 26.2 26.2 SE : 22.8 23.2 23.8 23.4 24.1 UK : : 34.4 32.4 32.9 33.9 Source: See figure 5.1. Income and living conditions in Europeeurostat 123 5Income poverty and income inequality of ECHP, and now EU-SILC, was a stimulus to collect distributional data on a regular basis, and the EU reference data provide the main national source, in quite a number of countries there are long-running regular series, typically annual, for income inequality and poverty. In the latter cases, it is important to compare the findings from EU- SILC with those from the national sources. (14) Differences between the results from EU-SILC and from national sources do not imply that one source is necessarily in error or that one source is to be preferred. Differences may arise for several reasons, including the following ones: • differences in the population covered (for example, the exclusion in EU-SILC of the non-household population, whereas national sources may cover people living in collective households or institutions); • differences in the definitions adopted (for example, of the unit of analysis or of total income or of the equivalence scale); • differences in timing (for example, in the definition of the income reference period or in the scheduling of the interviews). On the other hand, differences may be attributable to identifiable shortcomings. Response rates may be different, particularly where there is attrition from a panel survey. The extent of reporting may vary, as may be indicated by checks against known income totals (as discussed in Chapter 18). In this section, we take one comparison with national sources as a case study. The case study is that of Germany. There are three reasons for this choice. First, Germany i