Indonésie

The 3-months interest rate is a representative short-term interest rate series for the domestic money market. From January 1999, the euro area rate is the 3-month "EURo InterBank Offered Rate" (EURIBOR) EURIBOR is the benchmark rate of the large euro money market that has emerged since 1999. It is the rate at which euro InterBank term deposits are offered by one prime bank to another prime bank. The contributors to EURIBOR are the banks with the highest volume of business in the euro area money markets. The panel of banks consists of banks from EU countries participating in the euro from the outset, banks from EU countries not participating in the euro from the outset, and large international banks from non-EU countries but with important euro area operations. Monthly data are calculated as averages of daily values. Data are presented in raw form. Source: European Central Bank (ECB)

World Electricity access database Hundreds of millions of people have gained access to modern energy over the last two decades, especially in China and India. Rapid economic development in several developing countries, increasing urbanisation and ongoing energy access programmes have been important factors in this achievement. The IEA Access to Energy database provides a snapshot of progress made toward meeting the ultimate goal of universal access. Note: For indicator population without access, value 1 represent <1 except Botswana, Guatemala countries

Number of grants of citizenship of the reporting country to persons usually resident in the reporting country who have previously been citizens of another country or who have been stateless.

The annual Eurostat's collection on statistics on acquisitions of citizenship is structured as follows:   Data Collection Info & Legislation UNIDEMO Unified Demographic The most extended annual collection on demography and migration, collecting data at national and regional level for population, births, deaths, immigrants, emigrants, acquisition and loss of citizenship, marriages and divorces by a large number of breakdowns. (Art. 3 of the Regulation (EU) No 1260/2013 and Art. 3 of the Regulation (EC) No 862/2007)   The annual demography data collections aim at collecting from the National Statistical Institutes both mandatory data and voluntary data. The mandatory data are those defined by the legislation listed on "6.1. Institutional Mandate - legal acts and other agreements". The demographic data collected on voluntary basis depend on the availability and on the quality of information available in the National Statistical Institutes. For more specific information on mandatory/voluntary data collection see 6.1. Institutional Mandate - legal acts and other agreements.   The following data on acquisition and loss of citizenship are collected:Acquisitions of  citizenship by age, sex and former citizenshipLoss of citizenship by sex and new citizenship   Naturalisation rates: based on the different breakdowns of data on acquisition of citizenship and migrant population received, Eurostat produces the following:Statistics available in migr_acqs:                  a.   share of foreign citizens who have acquired citizenship                  b.   share of EU citizens who have acquired citizenship                  c.   share of  non-EU citizens who have acquired citizenship

The section 'LFS series - detailed quarterly survey results' reports detailed quarterly results going beyond the EU-LFS main aggregates, which have a separate data domain and some methodological differences. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.

The section 'LFS series - detailed annual survey results' reports annual results from the EU-LFS. While LFS is a quarterly survey, it is also possible to produce annual results. There are several ways of doing it, see section '18.5 Data compilation' below for details. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metadata. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.

The section 'LFS series - detailed annual survey results' reports annual results from the EU-LFS. While LFS is a quarterly survey, it is also possible to produce annual results. There are several ways of doing it, see section '18.5 Data compilation' below for details. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metadata. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.

The section 'LFS series - detailed quarterly survey results' reports detailed quarterly results going beyond the EU-LFS main aggregates, which have a separate data domain and some methodological differences. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.

The indicator reflects the purchasing power of households and their ability to invest in goods and services or save for the future, by accounting for taxes and social contributions and monetary in-kind social benefits. It is calculated as the adjusted gross disposable income of households and Non-Profit Institutions Serving Households (NPISH) divided by the purchasing power parities (PPP) of the actual individual consumption of households and by the total resident population. The values are also offered as an index calculated in relation to the European Union average set to equal 100. If the index of a country is higher than 100, this country's level of adjusted gross disposable income of households per person is higher than the EU average and vice versa. Please note that this index is intended for cross-country comparisons rather than for temporal comparisons. Finally, the disparities indicator offered for EU27 (from 2020) is calculated as the coefficient of variation of the national figures. This time series offers a measure of the convergence of household income between the Member States of the EU.

L'âge médian marque le point où la moitié du groupe est plus âgée que cet âge et la moitié est plus jeune. La main-d'oeuvre comprend toutes les personnes en âge de travailler qui fournissent, durant une période de référence spécifiée, la main-d'oeuvre disponible pour la production de biens et services. Elle correspond à la somme des personnes ayant un emploi et celles qui sont au chômage. Cette série fait partie des estimations du BIT et est harmonisée pour tenir compte des différences entre les données nationales, la portée de la couverture, les méthodologies de collecte et de tabulation, et de facteurs spécifiques aux pays. Les données pour 1990-2015 sont des estimations tandis que les données pour 2016-2030 sont des projections. La base de données a été mise à jour en juillet 2017. Pour plus d'informations, consultez la note méthodologique général (en anglais) et le document méthodologique sur les estimations et projections de la main d'oeuvre (en anglais).

The Economic Accounts for Agriculture (EAA) provide detailed information on income in the agricultural sector. The purpose is to analyse the production process of the agricultural industry and the primary income generated by this production. The accounts are therefore based on the industry concept. The EAA accounts are detailed data on value of output (producer prices and basic prices), intermediate consumption, subsidies and taxes, consumption of fixed capital, rent and interests, capital formation etc. The values are in current as well as in constant prices. Agricultural Labour Input (ALI) and Unit Values (UV) are an integrated part of the overall concept of Economic Accounts for Agriculture. The Economic accounts for agriculture (EAA) are a satellite account of the European System of Accounts (ESA2010), providing complementary information and concepts adapted to the particular nature of the agricultural industry. Although their structure very closely matches that of the national accounts, their compilation requires the formulation of appropriate rules and methods. National Statistical Institutes or Ministries of Agriculture are responsible for data collection and calculation of national EAA, in accordance with EC Regulations. Eurostat is responsible for the EU aggregations. Regional data EAA accounts are compiled at regional level (NUTS2), but only in values in current prices. The labour input data and Unit values are not broken down to regional level. Please note that for paragraphs where no metadata for regional data has been specified, the regional metadata is identical to the metadata provided for the national data. Frequency of data collection for data under Regulation (EC) 138/2004 and gentlemen's agreement, deadline for transmission for years 2015-2016.

This dataset contains indicators related to Agriculture, Horticulture and Forestry topics for provincial level.

The data describe the average use of chemical and mineral fertilizers per area of cropland (arable land and permanent crops) at national, regional, and global level in a time series from 2002 to 2014The data describe the average use of chemical and mineral fertilizers per area of cropland (arable land and permanent crops) at national, regional, and global level in a time series from 2002 to 2015

The Livestock Patterns domain of the FAOSTAT Agri-Environmental Indicators contains data on livestock numbers, shares of major livestock species and livestock densities in the agricultural area. Values are calculated using Livestock Units (LSU), which facilitate aggregating information for different livestock types. Data are available by country, with global coverage, for the period 1961–2014. This methodology applies the LSU coefficients reported in the "Guidelines for the preparation of livestock sector reviews" (FAO, 2011). From this publication, LSU coefficients are computed by livestock type and by country. The reference unit used for the calculation of livestock units (=1 LSU) is the grazing equivalent of one adult dairy cow producing 3000 kg of milk annually, fed without additional concentrated foodstuffs. FAOSTAT agri-environmental indicators on livestock patterns closely follow the structure of the indicators in EUROSTAT.

The data describe the average use of pesticides per area of cropland (arable land and permanent crops) at national level in a time series from 1990 to 2014. 

The gross nutrient balances (N and P) are calculated as the difference between the total quantity of nutrient inputs entering an agricultural system (mainly fertilisers, livestock manure), and the quantity of nutrient outputs leaving the system (mainly uptake of nutrients by crops and grassland). Gross nutrient balances are expressed in tonnes of nutrient surplus (when positive) or deficit (when negative). This calculation can be used as a proxy to reveal the status of environmental pressures, such as declining soil fertility in the case of a nutrient deficit, or for a nutrient surplus the risk of polluting soil, water and air. The nutrient balance indicator is also expressed in terms of kilogrammes of nutrient surplus per hectare of agricultural land to facilitate the comparison of the relative intensity of nutrients in agricultural systems between countries.

The gross nutrient balances (N and P) are calculated as the difference between the total quantity of nutrient inputs entering an agricultural system (mainly fertilizers, livestock manure), and the quantity of nutrient outputs leaving the system (mainly uptake of nutrients by crops and grassland). Gross nutrient balances are expressed in tonnes of nutrient surplus (when positive) or deficit (when negative). This calculation can be used as a proxy to reveal the status of environmental pressures, such as declining soil fertility in the case of a nutrient deficit, or for a nutrient surplus the risk of polluting soil, water and air. The nutrient balance indicator is also expressed in terms of kilogrammes of nutrient surplus per hectare of agricultural land to facilitate the comparison of the relative intensity of nutrients in agricultural systems between countries.

The air accident data are provided to Eurostat by the European Aviation Safety Agency (EASA). EASA as an Agency is responsible for providing common standards of safety and environmental protection in civil aviation in Europe and worldwide. It is the centrepiece of regulations creating a single European market in the aviation industry. The Agency’s responsibilities include aviation safety analysis and research for which it also collects statistics on European and worldwide aviation safety. The statistics are grouped according to type of operation, such as commercial air transport or general aviation, and aircraft category, such as aeroplanes, helicopters or gliders. The EASA manages and is responsible for the entire data collection. The selection of data made available to Eurostat does not differ from those available through the EASA (http://easa.europa.eu). In Eurobase, the following data are available: Air accident victims in commercial air transport, by country of occurrence and country of registry of aircraft (EASA data) (tran_sf_aviaca); Air accident victims in aerial works, by country of occurrence and country of registry of aircraft (EASA data) (tran_sf_aviaaw); Air accident victims in general aviation, by country of occurrence and country of registry of aircraft – maximum take-off mass above 2250 kg (EASA data) (tran_sf_aviagah); Air accident victims in general aviation by country of occurrence and country of registry of aircraft – maximum take-off mass under 2250 kg (EASA data) (tran_sf_aviagal).

The air accident data are provided to Eurostat by the European Aviation Safety Agency (EASA). EASA as an Agency is responsible for providing common standards of safety and environmental protection in civil aviation in Europe and worldwide. It is the centrepiece of regulations creating a single European market in the aviation industry. The Agency’s responsibilities include aviation safety analysis and research for which it also collects statistics on European and worldwide aviation safety. The statistics are grouped according to type of operation, such as commercial air transport or general aviation, and aircraft category, such as aeroplanes, helicopters or gliders. The EASA manages and is responsible for the entire data collection. The selection of data made available to Eurostat does not differ from those available through the EASA (http://easa.europa.eu). In Eurobase, the following data are available: Air accident victims in commercial air transport, by country of occurrence and country of registry of aircraft (EASA data) (tran_sf_aviaca); Air accident victims in aerial works, by country of occurrence and country of registry of aircraft (EASA data) (tran_sf_aviaaw); Air accident victims in general aviation, by country of occurrence and country of registry of aircraft – maximum take-off mass above 2250 kg (EASA data) (tran_sf_aviagah); Air accident victims in general aviation by country of occurrence and country of registry of aircraft – maximum take-off mass under 2250 kg (EASA data) (tran_sf_aviagal).

The air accident data are provided to Eurostat by the European Aviation Safety Agency (EASA). EASA as an Agency is responsible for providing common standards of safety and environmental protection in civil aviation in Europe and worldwide. It is the centrepiece of regulations creating a single European market in the aviation industry. The Agency’s responsibilities include aviation safety analysis and research for which it also collects statistics on European and worldwide aviation safety. The statistics are grouped according to type of operation, such as commercial air transport or general aviation, and aircraft category, such as aeroplanes, helicopters or gliders. The EASA manages and is responsible for the entire data collection. The selection of data made available to Eurostat does not differ from those available through the EASA (http://easa.europa.eu). In Eurobase, the following data are available: Air accident victims in commercial air transport, by country of occurrence and country of registry of aircraft (EASA data) (tran_sf_aviaca); Air accident victims in aerial works, by country of occurrence and country of registry of aircraft (EASA data) (tran_sf_aviaaw); Air accident victims in general aviation, by country of occurrence and country of registry of aircraft – maximum take-off mass above 2250 kg (EASA data) (tran_sf_aviagah); Air accident victims in general aviation by country of occurrence and country of registry of aircraft – maximum take-off mass under 2250 kg (EASA data) (tran_sf_aviagal).

The air accident data are provided to Eurostat by the European Aviation Safety Agency (EASA). EASA as an Agency is responsible for providing common standards of safety and environmental protection in civil aviation in Europe and worldwide. It is the centrepiece of regulations creating a single European market in the aviation industry. The Agency’s responsibilities include aviation safety analysis and research for which it also collects statistics on European and worldwide aviation safety. The statistics are grouped according to type of operation, such as commercial air transport or general aviation, and aircraft category, such as aeroplanes, helicopters or gliders. The EASA manages and is responsible for the entire data collection. The selection of data made available to Eurostat does not differ from those available through the EASA (http://easa.europa.eu). In Eurobase, the following data are available: Air accident victims in commercial air transport, by country of occurrence and country of registry of aircraft (EASA data) (tran_sf_aviaca); Air accident victims in aerial works, by country of occurrence and country of registry of aircraft (EASA data) (tran_sf_aviaaw); Air accident victims in general aviation, by country of occurrence and country of registry of aircraft – maximum take-off mass above 2250 kg (EASA data) (tran_sf_aviagah); Air accident victims in general aviation by country of occurrence and country of registry of aircraft – maximum take-off mass under 2250 kg (EASA data) (tran_sf_aviagal).

This database includes annual, quarterly and monthly information on carbon dioxide (CO2) emissions related to commercial passenger, freight, and general aviation flights, on both a territory and a residence basis, for 186 countries. These CO2 emissions are estimated by the OECD, based on a consistent methodology across countries. The main source used for the estimation of these CO2 emissions is a database compiled by the International Civil Aviation Organisation (ICAO) with all commercial passenger and freight flights around the world.

The focus of this domain is on the European Neighbourhood Policy (ENP) countries on the southern and eastern shores of the Mediterranean (ENP-South), namely: Algeria (DZ),Egypt (EG),Israel (IL),Jordan (JO),Lebanon (LB),Libya (LY),Morocco (MA),Palestine (PS) (1),Syria (SY) andTunisia (TN). An extensive range of indicators is presented in this domain, including indicators from almost every theme covered by European statistics. Generally, only annual data are published in this domain. The data and their denomination in no way constitute the expression of an opinion by the European Commission on the legal status of a country or territory or on the delimitation of its borders. (1) This designation shall not be construed as recognition of a State of Palestine and is without prejudice to the individual positions of the Member States on this issue.

The focus of this domain is on the European Neighbourhood Policy (ENP) countries on the southern and eastern shores of the Mediterranean (ENP-South), namely: Algeria (DZ),Egypt (EG),Israel (IL),Jordan (JO),Lebanon (LB),Libya (LY),Morocco (MA),Palestine (PS) (1),Syria (SY) andTunisia (TN). An extensive range of indicators is presented in this domain, including indicators from almost every theme covered by European statistics. Generally, only annual data are published in this domain. The data and their denomination in no way constitute the expression of an opinion by the European Commission on the legal status of a country or territory or on the delimitation of its borders. (1) This designation shall not be construed as recognition of a State of Palestine and is without prejudice to the individual positions of the Member States on this issue.

The indicator shows the volume of goods transported in Europe (in tonnes), broken down by country and by year. The data covers the total volume of freight and mail loaded/unloaded.

Residence permits statistics refers to third-country nationals (persons who are not EU citizens) receiving a residence permits or an authorisation to reside in one of the EU or EFTA Member States. The definitions used for residence permits and other concepts (e.g. first permit) are presented in the section 3.4. Statistical concepts and definitions. The detailed data collection methodology is presented in Annex 8 of this metadta file. LEGAL FRAMEWORK - Residence data contain statistical information based on Article 6 of Council Regulation (CE) No 862 of 11 July 2007.  This legal framework refers to the initial residence permits data colection with 2008 first reference period (e.g. first residence permits; change of immigration status or reason to stay; all valid residence permits in the end of the year and long-term residence permits valid in the end of the year) and it provides also a general framework for newer data collections based on speciffic European legal acts (e.g. statistics on EU Blue Cards and statistics on single permits) or provided on voluntary basis (e.g. new long-term residence permits issued during the year and residence permits issued for family reunification with beneficiaries of  protection status). DATA SOURCE - Data are entirely based on administrative sources with the exception of the United Kingdom1 and are provided mainly by the Ministries of Interior or related Immigration Agencies. Data are generally disseminated in June and July in the year following the reference year. AVAILABLE DATASETS I. Residence permits statistics by reason to stay, citizenship and permit's lenght of validity based on Article 6 of Council Regulation (CE) No 862 of 11 July 2007. These statistics are avilable from 2008 reference year.     First Permits - see the definition in the section 3.4. Statistical concepts and definitions. First permits by reason, length of validity and citizenship (migr_resfirst)2. The totals presented in this tables are depended on data availability in the following four tables migr_resfam + migr_resedu+ migr_resocc+ migr_resoth.First permits issued for family reasons by reason, length of validity and citizenship (migr_resfam)First permits issued for education reasons by reason, length of validity and citizenship (migr_resedu)First permits issued for remunerated activities by reason, length of validity and citizenship (migr_resocc)First permits issued for other reasons by reason, length of validity and citizenship (migr_resoth)     Residence Permits issued with the occasion of changing the immigration status or reason to stay Change of immigration status permits by reason and citizenship (migr_reschange)               Residence permits valid in the end of the year All valid permits by reason, length of validity and citizenship on 31 December of each year (migr_resvalid)Long-term residents by citizenship on 31 December of each year (migr_reslong)     Share of long term residence permitsLong-term residents among all non-EU citizens holding residence permits by citizenship on 31 December (%) (migr_resshare) II. Residence permits statistics by age (5-year age groups) and sex collected on voluntary basis. These statistics are avilable from 2010 reference year. First permits by reason, age, sex and citizenship (migr_resfas)  All valid permits by age, sex and citizenship on 31 December of each year (migr_resvas)               Long-term residents by age, sex and citizenship on 31 December of each year (migr_reslas) III. EU Blue Cards data collection based on Article 20 of the Directive 2009/50/EC. These statistics are avilable from 2012 reference year2. EU Blue Cards by type of decision, occupation and citizenship (migr_resbc1)       Admitted family members of EU Blue Cards holders by type of decision and citizenship (migr_resbc2)EU Blue Cards holders and family members by Member State of previous residence (migr_resbc3) IV. Single Permit data collection based on Art 15 Directive 2011/98/EU. These statistics are avilable from 2013 reference year. Single Permits issued by type of decision, length of validity (migr_ressing)  V. Pilot data collections collected on voluntary basis. These statistics are avilable from 2016 reference year and the data quality assesment is ongoing. Long-term residence permits issued during the year (migr_resltr)First permits issued for family reunification with a beneficiary of protection status (migr_resfrps1)Permits valid at the end of the year for family reunification with a beneficiary of protection status (migr_resfrps2) VI. New statistics on Intra-Corporate Transfers and Seasonal Workers New data collections with 2017 first reference period are in the preparetion phase to be released in 2018: Intra-Corporate Transfers data collection under Art 24 of Directive 2014/66/EU and Seasonal Workers data collection under Art 26 Directive 2014/36/EU.   Share of long-tem residence permits The indicators presented in the table 'Long-term residents among all non-EU citizens holding residence permits by citizenship on 31 December (%)' are produced within the framework of the pilot study related to the integration of migrants in the Member States, following the Zaragoza Declaration. The Zaragoza Declaration, adopted in April 2010 by EU Ministers responsible for immigrant integration issues, and approved at the Justice and Home Affairs Council on 3-4 June 2010, called upon the Commission to undertake a pilot study to examine proposals for common integration indicators and to report on the availability and quality of the data from agreed harmonised sources necessary for the calculation of these indicators. In June 2010 the ministers agreed "to promote the launching of a pilot project with a view to the evaluation of integration policies, including examining the indicators and analysing the significance of the defined indicators taking into account the national contexts, the background of diverse migrant populations and different migration and integration policies of the Member States, and reporting on the availability and quality of the data from agreed harmonised sources necessary for the calculation of these indicators". These indicators are produced on the basis of residence permit statistics collected by Eurostat on the basis of Article 6 of the Migration Statistics Regulation 862/2007. As a denominator data on the stock of all valid permits to stay at the end of each reporting year are used. As a numerator data on the stock of long-term residents are used.  Two types of long term residents are distinguished in accordance with the residence permit statistics: EU long-term resident status (as regulated by the Council Directive 2003/109/EC) and the National long-term resident status (as regulated by the national legislation in the Member States). Data for some countries may be a subject of revisions due to certain inconsistencies between categories.   Data consistency between tables The data providers should use the same methodological specifications provided by Eurostat and some tables from Resper statistics should be consistent between them according to this methodology.  However, consistency issues between tables exist due to some technical limitations (e.g. different data sources) or different methodology applied to each table (see the quality information from below or the national metadata files) or different point in time of producing each tables.   1Please note that the statistics for the United Kingdom use different data sources to those used in other Member States. For that reason, the statistics on residence permits published by Eurostat for UK may not be fully comparable with the statistics reported by other countries. Statistics for the United Kingdom are not based on records of residence permits issued (as the United Kingdom does not operate a system of residence permits), but instead relate to the numbers of arriving non-EU citizens permitted to enter the country under selected immigration categories. According to the United Kingdom authorities, data are estimated from a combination of information due to be published in the Home Office Statistical Bulletin 'Control of Immigration: Statistics, United Kingdom' and unpublished management information. The 'Other reasons' category includes: diplomat, consular officer treated as exempt from control; retired persons of independent means; all other passengers given limited leave to enter who are not included in any other category; non-asylum discretionary permissions. 2 The EU Blue cards issued during the year are collected in two datasets: 1. in the table migr_resocc countig the EU Blue Cards issued as "first permits" and 2. in the EU Blue Cards counting all EU Blue Cards issued. The diference between these two categories is represented by the EU Blue cards that are not first permits. However these two tables might be updated/revised at a different point in time and the consistency between tables might be affected.

Residence permit means any authorisation valid for at least 3 months issued by the authorities of a Member State allowing a third country national to stay legally on its territory. All valid permits on 31st December (end of the year). This data include statistics on all valid permits at the end of reference period, therefore including first permits, change of status or reasons to stay and renewed permits.

Residence permits data contain statistical information based on Article 6 of Council Regulation (CE) No 862 of 11 July 2007 with reference to:first permits granted to third-country nationals during the reference year, disaggregated by citizenship, reason for the permit being issued and by the length of validity of the permit; permits granted during the reference period on the occasion of person changing immigration status or reason to stay, disaggregated by citizenship, reason for the permit being issued and by the length of validity; permits valid at the end of the reference period, disaggregated by citizenship, reasons for the permit being issued and by the length of validity; number of long-term residents at the end of reference period. Statistics on EU Blue Cards contain information based on the Article 20 of the Council Directive 2009/50/EC of 25 May 2009 on:EU Blue Cards granted, renewed and withdrawn;Admitted family members of EU Blue Cards holders;EU Blue Cards holders and family members by Member State of previous residenceStatistics on Single permits contain information based on the Article 15 (2) Directive 2011/98/EU of the European Parliament and of the Council of 13 December 2011 on a single application procedure for a single permit for third-country nationals to reside and work in the territory of a Member State and on a common set of rights for third-country workers legally residing in a Member State. Eurostat collects data on first permits granted to third-country nationals (persons who are not EU citizens) during the reference year and data on permits valid at the end of the reference period. Statistics are disaggregated by citizenship, reason for the permit being issued and by the length of validity of the permit. In addition, Eurostat collects data on permits granted during the reference period on the occasion of the person changing immigration status or reason for stay (disaggregated by reason for the new permit being issued) and on the number of long-term residents at the end of the reference period. Since the 2010 reference year, data on first permits issued, stock of all valid permits and the number of long-term residents are additionally collected with a voluntary disaggregation by age (5-year age groups) and sex. These statistics are collected by Eurostat on an annual basis. Data are entirely based on administrative sources with the exception of the United Kingdom1 and are provided mainly by the Ministries of Interior or related Immigration Agencies. Data are generally disseminated in June and July in the year following  the  reference year. The indicators presented in the table 'Long-term residents among all non-EU citizens holding residence permits by citizenship on 31 December (%)' are produced within the framework of the pilot study related to the integration of migrants in the Member States, following the Zaragoza Declaration. The Zaragoza Declaration, adopted in April 2010 by EU Ministers responsible for immigrant integration issues, and approved at the Justice and Home Affairs Council on 3-4 June 2010, called upon the Commission to undertake a pilot study to examine proposals for common integration indicators and to report on the availability and quality of the data from agreed harmonised sources necessary for the calculation of these indicators. In June 2010 the ministers agreed "to promote the launching of a pilot project with a view to the evaluation of integration policies, including examining the indicators and analysing the significance of the defined indicators taking into account the national contexts, the background of diverse migrant populations and different migration and integration policies of the Member States, and reporting on the availability and quality of the data from agreed harmonised sources necessary for the calculation of these indicators". These indicators are produced on the basis of residence permit statistics collected by Eurostat on the basis of Article 6 of the Migration Statistics Regulation 862/2007. As a denominator data on the stock of all valid permits to stay at the end of each reporting year are used. As a numerator data on the stock of long-term residents are used.  Two types of long term residents are distinguished in accordance with the residence permit statistics: EU long-term resident status (as regulated by the Council Directive 2003/109/EC) and the National long-term resident status (as regulated by the national legislation in the Member States). Data for some countries may be a subject of revisions due to certain inconsistencies between categories. 1Please note that the statistics for the United Kingdom use different data sources to those used in other Member States. For that reason, the statistics on residence permits published by Eurostat for UK may not be fully comparable with the statistics reported by other countries. Statistics for the United Kingdom are not based on records of residence permits issued (as the United Kingdom does not operate a system of residence permits), but instead relate to the numbers of arriving non-EU citizens permitted to enter the country under selected immigration categories. According to the United Kingdom authorities, data are estimated from a combination of information due to be published in the Home Office Statistical Bulletin 'Control of Immigration: Statistics, United Kingdom' and unpublished management information. The 'Other reasons' category includes: diplomat, consular officer treated as exempt from control; retired persons of independent means; all other passengers given limited leave to enter who are not included in any other category; non-asylum discretionary permissions.

Residential Property Prices Indices (RPPIs) – also named House price indices (HPIs), are index numbers that measure the prices of residential properties over time. RPPIs are key statistics not only for citizens and households across the world, but also for economic and monetary policy makers. They can help, for example, to monitor potential macroeconomic imbalances and the risk exposure of the household and financial sectors. This dataset covers the 34 OECD member countries and some non-member countries. In addition to the nominal RPPIs it contains information on real house prices, rental prices and the ratios of nominal prices to rents and to disposable household income per capita. This dataset contains quarterly statistics for each country. House prices differ widely across OECD countries, both with respect to recent changes and to valuation levels. The OECD has identified one main nominal index for each country that covers the prices for the sale of newly-built and existing dwellings. The datasets “Analytical house price indicators” and “Residential Property Price Indices (RPPIs) – Headline Indicators” refer to the same price indices for all countries apart from Brazil, Canada, China, the United States and the Euro area. These differences are further documented in country-specific metadata. For the United States, the series used in “Analytical house price indicators” is included in the dataset called “Residential Property Price Indices (RPPIs) – Complete database”, but is not the headline indicator. For all other countries, non-seasonally adjusted price indices in both datasets are identical in the period in which they overlap. This research dataset provides extended time series coverage for many countries. The objective is to provide information on the long term trend of house prices and develop indicators which can be used to help track and analyse macroeconomic developments and risks. The extended data supplement the OECD RPPI data with historical data from a variety of sources, including other international organisations, central banks and national statistical offices. The methodological basis on the historical data and the types of geographical areas and dwellings they cover can differ from those used in the OECD RPPI data. The database contains a number of additional series. Real house prices are given by the ratio of seasonally adjusted nominal house prices to the seasonally adjusted consumers’ expenditure deflator in each country, from the OECD national accounts database. This provides information on how nominal house prices have changed over time relative to prices in the general economy. The rental prices come from the OECD Main Economic Indicators database and refer to Consumer Price Indices (CPIs) for Actual rentals for housing (COICOP 04.1). If this indicator is missing for a country, another indicator is chosen. The chosen indicator are usually those corresponding to the CPI aggregate for Housing including Actual rentals for housing (COICOP 04.1), imputed rentals for housing (COICOP 04.2) and Maintenance and repair of the dwelling (COICOP 04.3). The disposable income indicators come from the OECD national accounts database. Net household disposable income is used. The population data come from the OECD national accounts database. The price-to-rent ratio is given by the ratio of nominal house prices to rental prices. This is a measure of the profitability of owning a house. The price-to-income ratio is given by the ratio of nominal house prices to nominal household disposable income per capita. This is a measure of the affordability of purchasing a house. An indication that house prices may be overvalued is provided if either of these ratios is above their long-term averages. The standardised price-rent and price-income ratios show the current price-rent and price-income ratios relative to their respective long-term averages. The long-term average, which is used as a reference value, is calculated over the whole period available when the indicator begins after 1980 or 1980 if the indicator is available over a longer time period. The standardised ratio is indexed to a reference value equal to 100 over the full sample period. Values over 100 indicate that the present price-rent ratio, or price-income ratio, is above its long-run norms. This provides an indication of possible housing market pressures.

Animal output is valued at basic prices. The basic price is defined as the price received by the producer, after deduction of all taxes on products but including all subsidies on products. The concept of output comprises sales, changes in stocks, and products used for processing and own final use by the producers.

Animal production statistics cover three main sub-domains based on three pieces of relevant legislation and related gentlemen’s agreements. Livestock and meat statistics are collected under Regulation (EC) No 1165/2008. They cover meat production, as activity of slaughterhouses (monthly) and as other slaughtering (annual), meat production (gross indigenous production) forecast (semi-annual or quarterly), livestock statistics, including regional statistics. A quality report is also collected every third year.Milk and milk product statistics are collected under Decision 97/80/EC implementing Directive 96/16/EC. They cover farm production and utilisation of milk (annual), collection (monthly for cows’ milk) and production activity by dairies (annual) and statistics on the structure of dairies (every third year). An annual methodological report is also collected.Statistics on eggs for hatching and farmyard poultry chicks are collected under Regulation (EC) No 617/2008, implementing Regulation (EC) No 1234/2007 (Single CMO Regulation). They cover statistics on the structure (annual) and the activity (monthly) of hatcheries as well as reports on the external trade of chicks. European Economic Area countries (EEA, Iceland, Liechtenstein and Norway) are requested to provide milk statistics, with the exception of those related to home consumption, as stated in Annex XXI of the EEA Agreement. As Iceland is now a candidate country and Liechtenstein is exempted in the Agreement, only Norway is concerned. The Agreement between the European Community and the Swiss Confederation on cooperation in the field of statistics states that Switzerland must provide Eurostat with national milk statistics. It has been amended in 2013 for covering also some livestock and meat statistics. The same statistics are requested from the candidate countries as acquis communautaire. Further data about the same topics refer to repealed legal acts or agreements. The tables on animal product supply balance sheets (apro_mk_bal, apro_mt_bal and apro_ec_bal), statistics on the structure of rearing (apro_mt_str) and the number of laying hens (apro_ec_lshen) are therefore no longer updated. The same applies to some variables (external trade of animals and meat), periods (surveys in April or August) or items (number of horses) included in other tables. The statistical tables disseminated by Eurostat are organised into three groups of tables on Agricultural products (apro), i.e. Milk and milk products (apro_mk), Livestock and meat (apro_mt) and Poultry farming (apro_ec). This last label covers statistics on hatcheries and on trade in chicks. The regional animal production statistics collected on livestock (agr_r_animal) and on cows’ milk production on farms (agr_r_milk_pr) are disseminated separately. Due to the change in the legal basis or in the methodology, the time series may be broken. This is indicated by a flag in the tables. The detailed content of each table and the reference to its legal definition is provided in the table below.   Table 3.1: Data tables disseminated regarding animal production statistics

Livestock numbers are derived from surveys of farms or from administrative sources in November or December for each Member State.

The focus of this domain is on the European Neighbourhood Policy (ENP) countries on the southern and eastern shores of the Mediterranean (ENP-South), namely: Algeria (DZ),Egypt (EG),Israel (IL),Jordan (JO),Lebanon (LB),Libya (LY),Morocco (MA),Palestine (PS),Syria (SY) andTunisia (TN). An extensive range of indicators is presented in this domain, including indicators from almost every theme covered by European statistics. Only annual data are published in this domain. The data and their denomination in no way constitute the expression of an opinion by the European Commission on the legal status of a country or territory or on the delimitation of its borders.

'Statistics on high-tech industry and knowledge-intensive services' (sometimes referred to as simply 'high-tech statistics') comprise economic, employment and science, technology and innovation (STI) data describing manufacturing and services industries or products traded broken down by technological intensity. The domain uses various other domains and sources of  Eurostat's official statistics (CIS, COMEXT, HRST, LFS, PATENT, R&D and SBS) and its coverage is therefore dependent on these other primary sources. Two main approaches are used in the domain to identify technology-intensity: the sectoral approach and the product approach. A third approach is used for data on high-tech and biotechnology patents aggregated on the basis of the International Patent Classification (IPC) 8th edition (see summary table in Annex 1 for which approach is used by each type of data). The sectoral approach: The sectoral approach is an aggregation of the manufacturing industries according to technological intensity (R&D expenditure/value added) and based on the Statistical classification of economic activities in the European Community (NACE) at 2-digit level. The level of R&D intensity served as a criterion of classification of economic sectors into high-technology, medium high-technology, medium low-technology and low-technology industries. Services are mainly aggregated into knowledge-intensive services (KIS) and less knowledge-intensive services (LKIS) based on the share of tertiary educated persons at NACE 2-digit level. The sectoral approach is used for all indicators except data on high-tech trade and patents. Note that due to the revision of the NACE from NACE Rev. 1.1 to NACE Rev. 2 the definition of high-technology industries and knowledge-intensive services has changed in 2008. For high-tech statistics it means that two different definitions (one according NACE Rev. 1.1 and one according NACE Rev. 2) are used in parallel and the data according to both NACE versions are presented in separated tables depending on the data availability. For example as the LFS provides the results both by NACE Rev. 1.1 and NACE Rev. 2, all the table using this source have been duplicated to present the results by NACE Rev. 2 from 2008. For more details, see both definitions of high-tech sectors in Annex 2 and 3. Within the sectoral approach, a second classification was created, named Knowledge Intensive Activities KIA) and based on the share of tertiary educated people in each sectors of industries and services according to NACE at 2-digit level and for all EU28 Member States. A threshold was applied to judge sectors as knowledge intensive. In contrast to first sectoral approach mixing two methodologies, one for manufacturing industries and one for services, the KIA classification is based on one methodology for all the sectors of industries and services covering even public sector activities. The aggregations in use are Total Knowledge Intensive Activities (KIA) and Knowledge Intensive Activities in Business Industries (KIABI). Both classifications are made according to NACE Rev. 1.1 and NACE Rev. 2 at 2- digit level. Note that due to revision of the NACE Rev.1.1 to NACE Rev. 2 the list of Knowledge Intensive Activities has changed as well, the two definitions are used in parallel and the data are shown in two separate tables. NACE Rev.2 collection includes data starting from 2008 reference year. For more details please see the definitions in Annex 7 and 8. The product approach: The product approach was created to complement the sectoral approach and it is used for data on high-tech trade. The product list is based on the calculations of R&D intensity by groups of products (R&D expenditure/total sales). The groups classified as high-technology products are aggregated on the basis of the Standard International Trade Classification (SITC). The initial definition was built based on SITC Rev.3 and served to compile the high-tech product aggregates until 2007. With the implementation in 2007 of the new version of SITC Rev.4, the definition of high-tech groups was revised and adapted according to new classification. Starting from 2007 the Eurostat presents the trade data for high-tech groups aggregated based on the SITC Rev.4. For more details, see definition of high-tech products in Annex 4 and 5. High-tech patents: High-tech patents are defined according to another approach. The groups classified as high-tech patents are aggregated on the basis of the International Patent Classification (IPC 8th edition). Biotechnology patents are also aggregated on the basis of the IPC 8th edition. For more details, see the aggregation list of high-tech and biotechnology patents in Annex 6. The high-tech domain also comprises the sub-domain Venture Capital Investments: data are provided by INVEST Europe (formerly named the European Private Equity and Venture Capital Association EVCA). More details are available in the Eurostat metadata under Venture capital investments. Please note that for paragraphs where no metadata for regional data has been specified, the regional metadata is identical to the metadata provided for the national data.

Structural business statistics (SBS) describes the structure, conduct and performance of economic activities, down to the most detailed activity level (several hundred economic sectors). SBS are transmitted annually by the EU Member States on the basis of a legal obligation from 1995 onwards.   SBS covers all activities of the business economy with the exception of agricultural activities and personal services and the data are provided by all EU Member States, Norway and Switzerland, some candidate and potential candidate countries. The data are collected by domain of activity (annex) : Annex I - Services, Annex II - Industry, Annex III - Trade and Annex IV- Constructions and by datasets. Each annex contains several datasets as indicated in the SBS Regulation. The majority of the data is collected by National Statistical Institutes (NSIs) by means of statistical surveys, business registers or from various administrative sources. Regulatory or controlling national offices for financial institutions or central banks often provide the information required for the financial sector (NACE Rev 2 Section K / NACE Rev 1.1 Section J). Member States apply various statistical methods, according to the data source, such as grossing up, model based estimation or different forms of imputation, to ensure the quality of SBSs produced. Main characteristics (variables) of the SBS data category:Business Demographic variables (e.g. Number of enterprises)"Output related" variables (e.g. Turnover, Value added)"Input related" variables: labour input (e.g. Employment, Hours worked); goods and services input (e.g. Total of purchases); capital input (e.g. Material investments) All SBS characteristics are published on Eurostat’s website by tables and an example of the existent tables is presented below:Annual enterprise statistics: Characteristics collected are published by country and detailed on NACE Rev 2 and NACE Rev 1.1 class level (4-digits). Some classes or groups in 'services' section have been aggregated.Annual enterprise statistics broken down by size classes: Characteristics are published by country and detailed down to NACE Rev 2 and NACE Rev 1.1 group level (3-digits) and employment size class. For trade (NACE Rev 2 and NACE Rev 1.1 Section G) a supplementary breakdown by turnover size class is available.Annual regional statistics: Four characteristics are published by NUTS-2 country region and detailed on NACE Rev 2 and NACE Rev 1.1 division level (2-digits) (but to group level (3-digits) for the trade section). More information on the contents of different tables: the detail level and breakdowns required starting with the reference year 2008 is defined in Commission Regulation N° 251/2009. For previous reference years it is included in Commission Regulations (EC) N° 2701/98 and amended by Commission Regulation N°1614/2002 and Commission Regulation N°1669/2003. Several important derived indicators are generated in the form of ratios of certain monetary characteristics or per head values. A list with the available derived indicators is available below in the Annex.

Structural business statistics (SBS) describes the structure, conduct and performance of economic activities, down to the most detailed activity level (several hundred economic sectors). SBS are transmitted annually by the EU Member States on the basis of a legal obligation from 1995 onwards.   SBS covers all activities of the business economy with the exception of agricultural activities and personal services and the data are provided by all EU Member States, Norway and Switzerland, some candidate and potential candidate countries. The data are collected by domain of activity (annex) : Annex I - Services, Annex II - Industry, Annex III - Trade and Annex IV- Constructions and by datasets. Each annex contains several datasets as indicated in the SBS Regulation. The majority of the data is collected by National Statistical Institutes (NSIs) by means of statistical surveys, business registers or from various administrative sources. Regulatory or controlling national offices for financial institutions or central banks often provide the information required for the financial sector (NACE Rev 2 Section K / NACE Rev 1.1 Section J). Member States apply various statistical methods, according to the data source, such as grossing up, model based estimation or different forms of imputation, to ensure the quality of SBSs produced. Main characteristics (variables) of the SBS data category:Business Demographic variables (e.g. Number of enterprises)"Output related" variables (e.g. Turnover, Value added)"Input related" variables: labour input (e.g. Employment, Hours worked); goods and services input (e.g. Total of purchases); capital input (e.g. Material investments) All SBS characteristics are published on Eurostat’s website by tables and an example of the existent tables is presented below:Annual enterprise statistics: Characteristics collected are published by country and detailed on NACE Rev 2 and NACE Rev 1.1 class level (4-digits). Some classes or groups in 'services' section have been aggregated.Annual enterprise statistics broken down by size classes: Characteristics are published by country and detailed down to NACE Rev 2 and NACE Rev 1.1 group level (3-digits) and employment size class. For trade (NACE Rev 2 and NACE Rev 1.1 Section G) a supplementary breakdown by turnover size class is available.Annual regional statistics: Four characteristics are published by NUTS-2 country region and detailed on NACE Rev 2 and NACE Rev 1.1 division level (2-digits) (but to group level (3-digits) for the trade section). More information on the contents of different tables: the detail level and breakdowns required starting with the reference year 2008 is defined in Commission Regulation N° 251/2009. For previous reference years it is included in Commission Regulations (EC) N° 2701/98 and amended by Commission Regulation N°1614/2002 and Commission Regulation N°1669/2003. Several important derived indicators are generated in the form of ratios of certain monetary characteristics or per head values. A list with the available derived indicators is available below in the Annex.

Structural business statistics (SBS) describes the structure, conduct and performance of economic activities, down to the most detailed activity level (several hundred economic sectors). SBS are transmitted annually by the EU Member States on the basis of a legal obligation from 1995 onwards.   SBS covers all activities of the business economy with the exception of agricultural activities and personal services and the data are provided by all EU Member States, Norway and Switzerland, some candidate and potential candidate countries. The data are collected by domain of activity (annex) : Annex I - Services, Annex II - Industry, Annex III - Trade and Annex IV- Constructions and by datasets. Each annex contains several datasets as indicated in the SBS Regulation. The majority of the data is collected by National Statistical Institutes (NSIs) by means of statistical surveys, business registers or from various administrative sources. Regulatory or controlling national offices for financial institutions or central banks often provide the information required for the financial sector (NACE Rev 2 Section K / NACE Rev 1.1 Section J). Member States apply various statistical methods, according to the data source, such as grossing up, model based estimation or different forms of imputation, to ensure the quality of SBSs produced. Main characteristics (variables) of the SBS data category: Business Demographic variables (e.g. Number of enterprises)"Output related" variables (e.g. Turnover, Value added)"Input related" variables: labour input (e.g. Employment, Hours worked); goods and services input (e.g. Total of purchases); capital input (e.g. Material investments) All SBS characteristics are published on Eurostat’s website by tables and an example of the existent tables is presented below: Annual enterprise statistics: Characteristics collected are published by country and detailed on NACE Rev 2 and NACE Rev 1.1 class level (4-digits). Some classes or groups in 'services' section have been aggregated.Annual enterprise statistics broken down by size classes: Characteristics are published by country and detailed down to NACE Rev 2 and NACE Rev 1.1 group level (3-digits) and employment size class. For trade (NACE Rev 2 and NACE Rev 1.1 Section G) a supplementary breakdown by turnover size class is available.Annual regional statistics: Four characteristics are published by NUTS-2 country region and detailed on NACE Rev 2 and NACE Rev 1.1 division level (2-digits) (but to group level (3-digits) for the trade section). More information on the contents of different tables: the detail level and breakdowns required starting with the reference year 2008 is defined in Commission Regulation N° 251/2009. For previous reference years it is included in Commission Regulations (EC) N° 2701/98 and amended by Commission Regulation N°1614/2002 and Commission Regulation N°1669/2003. Several important derived indicators are generated in the form of ratios of certain monetary characteristics or per head values. A list with the available derived indicators is available below in the Annex.

Structural business statistics (SBS) describes the structure, conduct and performance of economic activities, down to the most detailed activity level (several hundred economic sectors). SBS are transmitted annually by the EU Member States on the basis of a legal obligation from 1995 onwards.   SBS covers all activities of the business economy with the exception of agricultural activities and personal services and the data are provided by all EU Member States, Norway and Switzerland, some candidate and potential candidate countries. The data are collected by domain of activity (annex) : Annex I - Services, Annex II - Industry, Annex III - Trade and Annex IV- Constructions and by datasets. Each annex contains several datasets as indicated in the SBS Regulation. The majority of the data is collected by National Statistical Institutes (NSIs) by means of statistical surveys, business registers or from various administrative sources. Regulatory or controlling national offices for financial institutions or central banks often provide the information required for the financial sector (NACE Rev 2 Section K / NACE Rev 1.1 Section J). Member States apply various statistical methods, according to the data source, such as grossing up, model based estimation or different forms of imputation, to ensure the quality of SBSs produced. Main characteristics (variables) of the SBS data category:Business Demographic variables (e.g. Number of enterprises)"Output related" variables (e.g. Turnover, Value added)"Input related" variables: labour input (e.g. Employment, Hours worked); goods and services input (e.g. Total of purchases); capital input (e.g. Material investments) All SBS characteristics are published on Eurostat’s website by tables and an example of the existent tables is presented below:Annual enterprise statistics: Characteristics collected are published by country and detailed on NACE Rev 2 and NACE Rev 1.1 class level (4-digits). Some classes or groups in 'services' section have been aggregated.Annual enterprise statistics broken down by size classes: Characteristics are published by country and detailed down to NACE Rev 2 and NACE Rev 1.1 group level (3-digits) and employment size class. For trade (NACE Rev 2 and NACE Rev 1.1 Section G) a supplementary breakdown by turnover size class is available.Annual regional statistics: Four characteristics are published by NUTS-2 country region and detailed on NACE Rev 2 and NACE Rev 1.1 division level (2-digits) (but to group level (3-digits) for the trade section). More information on the contents of different tables: the detail level and breakdowns required starting with the reference year 2008 is defined in Commission Regulation N° 251/2009. For previous reference years it is included in Commission Regulations (EC) N° 2701/98 and amended by Commission Regulation N°1614/2002 and Commission Regulation N°1669/2003. Several important derived indicators are generated in the form of ratios of certain monetary characteristics or per head values. A list with the available derived indicators is available below in the Annex.

Structural business statistics (SBS) describes the structure, conduct and performance of economic activities, down to the most detailed activity level (several hundred economic sectors). SBS are transmitted annually by the EU Member States on the basis of a legal obligation from 1995 onwards.   SBS covers all activities of the business economy with the exception of agricultural activities and personal services and the data are provided by all EU Member States, Norway and Switzerland, some candidate and potential candidate countries. The data are collected by domain of activity (annex) : Annex I - Services, Annex II - Industry, Annex III - Trade and Annex IV- Constructions and by datasets. Each annex contains several datasets as indicated in the SBS Regulation. The majority of the data is collected by National Statistical Institutes (NSIs) by means of statistical surveys, business registers or from various administrative sources. Regulatory or controlling national offices for financial institutions or central banks often provide the information required for the financial sector (NACE Rev 2 Section K / NACE Rev 1.1 Section J). Member States apply various statistical methods, according to the data source, such as grossing up, model based estimation or different forms of imputation, to ensure the quality of SBSs produced. Main characteristics (variables) of the SBS data category:Business Demographic variables (e.g. Number of enterprises)"Output related" variables (e.g. Turnover, Value added)"Input related" variables: labour input (e.g. Employment, Hours worked); goods and services input (e.g. Total of purchases); capital input (e.g. Material investments) All SBS characteristics are published on Eurostat’s website by tables and an example of the existent tables is presented below:Annual enterprise statistics: Characteristics collected are published by country and detailed on NACE Rev 2 and NACE Rev 1.1 class level (4-digits). Some classes or groups in 'services' section have been aggregated.Annual enterprise statistics broken down by size classes: Characteristics are published by country and detailed down to NACE Rev 2 and NACE Rev 1.1 group level (3-digits) and employment size class. For trade (NACE Rev 2 and NACE Rev 1.1 Section G) a supplementary breakdown by turnover size class is available.Annual regional statistics: Four characteristics are published by NUTS-2 country region and detailed on NACE Rev 2 and NACE Rev 1.1 division level (2-digits) (but to group level (3-digits) for the trade section). More information on the contents of different tables: the detail level and breakdowns required starting with the reference year 2008 is defined in Commission Regulation N° 251/2009. For previous reference years it is included in Commission Regulations (EC) N° 2701/98 and amended by Commission Regulation N°1614/2002 and Commission Regulation N°1669/2003. Several important derived indicators are generated in the form of ratios of certain monetary characteristics or per head values. A list with the available derived indicators is available below in the Annex.

Structural business statistics (SBS) describes the structure, conduct and performance of economic activities, down to the most detailed activity level (several hundred economic sectors). SBS are transmitted annually by the EU Member States on the basis of a legal obligation from 1995 onwards.   SBS covers all activities of the business economy with the exception of agricultural activities and personal services and the data are provided by all EU Member States, Norway and Switzerland, some candidate and potential candidate countries. The data are collected by domain of activity (annex) : Annex I - Services, Annex II - Industry, Annex III - Trade and Annex IV- Constructions and by datasets. Each annex contains several datasets as indicated in the SBS Regulation. The majority of the data is collected by National Statistical Institutes (NSIs) by means of statistical surveys, business registers or from various administrative sources. Regulatory or controlling national offices for financial institutions or central banks often provide the information required for the financial sector (NACE Rev 2 Section K / NACE Rev 1.1 Section J). Member States apply various statistical methods, according to the data source, such as grossing up, model based estimation or different forms of imputation, to ensure the quality of SBSs produced. Main characteristics (variables) of the SBS data category:Business Demographic variables (e.g. Number of enterprises)"Output related" variables (e.g. Turnover, Value added)"Input related" variables: labour input (e.g. Employment, Hours worked); goods and services input (e.g. Total of purchases); capital input (e.g. Material investments) All SBS characteristics are published on Eurostat’s website by tables and an example of the existent tables is presented below:Annual enterprise statistics: Characteristics collected are published by country and detailed on NACE Rev 2 and NACE Rev 1.1 class level (4-digits). Some classes or groups in 'services' section have been aggregated.Annual enterprise statistics broken down by size classes: Characteristics are published by country and detailed down to NACE Rev 2 and NACE Rev 1.1 group level (3-digits) and employment size class. For trade (NACE Rev 2 and NACE Rev 1.1 Section G) a supplementary breakdown by turnover size class is available.Annual regional statistics: Four characteristics are published by NUTS-2 country region and detailed on NACE Rev 2 and NACE Rev 1.1 division level (2-digits) (but to group level (3-digits) for the trade section). More information on the contents of different tables: the detail level and breakdowns required starting with the reference year 2008 is defined in Commission Regulation N° 251/2009. For previous reference years it is included in Commission Regulations (EC) N° 2701/98 and amended by Commission Regulation N°1614/2002 and Commission Regulation N°1669/2003. Several important derived indicators are generated in the form of ratios of certain monetary characteristics or per head values. A list with the available derived indicators is available below in the Annex.

The data correspond to quarterly financial accounts for the general government sector and follows the ESA2010 methodology. The data covers financial transactions and balance sheet items for general government (consolidated and non-consolidated) and its subsectors. This includes a number of financial instruments (F.1, F.2, F.3, F.4, ...) as well as some balancing items such as net financial transactions, net financial worth and net financial assets and liabilties. Data are available in million of euro, million of national currency (average exchange rates are used for transactions and end of period exchange rates are used for stocks) and as a percentage of GDP (for transactions quarterly GDP is used; for stocks a rolling sum of the last four quarters is used). In the table gov_10a_ggfa, annualised quarterly financial accounts for general government are presented. For financial transactions, data is summed over the four quarters of each year. For the conversion from national currency into euro, the yearly average exchange rate is used. For balance sheet items (stocks), the annualised data corresponds to the data of the fourth quarter. The percentage of GDP data of annualised data uses annual GDP transmitted by the Member States. In the course of the annualisation, small rounding differences may be amplified. Geographic coverage: EU and euro area. Main data sources are the tables provided according to the European Parliament and Council Regulation (EU) N° 549/2013 of 21 May 2013 (OJ No L174/1).

Yearly data on freshwater resources, water abstraction and use, wastewater treatment (connection rates of resident population to wastewater treatment and treatment capacities of wastewater treatment plants), sewage sludge production and disposal, generation and discharge of wastewater collected biennially by means of the OECD/Eurostat Joint Questionnaire - Inland Waters. Data aggregation: national territories.

Eurostat collects road transport statistics by two means: 1. Data on infrastructure, transport equipment, enterprises, economic performance, employment, traffic, aggregated data on transport of passengers and goods as well as data on accidents are collected using the Common Questionnaire of the United Nations Economic Commission for Europe (UNECE), Eurostat and the International Transport Forum (ITF, in the framework of OECD). The method of the Common Questionnaire data collection is presented in a separate document. 2. Data on carriage of goods by road, using heavy goods vehicles, are based on a continuum of legal acts: 2.1 Data collection on carriage of goods by road until 1998 (included) was based on Directives 78/546/EEC and 89/462/EEC and covered tonnes and tonne-kilometres only. 2.2 Data since the reference period 1999 are derived from micro-data collected in the framework of Regulation (EU) No 70/2012 of the European parliament and of the council on statistical returns in respect of the carriage of goods by road, a recast of Council Regulation (EC) 1172/98 which has replaced the previous Directives. The figures are aggregated on the basis of sample surveys carried out by the reporting countries. The data cover tonnes, tonne-kilometres, vehicle-kilometres and numbers of journeys. These metadata pages only refer to road freight statistics based on the European Union's legal acts (point 2 above) and, in particular, to the data for reference years 1999 and after (2.2).

Eurostat collects road transport statistics by two means: 1. Data on infrastructure, transport equipment, enterprises, economic performance, employment, traffic, aggregated data on transport of passengers and goods as well as data on accidents are collected using the Common Questionnaire of the United Nations Economic Commission for Europe (UNECE), Eurostat and the International Transport Forum (ITF, in the framework of OECD). The method of the Common Questionnaire data collection is presented in a separate document. 2. Data on carriage of goods by road, using heavy goods vehicles, are based on a continuum of legal acts: 2.1 Data collection on carriage of goods by road until 1998 (included) was based on Directives 78/546/EEC and 89/462/EEC and covered tonnes and tonne-kilometres only. 2.2 Data since the reference period 1999 are derived from micro-data collected in the framework of Regulation (EU) No 70/2012 of the European parliament and of the council on statistical returns in respect of the carriage of goods by road, a recast of Council Regulation (EC) 1172/98 which has replaced the previous Directives. The figures are aggregated on the basis of sample surveys carried out by the reporting countries. The data cover tonnes, tonne-kilometres, vehicle-kilometres and numbers of journeys. These metadata pages only refer to road freight statistics based on the European Union's legal acts (point 2 above) and, in particular, to the data for reference years 1999 and after (2.2).

Eurostat's database covers 1) Production and trade in roundwood and wood products, including primary and secondary products 2) Economic data on forestry and logging, including employment data 3) Sustainable forest management, comprising forest resources (assets) and environmental data. The main types of primary forest products included in (1) are: roundwood, sawnwood, wood-based panels, pulp, and paper and paperboard. Secondary products include further processed wood and paper products. These products are presented in greater detail; definitions are available. All of the data are compiled from the Joint Forest Sector Questionnaire (JFSQ), except for table (e), which is directly extracted from Eurostat's international trade database COMEXT (HS/CN Chapter 44). The tables in (1) cover details of the following topics: - Roundwood removals and production by type of wood and assortment (a) - Roundwood production by type of ownership (b) - Production and trade in roundwood, fuelwood and other basic products (c) - Trade in industrial roundwood by assortment and species (d) - Tropical wood imports to the EU from Chapter 44 of the Harmonised System (e) - Production and trade in sawnwood, panels and other primary products (f) - Sawnwood trade by species (g) - Production and trade in pulp and paper & paperboard (h) - Trade in secondary wood and paper products (i) Data in (2) include the output, intermediate consumption, gross value added, fixed capital consumption, gross fixed capital formation and different measures of income of forestry and logging.  The data are in current basic prices and are compatible with National Accounts. They are collected as part of Intergrated environmental and economic accounting for forests (IEEAF), which also covers labour input in annual work units (AWU).  Under (2), two separate tables cover the number of employees of forestry and logging, the manufacture of wood and products of wood and cork, and the manufacture of paper and paper products, as estimated from the Labour Force Survey results. There are two separate tables because of the change in the EU's classification of economic activities from NACE Rev. 1.1 to NACE Rev. 2 in 2008. More detailed information on wood products and accounting, including definitions and questionnaires, can be found on our open-access communication platform under the interest group 'Forestry statistics and accounts'.  Data in (3) are not collected by Eurostat, but by the FAO, UNECE, Forest Euope, the European Commission's departments for Environment and the Joint Research Centre. They include forest area, wood volume, defoliation on sample plots, fires and areas with protective functions.

Data are the result of the annual structure of government debt survey and cover the EU countries as well as Norway. The following series are available: Central government gross debt by initital maturity and sector of debt holder; State government gross debt by initital maturity and sector of debt holder; Local government gross debt by initital maturity and sector of debt holder; Social security funds gross debt by initital maturity and sector of debt holder; General government gross debt by initital maturity and sector of debt holder; Debt by currency of issuance; Government guarantees (contingent liabilities); Average remaining maturity of debt; Apparent cost of the debt; Market value of debt.

Data source used: The aquaculture production data collection is part of the more comprehensive data gathering carried out on an annual basis by the Fisheries Committee (COFI) of the Trade and Agriculture Directorate (TAD) from OECD members and participating non-OECD economies. Data on marine landings, aquaculture production, inland fisheries catch, fleet, employment, total allowable catch (TAC) and fisheries support estimate (FSE) are collected from Fisheries Ministries, National Statistics Offices and other institutions designated as an official data source. The surveys used for this exercise are the OECD Fisheries questionnaires.

Total production of fish, crustaceans, molluscs and other aquatic organisms from aquaculture ("fish-farming") from 2008 onwards, excluding hatcheries and nurseries. The data are expressed in Tonnes Live Weight (TLW, incl. shells, skeletons, etc.), Euro and Euro/Tonne. Production data from hatcheries and nurseries can be found in the fish_aq2b, fish_aq4a and fish_aq4b tables. Older data (1950-2007), excluding production from hatcheries and nurseries, are contained in the "Aquaculture production until 2007 (fish_aq08)" database.

AQUASTAT is FAO's global information system on water and agriculture, developed by the Land and Water Division. The main mandate of the program is to collect, analyze and disseminate information on water resources, water uses, and agricultural water management with an emphasis on countries in Africa, Asia, Latin America and the Caribbean. This allows interested users to find comprehensive and regularly updated information at global, regional, and national levels.

Recent Stress Ranking can be checked here: https://knoema.com/WRINWS2020/national-water-stress-rankings Suggested citation: Luo, T., R. Young, and P. Reig. 2015. "Aqueduct projected water stress rankings." Technical note. Washington, DC: World Resources Institute, August 215. Available online at http://www.wri.org/publication/aqueduct-projected-water-stress-country-rankings.    Supplemental Materials: Country Scores                         WRI projected future country-level water stress for 2020, 2030, and 2040 under business-as-usual (BAU), optimistic, and pessimistic scenarios. Each tab lists country projected water stress scores for each scenario and year, weighted by overall water withdrawals. Scores weighted by individual sectors (agricultural, domestic, and industrial) are provided as well.   These global projections are best suited to making comparisons among countries for the same year and among scenarios and decades for the same region. More detailed and localized data or scenarios can better estimate potential outcomes for specific regions and expose large sub-national variations that are subsumed under countrywide water-stress values. The country indicators face persistent limitations in attempting to simplify complex information, such as spatial and temporal variations, into a single number. They also do not account for the governance and investment structure of the water sector in different countries.    It is important to note the inherent uncertainty in estimating any future conditions, particularly those associated with climate change, future population and economic trends, and water demand. Additionally, care should be taken when examining the change rates of a country’s projected stress levels between one year and another, because the risk-score thresholds are not linear. For more information on these limitations, see the technical note.   Projections are described in further detail in: Luck, M., M. Landis, and F. Gassert, “Aqueduct Water Stress Projections: Decadal Projections of Water Supply and Demand Using CMIP5 GCMs,” Technical note (Washington, DC: World Resources Institute, April 2015), http://www.wri.org/publication/aqueduct-water-stress-projections.   Water Stress withdrawals / available flow Water stress measures total annual water withdrawals (municipal, industrial, and agricultural) expressed as a percentage of the total annual available blue water. Higher values indicate more competition among users. Score Value [0-1) Low (<10%) [1-2) Low to medium (10-20%) [2-3) Medium to high (20-40%) [3-4) High (40-80%) [4-5] Extremely high (>80%)    

Eurostat's database covers 1) Production and trade in roundwood and wood products, including primary and secondary products 2) Economic data on forestry and logging, including employment data 3) Sustainable forest management, comprising forest resources (assets) and environmental data. The main types of primary forest products included in (1) are: roundwood, sawnwood, wood-based panels, pulp, and paper and paperboard. Secondary products include further processed wood and paper products. These products are presented in greater detail; definitions are available. All of the data are compiled from the Joint Forest Sector Questionnaire (JFSQ), except for table (e), which is directly extracted from Eurostat's international trade database COMEXT (HS/CN Chapter 44). The tables in (1) cover details of the following topics: - Roundwood removals and production by type of wood and assortment (a) - Roundwood production by type of ownership (b) - Production and trade in roundwood, fuelwood and other basic products (c) - Trade in industrial roundwood by assortment and species (d) - Tropical wood imports to the EU from Chapter 44 of the Harmonised System (e) - Production and trade in sawnwood, panels and other primary products (f) - Sawnwood trade by species (g) - Production and trade in pulp and paper & paperboard (h) - Trade in secondary wood and paper products (i) Data in (2) include the output, intermediate consumption, gross value added, fixed capital consumption, gross fixed capital formation and different measures of income of forestry and logging.  The data are in current basic prices and are compatible with National Accounts. They are collected as part of Intergrated environmental and economic accounting for forests (IEEAF), which also covers labour input in annual work units (AWU).  Under (2), two separate tables cover the number of employees of forestry and logging, the manufacture of wood and products of wood and cork, and the manufacture of paper and paper products, as estimated from the Labour Force Survey results. There are two separate tables because of the change in the EU's classification of economic activities from NACE Rev. 1.1 to NACE Rev. 2 in 2008. More detailed information on wood products and accounting, including definitions and questionnaires, can be found on our open-access communication platform under the interest group 'Forestry statistics and accounts'.  Data in (3) are not collected by Eurostat, but by the FAO, UNECE, Forest Euope, the European Commission's departments for Environment and the Joint Research Centre. They include forest area, wood volume, defoliation on sample plots, fires and areas with protective functions.

The domain "Income and living conditions" covers four topics: people at risk of poverty or social exclusion, income distribution and monetary poverty, living conditions and material deprivation, which are again structured into collections of indicators on specific topics. The collection "People at risk of poverty or social exclusion" houses main indicator on risk of poverty or social inclusion included in the Europe 2020 strategy as well as the intersections between sub-populations of all Europe 2020 indicators on poverty and social exclusion. The collection "Income distribution and monetary poverty" houses collections of indicators relating to poverty risk, poverty risk of working individuals as well as the distribution of income. The collection "Living conditions" hosts indicators relating to characteristics and living conditions of households, characteristics of the population according to different breakdowns, health and labour conditions, housing conditions as well as childcare related indicators. The collection "Material deprivation" covers indicators relating to economic strain, durables, housing deprivation and environment of the dwelling.

Accommodation statistics are a key part of the system of tourism statistics in the EU and have a long history of data collection. Annex I of the Regulation (EU) 692/2011 of the European Parliament and of the Council deals with accommodation statistics and includes 4 sections focusing on accommodation statistics of which sections 1 and 2 include the requirements concerning rented accommodation (capacity and occupancy respectively). Data are collected by the competent national authorities of the Member States and are compiled according to a harmonised methodology established by EU regulations before transmission to Eurostat. Most of the time, data are collected via sample or census surveys. However, in a few cases data are compiled from a demand-side perspective (i.e. via visitor surveys or border surveys). Surveys on the occupancy of accommodation establishments are generally conducted on a monthly basis. The concepts and definitions used in the collection of data shall conform to the specifications described in the Methodological manual for tourism statistics. Accommodation statistics comprise the following information: Monthly data on tourism industries (NACE 55.1, 55.2 and 55.3) Monthly occupancy of tourist accommodation establishments: arrivals and nights spent by residents and non-residents Net occupancy rate of bed-places and bedrooms in hotels and similar accommodation Annual data on tourism industries(NACE 55.1, 55.2 and 55.3) Occupancy of tourist accommodation establishments: arrivals and nights spent by residents and non-residents Capacity of tourist accommodation establishments: number of establishments, bedrooms and bed places Regional data  Annual occupancy (arrivals and nights spent by residents and non-residents) of tourist accommodation establishments at NUTS 2 level, by degree of urbanisation and by coastal/non-coastal area Annual data on number of establishments, bedrooms and bed places at NUTS 2 level, by degree of urbanisation and by coastal/non-coastal area Data on number of establishments, bedrooms and bed places are available by activity at NUTS 3 level until 2011. Please note that for paragraphs where no metadata for regional data has been specified, the regional metadata is identical to the metadata provided for the national data.

APD Regional Economic Outlook (REO) provides information on recent economic developments and prospects for countries in Asia and Pacific. Data for the REO for Asia and Pacific is prepared in conjunction with the semi-annual World Economic Outlook (WEO) exercises, spring and fall. Data are consistent with the projections underlying the WEO. REO aggregate data may differ from WEO aggregates due to differences in group membership. Composite data for country groups are weighted averages of data for individual countries. Arithmetic weighted averages are used for all concepts except for inflation and broad money, for which geometric averages are used. PPP GDP weights from the WEO database are used for the aggregation of real GDP growth, real non-oil GDP growth, real per capita GDP growth, investment, national savings, broad money, claims on the nonfinancial private sector, and real and nominal effective exchange rates. Aggregates for other concepts are weighted by GDP in U.S. dollars at market exchange rates.

Data series on asylum applications contain statistical information based on Article 4 of the Council Regulation (EC) No 862/2007 with reference to:Asylum and first time asylum applicants by age, sex and citizenshipPersons subject to applications pending at the end of reference period by age, sex and citizenshipApplications for asylum withdrawn by age, sex and citizenshipAsylum applicants considered to be unaccompanied minors by age, sex and citizenship These data are supplied to Eurostat by the national Ministries of Interior and related official agencies. Data is presented by country and for groups of countries: the European Union (EU28, EU27) and the European Free Trade Association (EFTA). Data has been rounded to the nearest 5.

Data series on asylum applications contain statistical information based on Article 4 of the Council Regulation (EC) No 862/2007 with reference to:Asylum and first time asylum applicants by age, sex and citizenshipPersons subject to applications pending at the end of reference period by age, sex and citizenshipApplications for asylum withdrawn by age, sex and citizenshipAsylum applicants considered to be unaccompanied minors by age, sex and citizenship These data are supplied to Eurostat by the national Ministries of Interior and related official agencies. Data is presented by country and for groups of countries: the European Union (EU28, EU27) and the European Free Trade Association (EFTA). Data has been rounded to the nearest 5.

Asylum applicant means a person having submitted an application for international protection or having been included in such application as a family member during the reference period. New asylum applicant means a person having submitted an application for international protection for the first time

Asylum applicant means a person having submitted an application for international protection or having been included in such application as a family member during the reference period. New asylum applicant means a person having submitted an application for international protection for the first time.

Asylum applicants considered to be unaccompanied minors.

Data series on asylum applications contain statistical information based on Article 4 of the Council Regulation (EC) No 862/2007 with reference to:Asylum and first time asylum applicants by age, sex and citizenshipPersons subject to applications pending at the end of reference period by age, sex and citizenshipApplications for asylum withdrawn by age, sex and citizenshipAsylum applicants considered to be unaccompanied minors by age, sex and citizenship These data are supplied to Eurostat by the national Ministries of Interior and related official agencies. Data is presented by country and for groups of countries: the European Union (EU28, EU27) and the European Free Trade Association (EFTA). Data has been rounded to the nearest 5.

The indicator shows the number of first-time asylum applicants per million inhabitants and the number of positive first instance decisions per million inhabitants. Please note that caution is required when comparing these two values, since applications received in a given year might not be processed until a later year. Source data are supplied to Eurostat by the national Ministries of Interior and related official agencies. A first-time applicant for international protection is a person who lodged an application for asylum for the first time in a given Member State. First instance decisions are decisions granted by the respective authority acting as a first instance of the administrative/judicial asylum procedure in the receiving country.

The sum of elderly (65+) who are: at-risk-of-poverty or severely materially deprived or living in (quasi-)jobless households (i.e. with very low work intensity) as a share of the total population in the same age group.

The share of persons with an equivalised disposable income below the risk-of-poverty threshold, which is set at 60 % of the national median equivalised disposable income (after social transfers).

The domain "Income and living conditions" covers four topics: people at risk of poverty or social exclusion, income distribution and monetary poverty, living conditions and material deprivation, which are again structured into collections of indicators on specific topics. The collection "People at risk of poverty or social exclusion" houses main indicator on risk of poverty or social inclusion included in the Europe 2020 strategy as well as the intersections between sub-populations of all Europe 2020 indicators on poverty and social exclusion. The collection "Income distribution and monetary poverty" houses collections of indicators relating to poverty risk, poverty risk of working individuals as well as the distribution of income. The collection "Living conditions" hosts indicators relating to characteristics and living conditions of households, characteristics of the population according to different breakdowns, health and labour conditions, housing conditions as well as childcare related indicators. The collection "Material deprivation" covers indicators relating to economic strain, durables, housing deprivation and environment of the dwelling.

The domain "Income and living conditions" covers four topics: people at risk of poverty or social exclusion, income distribution and monetary poverty, living conditions and material deprivation, which are again structured into collections of indicators on specific topics. The collection "People at risk of poverty or social exclusion" houses main indicator on risk of poverty or social inclusion included in the Europe 2020 strategy as well as the intersections between sub-populations of all Europe 2020 indicators on poverty and social exclusion. The collection "Income distribution and monetary poverty" houses collections of indicators relating to poverty risk, poverty risk of working individuals as well as the distribution of income. The collection "Living conditions" hosts indicators relating to characteristics and living conditions of households, characteristics of the population according to different breakdowns, health and labour conditions, housing conditions as well as childcare related indicators. The collection "Material deprivation" covers indicators relating to economic strain, durables, housing deprivation and environment of the dwelling.

The at-risk-of-poverty rate is the share of people with an equivalised disposable income (after social transfer) below the at-risk-of-poverty threshold, which is set at 60 % of the national median equivalised disposable income after social transfers. This indicator does not measure wealth or poverty, but low income in comparison to other residents in that country, which does not necessarily imply a low standard of living.

The share of persons with an equivalised disposable income below the risk-of-poverty threshold, which is set at 60 % of the national median equivalised disposable income (after social transfers).

The domain "Income and living conditions" covers four topics: people at risk of poverty or social exclusion, income distribution and monetary poverty, living conditions and material deprivation, which are again structured into collections of indicators on specific topics. The collection "People at risk of poverty or social exclusion" houses main indicator on risk of poverty or social inclusion included in the Europe 2020 strategy as well as the intersections between sub-populations of all Europe 2020 indicators on poverty and social exclusion. The collection "Income distribution and monetary poverty" houses collections of indicators relating to poverty risk, poverty risk of working individuals as well as the distribution of income. The collection "Living conditions" hosts indicators relating to characteristics and living conditions of households, characteristics of the population according to different breakdowns, health and labour conditions, housing conditions as well as childcare related indicators. The collection "Material deprivation" covers indicators relating to economic strain, durables, housing deprivation and environment of the dwelling.

The domain "Income and living conditions" covers four topics: people at risk of poverty or social exclusion, income distribution and monetary poverty, living conditions and material deprivation, which are again structured into collections of indicators on specific topics. The collection "People at risk of poverty or social exclusion" houses main indicator on risk of poverty or social inclusion included in the Europe 2020 strategy as well as the intersections between sub-populations of all Europe 2020 indicators on poverty and social exclusion. The collection "Income distribution and monetary poverty" houses collections of indicators relating to poverty risk, poverty risk of working individuals as well as the distribution of income. The collection "Living conditions" hosts indicators relating to characteristics and living conditions of households, characteristics of the population according to different breakdowns, health and labour conditions, housing conditions as well as childcare related indicators. The collection "Material deprivation" covers indicators relating to economic strain, durables, housing deprivation and environment of the dwelling.

The domain "Income and living conditions" covers four topics: people at risk of poverty or social exclusion, income distribution and monetary poverty, living conditions and material deprivation, which are again structured into collections of indicators on specific topics. The collection "People at risk of poverty or social exclusion" houses main indicator on risk of poverty or social inclusion included in the Europe 2020 strategy as well as the intersections between sub-populations of all Europe 2020 indicators on poverty and social exclusion. The collection "Income distribution and monetary poverty" houses collections of indicators relating to poverty risk, poverty risk of working individuals as well as the distribution of income. The collection "Living conditions" hosts indicators relating to characteristics and living conditions of households, characteristics of the population according to different breakdowns, health and labour conditions, housing conditions as well as childcare related indicators. The collection "Material deprivation" covers indicators relating to economic strain, durables, housing deprivation and environment of the dwelling.

The indicator is defined as the share of persons with an equivalised disposable income below the risk-of-poverty threshold, which is set at 60 % of the national median equivalised disposable income (after social transfers).

The domain "Income and living conditions" covers four topics: people at risk of poverty or social exclusion, income distribution and monetary poverty, living conditions and material deprivation, which are again structured into collections of indicators on specific topics. The collection "People at risk of poverty or social exclusion" houses main indicator on risk of poverty or social inclusion included in the Europe 2020 strategy as well as the intersections between sub-populations of all Europe 2020 indicators on poverty and social exclusion. The collection "Income distribution and monetary poverty" houses collections of indicators relating to poverty risk, poverty risk of working individuals as well as the distribution of income. The collection "Living conditions" hosts indicators relating to characteristics and living conditions of households, characteristics of the population according to different breakdowns, health and labour conditions, housing conditions as well as childcare related indicators. The collection "Material deprivation" covers indicators relating to economic strain, durables, housing deprivation and environment of the dwelling.

The share of persons with an equivalised disposable income below the risk-of-poverty threshold, which is set at 60 % of the national median equivalised disposable income (after social transfers). The indicator is based on the EU-SILC (statistics on income, social inclusion and living conditions).

The share of persons with an equivalised disposable income below the at-risk-of-poverty threshold, which is set at 60 % of the national median equivalised disposable income (after social transfers).

The share of persons with an equivalised disposable income, before social transfers, below the risk-of-poverty threshold, which is set at 60 % of the national median equivalised disposable income (after social transfers). Retirement and survivor's pensions are counted as income before transfers and not as social transfers.

The domain "Income and living conditions" covers four topics: people at risk of poverty or social exclusion, income distribution and monetary poverty, living conditions and material deprivation, which are again structured into collections of indicators on specific topics. The collection "People at risk of poverty or social exclusion" houses main indicator on risk of poverty or social inclusion included in the Europe 2020 strategy as well as the intersections between sub-populations of all Europe 2020 indicators on poverty and social exclusion. The collection "Income distribution and monetary poverty" houses collections of indicators relating to poverty risk, poverty risk of working individuals as well as the distribution of income. The collection "Living conditions" hosts indicators relating to characteristics and living conditions of households, characteristics of the population according to different breakdowns, health and labour conditions, housing conditions as well as childcare related indicators. The collection "Material deprivation" covers indicators relating to economic strain, durables, housing deprivation and environment of the dwelling.

The domain "Income and living conditions" covers four topics: people at risk of poverty or social exclusion, income distribution and monetary poverty, living conditions and material deprivation, which are again structured into collections of indicators on specific topics. The collection "People at risk of poverty or social exclusion" houses main indicator on risk of poverty or social inclusion included in the Europe 2020 strategy as well as the intersections between sub-populations of all Europe 2020 indicators on poverty and social exclusion. The collection "Income distribution and monetary poverty" houses collections of indicators relating to poverty risk, poverty risk of working individuals as well as the distribution of income. The collection "Living conditions" hosts indicators relating to characteristics and living conditions of households, characteristics of the population according to different breakdowns, health and labour conditions, housing conditions as well as childcare related indicators. The collection "Material deprivation" covers indicators relating to economic strain, durables, housing deprivation and environment of the dwelling.

The threshold is set at 60 % of the national median equivalised disposable income (after social transfers). It is expressed in Purchase Parity Standards (PPS) in order to take into account differences in cost of living across EU Member States.

The at-risk-of-poverty threshold is set at 60 % of national median equivalised disposable income.

Eurostat Dataset Id:ilc_li01 The domain "Income and living conditions" covers four topics: people at risk of poverty or social exclusion, income distribution and monetary poverty, living conditions and material deprivation, which are again structured into collections of indicators on specific topics. The collection "People at risk of poverty or social exclusion" houses main indicator on risk of poverty or social inclusion included in the Europe 2020 strategy as well as the intersections between sub-populations of all Europe 2020 indicators on poverty and social exclusion. The collection "Income distribution and monetary poverty" houses collections of indicators relating to poverty risk, poverty risk of working individuals as well as the distribution of income. The collection "Living conditions" hosts indicators relating to characteristics and living conditions of households, characteristics of the population according to different breakdowns, health and labour conditions, housing conditions as well as childcare related indicators. The collection "Material deprivation" covers indicators relating to economic strain, durables, housing deprivation and environment of the dwelling.

The estimated resident population (ERP) is the official measure of the Australian population. This dataset contains annual ERP by country of birth, age and sex at the Australia level. At the state/territory level it is available for Census years only. Population_Estimates:_Concepts,_Sources_and_Methods_2009

This dataset presents the average number of students in a class by type of institution.

The average effective age of retirement is calculated as a weighted average of (net) withdrawals from the labour market at different ages over a 5-year period for workers initially aged 40 and over. In order to abstract from compositional effects in the age structure of the population, labour force withdrawals are estimated based on changes in labour force participation rates rather than labour force levels. These changes are calculated for each (synthetic) cohort divided into 5-year age groups. The estimates shown in red are less reliable as they have been derived from interpolations of census data rather than from annual labour force surveys. The estimates for women in Turkey are based on 3-yearly moving averages of participation rates for each 5-year age group. OECD estimates based on the results of national labour force surveys, the European Union Labour Force Survey and, for earlier years in some countries, national censuses.

Data are the result of the annual structure of government debt survey and cover the EU countries as well as Norway. The following series are available: Central government gross debt by initital maturity and sector of debt holder; State government gross debt by initital maturity and sector of debt holder; Local government gross debt by initital maturity and sector of debt holder; Social security funds gross debt by initital maturity and sector of debt holder; General government gross debt by initital maturity and sector of debt holder; Debt by currency of issuance; Government guarantees (contingent liabilities); Average remaining maturity of debt; Apparent cost of the debt; Market value of debt.

Data cited at: Balance of Payments, The International Monetary Fund. The Balance of Payments provides a framework that is applicable for a range of economies, from the smallest and least developed economies to the more advanced and complex economies. As a result, it is recognized that some items may not be relevant in all cases. The balance of payments is a statistical statement that summarizes transactions between residents and nonresidents during a period. It consists of the goods and services account, the primary income account, the secondary income account, the capital account, and the financial account. Contains balance of payments and international investment position (IIP) data of individual countries, jurisdictions, and other reporting entities, and regional and world totals for major components of the balance of payments. Both balance of payments and IIP data are presented in accordance with the standard components of the sixth edition of the Balance of Payments and International Investment Position Manual, BPM6. Balance of payments data are available for approximately 192 economies and international investment position data are available for approximately 152 economies.

The balance of payments is a statistical statement that provides a systematic summary of economic transactions of an economy with the rest of the world, for a specific time period. The transactions are for the most part between residents and non-residents of the economy. A transaction is defined as an economic flow that reflects the creation, transformation, exchange, transfer, or extinction of economic value and involves changes in ownership, of goods or assets, the provision of services, labour or capital.  This dataset presents countries compiling balance of payments statistics in accordance with the 6th edition of the Balance of Payments and International Investment Position Manual published by the IMF (BPM6). Transactions include: the goods and services accounts, the primary income account (income account in BPM5), the secondary income account (transfers in BPM5), the capital account, and the financial account. Changes in BPM6 compared to BPM5 are often a consequence of a stricter application of the change of ownership principle in particular in the goods and services accounts. They relate to transactions on goods and services (merchanting, goods for processing, Insurance), income (investment income), and financial operations (direct investment) .

Since the collection of 2009 data, the scope of the OECD Global Insurance Statistics questionnaire has been expanded. These changes led to the collection of key balance sheet and income statement items for direct insurance and reinsurance sectors, such as: gross claims paid, outstanding claims provision (changes), gross operating expenses, commissions, total assets, gross technical provisions (of which: unit-linked), shareholder equity, net income.

National accounts are a coherent and consistent set of macroeconomic indicators, which provide an overall picture of the economic situation and are widely used for economic analysis and forecasting, policy design and policy making. Eurostat publishes annual and quarterly national accounts, annual and quarterly sector accounts as well as supply, use and input-output tables, which are each presented with associated metadata. Annual national accounts are compiled in accordance with the European System of Accounts - ESA 2010 as defined in Annex B of the Council Regulation (EU) No 549/2013 of the European Parliament and of the Council of 21 May 2013.   The previous European System of Accounts, ESA95, was reviewed to bring national accounts in the European Union, in line with new economic environment, advances in methodological research and needs of users and the updated national accounts framework at the international level, the SNA 2008. The revisions are reflected in an updated Regulation of the European Parliament and of the Council on the European system of national and regional accounts in the European Union of 2010 (ESA 2010). The associated transmission programme is also updated and data transmissions in accordance with ESA 2010 are compulsory from September 2014 onwards. Further information on the transition from ESA 95 to ESA 2010 is presented on the Eurostat website. The domain consists of the following collections:   1. Main GDP aggregates: main components from the output, expenditure and income side, expenditure breakdowns by durability and exports and imports by origin. <

The indicator measures the number and proportion of coastal and inland bathing sites with excellent water quality. The indicator assessment is based on microbiological parameters (intestinal enterococci and Escherichia coli). The new Bathing Water Directive requires Member States to identify and assess the quality of all inland and marine bathing waters and to classify these waters as ‘poor’, ‘sufficient’, ‘good’ or ‘excellent’.

The Bertelsmann Stiftung’s Transformation Index (BTI) analyzes and evaluates the quality of democracy, a market economy and political management in 128 developing and transition countries. It measures successes and setbacks on the path toward a democracy based on the rule of law and a market economy flanked by sociopolitical safeguards. Within this framework, the BTI publishes two rankings, the Status Index and the Management Index. Countries are further categorized on the basis of these status index and management rankings/scores. For instance, countries are categorized in to 5 groups – viz; 5 or failed, 4 or very limited, 3 or limited, 2 or advanced, and 1 or highly advanced—based on their status index score of 1 to 10. A country with a high score, 8.5 and above, is categorized as highly advanced. A country with a low score, below 4, is categorized as failed. A country is categorized as ‘very limited’ if it has a status index score between 4 and 5.5. A score between 5.5 and 7 means the country is categorized as ‘limited’ and a country is categorized as ‘advanced’ for a score between 7.1 and 8.5. On the basis of the democratic status ranking, countries are further categorized as 5 or ‘hard - line autocracies,’ 4 or ‘moderate autocracies,’ 3 or ‘highly defective democracies,’ 2 or ‘defective democracies,’ and 1 or ‘democracies in consolidation.’ A country with a democratic status ranking below 4 is categorized as a hard line autocracy. A democratic status score between 4 and 5 means that the country is part of the ‘moderate autocracy’ group. A country is grouped as a ‘highly defective democracy’ for a score between 5 and 6. A country is recognized as a ‘defective democracy’ for a score between 6 and 8, and a score of 8 and above earns a country the status of a ‘democracy in consolidation.’ Countries are also categorized in to 5 groups based on their market economy status ranking. The countries are categorized as ‘rudimentary’ or group 5, ‘poorly functioning’ or group 4, ‘functional flaws’ or group 3, ‘functioning’ or group 2, and ‘developed’ or group 1. A country is recognized as a member of the ‘developed’ group with a market economy status ranking/score of 8 and above. A country is grouped as ‘functioning’ if it has a score between 7 and 8. A market economy status ranking between 5 and 7 means the country is categorized to group 3 or the ‘functional flaws’ group. A score between 3 and 5 means that the country is ‘poorly functioning’ and a score below 3 means the country enjoys a ‘rudimentary’ status. Based on the management index ranking, countries are categorized as 5 or failed, 4 or weak, 3 or moderate, 2 or good, and1 or very good. A country is categorized as ‘very good’ for a score of 7 and above. It is categorized as ‘good’ for a score between 5.6 and 7, and as ‘moderate’ for a score between 4.4 and 5.5. A score between 3 and 4.3 means a country is categorized as ‘weak,’ and a score below 3 means the categorization of a country as ‘failed.’ Countries are ranked between 1 and 10 on the basis of the level of difficulty they face. The level of difficulty is further categorized as 5 or negligible, 4 or minor, 3 or moderate, 2 or substantial, and 1 or massive. A score of 8.5 and above means the categorization of the country’s level of difficulty as ‘massive, and a score below 2.5 means the categorization of the level of difficulty faced by the country as ‘negligible.’ The level of difficulty score of 2.5 to 4.4 means a country faces a ‘minor’ level of difficulty and a score between 4.5 and 6.4 means the level of difficulty faced by a country is ‘moderate.’ A country with a score of 6.5 to 8.4 faces a ‘substantial’ level of difficulty.

STAN Bilateral Trade Database by Industry and End-use category (BTDIxE) provides values of imports and exports (as well as re-imports and re-exports) of goods broken down by industrial sectors and by end-use categories. BTDIxE was designed to extend the old BTD database which provided bilateral trade in goods by industry only.  BTDIxE allows, for example, insights into the patterns of trade in intermediate goods between countries to track global production networks and supply chains, and it helps to address policy issues such as trade in value added and trade in tasks.  The database presents estimates of bilateral flows of goods from 1990 to the latest available year, i.e. 2018; the latest year shown is subject to the availability of underlying product-based annual trade statistics.  Reporters are the OECD member countries and a large number of non-OECD economies, including the BRIICS: Brazil, the Russian Federation, India, Indonesia, People's Republic of China and South Africa; other selected G20 and Asian economies; and major African and Latin American nations.  It should be noted that starting from mid-2012, the OECD and the United Nations agreed to centralise the data collection and processing procedures within UNSD Comtrade.  The list of partners covers the OECD countries, more than a hundred of non-member economies as well as the partners "World", "Rest of the World" and "Unspecified". The partner "Total foreign trade" corresponds to the flows with partner "World" excluding intra-country flows. Trade flows are divided into economic activities based on the Revision 4 of ISIC and nine end-use categories including capital goods, intermediate goods and household consumption.

The residential property price statistics collect data from different countries. The BIS has obtained permission from various national data providers, with the assistance of its member central banks, to disseminate these statistics. The topic ‘Property prices: Selected series,’ contains nominal and real quarterly values for 58 countries, both in levels and in growth rates (ie four series per country). Real series are the nominal price series deflated by the consumer price index. The BIS has made the selection based on the Handbook on Residential Property Prices and the experience and metadata of central banks.   Data cited at : https://www.bis.org/statistics/index.htm

The BP Statistical Review of World Energy has provided high-quality, objective and globally consistent data on world energy markets. The Review is one of the most widely respected and authoritative publications in the field of energy economics, used for reference by the media, academia, world governments and energy companies. A new edition is published every June. Historical data from 1965 for many sections. Note:  1. For the below indicators "Converted data from Exajoules to Million tonnes ((Exajoules value *23884589.66)/1000000)=Million tonnes" Coal Consumption, Million tonnes oil equivalent Coal Production, Million tonnes oil equivalent Natural gas Consumption, Million tonnes oil equivalent Natural gas Production, Million tonnes oil equivalent Hydroelectricity Consumption, Million tonnes oil equivalent Nuclear energy Consumption, Million tonnes oil equivalent Oil Consumption, Million tonnes oil equivalent Primary energy Consumption, Million tonnes oil equivalent Primary Energy Consumption by fuel: Coal, Million tonnes oil equivalent Primary Energy Consumption by fuel: Oil, Million tonnes oil equivalent Primary Energy Consumption by fuel: Natural Gas, Million tonnes oil equivalent Primary Energy Consumption by fuel: Nuclear Energy, Million tonnes oil equivalent Primary Energy Consumption by fuel: Hydro electric, Million tonnes oil equivalent Primary Energy Consumption by fuel: Renewables, Million tonnes oil equivalent Primary Energy Consumption by fuel: Total Consumption, Million tonnes oil equivalent Renewable Consumption, Million tonnes oil equivalent Renewables Consumption - Solar, Million tonnes oil equivalent Renewables Consumption - Wind, Million tonnes oil equivalent Renewables Consumption - Geothermal, Biomass and Other, Million tonnes oil equivalent 2. For the below indicator "Converted data from Petajoules to Thousand tonnes ((Petajoules Values* 23884.58966275)/1000)=Thousand tonnes" Renewable energy - Bio-fuels Production, Thousand tonnes of oil equivalent

The objective of the building permit index is to show the future development of construction activity in terms of per square meter of useful floor. The current base year is 2015 (Index 2015 = 100). The index is presented in seasonally adjusted form. Growth rates with respect to the previous month (M/M-1) are calculated from seasonally adjusted figures while growth rates with respect to the same month of the previous year (M/M-12) are calculated from raw data.

The annual Business demography data collection covers variables which explain the characteristics and demography of the business population. The methodology allows for the production of data on enterprise births (and deaths), that is, enterprise creations (cessations) that amount to the creation (dissolution) of a combination of production factors and where no other enterprises are involved. In other words, enterprises created or closed solely as a result of e.g. restructuring, merger or break-up are not considered. The data are drawn from business registers, although some countries improve the availability of data on employment and turnover by integrating other sources. Until 2010 reference year the harmonised data collection is carried out to satisfy the requirements for the Structural Indicators, used for monitoring progress of the Lisbon process, regarding business births, deaths and survival. Currently, business demography delivers key information for policy decision-making and for the indicators to support the Europe 2020 strategy. It also provides key data for the joint OECD-Eurostat "Entrepreneurship Indicators Programme". In summary, the collected indicators are as follows:Population of active enterprisesNumber of enterprise birthsNumber of enterprise survivals up to five yearsNumber of enterprise deathsRelated variables on employmentDerived indicators such as birth rates, death rates, survival rates and employment sharesAn additional set of indicators on high-growth enterprises and 'gazelles' (high-growth enterprises that are up to five years old) The complete list of the basic variables, delivered from the data providers (National Statistical Institutes) and the derived indicators, calculated by Eurostat, is attached in the Annexes of this document (see Business demography indicators).  Geographically EU Member States and EFTA countries are covered. In practice not all Member States have participated in the first harmonised data collection exercises. The methodology laid down in the Eurostat-OECD Manual on Business Demography Statistics  is followed closely by most of the countries (see Country specific notes in the Annexes).

Data on cultural enterprises come from 2 data collections and are summarised in 4 Tables : a) SBS (Structural Business Statistics) Table 1. Number and average size of enterprises in the cultural sectors by NACE Rev. 2 activity (cult_ent_num) Table 2. Value added and turnover of enterprises in the cultural sectors by NACE Rev. 2 activity (cult_ent_val), in millions of EUR and as a percentage of services except trade and financial and insurance activities (i.e. NACE Rev. 2 sections H to N, without K) Table 3. Services by employment size class (NACE Rev. 2, H-N, S95) (sbs_sc_1b_se_r2)   b) Business Demography (BD) Table 4. Business demography by size class (from 2004 onwards, NACE Rev. 2) (bd_9bd_sz_cl_r2)   The data focus on culture-related sectors of activity, as identified by international experts in the final report of the European Statistical System Network on Culture (ESS-Net Culture Report 2012).   The cultural sphere in business statistics is therefore captured through the following NACE Rev. 2 codes, when they are covered (see 3.3. Sector coverage for details): J58.11 Book publishing J58.13 Publishing of newspapers J58.14 Publishing of journals and periodicals J58.21 Publishing of computer games J59 Motion picture, video and television programme production, sound recording and music publishing activities J60 Programming and broadcasting activities J63.91 News agency activities M71.11 Architectural activities M74.1 Specialised design activities R90 Creative, arts and entertainment activities R91 Libraries, archives, museums and other cultural activities

Business demography produces information such as birth rates, death rates, survival rates, and their employment shares. These main derived indicators are expressed as ratios of total active enterprises or enterprises born in the reference period. Presented data refer to the business economy, covering sections B to N (excluding activities of holding companies – K64.2) according to NACE Rev. 2.

Business demography contains information for characteristics and demography of the business population. The category presents main variables, such as enterprise births (often referred to as business entries), enterprise deaths (often referred to as business exits), and enterprise survivals. Enterprises created or closed solely as a result of e.g. restructuring, merger or break-up are not included in this data. Information refers to the business economy, covering sections B to N (excluding activities of holding companies – K64.2) according to NACE Rev. 2.

This collection provides users with data concerning R&D expenditure and R&D personnel broken down by following institutional sectors: business enterprise (BES), government (GOV), higher education (HES), private non-profit (PNP) with the total of sectors. All data are broken down by the above mentioned sectors of performance. The R&D expenditure is further broken down by source of funds, by type of costs, by economic activity (NACE Rev.2), by size class, by type of R&D, by fields of science, by socio-economic objectives and by regions (NUTS 2 level). Besides R&D expenditures in basic unit National currency (MIO_NAC) the following units are available: Euro (MIO_EUR), Euro per inhabitant (EUR_HAB) Purchasing Power Standard (MIO_PPS), Purchasing Power Standard at 2005 prices (MIO_PPS_KP05), Purchasing Power Standard per inhabitant at constant 2005 prices (PPS_KP05_HAB), Percentage of GDP (PC_GDP) and Percentage of total R&D expenditure (PC_TOT - for the breakdown by source of funds). R&D personnel data is available in full-time equivalent (FTE), in head count (HC), as a % of employment and as a % of labour force. The data is further broken down by occupation, by qualification, by gender, by size class, by citizenship, by age groups, by fields of science, by economic activity (NACE Rev.2) and by regions (NUTS 2 level). The periodicity of R&D data is biennial except for the key R&D indicators (R&D expenditure, R&D personnel and Researchers by sectors of performance) which are transmitted annually by the EU Member States on the basis of a legal obligation from 2003 onwards. Some other breakdowns of the data may appear on annual basis based on voluntary data provisions. The data are collected through sample or census surveys, from administrative registers or through a combination of sources. R&D data are available for following countries and country groups: - All EU Member States, plus Candidate Countries, EFTA Countries, the Russian Federation, China, Japan, the United States and South Korea. - Country groups: EU-28, EU-15 and EA-18. R&D data are compiled in accordance to the guidelines laid down in the Proposed standard practice for surveys of research and experimental development - Frascati Manual (FM), OECD, 2002 .

This collection provides users with data concerning R&D expenditure and R&D personnel broken down by following institutional sectors: business enterprise (BES), government (GOV), higher education (HES), private non-profit (PNP) with the total of sectors. All data are broken down by the above mentioned sectors of performance. The R&D expenditure is further broken down by source of funds, by type of costs, by economic activity (NACE Rev.2), by size class, by type of R&D, by fields of science, by socio-economic objectives and by regions (NUTS 2 level). Besides R&D expenditures in basic unit National currency (MIO_NAC) the following units are available: Euro (MIO_EUR), Euro per inhabitant (EUR_HAB) Purchasing Power Standard (MIO_PPS), Purchasing Power Standard at 2005 prices (MIO_PPS_KP05), Purchasing Power Standard per inhabitant at constant 2005 prices (PPS_KP05_HAB), Percentage of GDP (PC_GDP) and Percentage of total R&D expenditure (PC_TOT - for the breakdown by source of funds). R&D personnel data is available in full-time equivalent (FTE), in head count (HC), as a % of employment and as a % of labour force. The data is further broken down by occupation, by qualification, by gender, by size class, by citizenship, by age groups, by fields of science, by economic activity (NACE Rev.2) and by regions (NUTS 2 level). The periodicity of R&D data is biennial except for the key R&D indicators (R&D expenditure, R&D personnel and Researchers by sectors of performance) which are transmitted annually by the EU Member States on the basis of a legal obligation from 2003 onwards. Some other breakdowns of the data may appear on annual basis based on voluntary data provisions. The data are collected through sample or census surveys, from administrative registers or through a combination of sources. R&D data are available for following countries and country groups: - All EU Member States, plus Candidate Countries, EFTA Countries, the Russian Federation, China, Japan, the United States and South Korea. - Country groups: EU-28, EU-15 and EA-18. R&D data are compiled in accordance to the guidelines laid down in the Proposed standard practice for surveys of research and experimental development - Frascati Manual (FM), OECD, 2002 .

This collection provides users with data concerning R&D expenditure and R&D personnel broken down by following institutional sectors: business enterprise (BES), government (GOV), higher education (HES), private non-profit (PNP) with the total of sectors. All data are broken down by the above mentioned sectors of performance. The R&D expenditure is further broken down by source of funds, by type of costs, by economic activity (NACE Rev.2), by size class, by type of R&D, by fields of science, by socio-economic objectives and by regions (NUTS 2 level). Besides R&D expenditures in basic unit National currency (MIO_NAC) the following units are available: Euro (MIO_EUR), Euro per inhabitant (EUR_HAB), Purchasing Power Standard (MIO_PPS), Purchasing Power Standard at 2005 prices (MIO_PPS_KP05), Purchasing Power Standard per inhabitant at constant 2005 prices (PPS_KP05_HAB), Percentage of GDP (PC_GDP) and Percentage of total R&D expenditure (PC_TOT - for the breakdown by source of funds). R&D personnel data is available in full-time equivalent (FTE), in head count (HC), as a % of employment and as a % of labour force. The data is further broken down by occupation, by qualification, by gender, by size class, by citizenship, by age groups, by fields of science, by economic activity (NACE Rev.2) and by regions (NUTS 2 level). The periodicity of R&D data is biennial except for the key R&D indicators (R&D expenditure, R&D personnel and Researchers by sectors of performance) which are transmitted annually by the EU Member States on the basis of a legal obligation from 2003 onwards. Some other breakdowns of the data may appear on annual basis based on voluntary data provisions. The data are collected through sample or census surveys, from administrative registers or through a combination of sources. R&D data are available for following countries and country groups: - All EU Member States, plus Candidate Countries, EFTA Countries, the Russian Federation, China, Japan, the United States and South Korea. - Country groups: EU-28, EU-15 and EA-18. R&D data are compiled in accordance to the guidelines laid down in the Proposed standard practice for surveys of research and experimental development - Frascati Manual (FM), OECD, 2002 .

This collection provides users with data concerning R&D expenditure and R&D personnel broken down by following institutional sectors: business enterprise (BES), government (GOV), higher education (HES), private non-profit (PNP) with the total of sectors. All data are broken down by the above mentioned sectors of performance. The R&D expenditure is further broken down by source of funds, by type of costs, by economic activity (NACE Rev.2), by size class, by type of R&D, by fields of science, by socio-economic objectives and by regions (NUTS 2 level). Besides R&D expenditures in basic unit National currency (MIO_NAC) the following units are available: Euro (MIO_EUR), Euro per inhabitant (EUR_HAB) Purchasing Power Standard (MIO_PPS), Purchasing Power Standard at 2005 prices (MIO_PPS_KP05), Purchasing Power Standard per inhabitant at constant 2005 prices (PPS_KP05_HAB), Percentage of GDP (PC_GDP) and Percentage of total R&D expenditure (PC_TOT - for the breakdown by source of funds). R&D personnel data is available in full-time equivalent (FTE), in head count (HC), as a % of employment and as a % of labour force. The data is further broken down by occupation, by qualification, by gender, by size class, by citizenship, by age groups, by fields of science, by economic activity (NACE Rev.2) and by regions (NUTS 2 level). The periodicity of R&D data is biennial except for the key R&D indicators (R&D expenditure, R&D personnel and Researchers by sectors of performance) which are transmitted annually by the EU Member States on the basis of a legal obligation from 2003 onwards. Some other breakdowns of the data may appear on annual basis based on voluntary data provisions. The data are collected through sample or census surveys, from administrative registers or through a combination of sources. R&D data are available for following countries and country groups: - All EU Member States, plus Candidate Countries, EFTA Countries, the Russian Federation, China, Japan, the United States and South Korea. - Country groups: EU-28, EU-15 and EA-18. R&D data are compiled in accordance to the guidelines laid down in the Proposed standard practice for surveys of research and experimental development - Frascati Manual (FM), OECD, 2002 .

'Statistics on high-tech industry and knowledge-intensive services' (sometimes referred to as simply 'high-tech statistics') comprise economic, employment and science, technology and innovation (STI) data describing manufacturing and services industries or products traded broken down by technological intensity. The domain uses various other domains and sources of  Eurostat's official statistics (CIS, COMEXT, HRST, LFS, PATENT, R&D and SBS) and its coverage is therefore dependent on these other primary sources. Two main approaches are used in the domain to identify technology-intensity: the sectoral approach and the product approach. A third approach is used for data on high-tech and biotechnology patents aggregated on the basis of the International Patent Classification (IPC) 8th edition (see summary table in Annex 1 for which approach is used by each type of data). The sectoral approach: The sectoral approach is an aggregation of the manufacturing industries according to technological intensity (R&D expenditure/value added) and based on the Statistical classification of economic activities in the European Community (NACE)  at 2-digit level. The level of R&D intensity served as a criterion of classification of economic sectors into high-technology, medium high-technology, medium low-technology and low-technology industries. Services are mainly aggregated into knowledge-intensive services (KIS) and less knowledge-intensive services (LKIS) based on the share of tertiary educated persons at NACE 2-digit level. The sectoral approach is used for all indicators except data on high-tech trade and patents. Note that due to the revision of the NACE from NACE Rev. 1.1 to NACE Rev. 2 the definition of high-technology industries and knowledge-intensive services has changed in 2008. For high-tech statistics it means that two different definitions (one according NACE Rev. 1.1 and one according NACE Rev. 2) are used in parallel and the data according to both NACE versions are presented in separated tables depending on the data availability. For example as the LFS provides the results both by NACE Rev. 1.1 and NACE Rev. 2, all the table using this source have been duplicated to present the results by NACE Rev. 2 from 2008. For more details, see both definitions of high-tech sectors under Annexes section. Within the sectoral approach, a second classification was created , named Knowledge Intensive Activities KIA) and based on the share of tertiary educated people in each sectors of industries and services according to NACE at 2-digit level and for all EU28 Member States. A threshold was applied to judge sectors as knowledge intensive. In contrast to first sectoral approach mixing two methodologies, one for manufacturing industries and one for services, the KIA classification is based on one methodology for all the sectors of industries and services covering even public sector activities. The aggregations in use are Total Knowledge Intensive Activities (KIA) and Knowledge Intensive Activities in Business Industries (KIABI). Both classifications are made according to NACE Rev. 1.1 and NACE Rev. 2 at 2- digit level. Note that due to revision of the NACE Rev.1.1 to NACE Rev. 2 the list of Knowledge Intensive Activities has changed as well, the two definitions are used in parallel and the data are shown in two separate tables. NACE Rev.2 collection includes data starting from 2008 reference year. For more details please see the definitions under Annexes section. The product approach: The product approach was created to complement the sectoral approach and it is used for data on high-tech trade. The product list is based on the calculations of R&D intensity by groups of products (R&D expenditure/total sales). The groups classified as high-technology products are aggregated on the basis of the Standard International Trade Classification (SITC). The initial definition was built based on SITC Rev.3 and served to compile the high-tech product aggregates until 2007. With the implementation in 2007 of the new version of SITC Rev.4, the definition of high-tech groups was revised and adapted according to new classification. Starting from 2007 the Eurostat presents the trade data for high-tech groups aggregated based on the SITC Rev.4. . For more details, see definition of high-tech products under Annexes section. High-tech patents: High-tech patents are defined according to another approach. The groups classified as high-tech patents are aggregated on the basis of the International Patent Classification (IPC 8th edition). Biotechnology patents are also aggregated on the basis of the IPC 8th edition. For more details, see the aggregation list of high-tech and biotechnology patents under Annexes section. The high-tech domain also comprises the sub-domain Venture Capital Investments: data are provided by INVEST Europe (formerly named the European Private Equity and Venture Capital Association EVCA). More details are available in the Eurostat metadata under Venture capital investments. Please note that for paragraphs where no metadata for regional data has been specified, the regional metadata is identical to the metadata provided for the national data.

'Statistics on high-tech industry and knowledge-intensive services' (sometimes referred to as simply 'high-tech statistics') comprise economic, employment and science, technology and innovation (STI) data describing manufacturing and services industries or products traded broken down by technological intensity. The domain uses various other domains and sources of  Eurostat's official statistics (CIS, COMEXT, HRST, LFS, PATENT, R&D and SBS) and its coverage is therefore dependent on these other primary sources. Two main approaches are used in the domain to identify technology-intensity: the sectoral approach and the product approach. A third approach is used for data on high-tech and biotechnology patents aggregated on the basis of the International Patent Classification (IPC) 8th edition (see summary table in Annex 1 for which approach is used by each type of data). The sectoral approach: The sectoral approach is an aggregation of the manufacturing industries according to technological intensity (R&D expenditure/value added) and based on the Statistical classification of economic activities in the European Community (NACE) at 2-digit level. The level of R&D intensity served as a criterion of classification of economic sectors into high-technology, medium high-technology, medium low-technology and low-technology industries. Services are mainly aggregated into knowledge-intensive services (KIS) and less knowledge-intensive services (LKIS) based on the share of tertiary educated persons at NACE 2-digit level. The sectoral approach is used for all indicators except data on high-tech trade and patents. Note that due to the revision of the NACE from NACE Rev. 1.1 to NACE Rev. 2 the definition of high-technology industries and knowledge-intensive services has changed in 2008. For high-tech statistics it means that two different definitions (one according NACE Rev. 1.1 and one according NACE Rev. 2) are used in parallel and the data according to both NACE versions are presented in separated tables depending on the data availability. For example as the LFS provides the results both by NACE Rev. 1.1 and NACE Rev. 2, all the table using this source have been duplicated to present the results by NACE Rev. 2 from 2008. For more details, see both definitions of high-tech sectors in Annex 2 and 3. Within the sectoral approach, a second classification was created, named Knowledge Intensive Activities KIA) and based on the share of tertiary educated people in each sectors of industries and services according to NACE at 2-digit level and for all EU28 Member States. A threshold was applied to judge sectors as knowledge intensive. In contrast to first sectoral approach mixing two methodologies, one for manufacturing industries and one for services, the KIA classification is based on one methodology for all the sectors of industries and services covering even public sector activities. The aggregations in use are Total Knowledge Intensive Activities (KIA) and Knowledge Intensive Activities in Business Industries (KIABI). Both classifications are made according to NACE Rev. 1.1 and NACE Rev. 2 at 2- digit level. Note that due to revision of the NACE Rev.1.1 to NACE Rev. 2 the list of Knowledge Intensive Activities has changed as well, the two definitions are used in parallel and the data are shown in two separate tables. NACE Rev.2 collection includes data starting from 2008 reference year. For more details please see the definitions in Annex 7 and 8. The product approach: The product approach was created to complement the sectoral approach and it is used for data on high-tech trade. The product list is based on the calculations of R&D intensity by groups of products (R&D expenditure/total sales). The groups classified as high-technology products are aggregated on the basis of the Standard International Trade Classification (SITC). The initial definition was built based on SITC Rev.3 and served to compile the high-tech product aggregates until 2007. With the implementation in 2007 of the new version of SITC Rev.4, the definition of high-tech groups was revised and adapted according to new classification. Starting from 2007 the Eurostat presents the trade data for high-tech groups aggregated based on the SITC Rev.4. For more details, see definition of high-tech products in Annex 4 and 5. High-tech patents: High-tech patents are defined according to another approach. The groups classified as high-tech patents are aggregated on the basis of the International Patent Classification (IPC 8th edition). Biotechnology patents are also aggregated on the basis of the IPC 8th edition. For more details, see the aggregation list of high-tech and biotechnology patents in Annex 6. The high-tech domain also comprises the sub-domain Venture Capital Investments: data are provided by INVEST Europe (formerly named the European Private Equity and Venture Capital Association EVCA). More details are available in the Eurostat metadata under Venture capital investments. Please note that for paragraphs where no metadata for regional data has been specified, the regional metadata is identical to the metadata provided for the national data.

The business tendency survey indicators cover a standard set of indicators for four economic sectors: manufacturing, construction, retail trade and other services. This includes an indicator of overall business conditions or business confidence in each sector. The consumer opinion survey indicators cover a restricted set of indicators on consumer confidence, expected economic situation and price expectations.   Business and consumer opinion (tendency) surveys provide qualitative information that has proved useful for monitoring the current economic situation. Typically they are based on a sample of enterprises or households and respondents are asked about their assessments of the current situation and expectations for the immediate future. For enterprise surveys this concerns topics such as production, orders, stocks etc. and in the case of consumer surveys their intentions concerning major purposes, economic situation now compared with the recent past and expectations for the immediate future. Many survey series provide advance warning of turning points in aggregate economic activity as measured by GDP or industrial production. Such series are known as leading indicators in cyclical analysis. These types of survey series are widely used as component series in composite leading indicators.   The main characteristic of these types of surveys is that instead of asking for exact figures, they usually ask for the direction of change e.g. a question on tendency by reference to a “normal” state, e.g. of production level. Possible answers are generally of the three point scale type e.g. up/same/down or above normal/normal/below normal for enterprise surveys and of the five point scale type e.g. increase sharply/increase slightly/remain the same/fall slightly/fall sharply for consumer surveys. In presenting the results as a time series, only the balance is shown. That is “same” or “normal” answers are ignored and the balance is obtained by taking the difference between percentages of respondents giving favourable and unfavourable answers.   Virtually all business tendency and consumer opinion survey data are presented as time series of balances in this dataset, either in raw or seasonally adjusted form. Very few series are presented as indices, and where these exist they have generally been converted from underlying balances by countries before submitting the data to the OECD.

Institutional coverage As a consequence of the implementation of the new OECD Global Insurance Statistics' framework, there is a break in series between 2008 and 2009 regarding life and non-life business data where composite insurance undertakings exist. Up until 2008, the insurance business is broken down between life and non-life business. As of 2009, the insurance business is broken down between the business of pure life, pure non-life and composite undertakings and composite undertakings' business is further broken down between life and non-life business. Some countries do not allow for insurance undertakings to be active in both life and non-life insurance business and therefore composite insurance undertakings do not exist in these countries. In other countries (e.g., Austria, Belgium, Hungary, Italy, Mexico, Portugal, Spain) however, the share of employment in composite insurance undertakings accounts for more than half of the whole domestic insurance sector. Therefore, to have comparable data across years for life business data (resp. non-life), one has to sum up the life (resp. non-life) business of pure life (resp. non-life) undertakings and the life (resp. non-life) business of composite undertakings as of 2009. Item coverage Business written in the reporting country on a gross and net premium basis. It contains a breakdown between domestic companies, foreign-controlled companies and branches and agencies or foreign companies.

The energy balance is the most complete statistical accounting of energy products and their flow in the economy. The energy balance allows users to see the total amount of energy extracted from the environment, traded, transformed and used by different types of end-users. It also allows seeing the relative contribution of each energy carrier (fuel, product). The energy balance allows studying the overall domestic energy market and monitoring impacts of energy policies. The energy balance offers a complete view on the energy situation of a country in a compact format, such as on energy consumption of the whole economy and of individual sectors. The energy balance presents all statistically significant energy products (fuels) of a country and their production, transformation and consumption by different type of economic actors (industry, transport, etc.). Therefore, an energy balance is the natural starting point to study the energy sector. Annual data collection cover in principle the EU Member States, EFTA, EU candidate countries, and potential candidate countries. Time series starts mostly in year 1990. All data in energy balances are presented in terajoules, kilotonnes of oil equivalent and gigawatt hours.

This dataset is updated with data obtained from Statistics Canada and the U.S. Census Bureau. Trade Data is updated on a monthly and annual basis, with revisions in March, April, May, August and November to previous year's data. Trade Data is available on both product and industry-based versions. The product Trade Data is classified by Harmonized System (HS) codes while the industry data is based on North American Industry Classification System(NAICS) classification codes. Source: Statistics Canada and the U.S.Census Bureau

This dataset provides detailed trade statistics on iron and steel commodities using the Harmonized System (HS) classification of goods (based on the 6-digit commodity level).

This data set provides the Estimated numbers of new cancer cases and deaths in 2023. In 2023, 1,958,310 new cancer cases and 609,820 cancer deaths are projected to occur in the United States. 

Data cited: Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2016 (GBD 2016) Cancer Incidence, Mortality, Years of Life Lost, Years Lived with Disability, and Disability-Adjusted Life Years 1990-2016. Seattle, United States: Institute for Health Metrics and Evaluation (IHME), 2018.   The Global Burden of Disease Study 2016 (GBD 2016), coordinated by the Institute for Health Metrics and Evaluation (IHME), estimated the burden of diseases, injuries, and risk factors for 195 countries and territories and at the subnational level for a subset of countries. Estimates for deaths, disability-adjusted life years (DALYs), years lived with disability (YLDs), years of life lost (YLLs), prevalence, and incidence for 29 cancer groups by age and sex for 1990-2016 are available from the GBD Results Tool. Files available in this record are the web tables published in JAMA Oncology in June 2018 in "Global, Regional, and National Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life-years for 29 Cancer Groups, 1990 to 2016."

Eurostat Dataset Id:cpc_ecgov  The focus of this domain is on the following country groups:Acceeding country: Croatia (HR)Candidate countries: the former Yugoslav Republic of Macedonia (MK), Montenegro (ME), Iceland (IS), Serbia (RS) and Turkey (TR)Potential candidate countries: Albania (AL), Bosnia and Herzegovina (BA), as well as Kosovo under UNSCR 1244/99 (XK)

The focus of this domain is on enlargement countries, in other words the following country groups: candidate countries — Albania (AL), the former Yugoslav Republic of Macedonia (MK), Montenegro (ME), Serbia (RS) and Turkey (TR)potential candidates — Bosnia and Herzegovina (BA), and Kosovo (XK) (*) An extensive range of indicators is presented in this domain, including indicators from almost every theme covered by European statistics. Only annual data are published in this domain. (*) This designation is without prejudice to positions on status and is in line with UNSCR 1244/1999 and the ICJ Opinion on the Kosovo declaration of independence.

OICA Car Production Statistics 1999-2021 contains world motor vehicle production statistics, obtained from national trade organisations, OICA members or correspondents. Passenger cars are motor vehicles with at least four wheels, used for the transport of passengers, and comprising no more than eight seats in addition to the driver's seat. Commercial vehicles include light commercial vehicles, heavy trucks, coaches and buses.

The data in this dataset comes from the Common Questionnaire for Transport Statistics, developed and surveyed in co-operation between the United Nations Economic Commission for Europe (UNECE), the International Transport Forum (ITF) and Eurostat. The Common Questionnaire is not supported by a legal act, but is based on a gentlemen's agreement with the participating countries; the completeness varies from country to country. Eurostat’s datasets based on the Common Questionnaire cover annual data for the EU Member States, EFTA states and Candidate countries to the EU. Data for other participating countries are available through the ITF and the UNECE. In total, comparable transport data collected through the Common Questionnaire is available for close to 60 countries worldwide. The Common Questionnaire collects aggregated annual data on: Railway transportRoad transportInland waterways transportOil pipelines transportGas pipelines transport For each mode of transport, the Common Questionnaire cover some or all of the following sub-modules (the number of questions/variables within each sub-module varies between the different modes of transport): Infrastructure (All modes)Transport equipment (RAIL, ROAD and INLAND WATERWAYS)Enterprises, economic performance and employment (All modes)Traffic (RAIL, ROAD and INLAND WATERWAYS)Transport measurement (All modes) Accidents (ROAD only) The Common Questionnaire is completed by the competent national authorities. The responsibility for completing specific modules (e.g. Transport by Rail) or part of modules (e.g. Road Infrastructure) may be delegated to other national authorities in charge of specific fields.

Catches of fish, crustaceans, molluscs and other aquatic organisms by species and fishing area for EU and associated countries (in live weight equivalent of the landings). European data on fish catches, in tonnes live weight (TLW) have been recorded since 1950. Historical data up to 1999 are displayed under [fish_ca_h] while data from 2000 onwards are released in the [fish_ca] tables. These exclude catches in inland waters and focus on marine fishing areas that are legally covered, namely: 21 - Atlantic, Northwest 27 - Atlantic, Northeast 34 - Atlantic, Eastern Central 37 - Mediterranean and Black Sea 41 - Atlantic, Southwest 47 - Atlantic, Southeast 51 - Indian Ocean, Western

Catches of fish, crustaceans, molluscs and other aquatic organisms by species and fishing area for EU and associated countries (in live weight equivalent of the landings). European data on fish catches, in tonnes live weight (TLW) have been recorded since 1950. Historical data up to 1999 are displayed under [fish_ca_h] while data from 2000 onwards are released in the [fish_ca] tables. These exclude catches in inland waters and focus on marine fishing areas that are legally covered, namely: 21 - Atlantic, Northwest 27 - Atlantic, Northeast 34 - Atlantic, Eastern Central 37 - Mediterranean and Black Sea 41 - Atlantic, Southwest 47 - Atlantic, Southeast 51 - Indian Ocean, Western

Catches of fish, crustaceans, molluscs and other aquatic organisms by species and fishing area for EU and associated countries (in live weight equivalent of the landings). European data on fish catches, in tonnes live weight (TLW) have been recorded since 1950. Historical data up to 1999 are displayed under [fish_ca_h] while data from 2000 onwards are released in the [fish_ca] tables. These exclude catches in inland waters and focus on marine fishing areas that are legally covered, namely: 21 - Atlantic, Northwest 27 - Atlantic, Northeast 34 - Atlantic, Eastern Central 37 - Mediterranean and Black Sea 41 - Atlantic, Southwest 47 - Atlantic, Southeast 51 - Indian Ocean, Western

Catches of fish, crustaceans, molluscs and other aquatic organisms by species and fishing area for EU and associated countries (in live weight equivalent of the landings). European data on fish catches, in tonnes live weight (TLW) have been recorded since 1950. Historical data up to 1999 are displayed under [fish_ca_h] while data from 2000 onwards are released in the [fish_ca] tables. These exclude catches in inland waters and focus on marine fishing areas that are legally covered, namely: 21 - Atlantic, Northwest 27 - Atlantic, Northeast 34 - Atlantic, Eastern Central 37 - Mediterranean and Black Sea 41 - Atlantic, Southwest 47 - Atlantic, Southeast 51 - Indian Ocean, Western

Catches of fish, crustaceans, molluscs and other aquatic organisms by species and fishing area for EU and associated countries (in live weight equivalent of the landings). European data on fish catches, in tonnes live weight (TLW) have been recorded since 1950. Historical data up to 1999 are displayed under [fish_ca_h] while data from 2000 onwards are released in the [fish_ca] tables. These exclude catches in inland waters and focus on marine fishing areas that are legally covered, namely: 21 - Atlantic, Northwest 27 - Atlantic, Northeast 34 - Atlantic, Eastern Central 37 - Mediterranean and Black Sea 41 - Atlantic, Southwest 47 - Atlantic, Southeast 51 - Indian Ocean, Western

Catches of fish, crustaceans, molluscs and other aquatic organisms by species and fishing area for EU and associated countries (in live weight equivalent of the landings). European data on fish catches, in tonnes live weight (TLW) have been recorded since 1950. Historical data up to 1999 are displayed under [fish_ca_h] while data from 2000 onwards are released in the [fish_ca] tables. These exclude catches in inland waters and focus on marine fishing areas that are legally covered, namely: 21 - Atlantic, Northwest 27 - Atlantic, Northeast 34 - Atlantic, Eastern Central 37 - Mediterranean and Black Sea 41 - Atlantic, Southwest 47 - Atlantic, Southeast 51 - Indian Ocean, Western

Catches of fish, crustaceans, molluscs and other aquatic organisms by species and fishing area for EU and associated countries (in live weight equivalent of the landings). European data on fish catches, in tonnes live weight (TLW) have been recorded since 1950. Historical data up to 1999 are displayed under [fish_ca_h] while data from 2000 onwards are released in the [fish_ca] tables. These exclude catches in inland waters and focus on marine fishing areas that are legally covered, namely: 21 - Atlantic, Northwest 27 - Atlantic, Northeast 34 - Atlantic, Eastern Central 37 - Mediterranean and Black Sea 41 - Atlantic, Southwest 47 - Atlantic, Southeast 51 - Indian Ocean, Western

Catches of fish, crustaceans, molluscs and other aquatic organisms by species and fishing area for EU and associated countries (in live weight equivalent of the landings). European data on fish catches, in tonnes live weight (TLW) have been recorded since 1950. Historical data up to 1999 are displayed under [fish_ca_h] while data from 2000 onwards are released in the [fish_ca] tables. These exclude catches in inland waters and focus on marine fishing areas that are legally covered, namely: 21 - Atlantic, Northwest 27 - Atlantic, Northeast 34 - Atlantic, Eastern Central 37 - Mediterranean and Black Sea 41 - Atlantic, Southwest 47 - Atlantic, Southeast 51 - Indian Ocean, Western

The total annual catch of fishery products by EU Member States, Iceland and Norway and other major fishing nations from all oceans and internal waters of the world. The data are expressed in the live weight equivalent of the landings. This is the weight as the product is taken from the water (that is, before processing) but excludes any products which, for a variety of reasons, are not landed.

The total annual catches by EU Member States, Iceland, Norway and other major fishing nations in the Eastern Central Atlantic. This region of the Atlantic Ocean, is roughly the area to the east of 40°W longitude between latitudes 36°N and 6°S. The data are expressed in the live weight equivalent of the landings. This is the weight as the product is taken from the water (that is, before processing) but excludes any products which, for a variety of reasons, are not landed.

The total annual catches by EU Member States, Iceland, Norway and other major fishing nations in the Mediterranean. This region, known as FAO Major Fishing Area 37, comprises the Mediterranean and the adjacent Black Sea. The data are expressed in the live weight equivalent of the landings. This is the weight as the product is taken from the water (that is, before processing) but excludes any products which, for a variety of reasons, are not landed.

The total annual catches by EU Member States, Iceland, Norway and other major fishing nations in the North-east Atlantic. This region of the Atlantic Ocean, is roughly the area to the east of 42°W longitude and north of 36°N latitude. It includes the waters of the Baltic Sea. The data are expressed in the live weight equivalent of the landings. This is the weight as the product is taken from the water (that is, before processing) but excludes any products which, for a variety of reasons, are not landed.

The total annual catches by EU Member States, Iceland, Norway and other major fishing nations in the North-west Atlantic. This region of the Atlantic Ocean, is roughly the area to the west of 42°W longitude and north of 35°N latitude. The data are expressed in the live weight equivalent of the landings. This is the weight as the product is taken from the water (that is, before processing) but excludes any products which, for a variety of reasons, are not landed.

Animal production statistics cover three main sub-domains based on three pieces of relevant legislation and related gentlemen’s agreements. Livestock and meat statistics are collected under Regulation (EC) No 1165/2008. They cover meat production, as activity of slaughterhouses (monthly) and as other slaughtering (annual), meat production (gross indigenous production) forecast (semi-annual or quarterly), livestock statistics, including regional statistics. A quality report is also collected every third year.Milk and milk product statistics are collected under Decision 97/80/EC implementing Directive 96/16/EC. They cover farm production and utilisation of milk (annual), collection (monthly for cows’ milk) and production activity by dairies (annual) and statistics on the structure of dairies (every third year). An annual methodological report is also collected.Statistics on eggs for hatching and farmyard poultry chicks are collected under Regulation (EC) No 617/2008, implementing Regulation (EC) No 1234/2007 (Single CMO Regulation). They cover statistics on the structure (annual) and the activity (monthly) of hatcheries as well as reports on the external trade of chicks. European Economic Area countries (EEA, Iceland, Liechtenstein and Norway) are requested to provide milk statistics, with the exception of those related to home consumption, as stated in Annex XXI of the EEA Agreement. As Iceland is now a candidate country and Liechtenstein is exempted in the Agreement, only Norway is concerned. The Agreement between the European Community and the Swiss Confederation on cooperation in the field of statistics states that Switzerland must provide Eurostat with national milk statistics. It has been amended in 2013 for covering also some livestock and meat statistics. The same statistics are requested from the candidate countries as acquis communautaire. Further data about the same topics refer to repealed legal acts or agreements. The tables on animal product supply balance sheets (apro_mk_bal, apro_mt_bal and apro_ec_bal), statistics on the structure of rearing (apro_mt_str) and the number of laying hens (apro_ec_lshen) are therefore no longer updated. The same applies to some variables (external trade of animals and meat), periods (surveys in April or August) or items (number of horses) included in other tables. The statistical tables disseminated by Eurostat are organised into three groups of tables on Agricultural products (apro), i.e. Milk and milk products (apro_mk), Livestock and meat (apro_mt) and Poultry farming (apro_ec). This last label covers statistics on hatcheries and on trade in chicks. The regional animal production statistics collected on livestock (agr_r_animal) and on cows’ milk production on farms (agr_r_milk_pr) are disseminated separately. Due to the change in the legal basis or in the methodology, the time series may be broken. This is indicated by a flag in the tables. The detailed content of each table and the reference to its legal definition is provided in the table below. Table 3.1: Data tables disseminated regarding animal production statistics <

Data cited at : https://www.bis.org/statistics/index.htm

Data are the result of the annual structure of government debt survey and cover the EU countries as well as Norway. The following series are available: Central government gross debt by initital maturity and sector of debt holder; State government gross debt by initital maturity and sector of debt holder; Local government gross debt by initital maturity and sector of debt holder; Social security funds gross debt by initital maturity and sector of debt holder; General government gross debt by initital maturity and sector of debt holder; Debt by currency of issuance; Government guarantees (contingent liabilities).

Residence permits data contain statistical information based on Article 6 of Council Regulation (CE) No 862 of 11 July 2007 with reference to:first permits granted to third-country nationals during the reference year, disaggregated by citizenship, reason for the permit being issued and by the length of validity of the permit; permits granted during the reference period on the occasion of person changing immigration status or reason to stay, disaggregated by citizenship, reason for the permit being issued and by the length of validity; permits valid at the end of the reference period, disaggregated by citizenship, reasons for the permit being issued and by the length of validity; number of long-term residents at the end of reference period. Statistics on EU Blue Cards contain information based on the Article 20 of the Council Directive 2009/50/EC of 25 May 2009 on:EU Blue Cards granted, renewed and withdrawn;Admitted family members of EU Blue Cards holders;EU Blue Cards holders and family members by Member State of previous residenceStatistics on Single permits contain information based on the Article 15 (2) Directive 2011/98/EU of the European Parliament and of the Council of 13 December 2011 on a single application procedure for a single permit for third-country nationals to reside and work in the territory of a Member State and on a common set of rights for third-country workers legally residing in a Member State. Eurostat collects data on first permits granted to third-country nationals (persons who are not EU citizens) during the reference year and data on permits valid at the end of the reference period. Statistics are disaggregated by citizenship, reason for the permit being issued and by the length of validity of the permit. In addition, Eurostat collects data on permits granted during the reference period on the occasion of the person changing immigration status or reason for stay (disaggregated by reason for the new permit being issued) and on the number of long-term residents at the end of the reference period. Since the 2010 reference year, data on first permits issued, stock of all valid permits and the number of long-term residents are additionally collected with a voluntary disaggregation by age (5-year age groups) and sex. These statistics are collected by Eurostat on an annual basis. Data are entirely based on administrative sources with the exception of the United Kingdom1 and are provided mainly by the Ministries of Interior or related Immigration Agencies. Data are generally disseminated in June and July in the year following  the  reference year. The indicators presented in the table 'Long-term residents among all non-EU citizens holding residence permits by citizenship on 31 December (%)' are produced within the framework of the pilot study related to the integration of migrants in the Member States, following the Zaragoza Declaration. The Zaragoza Declaration, adopted in April 2010 by EU Ministers responsible for immigrant integration issues, and approved at the Justice and Home Affairs Council on 3-4 June 2010, called upon the Commission to undertake a pilot study to examine proposals for common integration indicators and to report on the availability and quality of the data from agreed harmonised sources necessary for the calculation of these indicators. In June 2010 the ministers agreed "to promote the launching of a pilot project with a view to the evaluation of integration policies, including examining the indicators and analysing the significance of the defined indicators taking into account the national contexts, the background of diverse migrant populations and different migration and integration policies of the Member States, and reporting on the availability and quality of the data from agreed harmonised sources necessary for the calculation of these indicators". These indicators are produced on the basis of residence permit statistics collected by Eurostat on the basis of Article 6 of the Migration Statistics Regulation 862/2007. As a denominator data on the stock of all valid permits to stay at the end of each reporting year are used. As a numerator data on the stock of long-term residents are used.  Two types of long term residents are distinguished in accordance with the residence permit statistics: EU long-term resident status (as regulated by the Council Directive 2003/109/EC) and the National long-term resident status (as regulated by the national legislation in the Member States). Data for some countries may be a subject of revisions due to certain inconsistencies between categories. 1Please note that the statistics for the United Kingdom use different data sources to those used in other Member States. For that reason, the statistics on residence permits published by Eurostat for UK may not be fully comparable with the statistics reported by other countries. Statistics for the United Kingdom are not based on records of residence permits issued (as the United Kingdom does not operate a system of residence permits), but instead relate to the numbers of arriving non-EU citizens permitted to enter the country under selected immigration categories. According to the United Kingdom authorities, data are estimated from a combination of information due to be published in the Home Office Statistical Bulletin 'Control of Immigration: Statistics, United Kingdom' and unpublished management information. The 'Other reasons' category includes: diplomat, consular officer treated as exempt from control; retired persons of independent means; all other passengers given limited leave to enter who are not included in any other category; non-asylum discretionary permissions.

The sum of children (0-17) who are: at-risk-of-poverty or severely materially deprived or living in (quasi-)jobless households (i.e. households with very low work intensity (below 20%) as a share of the total population in the same age group.

The domain "Income and living conditions" covers four topics: people at risk of poverty or social exclusion, income distribution and monetary poverty, living conditions and material deprivation, which are again structured into collections of indicators on specific topics. The collection "People at risk of poverty or social exclusion" houses main indicator on risk of poverty or social inclusion included in the Europe 2020 strategy as well as the intersections between sub-populations of all Europe 2020 indicators on poverty and social exclusion. The collection "Income distribution and monetary poverty" houses collections of indicators relating to poverty risk, poverty risk of working individuals as well as the distribution of income. The collection "Living conditions" hosts indicators relating to characteristics and living conditions of households, characteristics of the population according to different breakdowns, health and labour conditions, housing conditions as well as childcare related indicators. The collection "Material deprivation" covers indicators relating to economic strain, durables, housing deprivation and environment of the dwelling.

Note: Import and Export values for the month January and February 2020 are not available at source. HS Classified Names are as per https://www.seair.co.in/hs-codes/china.aspx

Preliminary Release: China's Total Export & Import Values ​​by Country/Region (in USD)

Le taux de chômage est le nombre de personnes qui sont au chômage exprimé en pourcentage du nombre total de personnes pourvues d'un emploi et des chômeurs (c'est-à-dire, la population active). Cette série fait partie des estimations du BIT et est harmonisée pour tenir compte des différences entre les données nationales, la portée de la couverture, les méthodologies de collecte et de tabulation, et de facteurs spécifiques aux pays. Les données pour 1991-2016 sont des estimations tandis que les données pour 2017-2021 sont des projections. La base de données a été mise à jour en Novembre 2017. Pour plus d'informations, consultez la description de l'indicateur et le document méthodologique sur les estimations et projections du BIT (en anglais).

Dans le but de promouvoir la comparabilité internationale, les statistiques présentées sur ILOSTAT sont basées sur des définitions internationales standard dans la mesure du possible et peuvent différer des chiffres nationaux officiels. Cette série est basée sur les définitions de la 13e CIST. Pour la comparabilité des séries chronologiques, elle inclut les pays qui ont mis en >uvre les normes de la 19e CIST, pour lesquels des données sont également disponibles dans la base de données Statistiques du travail -- 19e CIST (WORK). Les chômeurs comprennent toutes les personnes qui se trouvent: a) sans travail pendant la période de référence, c'est-à-dire pas en emploi ni salarié ni non salarié; b) disponibles pour travailler dans un emploi salarié ou non salarié durant la période de référence; et c) à la recherche d'un travail, c'est-à-dire ayant pris des dispositions spécifiques au cours d'une période récente spécifiée pour chercher un emploi salarié ou non salarié. Pour plus d'informations, reportez-vous à la description de la base de données Statistiques sur la main-d'oeuvre (LFS et STLFS).

Dans le but de promouvoir la comparabilité internationale, les statistiques présentées sur ILOSTAT sont basées sur des définitions internationales standard dans la mesure du possible et peuvent différer des chiffres nationaux officiels. Cette série est basée sur les définitions de la 13e CIST. Pour la comparabilité des séries chronologiques, elle inclut les pays qui ont mis en >uvre les normes de la 19e CIST, pour lesquels des données sont également disponibles dans la base de données Statistiques du travail -- 19e CIST (WORK). Les chômeurs comprennent toutes les personnes qui se trouvent: a) sans travail pendant la période de référence, c'est-à-dire pas en emploi ni salarié ni non salarié; b) disponibles pour travailler dans un emploi salarié ou non salarié durant la période de référence; et c) à la recherche d'un travail, c'est-à-dire ayant pris des dispositions spécifiques au cours d'une période récente spécifiée pour chercher un emploi salarié ou non salarié. Pour plus d'informations, reportez-vous à la description de la base de données Statistiques sur la main-d'oeuvre (LFS et STLFS).

Starting from 1750 Population statistics have been digitised into PDF format in the National Library's Doria service: Publications on Population structure and Vital statistics in Doria (in Finnish) Publications on Population censuses in Doria (in Finnish) --- Description of statistic Quality descriptions Concepts and definitionsRegionThese statistics apply the regional division of 1 January 2019 to the whole time series.NationalityCountries (ISO 3166). The used classification of continents is the classification of Eurostat, where Cyprus and Turkey belong to Europe. In the classification of continents, Europe does not include the figures for Finland. Non-autonomous states are combined with the mother country. Czech Republic = Czech Republic + Former Czechoslovakia Sudan = Sudan + Former Sudan If a person has two nationalities and one of them is Finnish, he/she will be included in statistics as a Finnish national. --- HDI = Human Development Index Ranking according to the 2016 HDI index by country: The former Soviet Union is included in the Russia HDI category The former Czechoslovakia is included in the Czech Republic HDI category The former Yugoslavia and Serbia and Montenegro are included in the Serbia HDI categoryAgeAge refers to a person's age in whole years as at 31 December.InformationPopulation 31 DecPopulation at the end of the statistical reference period.

... confidentialFor reasons of privacy protection, cells with less than 10 cases of citizenship by municipality have been marked with three dots. Starting from 1750 Population statistics have been digitized into PDF format in the National Library's Doria service: Publications on Population structure and Vital statistics in Doria (in Finnish) Publications on Population censuses in Doria (in Finnish) --- Description of statistic Quality descriptions Concepts and definitionsAreaThese statistics apply the regional division of 1 January 2019 to the whole time series. Data for merged municipalities have been combined. Partial municipal mergers have not been taken into account in the years preceding the merger.NationalityCountries (ISO 3166). The used classification of continents is the classification of Eurostat, where Cyprus and Turkey belong to Europe. In the classification of continents, Europe does not include the figures for Finland. Non-autonomous states are combined with the mother country. Czech Republic = Czech Republic + Former Czechoslovakia Sudan = Sudan + Former Sudan If a person has two nationalities and one of them is Finnish, he/she will be included in statistics as a Finnish national. --- HDI = Human Development Index Ranking according to the 2016 HDI index by country: The former Soviet Union is included in the Russia HDI category The former Czechoslovakia is included in the Czech Republic HDI category The former Yugoslavia and Serbia and Montenegro are included in the Serbia HDI categoryInformationPopulation 31 DecPopulation at the end of the statistical reference period.

Climate Change Knowledge Portal: CMIP6 Mean Projections

The Questionnaire on Air Transport Statistics is aimed to collect aggregated annual data on the air transport sector for the following domains: I. Infrastructure (covering commercial airports only) (status at 31/12) II. Transport equipment (covering commercial aircrafts only) (status at 31/12) III. Enterprises, economic performance and employment (status at 31/12) IV. Accidents (annual data) - as from 2015 data on accidents are no longer collected by the questionnaire but are obtained from European Aviation Safety Agency (EASA) and disseminated under Air Transport Safety (tran_sf_avia) part of Eurobase Data are collected and disseminated at country level or at airport level for major European airports. The questionnaire is not supported by any legal acts and it is based on a gentlemen's agreement with the participating countries (Member States, Iceland, Norway, Switzerland, Turkey, FYROM and Montenegro). It is usually prefilled by Eurostat using its own sources as well as other ones (e.g. Airclaims or EASA) and sent to the countries for completion and validation. The completeness varies from country to country. Please note that information concerning data collected in the frame of the Air Transport Statistics Regulation can be found in the metadata documentation provided for this domain (Air Transport Measurement). The section on "Infrastructure" contains three tables: Number of main airports (with more than 150 000 passenger units per year) and other airports (between 15 000 and 150 000 passenger units per year) at country levelAirport infrastructures by type at airport levelAirport connections to other modes of transport at airport level. The section on "Transport Equipment" contains two tables: Commercial aircraft fleet by type of aircraft at country levelCommercial aircraft fleet by age of aircraft at country level The section on "Enterprises economic performance and employment" contains three tables: Number of aviation and airport enterprises at country levelEmployment in aviation and airport enterprises by gender at country levelEmployment in main airports by gender at airport level The section on "Accidents" (which contained two tables Number of injury accidents at country level and Number of fatalities in injury accidents at country level) has been removed from dissemination (in September 2015) and replaced by Air Transport Safety (tran_sf_avia) tables: Air accident victims in commercial air transport, by country of occurrence and country of registry of aircraft (EASA data) (tran_sf_aviaca);Air accident victims in aerial works, by country of occurrence and country of registry of aircraft (EASA data) (tran_sf_aviaaw);Air accident victims in general aviation, by country of occurrence and country of registry of aircraft – maximum take-off mass above 2250 kg (EASA data) (tran_sf_aviagah);Air accident victims in general aviation by country of occurrence and country of registry of aircraft – maximum take-off mass under 2250 kg (EASA data) (tran_sf_aviagal). More information on air accident victims under the following link.

Institutional coverage As a consequence of the implementation of the new OECD Global Insurance Statistics' framework, there is a break in series between 2008 and 2009 regarding life and non-life business data where composite insurance undertakings exist. Up until 2008, the insurance business is broken down between life and non-life business. As of 2009, the insurance business is broken down between the business of pure life, pure non-life and composite undertakings and composite undertakings' business is further broken down between life and non-life business. Some countries do not allow for insurance undertakings to be active in both life and non-life insurance business and therefore composite insurance undertakings do not exist in these countries. In other countries (e.g., Austria, Belgium, Hungary, Italy, Mexico, Portugal, Spain) however, the share of employment in composite insurance undertakings accounts for more than half of the whole domestic insurance sector. Therefore, to have comparable data across years for life business data (resp. non-life), one has to sum up the life (resp. non-life) business of pure life (resp. non-life) undertakings and the life (resp. non-life) business of composite undertakings as of 2009. Item coverage Commissions in the reporting country, containing a breakdown between domestic companies, foreign-controlled companies and branches and agencies of foreign companies.

Compensation of employees is defined as the total remuneration, in cash or in kind, payable by an employer to an employee in return for work done by the latter. In particular, it also includes social contributions paid by the employer.

Compensation of employees (at current prices) is defined as the total remuneration, in cash or in kind, payable by an employer to an employee in return for work done by the latter during the accounting period. Compensation of employees consists of wages and salaries, and of employers' social contributions. The input data are obtained through official transmissions of national accounts' country data in the ESA 2010 transmission programme. The data are expressed in million national currency.

Compensation of employees (at current prices) (ESA 2010, 4.02) is defined as the total remuneration, in cash or in kind, payable by an employer to an employee in return for work done by the latter during the accounting period. Compensation of employees consists of wages and salaries, and of employers' social contributions. Seasonally and calendar adjusted data (SCA).

Compensation of employees (at current prices) is defined as the total remuneration, in cash or in kind, payable by an employer to an employee in return for work done by the latter during the accounting period. Compensation of employees consists of wages and salaries, and of employers' social contributions. The input data are obtained through official transmissions of national accounts' country data in the ESA 2010 transmission programme. The data are expressed in million national currency.

The energy balance is the most complete statistical accounting of energy products and their flow in the economy. The energy balance allows users to see the total amount of energy extracted from the environment, traded, transformed and used by different types of end-users. It also allows seeing the relative contribution of each energy carrier (fuel, product). The energy balance allows studying the overall domestic energy market and monitoring impacts of energy policies. The energy balance offers a complete view on the energy situation of a country in a compact format, such as on energy consumption of the whole economy and of individual sectors. The energy balance presents all statistically significant energy products (fuels) of a country and their production, transformation and consumption by different type of economic actors (industry, transport, etc.). Therefore, an energy balance is the natural starting point to study the energy sector. Annual data collection cover in principle the EU Member States, EFTA, EU candidate countries, and potential candidate countries. Time series starts mostly in year 1990. All data in energy balances are presented in terajoules, kilotonnes of oil equivalent and gigawatt hours.

Statistical population: CLIs are calculated for 33 OECD countries (Iceland is not included), 6 non-member economies and 8 zone aggregates. A country CLI comprises a set of component series selected from a wide range of key short-term economic indicators.   CLIs, reference series data (see below) and standardised business and consumer confidence indicators are presented in various forms.   Recommended uses and limitations: The composite leading indicator is a times series, formed by aggregating a variety of component indicators which show a reasonably consistent relationship with a reference series (e.g. industrial production IIP up to March 2012 and since then the reference series is GDP) at turning points. The OECD CLI is designed to provide qualitative information on short-term economic movements, especially at the turning points, rather than quantitative measures. Therefore, the main message of CLI movements over time is the increase or decrease, rather than the amplitude of the changes. The OECD’s headline indicator is the amplitude adjusted CLI. In practice, turning points in the de-trended reference series have been found about 4 to 8 months (on average) after the signals of turning points had been detected in the headline CLI.

Business surveys provide a rapid means of compiling simple statistics with the results available before those of traditional statistical methods, and provide also information on areas not covered by quantitative statistics. Details provided by respondents are generally of very high quality since the questions related to subjects with which they are familiar, thus the past and future performance of their business is highly reliable. Similarly, consumers provide a very high quality information on their purchasing information and price trend. Source: DG ECFIN

The consolidated banking statistics (CBS) measure international banking activity from a nationality perspective, focusing on the country where the banking group's parent is headquartered. While residence-based data such as the locational banking statistics indicate where positions are booked, they do not always identify where underlying decisions are made. This is because banking offices in one country may operate within a business model decided by the group's controlling parent, which may be headquartered in another country. The CBS capture the worldwide claims of banking groups based in reporting countries and exclude intragroup positions, similar to the consolidation approach followed by banking supervisors. The CBS provide several different measures of banking groups' country risk exposures, on either an immediate counterparty or an ultimate risk basis. The most appropriate exposure measure depends on the issue being analysed. The benchmark measure in the CBS is foreign claims, which capture credit to borrowers outside a banking group's home country.   Measure for all Combinations - Amounts Outstanding / Stocks   Note: Under "Reporting country" they have removed "Euro Area".   Data cited at : https://www.bis.org/statistics/index.htm

Below Parameters are common for all combinations : Frequency - Quarterly Measure -Amounts Outstanding / Stocks CBS Bank Type - Domestic Banks CBS Reporting Basis - Immediate Counterparty Basis Balance Sheet Position - Total Claims Type of Instruments - All Instruments Remaining Maturity - All Maturities Currency Type of Booking Location - All Currencies Counterparty Sector - All Sectors Data cited at : https://www.bis.org/statistics/index.htm

This dataset presents the Consolidated financial transactions by economic sector (Quarterly table 0610), according to SNA 2008 methodology. It comprises all flows, which record, by type of financial instruments, the financial transactions between institutional sectors.

Eurostat Dataset Id:ei_bsbu_m_r2 Six qualitative surveys are conducted on a monthly basis in the following areas: manufacturing industry, construction, consumers, retail trade, services and financial services. Some additional questions are asked on a quarterly basis in the surveys in industry, services, financial services, construction and among consumers. In addition, a survey is conducted twice a year on Investment in the manufacturing sector. The domain consists of a selection for variables from the following type of survey: Industry monthly questions for: production, employment expectations, order-book levels, stocks of finished products and selling price. Industry quarterly questions for:production capacity, order-books, new orders, export expectations, capacity utilization, Competitive position and factors limiting the production. Construction monthly questions for: trend of activity, order books, employment expectations, price expectations and factors limiting building activity. Construction quarterly questions for: operating time ensured by current backlog. Retail sales monthly questions for: business situation, stocks of goods, orders placed with suppliers and firm's employment. Services monthly questions for: business climate, evolution of demand, evolution of employment and selling prices. Services quarterly question for: factors limiting their business Consumer monthly questions for: financial situation, general economic situation, price trends, unemployment, major purchases and savings. Consumer quarterly questions for: intention to buy a car, purchase or build a home, home improvements. Financial services monthly questions for: business situation, evolution of demand and employment Financial services quarterly questions for: operating income, operating expenses, profitability of the company, capital expenditure and competitive position Monthly Confidence Indicators are computed for industry, services, construction, retail trade, consumers (at country level, EU and euro area level) and financial services (EU and euro area). An Economic Sentiment indicator (ESI) is calculated based on a selection of questions from industry, services, construction, retail trade and consumers at country level and aggregate level (EU and euro area). A monthly Euro-zone Business Climate Indicator is also available for industry. The data are published:as balance i.e. the difference between positive and negative answers (in percentage points of total answers)as indexas confidence indicators (arithmetic average of balances),at current level of capacity utilization (percentage)estimated months of production assured by orders (number of months)Unadjusted (NSA) and seasonally adjusted (SA)

Industry, Trade and Services statistics are part of Short-term statistics (STS), they give information on a wide range of economic activities according to NACE Rev.2 classification (Statistical Classification of Economic Activities in the European Community). The industrial import price indices offer information according to the CPA classification(Statistical Classification of Products by Activity in the European Economic Community). Construction indices are broken down by Classification of Types of Construction (CC). All data under this heading are index data. Percentage changes are also available for each indicator. The index data are presented in the following forms: UnadjustedCalendar adjustedSeasonally-adjusted Depending on the STS regulation, data are accessible monthly and quarterly. This heading covers the indicators listed below in four different sectors. Based on the national data, Eurostat compiles EU and euro area infra-annual economic statistics. Among these, a list of indicators, called Principal European Economic Indicators (PEEIs) has been identified by key users as being of prime importance for the conduct of monetary and economic policy of the euro area. These indicators are mainly released through Eurostat's website under the heading Euro-indicators. There are eight PEEIs contributed by STS and they are marked with * in the text below. INDUSTRYProduction Index*Turnover IndexProducer Prices (Domestic Output Prices index)*Import Prices Index*: Total, Euro area market, Non euro area market (euro area countries only)Labour Input Indicators: Number of Persons Employed, Hours Worked, Gross Wages and SalariesCONSTRUCTIONProduction Index*: Total of the construction sector, Building construction, Civil EngineeringLabour input indicators: Number of Persons Employed, Hours Worked, Gross Wages and SalariesConstruction costs IndexBuilding permits indicators*: Number of dwellingsWHOLESALE AND RETAIL TRADEVolume of sales (deflated turnover)*Turnover (in value)Labour input indicators: Number of Persons EmployedSERVICES  Turnover Index* Producer prices (Ouput prices)*

Industry, Trade and Services statistics are part of Short-term statistics (STS), they give information on a wide range of economic activities according to NACE Rev.2 classification (Statistical Classification of Economic Activities in the European Community). The industrial import price indices offer information according to the CPA classification(Statistical Classification of Products by Activity in the European Economic Community). Construction indices are broken down by Classification of Types of Construction (CC). All data under this heading are index data. Percentage changes are also available for each indicator. The index data are presented in the following forms:UnadjustedCalendar adjustedSeasonally-adjusted Depending on the STS regulation, data are accessible monthly and quarterly. This heading covers the indicators listed below in four different sectors. Based on the national data, Eurostat compiles EU and euro area infra-annual economic statistics. Among these, a list of indicators, called Principal European Economic Indicators (PEEIs) has been identified by key users as being of prime importance for the conduct of monetary and economic policy of the euro area. These indicators are mainly released through Eurostat's website under the heading Euro-indicators. There are eight PEEIs contributed by STS and they are marked with * in the text below. INDUSTRYProduction Index*Turnover IndexProducer Prices (Domestic Output Prices index)*Import Prices Index*: Total, Euro area market, Non euro area market (euro area countries only)Labour Input Indicators: Number of Persons Employed, Hours Worked, Gross Wages and Salaries CONSTRUCTIONProduction Index*: Total of the construction sector, Building construction, Civil EngineeringLabour input indicators: Number of Persons Employed, Hours Worked, Gross Wages and SalariesConstruction costs IndexBuilding permits indicators*: Number of dwellings WHOLESALE AND RETAIL TRADEVolume of sales (deflated turnover)*Turnover (in value)Labour input indicators: Number of Persons Employed SERVICES Turnover Index*Producer prices (Ouput prices)*

Eurostat Dataset Id:ei_bsbu_q_r2 Six qualitative surveys are conducted on a monthly basis in the following areas: manufacturing industry, construction, consumers, retail trade, services and financial services. Some additional questions are asked on a quarterly basis in the surveys in industry, services, financial services, construction and among consumers. In addition, a survey is conducted twice a year on Investment in the manufacturing sector. The domain consists of a selection for variables from the following type of survey: Industry monthly questions for: production, employment expectations, order-book levels, stocks of finished products and selling price. Industry quarterly questions for:production capacity, order-books, new orders, export expectations, capacity utilization, Competitive position and factors limiting the production. Construction monthly questions for: trend of activity, order books, employment expectations, price expectations and factors limiting building activity. Construction quarterly questions for: operating time ensured by current backlog. Retail sales monthly questions for: business situation, stocks of goods, orders placed with suppliers and firm's employment. Services monthly questions for: business climate, evolution of demand, evolution of employment and selling prices. Services quarterly question for: factors limiting their business Consumer monthly questions for: financial situation, general economic situation, price trends, unemployment, major purchases and savings. Consumer quarterly questions for: intention to buy a car, purchase or build a home, home improvements. Financial services monthly questions for: business situation, evolution of demand and employment Financial services quarterly questions for: operating income, operating expenses, profitability of the company, capital expenditure and competitive position Monthly Confidence Indicators are computed for industry, services, construction, retail trade, consumers (at country level, EU and euro area level) and financial services (EU and euro area). An Economic Sentiment indicator (ESI) is calculated based on a selection of questions from industry, services, construction, retail trade and consumers at country level and aggregate level (EU and euro area). A monthly Euro-zone Business Climate Indicator is also available for industry. The data are published: as balance i.e. the difference between positive and negative answers (in percentage points of total answers)as indexas confidence indicators (arithmetic average of balances),at current level of capacity utilization (percentage)estimated months of production assured by orders (number of months)Unadjusted (NSA) and seasonally adjusted (SA)

Structural business statistics (SBS) describes the structure, conduct and performance of economic activities, down to the most detailed activity level (several hundred economic sectors). SBS are transmitted annually by the EU Member States on the basis of a legal obligation from 1995 onwards.   SBS covers all activities of the business economy with the exception of agricultural activities and personal services and the data are provided by all EU Member States, Norway and Switzerland, some candidate and potential candidate countries. The data are collected by domain of activity (annex) : Annex I - Services, Annex II - Industry, Annex III - Trade and Annex IV- Constructions and by datasets. Each annex contains several datasets as indicated in the SBS Regulation. The majority of the data is collected by National Statistical Institutes (NSIs) by means of statistical surveys, business registers or from various administrative sources. Regulatory or controlling national offices for financial institutions or central banks often provide the information required for the financial sector (NACE Rev 2 Section K / NACE Rev 1.1 Section J). Member States apply various statistical methods, according to the data source, such as grossing up, model based estimation or different forms of imputation, to ensure the quality of SBSs produced. Main characteristics (variables) of the SBS data category:Business Demographic variables (e.g. Number of enterprises)"Output related" variables (e.g. Turnover, Value added)"Input related" variables: labour input (e.g. Employment, Hours worked); goods and services input (e.g. Total of purchases); capital input (e.g. Material investments) All SBS characteristics are published on Eurostat’s website by tables and an example of the existent tables is presented below:Annual enterprise statistics: Characteristics collected are published by country and detailed on NACE Rev 2 and NACE Rev 1.1 class level (4-digits). Some classes or groups in 'services' section have been aggregated.Annual enterprise statistics broken down by size classes: Characteristics are published by country and detailed down to NACE Rev 2 and NACE Rev 1.1 group level (3-digits) and employment size class. For trade (NACE Rev 2 and NACE Rev 1.1 Section G) a supplementary breakdown by turnover size class is available.Annual regional statistics: Four characteristics are published by NUTS-2 country region and detailed on NACE Rev 2 and NACE Rev 1.1 division level (2-digits) (but to group level (3-digits) for the trade section). More information on the contents of different tables: the detail level and breakdowns required starting with the reference year 2008 is defined in Commission Regulation N° 251/2009. For previous reference years it is included in Commission Regulations (EC) N° 2701/98 and amended by Commission Regulation N°1614/2002 and Commission Regulation N°1669/2003. Several important derived indicators are generated in the form of ratios of certain monetary characteristics or per head values. A list with the available derived indicators is available below in the Annex.

The objective of the construction cost index is to show the development of costs incurred by the contractor/producer to carry out the construction process, in others words the cost of labour, materials and plant and overheads. The construction sector corresponds to the NACE Rev. 2 section F but the split between building construction and civil engineering (CC1, CC2) is based on the Classification of types of Construction. Building construction (CC1) is further subdivided in residential building (CC11) and non-residential building (CC12). The data provided in this table concerns residential building excluding the subgroup of residencies for communities (CC11 excluding CC113). Data are not seasonally adjusted.

This dataset contains indicators related to construction for provincial level.

Data cited at: Consumer price indexes, The International Monetary Fund Consumer price indexes (CPIs) are index numbers that measure changes in the prices of goods and services purchased or otherwise acquired by households, which households use directly, or indirectly, to satisfy their own needs and wants. In practice, most CPIs are calculated as weighted averages of the percentage price changes for a specified set, or ‘‘basket’’, of consumer products, the weights reflecting their relative importance in household consumption in some period. CPIs are widely used to index pensions and social security benefits. CPIs are also used to index other payments, such as interest payments or rents, or the prices of bonds. CPIs are also commonly used as a proxy for the general rate of inflation, even though they measure only consumer inflation. They are used by some governments or central banks to set inflation targets for purposes of monetary policy. The price data collected for CPI purposes can also be used to compile other indices, such as the price indices used to deflate household consumption expenditures in national accounts, or the purchasing power parities used to compare real levels of consumption in different countries.

Eurostat Dataset Id:ei_bsco_m Six qualitative surveys are conducted on a monthly basis in the following areas: manufacturing industry, construction, consumers, retail trade, services and financial services. Some additional questions are asked on a quarterly basis in the surveys in industry, services, financial services, construction and among consumers. In addition, a survey is conducted twice a year on Investment in the manufacturing sector. The domain consists of a selection for variables from the following type of survey: Industry monthly questions for: production, employment expectations, order-book levels, stocks of finished products and selling price. Industry quarterly questions for:production capacity, order-books, new orders, export expectations, capacity utilization, Competitive position and factors limiting the production. Construction monthly questions for: trend of activity, order books, employment expectations, price expectations and factors limiting building activity. Construction quarterly questions for: operating time ensured by current backlog. Retail sales monthly questions for: business situation, stocks of goods, orders placed with suppliers and firm's employment. Services monthly questions for: business climate, evolution of demand, evolution of employment and selling prices. Services quarterly question for: factors limiting their business Consumer monthly questions for: financial situation, general economic situation, price trends, unemployment, major purchases and savings. Consumer quarterly questions for: intention to buy a car, purchase or build a home, home improvements. Financial services monthly questions for: business situation, evolution of demand and employment Financial services quarterly questions for: operating income, operating expenses, profitability of the company, capital expenditure and competitive position Monthly Confidence Indicators are computed for industry, services, construction, retail trade, consumers (at country level, EU and euro area level) and financial services (EU and euro area). An Economic Sentiment indicator (ESI) is calculated based on a selection of questions from industry, services, construction, retail trade and consumers at country level and aggregate level (EU and euro area). A monthly Euro-zone Business Climate Indicator is also available for industry. The data are published:as balance i.e. the difference between positive and negative answers (in percentage points of total answers)as indexas confidence indicators (arithmetic average of balances),at current level of capacity utilization (percentage)estimated months of production assured by orders (number of months)Unadjusted (NSA) and seasonally adjusted (SA)

Eurostat Dataset Id:ei_bsco_q Six qualitative surveys are conducted on a monthly basis in the following areas: manufacturing industry, construction, consumers, retail trade, services and financial services. Some additional questions are asked on a quarterly basis in the surveys in industry, services, financial services, construction and among consumers. In addition, a survey is conducted twice a year on Investment in the manufacturing sector. The domain consists of a selection for variables from the following type of survey: Industry monthly questions for: production, employment expectations, order-book levels, stocks of finished products and selling price. Industry quarterly questions for:production capacity, order-books, new orders, export expectations, capacity utilization, Competitive position and factors limiting the production. Construction monthly questions for: trend of activity, order books, employment expectations, price expectations and factors limiting building activity. Construction quarterly questions for: operating time ensured by current backlog. Retail sales monthly questions for: business situation, stocks of goods, orders placed with suppliers and firm's employment. Services monthly questions for: business climate, evolution of demand, evolution of employment and selling prices. Services quarterly question for: factors limiting their business Consumer monthly questions for: financial situation, general economic situation, price trends, unemployment, major purchases and savings. Consumer quarterly questions for: intention to buy a car, purchase or build a home, home improvements. Financial services monthly questions for: business situation, evolution of demand and employment Financial services quarterly questions for: operating income, operating expenses, profitability of the company, capital expenditure and competitive position Monthly Confidence Indicators are computed for industry, services, construction, retail trade, consumers (at country level, EU and euro area level) and financial services (EU and euro area). An Economic Sentiment indicator (ESI) is calculated based on a selection of questions from industry, services, construction, retail trade and consumers at country level and aggregate level (EU and euro area). A monthly Euro-zone Business Climate Indicator is also available for industry. The data are published: as balance i.e. the difference between positive and negative answers (in percentage points of total answers)as indexas confidence indicators (arithmetic average of balances),at current level of capacity utilization (percentage)estimated months of production assured by orders (number of months)Unadjusted (NSA) and seasonally adjusted (SA)

Fertilisers contain important nutrients, such as nitrogen (N) and phosphorus (P), which plants absorb from the soil for their growth. With the harvest of crops for human and livestock consumption and industrial uses, N and P is removed from the soil. Continuing agricultural production without fertilisation could lead to soil degradation and erosion. Fertilisers are therefore essential to sustain agricultural production. Fertilisers are also used to improve crop yields and soils. The use of manufactured fertilizers as a regular farming practice began in most European countries in the mid to late nineteenth century but the greatest increase in consumption in these countries occurred in the three decades following World War II. The manufacturing of fertilisers greatly enhanced crop yields and agricultural production, and aided the large increase in the world population in the 20th Century. However when the amount of fertiliser applied exceeds the plants' nutritional requirements, there is a greater risk of nutrient losses from agricultural soils into ground and surface water. The resulting higher concentration of nutrients (eutrophication) can cause serious degradation of ecosystems. With the storage and application to the land of manufactured fertilisers, Nitrogen can volatilise into the air as ammonia contributing to acidification, eutrophication and atmospheric particulate pollution, and nitrous oxides, a potent greenhouse gas contributing to climate change.  In addition fertilisers may also have adverse environmental effects resulting from their production processes. More specifically, nitrogenous fertilisers require large amounts of energy to be produced leading potentially to higher levels of greenhouse gas emissions. In a different way, phosphorus fertilisers also have an environmental impact, since the raw materials used to produce them are mined, therefore potentially leading to landscape destruction, water contamination, excessive water consumption or air pollution. This table contains data on the total use of manufactured fertilisers expressed in tonnes of N and tonnes of P received from the countries. Manufactured fertilisers are also often referred to as inorganic fertilisers or mineral fertilisers. For a definition see 3.4.

For the countries belonging to the euro area, these are factors for converting euro fixed series (NAC) into euro/ECU series, and vice-versa.

Data cited at: Coordinated Direct Investment Survey, The International Monetary Fund. The CDIS database presents detailed data on "inward" direct investment positions (i.e., direct investment into the reporting economy) cross-classified by economy of immediate investor, and data on "outward" direct investment positions (i.e., direct investment abroad by the reporting economy) cross-classified by economy of immediate investment. The CDIS database contains breakdowns of direct investment position data, including, in most instances, separate data on net equity and net debt positions, as well as tables that present "mirror" data (i.e., tables in which data from the reporting economy are shown side-by-side with the data obtained from all other counterpart reporting economies).

Data cited at: Coordinated Portfolio Investment Survey (CPIS), The International Monetary Fund. The Coordinated Portfolio Investment Survey (CPIS) is a voluntary data collection exercise conducted under the auspices of the IMF. An economy provides data on its holdings of portfolio investment securities (data are separately requested for equity and investment fund shares, long-term debt instruments, and short-term debt instruments).   Worldwide portfolio holdings of equity and investment fund shares (31 USD trillion) at end-2017 surpasses holdings of debt securities (29.7 USD trillion). After the peak of the financial crisis in 2008, the annual growth rate of equity holdings has exceeded substantially that for debt securities holdings. That pattern is similar in all the economies with the largest cross border portfolio assets and liabilities. As per G20 emerging economies, while the holdings of equity and investment fund shares had already been consistently higher than those of debt securities, during the last five years the gap has widened even further.

Bloom Consulting was founded in 2003 as a Nation Branding consultancy. Our Headquarters are located in Madrid, with offices in Lisbon and São Paulo. Bloom Consulting has been interviewed by The Economist, Forbes and CNN . According to Country Branding Central www.countrybrandingwiki.org, our CEO José Filipe Torres, a recurrent lecturer in Universities such as Harvard, is considered one of the top 3 international experts in the field of Nation Branding, Region and City Branding, providing advisory for the OECD. In addition, Bloom Consulting publishes the Bloom Consulting Country Brand Ranking © annually for both Trade and Tourism, to extensively analyze the brand performance of 193 countries and territories worldwide and the Digital Country Index - Measuring the Brand appeal of countries and territories in the Digital World.

Reports - Statistical Releases E.16 Country Exposure Lending Survey and Country Exposure Information Report

Data cited at: Our World in data-https://ourworldindata.org/coronavirus-source-data 

These Community Mobility Reports aim to provide insights into what has changed in response to policies aimed at combating COVID-19. The reports chart movement trends over time by geography, across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential.

Note: For Measures-"Lower Credible (50%), Upper Credible(50%),Probability of Control", the data is available as of 25th June 2020 and there will be no further update for these measures as discontinued at source level. Latest estimates of the number of confirmed cases by date of infection, the effective reproduction number, and the doubling time (when negative this corresponds to the halving time) in each region. The mean and 90% credible interval is shown. Data cited at https://epiforecasts.io/covid/posts/global/

The Credit to Agriculture dataset provides national data for over 100 countries on the amount of loans provided by the private/commercial banking sector to producers in agriculture, forestry and fisheries, including household producers, cooperatives, and agro-businesses. For some countries, the three sub sectors of agriculture, forestry, and fishing are completely specified. In other cases, complete dis aggregations are not available. The dataset also provides statistics on the total credit to all industries, indicators on the share of credit to agricultural producers, and an agriculture orientation index (the agriculture share of credit, over the agriculture share of GDP).

The data set on credit to the non-financial sector captures borrowing activity by the government sector and the private non-financial sector in more than 40 economies. Quarterly data on credit to the government sector cover on average 20 years, while those on credit to the private non-financial sector on average more than 45 years. The statistics follow the framework of the System of National Accounts.   Data cited at: Bank for International Settlements (2024), Credit to the non-financial sector, BIS WS_TC 2.0 (data set), https://data.bis.org/topics/TOTAL_CREDIT/data.

Crop output is valued at basic prices. The basic price is defined as the price received by the producer, after deduction of all taxes on products but including all subsidies on products. The concept of output comprises sales, changes in stocks, and crop products used as animal feedingstuffs, for processing and own final use by the producers.

Crop statistics refer to the following types of annual data: area under cultivation, harvested production, yield,  humidity and main area for cereals and for other main field crops (mainly dried pulses, root crops, fodder and industrial crops);harvested area, harvested production and main area for vegetables ;production area, harvested production and main area for permanent crop.The data are provided at national level. For some products regional figures (NUTS 1 or 2) are available too. The areas  are expressed in 1 000 hectares,  the harvested quantities in 1 000 tonnes and the yields in t/ha. The production and yield data are available in EU standard humidity (apro_cpsh) and in national humidity (apro_cpnh). The information concerns more than 100 crop products. The earliest data are available from 1955 for cereals and from the early 1960's for fruits and vegetables. However, most Member States have started to send in data in the 1970's and 1980's. The statistical system has progressively improved and enlarged. The current Regulation (EC) No 543/2009 entered into force in January 2010. The annex was updated in 2015 through a Commission Delegated Regulation (EU) No 2015/1557. At present Eurostat receives and publishes harmonised statistical data from 28 Member States, form the EFTA countries and from the candidate and potential candidate countries broken down in: 17 categories and subcategories for cereals;29 categories and  subcategories for other main crops (mainly dry pulses and protein crops, root crops industrial crops and plants harvested green from arable land);40 categories and subcategories for vegetables;41 categories and subcategories for permanent crops;18 categories and subcategories for the Utilised Agricultural Area (UAA). For the full list of crops, please consult Annex. Some additional crops and transmission deadlines are covered by an ESS agreement on annual crop statistics. The main data sources are administrative records, surveys and expert estimates. National Statistical Institutes or Ministries of Agriculture are responsible for the national data collection in accordance with the Regulations and agreements in force. Eurostat is responsible for drawing the EU aggregations. Regional metadata Please note that for paragraphs where no metadata for regional data has been specified the regional metadata is identical to the metadata for the national data.

Crop statistics refer to the following types of annual data: area under cultivation, harvested production, yield,  humidity and main area for cereals and for other main field crops (mainly dried pulses, root crops, fodder and industrial crops);harvested area, harvested production and main area for vegetables ;production area, harvested production and main area for permanent crop.The data are provided at national level. For some products regional figures (NUTS 1 or 2) are available too. The areas  are expressed in 1 000 hectares,  the harvested quantities in 1 000 tonnes and the yields in t/ha. The production and yield data are available in EU standard humidity (apro_cpsh) and in national humidity (apro_cpnh). The information concerns more than 100 crop products. The earliest data are available from 1955 for cereals and from the early 1960's for fruits and vegetables. However, most Member States have started to send in data in the 1970's and 1980's. The statistical system has progressively improved and enlarged. The current Regulation (EC) No 543/2009 entered into force in January 2010. The annex was updated in 2015 through a Commission Delegated Regulation (EU) No 2015/1557. At present Eurostat receives and publishes harmonised statistical data from 28 Member States, form the EFTA countries and from the candidate and potential candidate countries broken down in: 17 categories and subcategories for cereals;29 categories and  subcategories for other main crops (mainly dry pulses and protein crops, root crops industrial crops and plants harvested green from arable land);40 categories and subcategories for vegetables;41 categories and subcategories for permanent crops;18 categories and subcategories for the Utilised Agricultural Area (UAA). For the full list of crops, please consult Annex. Some additional crops and transmission deadlines are covered by an ESS agreement on annual crop statistics. The main data sources are administrative records, surveys and expert estimates. National Statistical Institutes or Ministries of Agriculture are responsible for the national data collection in accordance with the Regulations and agreements in force. Eurostat is responsible for drawing the EU aggregations. Regional metadata Please note that for paragraphs where no metadata for regional data has been specified the regional metadata is identical to the metadata for the national data.

Crop statistics refer to the following types of annual data:area under cultivation, harvested production, yield,  humidity and main area for cereals and for other main field crops (mainly dried pulses, root crops, fodder and industrial crops);harvested area, harvested production and main area for vegetables ;production area, harvested production and main area for permanent crop. The data are provided at national level. For some products regional figures (NUTS 1 or 2) are available too. The areas  are expressed in 1 000 hectares,  the harvested quantities in 1 000 tonnes and the yields in t/ha. The production and yield data are available in EU standard humidity (apro_cpsh) and in national humidity (apro_cpnh). The information concerns more than 100 crop products. The earliest data are available from 1955 for cereals and from the early 1960's for fruits and vegetables. However, most Member States have started to send in data in the 1970's and 1980's. The statistical system has progressively improved and enlarged. The current Regulation (EC) No 543/2009 entered into force in January 2010. The annex was updated in 2015 through a Commission Delegated Regulation (EU) No 2015/1557. At present Eurostat receives and publishes harmonised statistical data from 28 Member States, form the EFTA countries and from the candidate and potential candidate countries broken down in:17 categories and subcategories for cereals;29 categories and  subcategories for other main crops (mainly dry pulses and protein crops, root crops industrial crops and plants harvested green from arable land);40 categories and subcategories for vegetables;41 categories and subcategories for permanent crops;18 categories and subcategories for the Utilised Agricultural Area (UAA). For the full list of crops, please consult Annex. Some additional crops and transmission deadlines are covered by an ESS agreement on annual crop statistics. The main data sources are administrative records, surveys and expert estimates. National Statistical Institutes or Ministries of Agriculture are responsible for the national data collection in accordance with the Regulations and agreements in force. Eurostat is responsible for drawing the EU aggregations. Regional metadata Please note that for paragraphs where no metadata for regional data has been specified the regional metadata is identical to the metadata for the national data.

National accounts are a coherent and consistent set of macroeconomic indicators, which provide an overall picture of the economic situation and are widely used for economic analysis and forecasting, policy design and policy making. Eurostat publishes annual and quarterly national accounts, annual and quarterly sector accounts as well as supply, use and input-output tables, which are each presented with associated metadata. Annual national accounts are compiled in accordance with the European System of Accounts - ESA 2010 as defined in Annex B of the Council Regulation (EU) No 549/2013 of the European Parliament and of the Council of 21 May 2013. The previous European System of Accounts, ESA95, was reviewed to bring national accounts in the European Union, in line with new economic environment, advances in methodological research and needs of users and the updated national accounts framework at the international level, the SNA 2008. The revisions are reflected in an updated Regulation of the European Parliament and of the Council on the European system of national and regional accounts in the European Union of 2010 (ESA 2010). The associated transmission programme is also updated and data transmissions in accordance with ESA 2010 are compulsory from September 2014 onwards. Further information on the transition from ESA 95 to ESA 2010 is presented on the Eurostat website.

National accounts are a coherent and consistent set of macroeconomic indicators, which provide an overall picture of the economic situation and are widely used for economic analysis and forecasting, policy design and policy making. Eurostat publishes annual and quarterly national accounts, annual and quarterly sector accounts as well as supply, use and input-output tables, which are each presented with associated metadata. Even though consistency checks are a major aspect of data validation, temporary (usually limited) inconsistencies between datasets may occur, mainly due to vintage effects. Annual national accounts are compiled in accordance with the European System of Accounts - ESA 2010 as defined in Annex B of the Council Regulation (EU) No 549/2013 of the European Parliament and of the Council of 21 May 2013.   The previous European System of Accounts, ESA95, was reviewed to bring national accounts in the European Union, in line with new economic environment, advances in methodological research and needs of users and the updated national accounts framework at the international level, the SNA 2008. The revisions are reflected in an updated Regulation of the European Parliament and of the Council on the European system of national and regional accounts in the European Union of 2010 (ESA 2010). The associated transmission programme is also updated and data transmissions in accordance with ESA 2010 are compulsory from September 2014 onwards. Further information (including actual communications) is presented on the Eurostat website. The domain consists of the following collections:   1. Main GDP aggregates: main components from the output, expenditure and income side, expenditure breakdowns by durability and exports and imports by origin. <

Japan: Crude Oil/Petroleum Products Import (Amount in Yen), Monthly Update

Monthly data on crude oil imports by country and by oil type in Japan.

Note: (1) Source: Council Regulation (EC) n°2964/95 of 20 December 1995. (2) The cif price includes the fob price (the price actually invoiced at the port of loading), the cost of transport, insurance and certain charges linked to crude oil transfer operations. (3) Due to confidentiality Czech Republic is excluded from EU(28). (4) For Romania November-2016 and December-2016 are estimations derived from Eurostat data

Statistics on culture cover many aspects of economic and social life. According to the Europe 2020 strategy, the role of culture is crucial for achieving the goal of a "smart, sustainable and inclusive" growth. Employment in cultural sector statistics aim at investigating on the dimension of the contribution of cultural employment to the overall employment. Cultural employment statistics are derived from data on employment based on the results of the European Labour Force Survey (see EU-LFS metadata) that is the main source of information about the situation and trends on the labour market in the European Union. The final report of the European Statistical System Network on Culture (ESS-Net Culture Report 2012, in particular pp. 129-226) deals with the methodology applied to cultural statistics, including the scope of the 'cultural economic activities' and 'cultural occupations' based on two reference classifications: the NACE classification (‘Nomenclature générale des Activités économiques dans les Communautés Européennes’) which classifies the employer’s main activity, andthe ISCO classification(‘International Standard Classification of Occupations’) which classifies occupations. Results from the EU-LFS allow to characterize cultural employment by different variables such as gender, age, employment status, working time, educational attainment, permanency of jobs by cross-tabulating ISCO and NACE cultural codes as defined in the ESS-Net Culture Report 2012 (Annex 3 – Table 26 and Annex 4 – Table 27).

Current taxes on income, wealth, etc. (ESA 2010 code D.5) cover all compulsory, unrequited payments, in cash or in kind, levied periodically by general government and by the rest of the world on the income and wealth of institutional units, and some periodic taxes which are assessed neither on that income nor that wealth. In ESA 2010, current taxes on income, wealth, etc. are divided into taxes on income and other current taxes.

Global Surface Summary of the Day is derived from The Integrated Surface Hourly (ISH) dataset. The ISH dataset includes global data obtained from the USAF Climatology Center, located in the Federal Climate Complex with NCDC. The latest daily summary data are normally available 1-2 days after the date-time of the observations used in the daily summaries. Data cited at: https://www.ncei.noaa.gov/data/global-summary-of-the-day/archive/

Euro-zone series: Until December 1998 it is an aggregate of interbank deposit bid rates weighted by country GDP (Gross Domestic Product). Thereafter the rate is the EONIA (Euro OverNight Index Average), the effective overnight reference rate for the euro, computed as a weighted average of all overnight unsecured lending transactions in the interbank market, initiated within the euro area by the contributing panel banks. EONIA is computed with the help of the European Central Bank. EU15 series: Until December 1998, this is a theoretical rate based on an aggregation of day-to-day rates weighted by country GDP. Thereafter the rate is an average of the EONIA and the rates of the non-euro-zone countries, weighted by country GDP. National series: broadly speaking, these are day-to-day interbank rates. Source: European Central Bank.

Data cited at : https://www.bis.org/statistics/index.htm

The focus of this domain is on the European Neighbourhood Policy (ENP) countries on the southern and eastern shores of the Mediterranean (ENP-South), namely: Algeria (DZ),Egypt (EG),Israel (IL),Jordan (JO),Lebanon (LB),Libya (LY),Morocco (MA),Palestine (PS),Syria (SY) andTunisia (TN). An extensive range of indicators is presented in this domain, including indicators from almost every theme covered by European statistics. Only annual data are published in this domain. The data and their denomination in no way constitute the expression of an opinion by the European Commission on the legal status of a country or territory or on the delimitation of its borders.

This dataset contains indicators related to population for provincial level.

Data are collected directly from countries using the FAO questionnaire, which was developed by the International Monetary Fund, as the IMF collects global data on government expenditures and is the international organization responsible for developing guidelines on the Classification of Functions of Government (COFOG) . The FAO Questionnaire is also of interest to the questionnaire. The FAO dataset consists of a time series, from 2000 onwards, of government expenditures in: (1) Total; (2) Economic affairs; (3) Agriculture, Forestry, Fishing and Hunting, along with its three disaggregated subsectors of Agriculture, Forestry and Fishing; and 4) Environmental Protection. In addition, The purpose of the financial statements is to provide a more detailed description of the accounts and the future of the financial statements. Additional indicators include the total share of agriculture allocated to agriculture, and an agriculture index orientation (ratio of the total share of government expenditure to agriculture, over the total share of GDP from agricultural value-added). Contains the concept of a comprehensive set of data and provides a comprehensive set of information for the entire world. Additional indicators include the total share of agriculture allocated to agriculture, and an agriculture index orientation (ratio of the total share of government expenditure to agriculture, over the total share of GDP from agricultural value-added). Contains the concept of a comprehensive set of data and provides a comprehensive set of information for the entire world. Additional indicators include the total share of agriculture allocated to agriculture, and an agriculture index orientation (ratio of the total share of government expenditure to agriculture, over the total share of GDP from agricultural value-added). Contains the concept of a comprehensive set of data and provides a comprehensive set of information for the entire world.

As a consequence of the implementation of the new OECD Global Insurance Statistics' framework, there is a break in series between 2008 and 2009 regarding life and non-life business datawhere composite insurance undertakings exist. Up until 2008, the insurance business is broken down between life and non-life business. As of 2009, the insurance business is broken down between the business of pure life, pure non-life and composite undertakings and composite undertakings' business is further broken down between life and non-life business. Some countries do not allow for insurance undertakings to be active in both life and non-life insurance business and therefore composite insurance undertakings do not exist in these countries. In other countries (e.g., Austria, Belgium, Hungary, Italy, Mexico, Portugal, Spain) however, the share of employment in composite insurance undertakings accounts for more than half of the whole domestic insurance sector. Therefore, to have comparable data across years for life business data (resp. non-life), one has to sum up the life (resp. non-life) business of pure life (resp. non-life) undertakings and the life (resp. non-life) business of composite undertakings as of 2009. Click to collapse Item coverage Outstanding investment by direct insurance companies, classified by investment category, by the companies' nationality and by its destination (domestic or foreign). As of 2009, investment data exclude assets linked to unit-linked products sold to policyholders.

Financing Global Health 2019: Tracking Health Spending in a Time of Crisis This edition of the Institute for Health Metrics and Evaluation’s annual Financing Global Health report, the 11th in the series, provides up-to-date estimates of domestic spending on health, development assistance for health, spending for HIV/AIDS, tuberculosis, and malaria, as well as projections of future health spending. Our health spending tracking and estimates show patterns between income groups and regions over time, highlight variations in how much each country spends on health, and identify where more resources are needed most. Financing Global Health 2020: Tracking Health Spending in a Time of Crisis This edition of IHME's annual Financing Global Health report, the 12th in the series, provides updated estimates of spending on health, development assistance for health, and projections of future health spending. This year's report specifically highlights changes in health spending during a global pandemic and includes dedicated sections on COVID-19, as well as in-depth analysis of pandemic preparedness spending and seven other health focus areas.

In latest update, the total value of sovereign debt in default at US$443.2 billion in 2020 (0.5 percent of world public debt), up US$143.6 billion (48 percent) from the revised total of US$299.6 billion in 2019. This outpaces the 13 percent increase in gross world public debt. The data by major creditor categories show that the increase was driven mainly by foreign currency bonds in default, which rose by US$121.2 billion. This reflected:new defaults by Argentina, Belize, Ecuador and Surinamea first-time default on foreign currency bonds by Lebanona greater amount of interest arrears from ongoing bond defaults by Venezuela and Puerto Rico Local currency debt in default increased by US$0.9 billion. This was due mainly to Iraq’s restructuring of its short-term debt into debt with longer-term maturities and lower coupon rates.

Data cited at: Direction of Trade Statistics, The International Monetary Fund. The Direction of Trade Statistics (DOTS) presents current figures on the value of merchandise exports and imports dis-aggregated according to a country's primary trading partners. Area and world aggregates are included in the display of trade flows between major areas of the world. Reported data is supplemented by estimates whenever such data is not available or current. Imports are reported on a cost, insurance and freight (CIF) basis and exports are reported on a free on board (FOB) basis, with the exception of a few countries for which imports are also available FOB. Time series data includes estimates derived from reports of partner countries for non-reporting and slow-reporting countries.

This indicator is defined as the percentage of persons with an equivalised disposable income below respectively 40%, 50%, 60% and 70% of the national median equivalised disposable income.

The domain "Income and living conditions" covers four topics: people at risk of poverty or social exclusion, income distribution and monetary poverty, living conditions and material deprivation, which are again structured into collections of indicators on specific topics. The collection "People at risk of poverty or social exclusion" houses main indicator on risk of poverty or social inclusion included in the Europe 2020 strategy as well as the intersections between sub-populations of all Europe 2020 indicators on poverty and social exclusion. The collection "Income distribution and monetary poverty" houses collections of indicators relating to poverty risk, poverty risk of working individuals as well as the distribution of income. The collection "Living conditions" hosts indicators relating to characteristics and living conditions of households, characteristics of the population according to different breakdowns, health and labour conditions, housing conditions as well as childcare related indicators. The collection "Material deprivation" covers indicators relating to economic strain, durables, housing deprivation and environment of the dwelling.

The disposable income of private households is the balance of primary income (operating surplus/mixed income plus compensation of employees plus property income received minus property income paid) and the redistribution of income in cash. These transactions comprise social contributions paid, social benefits in cash received, current taxes on income and wealth paid, as well as other current transfers. Disposable income does not include social transfers in kind coming from public administrations or non-profit institutions serving households.

Les personnes pourvues d'un emploi sont toutes les personnes en âge de travailler qui, durant une brève période de référence spécifiée, se trouvaient dans une des catégories suivantes: a) emploi salarié (soit au travail ou ayant un emploi mais pas au travail), b) emploi non salarié (soit au travail ou ayant une entreprise mais pas au travail). Les données sont présentées par activité économique, qui fait référence à l'activité principale de l'établissement dans lequel la personne a travaillé pendant la période de référence, et ne dépend pas des tâches ou des fonctions spécifiques du travail de la personne, mais des caractéristiques de l'entité économique dans laquelle cette personne travaille. Cette série fait partie des estimations du BIT et est harmonisée pour tenir compte des différences entre les données nationales, la portée de la couverture, les méthodologies de collecte et de tabulation, et de facteurs spécifiques aux pays. Les données pour 1991-2016 sont des estimations tandis que les données pour 2017-2021 sont des projections. La base de données a été mise à jour en Novembre 2017. Pour plus d'informations, consultez la description de l'indicateur et le document méthodologique sur les estimations et projections du BIT (en anglais).

Les personnes pourvues d'un emploi sont toutes les personnes en âge de travailler qui, durant une brève période de référence spécifiée, se trouvaient dans une des catégories suivantes: a) emploi salarié (soit au travail ou ayant un emploi mais pas au travail), b) emploi non salarié (soit au travail ou ayant une entreprise mais pas au travail). Les données sont présentées par activité économique, qui fait référence à l'activité principale de l'établissement dans lequel la personne a travaillé pendant la période de référence, et ne dépend pas des tâches ou des fonctions spécifiques du travail de la personne, mais des caractéristiques de l'entité économique dans laquelle cette personne travaille. Cette série fait partie des estimations du BIT et est harmonisée pour tenir compte des différences entre les données nationales, la portée de la couverture, les méthodologies de collecte et de tabulation, et de facteurs spécifiques aux pays. Les données pour 1991-2016 sont des estimations tandis que les données pour 2017-2021 sont des projections. La base de données a été mise à jour en Novembre 2017. Pour plus d'informations, consultez la description de l'indicateur et le document méthodologique sur les estimations et projections du BIT (en anglais).

Measles cases are defined as laboratory confirmed, epidemiologically linked, and clinical cases as reported to the World Health Organization.

The domain "Income and living conditions" covers four topics: people at risk of poverty or social exclusion, income distribution and monetary poverty, living conditions and material deprivation, which are again structured into collections of indicators on specific topics. The collection "People at risk of poverty or social exclusion" houses main indicator on risk of poverty or social inclusion included in the Europe 2020 strategy as well as the intersections between sub-populations of all Europe 2020 indicators on poverty and social exclusion. The collection "Income distribution and monetary poverty" houses collections of indicators relating to poverty risk, poverty risk of working individuals as well as the distribution of income. The collection "Living conditions" hosts indicators relating to characteristics and living conditions of households, characteristics of the population according to different breakdowns, health and labour conditions, housing conditions as well as childcare related indicators. The collection "Material deprivation" covers indicators relating to economic strain, durables, housing deprivation and environment of the dwelling.

The domain "Income and living conditions" covers four topics: people at risk of poverty or social exclusion, income distribution and monetary poverty, living conditions and material deprivation, which are again structured into collections of indicators on specific topics. The collection "People at risk of poverty or social exclusion" houses main indicator on risk of poverty or social inclusion included in the Europe 2020 strategy as well as the intersections between sub-populations of all Europe 2020 indicators on poverty and social exclusion. The collection "Income distribution and monetary poverty" houses collections of indicators relating to poverty risk, poverty risk of working individuals as well as the distribution of income. The collection "Living conditions" hosts indicators relating to characteristics and living conditions of households, characteristics of the population according to different breakdowns, health and labour conditions, housing conditions as well as childcare related indicators. The collection "Material deprivation" covers indicators relating to economic strain, durables, housing deprivation and environment of the dwelling.

Structural business statistics (SBS) describes the structure, conduct and performance of economic activities, down to the most detailed activity level (several hundred economic sectors). SBS are transmitted annually by the EU Member States on the basis of a legal obligation from 1995 onwards.   SBS covers all activities of the business economy with the exception of agricultural activities and personal services and the data are provided by all EU Member States, Norway and Switzerland, some candidate and potential candidate countries. The data are collected by domain of activity (annex) : Annex I - Services, Annex II - Industry, Annex III - Trade and Annex IV- Constructions and by datasets. Each annex contains several datasets as indicated in the SBS Regulation. The majority of the data is collected by National Statistical Institutes (NSIs) by means of statistical surveys, business registers or from various administrative sources. Regulatory or controlling national offices for financial institutions or central banks often provide the information required for the financial sector (NACE Rev 2 Section K / NACE Rev 1.1 Section J). Member States apply various statistical methods, according to the data source, such as grossing up, model based estimation or different forms of imputation, to ensure the quality of SBSs produced. Main characteristics (variables) of the SBS data category: Business Demographic variables (e.g. Number of enterprises)"Output related" variables (e.g. Turnover, Value added)"Input related" variables: labour input (e.g. Employment, Hours worked); goods and services input (e.g. Total of purchases); capital input (e.g. Material investments) All SBS characteristics are published on Eurostat’s website by tables and an example of the existent tables is presented below: Annual enterprise statistics: Characteristics collected are published by country and detailed on NACE Rev 2 and NACE Rev 1.1 class level (4-digits). Some classes or groups in 'services' section have been aggregated.Annual enterprise statistics broken down by size classes: Characteristics are published by country and detailed down to NACE Rev 2 and NACE Rev 1.1 group level (3-digits) and employment size class. For trade (NACE Rev 2 and NACE Rev 1.1 Section G) a supplementary breakdown by turnover size class is available.Annual regional statistics: Four characteristics are published by NUTS-2 country region and detailed on NACE Rev 2 and NACE Rev 1.1 division level (2-digits) (but to group level (3-digits) for the trade section). More information on the contents of different tables: the detail level and breakdowns required starting with the reference year 2008 is defined in Commission Regulation N° 251/2009. For previous reference years it is included in Commission Regulations (EC) N° 2701/98 and amended by Commission Regulation N°1614/2002 and Commission Regulation N°1669/2003. Several important derived indicators are generated in the form of ratios of certain monetary characteristics or per head values. A list with the available derived indicators is available below in the Annex.

Tourism can be regarded as a social, cultural and economic phenomenon related to the movement of people outside their usual place of residence.

This table includes the areas, productions and humidity of field peas, broad and field beans, sweet lupins, dry beans, other dry peas, lentils, chickling vetch, chick peas, vetches and other protein crops sown in pure crops or as mixtures with cereals harvested dry for grain. This indicator uses the concepts of "area under cultivation", "harvested production" and "humidity". 1) The "area under cultivation" corresponds: • before the harvest, to the sown area; • after the harvest, to the sown area excluding the non-harvested area (e.g. area ruined by natural disasters, area not harvested for economic reasons, etc.) 2) The "harvested production" corresponds to the production for which harvesting started in year N, even thought harvesting may finish in year N+1. So N is the reference year for data published by Eurostat. 3) In order to facilitate the comparisons of production between the Members States, the publication of "humidity" for each country is needed. Only the EU-aggregate for the production is published with a standard EU humidity.

Earnings Of Foreign Direct Investment In Singapore By Country/Region (During The Year), Annual

There has been a growing interest in monitoring patterns of trade in services around the world, which is partly associated with ongoing trade negotiations and partly due to the increasing importance of services in OECD economies. It has been developed to supplement other OECD publications on trade in services to address the data needs of trade analysts. It is also an important part of OECD's programme to facilitate the implementation of the recommendations of the revised Manual on Statistics of International Trade in Services 2010.Other commentsThe Task Force on Statistics of International Trade in Services maintains a matrix summarising the status of the trade in services data collection performed by International Organisations. The table displays links to the databases as well as update timetables, availability of metadata, availability of bilateral data, and other important characteristics.

The economic accounts for forestry and logging are based on national accounts, but are collected with greater detail. Current prices means prices of that particular year. Annual inflation must yet be taken into account if one wishes to compare the values of different years. Basic prices means the price received by the producer after deduction of all taxes on products, but including all subsidies on products. Gross value added is the value of the output less the value of intermediate consumption. Fixed capital relates to longer-lived assets (e.g. equipment or buildings) that are either acquired (this is gross fixed capital formation) or consumed (this is fixed capital consumption, the annual reduction in the value of fixed assets). The definition of the activity of forestry and logging is based on Division 02 of NACE Rev. 2.

Eurostat's database covers 1) Production and trade in roundwood and wood products, including primary and secondary products 2) Economic data on forestry and logging, including employment data 3) Sustainable forest management, comprising forest resources (assets) and environmental data. The main types of primary forest products included in (1) are: roundwood, sawnwood, wood-based panels, pulp, and paper and paperboard. Secondary products include further processed wood and paper products. These products are presented in greater detail; definitions are available. All of the data are compiled from the Joint Forest Sector Questionnaire (JFSQ), except for table (e), which is directly extracted from Eurostat's international trade database COMEXT (HS/CN Chapter 44). The tables in (1) cover details of the following topics: - Roundwood removals and production by type of wood and assortment (a) - Roundwood production by type of ownership (b) - Production and trade in roundwood, fuelwood and other basic products (c) - Trade in industrial roundwood by assortment and species (d) - Tropical wood imports to the EU from Chapter 44 of the Harmonised System (e) - Production and trade in sawnwood, panels and other primary products (f) - Sawnwood trade by species (g) - Production and trade in pulp and paper & paperboard (h) - Trade in secondary wood and paper products (i) Data in (2) include the output, intermediate consumption, gross value added, fixed capital consumption, gross fixed capital formation and different measures of income of forestry and logging.  The data are in current basic prices and are compatible with National Accounts. They are collected as part of Intergrated environmental and economic accounting for forests (IEEAF), which also covers labour input in annual work units (AWU).  Under (2), two separate tables cover the number of employees of forestry and logging, the manufacture of wood and products of wood and cork, and the manufacture of paper and paper products, as estimated from the Labour Force Survey results. There are two separate tables because of the change in the EU's classification of economic activities from NACE Rev. 1.1 to NACE Rev. 2 in 2008. More detailed information on wood products and accounting, including definitions and questionnaires, can be found on our open-access communication platform under the interest group 'Forestry statistics and accounts'.  Data in (3) are not collected by Eurostat, but by the FAO, UNECE, Forest Euope, the European Commission's departments for Environment and the Joint Research Centre. They include forest area, wood volume, defoliation on sample plots, fires and areas with protective functions.

Latest Version is available here: https://knoema.com/OECDEO2020DEC/economic-outlook-no-108-december-2020   Economic Outlook No 107 (EO107) 2/2   Given the unusual level of uncertainty caused by the Covid-19 pandemic, this Economic Outlook (EO107) presents two scenarios for each country and economy – one scenario in which a second outbreak occurs in most economies towards the end of this year (double-hit scenario) and an alternative scenario where the second outbreak is avoided (single-hit scenario).Furthermore, only a limited number of series is made available compared to past editions.   This data set presents the double-hit scenario.   The OECD Economic Outlook analyses the major economic trends over the coming 2 years. It provides in-depth coverage of the main economic issues and the policy measures required to foster growth in each member country. Forthcoming developments in selected non-OECD economies are also evaluated in detail. Each edition of the Outlook provides a unique resource to keep abreast of world economic developments. The OECD Economic Outlook database is a comprehensive and consistent macroeconomic database of the OECD economies, covering expenditures, foreign trade, output, labour markets, interest and exchange rates, balance of payments, and government debt. For the non-OECD regions, foreign trade and current account series are available.   The database contains annual data (for all variables) and quarterly figures (for a subset of variables). Variables are defined in such a way that they are as homogenous as possible for the countries covered. Breaks in underlying series are corrected as far as possible. Sources for the historical data are publications of national statistical agencies and OECD data bases such as Quarterly National Accounts, Annual National Accounts, Labour Force Statistics and Main Economic Indicators. The cut-off date for information used in the compilation of the projections was the 4 June 2020.   Concerning the aggregation of world trade, a new composition has been introduced, since projections are now made for the major non-OECD economies. Thus, besides OECD and the OECD euro area, the following new regions are available: Dynamic Asian Economies (Chinese Taipei, Hong Kong, Malaysia, the Philippines, Singapore, Thailand, Vietnam); Oil Producers (Azerbaijan, Kazakhstan, Turkmenistan, Brunei, Timor-Leste, Bahrain, Iran, Iraq, Kuwait, Libya, Oman, Qatar, Saudi Arabia, United Arab Emirates, Yemen, Ecuador, Trinidad and Tobago, Venezuela, Algeria, Angola, Chad, Rep. of Congo, Equatorial Guinea, Gabon, Nigeria, Sudan); with the remaining countries in a residual 'Rest of the World' group.

Latest version is available here: https://knoema.com/OECDEO2020DEC/economic-outlook-no-108-december-2020   Economic Outlook No 107 (EO107) 1/2   Given the unusual level of uncertainty caused by the Covid-19 pandemic, this Economic Outlook (EO107) presents two scenarios for each country and economy – one scenario in which a second outbreak occurs in most economies towards the end of this year (double-hit scenario) and an alternative scenario where the second outbreak is avoided (single-hit scenario).Furthermore, only a limited number of series is made available compared to past editions.   This data set presents the single-hit scenario.   The OECD Economic Outlook analyses the major economic trends over the coming 2 years. It provides in-depth coverage of the main economic issues and the policy measures required to foster growth in each member country. Forthcoming developments in selected non-OECD economies are also evaluated in detail. Each edition of the Outlook provides a unique resource to keep abreast of world economic developments. The OECD Economic Outlook database is a comprehensive and consistent macroeconomic database of the OECD economies, covering expenditures, foreign trade, output, labour markets, interest and exchange rates, balance of payments, and government debt. For the non-OECD regions, foreign trade and current account series are available.   The database contains annual data (for all variables) and quarterly figures (for a subset of variables). Variables are defined in such a way that they are as homogenous as possible for the countries covered. Breaks in underlying series are corrected as far as possible. Sources for the historical data are publications of national statistical agencies and OECD data bases such as Quarterly National Accounts, Annual National Accounts, Labour Force Statistics and Main Economic Indicators. The cut-off date for information used in the compilation of the projections was the 4 June 2020.   Concerning the aggregation of world trade, a new composition has been introduced, since projections are now made for the major non-OECD economies. Thus, besides OECD and the OECD euro area, the following new regions are available: Dynamic Asian Economies (Chinese Taipei, Hong Kong, Malaysia, the Philippines, Singapore, Thailand, Vietnam); Oil Producers (Azerbaijan, Kazakhstan, Turkmenistan, Brunei, Timor-Leste, Bahrain, Iran, Iraq, Kuwait, Libya, Oman, Qatar, Saudi Arabia, United Arab Emirates, Yemen, Ecuador, Trinidad and Tobago, Venezuela, Algeria, Angola, Chad, Rep. of Congo, Equatorial Guinea, Gabon, Nigeria, Sudan); with the remaining countries in a residual 'Rest of the World' group.

The OECD Economic Outlook analyzes the major economic trends over the coming 2 years. It provides in-depth coverage of the main economic issues and the policy measures required to foster growth in each member country. Forthcoming developments in major non-OECD economies are also evaluated in detail. Each edition of the Outlook provides a unique resource to keep abreast of world economic developments. The OECD Economic Outlook database is a comprehensive and consistent macroeconomic database of the OECD economies, covering expenditures, foreign trade, output, labor markets, interest and exchange rates, balance of payments and government debt. For the non-OECD regions, foreign trade and current account series are available. Variables are defined in such a way that they are as homogenous as possible for the countries covered. Breaks in underlying series are corrected as far as possible. Sources for the historical data are publications of national statistical agencies and OECD data bases such as Quarterly National Accounts, Annual National Accounts, Labor Force Statistics and Main Economic Indicators. The cut-off date for information used in the compilation of the projections was June 1, 2023. The aggregation of world trade takes into account the projections made for the main non-OECD economies. Thus, besides OECD and the OECD euro area, the following regions are available: Dynamic Asian Economies (Chinese Taipei, Hong Kong, Malaysia, the Philippines, Singapore, Thailand, Vietnam); Oil Producers (Algeria, Angola, Azerbaijan Bahrain, Brunei, Chad, Rep. of Congo, Ecuador, Equatorial Guinea, Gabon, Iran, Iraq, Kazakhstan, Kuwait, Libya, Nigeria, Oman, Qatar, Saudi Arabia, Sudan, Timor-Leste , Trinidad and Tobago, Turkmenistan, United Arab Emirates, Yemen, Venezuela); with the remaining countries in a residual 'Rest of the World' group.

Edelman Trust Barometer reveals that trust is in crisis around the world. The general population’s trust in all four key institutions — business, government, NGOs, and media — has declined broadly, a phenomenon not reported since Edelman began tracking trust among this segment in 2012. With the fall of trust, the majority of respondents now lack full belief that the overall system is working for them. In this climate, people’s societal and economic concerns, including globalization, the pace of innovation and eroding social values, turn into fears, spurring the rise of populist actions now playing out in several Western-style democracies. To rebuild trust and restore faith in the system, institutions must step outside of their traditional roles and work toward a new, more integrated operating model that puts people — and the addressing of their fears — at the center of everything they do.

This dataset contains indicators related to Education for provincial level.

This indicator presents internationally comparable data regarding the labour force status and the educational attainment level by the National Educational Attainment Categories (NEAC) as reported by the labour force survey (LFS) and published in OECD Education at a Glance 2017. For trend data, the Education at a Glance Database includes data from 1981 to 2016 (or years with available data).

World: Electricity Prices

This indicator presents electricity prices charged to final consumers. Electricity prices for non-household consumers are defined as follows: Average national price in Euro per kWh without taxes applicable for the first semester of each year for medium size industrial consumers (Consumption Band Ic with annual consumption between 500 and 2000 MWh). Electricity prices for household consumers are defined as follows: Average national price in Euro per kWh including taxes and levies applicable for the first semester of each year for medium size household consumers (Consumption Band Dc with annual consumption between 2500 and 5000 kWh).

NEW METHODOLOGY (from 2007 onwards) Average half-yearly electricity prices for households and industrial end-users. The end-users are characterised by predefined annual consumption bands. The prices are collected and published considering three levels of taxation prices excluding taxes and levies;prices excluding VAT and other recoverable taxes;prices including all taxes, levies and VAT.  For the disaggregated electricity prices, separate price components are available for households and industrial consumers for production costs of electricitynetwork coststaxes and leviesOLD METHODOLOGY (until 2007) Electricity prices for households and industrial standard consumers, valid on 1st January and on 1st July of each calendar year. Standard consumers are characterised by predefined annual consumption. The prices include electricity/basic price, transmission, system services, meter rental, distribution and other services. The prices are collected and published considering three levels of taxation (see above). For structural indicators tables, where only annual data is displayed both for new and old methodology in the same table, the prices refer to the price on 1st January of each year (until 2007) and to the first semester of each year (2008 and later). Data on electricity prices for industrial consumers are collected according to Directive 2008/92/EC of the European Parliament and of the Council of 22 October 2008 concerning a Community procedure to improve the transparency of gas and electricity prices charged to industrial end-users (recast) (Text with EEA relevance). Data for electricity prices for household end-users are collected on a voluntary basis The data collection covers the full spectrum of the 28 Member States of the European Union, Candidate Countries, Potential candidate countries and EFTA countries (except Switzerland).

These are data collected via the annual electricity and heat questionnaire and the annual renewables questionnaire, according to Annex B of the Regulation (EC) No 1099/2008 of the European Parliament and of the Council of 22 October 2008 on energy statistics. The variables are: Total capacity (MWe)Capacity by source of electricity production (MWe)Capacity by type of generation in power plants using combustible fuels (MWe)Capacity by type of firing and by type of fuel used in power plants using combustible fuels (MWe) All reported capacities are broken down by type of supplier (main activity producer or auto-producer) in nrg_inf_epc. For plants based on combustion of fuels the capacity is further divided by type of technology of the generating plant (steam, internal combustion….) in nrg_inf_epct, by type of firing and by type of fuels.  The dataset nrg_inf_epcrw offers more detailed renewable fuels. Total capacity calculated from the aggregation of individual categories coming from each of the datasets could yield slightly different values. indeed, the data come from different sources (different questionnaires) and different use of decimal places and rounding could result in these differences.

ANNUAL Annual data on quantities for crude oil, petroleum products, natural gas and manufactures gases, electricity and derived heat, solid fossil fuels, renewables and wastes covering the full spectrum of the energy sector from supply through transformation to final consumption oby sector and fuel type. Also, annual imports and exports data of various energy carriers by country of origin and destination, as well as infrastructure information. Data on annual statistics are collected by standard questionnaires according to Annex B of the Regulation (EC) No 1099/2008 of the European Parliament and of the Council of 22 October 2008 on energy statistics   MONTHLY The monthly energy data collections cover the most important energy commodities: Crude oil & Petroleum productsNatural gasSolid fuelsElectricity For each of the above mentioned commodities the inflowing data are delivered by the reporting countries to Eurostat via separate dedicated questionnaires. Data on monthly statistics are collected by standard questionnaires according to Annex C of the Regulation (EC) No 1099/2008 of the European Parliament and of the Council of 22 October 2008 on energy statistics   SHORT-TERM MONTHLY Short-term monthly energy data collections cover the most important energy commodities: Oil & petroleum productsNatural gasElectricity Short-term monthly data provides information on main flows (quantities) on the supply side. Data on monthly short term statistics are collected by standard questionnaires according to Annex D of the Regulation (EC) No 1099/2008 of the European Parliament and of the Council of 22 October 2008 on energy statistics

This dataset contains indicators related to Electricity, Gas and Water Supply for provincial level.

Total number of long-term emigrantsleaving from the reporting country during the reference year

This domain comprises series of long-term international immigration and emigration during the reference year. Immigrants and emigrants are disaggregated by age group, sex, citizenship or country of previous/next residence. Since 2008 migration data by single age and immigration data by country of birth are also available. The data sources are administrative records or national surveys. For some datasets statistical estimation methods are applied. Data are presented country by country and for groups of countries. The completeness of the tables depends largely on the availability of data from the relevant national statistical institutes. For details on data sources used for compilation of statistics on immigration and emigration and for more country-specific data descriptions see Annexes attached.

Eurostat's annual collections of statistics on international migration flows are structured as follows:  

The annual Eurostat's collections on statistics on international migration flows are structured as follows:   Data Collection Info & Legislation NOWCAST Annual data collection on provisional monthly data on live births and deaths covering at least six months of the reference year (Article 4.3 of the Commission implementing regulation (EU) No 205/2014) and provisional monthly data on migrants covering at least six months of the reference year on voluntary basis DEMOBAL Demographic balance Annual data collection on provisional data on population, total live births and total deaths at national level (Article 4.1 of the Commission implementing regulation (EU) No 205/2014) and provisional data on total migrants at national level on voluntary basis UNIDEMO Unified Demographic The most extended annual collection on demography and migration, collecting data at national and regional level for population, births, deaths, immigrants,  emigrants, marriages and divorces by a large number of breakdowns. (Art. 3 of the Regulation (EU) No 1260/2013 and Art. 3 of the Regulation (EC) No 862/2007)   The annual demography data collections aim at collecting from the National Statistical Institutes both mandatory data and voluntary data. The mandatory data are those defined by the legislation listed on "6.1. Institutional Mandate - legal acts and other agreements".   The migration data collected on voluntary basis depend on the availability and on the quality of information available in the National Statistical Institutes.   For more specific information on mandatory/voluntary data collection see 6.1. Institutional Mandate - legal acts and other agreements.   The following data on migrants are collected under Unified Demographic data collection:   Immigrants by age, sex and by:                  a.   country of citizenship                  b.   country of birth                  c.   country of previous residenceImmigrants by country of citizenship and country of birthEmigrants by age, sex and by:                   a.   country of citizenship                   b.   country of birth                   c.   country of next residence 

This domain comprises series of long-term international immigration and emigration during the reference year. Immigrants and emigrants are disaggregated by age group, sex, citizenship or country of previous/next residence. Since 2008 migration data by single age and immigration data by country of birth are also available. The data sources are administrative records or national surveys. For some datasets statistical estimation methods are applied. Data are presented country by country and for groups of countries. The completeness of the tables depends largely on the availability of data from the relevant national statistical institutes. For details on data sources used for compilation of statistics on immigration and emigration and for more country-specific data descriptions see Annexes attached.

Greenhouse Gas (GHG) emissions from burning crop residues consist of methane (CH4) and nitrous oxide (N2O) gases produced by the combustion of a percentage of crop residues burnt on-site. The mass of fuel available for burning should be estimated taking into account the fractions removed before burning due to animal consumption, decay in the field, and use in other sectors (e.g., biofuel, domestic livestock feed, building materials, etc.). FAOSTAT emission estimates are computed at Tier 1 following the IPCC 2006 Guidelines for National GHG Inventories (http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol4.html). GHG emissions are provided by country, reguions and special groups, with global coverage, relative to the period 1961-present (with annual updates) and with projections for 2030 and 2050, expressed both as Gg CH4, Gg N2O, Gg CO2eq and CO2eq from CH4 and N2O, by crop (maize, rice, sugarcane and wheat) and by aggregates. Implied emission factors for N2O and CH4 as well activity data (biomass burned) are also provided.

Greenhouse gas (GHG) emissions from crop residues consist of direct and indirect nitrous oxide (N2O) emissions from nitrogen (N) in crop residues and forage/pasture renewal left on agricultural fields by farmers. Specifically, N2O is produced by microbial processes of nitrification and de-nitrification taking place on the deposition site (direct emissions), and after volatilization/re-deposition and leaching processes (indirect emissions). The FAOSTAT emissions database is computed following Tier 1 IPCC 2006 Guidelines for National GHG Inventories, Vol. 4, Ch. 2 and 11(http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol4.html). GHG emissions are provided as direct, indirect and total by country, regions and special groups, with global coverage, relative to the period 1961-present (with annual updates) and with projections for 2030 and 2050, expressed as Gg N2O and Gg CO2eq, by crop and N content in residues.

Greenhouse gas (GHG) emissions from enteric fermentation consist of methane gas produced in digestive systems of ruminants and to a lesser extent of non-ruminants. The FAOSTAT emissions database is computed following Tier 1 IPCC 2006 Guidelines for National GHG Inventories vol. 4, ch. 10 and 11 (http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol4.html). GHG emissions are provided by country, regions and special groups, with global coverage, relative to the period 1961-present (with annual updates) and with projections for 2030 and 2050, expressed both as Gg CH4 and Gg CO2eq, by livestock species (asses, buffaloes, camels, cattle (dairy and non-dairy), goats, horses, llamas, mules, sheep, swine (breeding and market)) and by species aggregates (all animals, camels and llamas, cattle, mules and asses, sheep and goats, swine). Implied emission factor for CH4 and activity data are also provided

GHG emissions from manure applied to soils consist of direct and indirect nitrous oxide (N2O) emissions from manure nitrogen (N) added to agricultural soils by farmers. Specifically, N2O is produced by microbial processes of nitrification and de-nitrification taking place on the application site (direct emissions), and after volatilization/re-deposition and leaching processes (indirect emissions). The FAOSTAT emissions database is computed following Tier 1 IPCC 2006 Guidelines for National GHG Inventories vol. 4, ch. 10 and 11 (http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol4.html). GHG emissions are provided as direct, indirect and total by country, regions and special groups, with global coverage, relative to the period 1961-present (with annual updates) and with projections for 2030 and 2050, expressed as Gg N2O and Gg CO2eq, by livestock species (asses, buffaloes, camels, cattle (dairy and non-dairy), chickens (broilers and layers), ducks, goats, horses, llamas, mules, sheep, swine (breeding and market) and turkeys) and by species aggregates (all animals, camels and llamas, cattle, chickens, mules and asses, poultry birds, sheep and goats, swine). Implied emission factor for N2O and activity data (N content in manure) are also provided.

Greenhouse gas (GHG) emissions from synthetic fertilizers consist of nitrous oxide gas from synthetic nitrogen additions to managed soils. Specifically, N2O is produced by microbial processes of nitrification and de-nitrification taking place on the addition site (direct emissions), and after volatilization/re-deposition and leaching processes (indirect emissions). The FAOSTAT emissions database is computed following Tier 1 IPCC 2006 Guidelines for National GHG Inventories vol. 4, ch. 11 (http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol4.html). GHG emissions are provided as direct, indirect and total by country, regions and special groups, with global coverage, relative to the period 1961-present (with annual updates) and with projections for 2030 and 2050, expressed as Gg N2O and Gg CO2eq. Implied emission factor for N2O and activity data (consumption) are also provided.

The FAOSTAT domain Organic soils consist of nitrous oxide (N2O) and carbon dioxide (CO2) emissions associated with the drainage of organic soils ? using histosols as proxy ? for agriculture. Data are computed geospatially, using the Tier 1 method of the 2006 IPCC Guidelines for National greenhouse gas Inventories (IPCC, 2006). Estimates are available by country and with global coverage, in complete time series for the period 1990?2019. The database is updated annually. The FAOSTAT domain Organic soils disseminates information by country on: activity data (in hectares of organic soils drained for agriculture); and greenhouse gas (GHG) emissions (in kilotonnes of N2O and CO2). Drainage and associated emissions are assessed separately for IPCC land use categories cropland and grassland, corresponding to FAO land use categories ??cropland?? and ??permanent meadows and pastures.?? Data are available for all countries and territories, for standard FAOSTAT regional aggregations, and for Annex I and non-Annex I country groups. This FAOSTAT domain also disseminates the activity data and emissions data reported by countries to the United Nations Framework Convention on Climate Change (UNFCCC), under ?Cultivation of organic soils? for the N2O component and categories ?Cropland drained organic soils? and ?Grassland drained organic soils? for the CO2. Activity data are sourced from the most recently available GHG National Inventories (NGHGI) or from National Communications. Emission data are sourced directly from the UNFCCC data portal or from Biennial Update Reports (BURs). UNFCCC data are disseminated in FAOSTAT with permission, formalized via a FAO-UNFCCC Memorandum of Understanding.

Dans le but de promouvoir la comparabilité internationale, les statistiques présentées sur ILOSTAT sont basées sur des définitions internationales standard dans la mesure du possible et peuvent différer des chiffres nationaux officiels. Cette série est basée sur les définitions de la 13e CIST. Pour la comparabilité des séries chronologiques, elle inclut les pays qui ont mis en >uvre les normes de la 19e CIST, pour lesquels des données sont également disponibles dans la base de données Statistiques du travail -- 19e CIST (WORK). Les personnes pourvues d'un emploi sont toutes les personnes en âge de travailler qui, durant une brève période de référence spécifiée, se trouvaient dans une des catégories suivantes: a) emploi salarié (soit au travail ou ayant un emploi mais pas au travail), b) emploi non salarié (soit au travail ou ayant une entreprise mais pas au travail). Les données ventilées par activité économique sont présentées conformément à la version plus récente de la Classification internationale type des industries (CITI) disponible. Les données peuvent avoir été reclassées à partir de classifications nationales, qui peuvent ne pas être strictement comparables à la CITI. Les données ventilées par profession sont présentées conformément à la version plus récente de la Classification Internationale Type des Professions (CITP) disponible. Les données peuvent avoir été reclassées à partir de classifications nationales, qui peuvent ne pas être strictement comparables à la CITP. Pour plus d'informations, reportez-vous à la description de la base de données Statistiques sur la main-d'oeuvre (LFS et STLFS).

Les personnes pourvues d'un emploi sont toutes les personnes en âge de travailler qui, durant une brève période de référence spécifiée, se trouvaient dans une des catégories suivantes: a) emploi salarié (soit au travail ou ayant un emploi mais pas au travail), b) emploi non salarié (soit au travail ou ayant une entreprise mais pas au travail). Les données sont présentées par activité économique, qui fait référence à l'activité principale de l'établissement dans lequel la personne a travaillé pendant la période de référence, et ne dépend pas des tâches ou des fonctions spécifiques du travail de la personne, mais des caractéristiques de l'entité économique dans laquelle cette personne travaille. Cette série fait partie des estimations du BIT et est harmonisée pour tenir compte des différences entre les données nationales, la portée de la couverture, les méthodologies de collecte et de tabulation, et de facteurs spécifiques aux pays. Les données pour 1991-2016 sont des estimations tandis que les données pour 2017-2021 sont des projections. La base de données a été mise à jour en Novembre 2017. Pour plus d'informations, consultez la description de l'indicateur et le document méthodologique sur les estimations et projections du BIT (en anglais).

Dans le but de promouvoir la comparabilité internationale, les statistiques présentées sur ILOSTAT sont basées sur des définitions internationales standard dans la mesure du possible et peuvent différer des chiffres nationaux officiels. Cette série est basée sur les définitions de la 13e CIST. Pour la comparabilité des séries chronologiques, elle inclut les pays qui ont mis en >uvre les normes de la 19e CIST, pour lesquels des données sont également disponibles dans la base de données Statistiques du travail -- 19e CIST (WORK). Les personnes pourvues d'un emploi sont toutes les personnes en âge de travailler qui, durant une brève période de référence spécifiée, se trouvaient dans une des catégories suivantes: a) emploi salarié (soit au travail ou ayant un emploi mais pas au travail), b) emploi non salarié (soit au travail ou ayant une entreprise mais pas au travail). Les données ventilées par activité économique sont présentées conformément à la version plus récente de la Classification internationale type des industries (CITI) disponible. Les données peuvent avoir été reclassées à partir de classifications nationales, qui peuvent ne pas être strictement comparables à la CITI. Pour plus d'informations, reportez-vous à la description de la base de données Statistiques sur la main-d'oeuvre (LFS et STLFS).

Dans le but de promouvoir la comparabilité internationale, les statistiques présentées sur ILOSTAT sont basées sur des définitions internationales standard dans la mesure du possible et peuvent différer des chiffres nationaux officiels. Cette série est basée sur les définitions de la 13e CIST. Pour la comparabilité des séries chronologiques, elle inclut les pays qui ont mis en >uvre les normes de la 19e CIST, pour lesquels des données sont également disponibles dans la base de données Statistiques du travail -- 19e CIST (WORK). Les personnes pourvues d'un emploi sont toutes les personnes en âge de travailler qui, durant une brève période de référence spécifiée, se trouvaient dans une des catégories suivantes: a) emploi salarié (soit au travail ou ayant un emploi mais pas au travail), b) emploi non salarié (soit au travail ou ayant une entreprise mais pas au travail). Les données ventilées par activité économique sont présentées conformément à la version plus récente de la Classification internationale type des industries (CITI) disponible. Les données peuvent avoir été reclassées à partir de classifications nationales, qui peuvent ne pas être strictement comparables à la CITI. Pour plus d'informations, reportez-vous à la description de la base de données Statistiques sur la main-d'oeuvre (LFS et STLFS).

Les personnes pourvues d'un emploi sont toutes les personnes en âge de travailler qui, durant une brève période de référence spécifiée, se trouvaient dans une des catégories suivantes: a) emploi salarié (soit au travail ou ayant un emploi mais pas au travail), b) emploi non salarié (soit au travail ou ayant une entreprise mais pas au travail). Cette série fait partie des estimations du BIT et est harmonisée pour tenir compte des différences entre les données nationales, la portée de la couverture, les méthodologies de collecte et de tabulation, et de facteurs spécifiques aux pays. Les données pour 1991-2016 sont des estimations tandis que les données pour 2017-2021 sont des projections. La base de données a été mise à jour en Novembre 2017. Pour plus d'informations, consultez la description de l'indicateur.

Les personnes pourvues d'un emploi sont toutes les personnes en âge de travailler qui, durant une brève période de référence spécifiée, se trouvaient dans une des catégories suivantes: a) emploi salarié (soit au travail ou ayant un emploi mais pas au travail), b) emploi non salarié (soit au travail ou ayant une entreprise mais pas au travail). Les données sont présentées par activité économique, qui fait référence à l'activité principale de l'établissement dans lequel la personne a travaillé pendant la période de référence, et ne dépend pas des tâches ou des fonctions spécifiques du travail de la personne, mais des caractéristiques de l'entité économique dans laquelle cette personne travaille. Cette série fait partie des estimations du BIT et est harmonisée pour tenir compte des différences entre les données nationales, la portée de la couverture, les méthodologies de collecte et de tabulation, et de facteurs spécifiques aux pays. Les données pour 1991-2016 sont des estimations tandis que les données pour 2017-2021 sont des projections. La base de données a été mise à jour en Novembre 2017. Pour plus d'informations, consultez la description de l'indicateur et le document méthodologique sur les estimations et projections du BIT (en anglais).

Les personnes pourvues d'un emploi sont toutes les personnes en âge de travailler qui, durant une brève période de référence spécifiée, se trouvaient dans une des catégories suivantes: a) emploi salarié (soit au travail ou ayant un emploi mais pas au travail), b) emploi non salarié (soit au travail ou ayant une entreprise mais pas au travail). Les données sont présentées par situation dans la profession utilisant la dernière version de la Classification internationale type de situation dans la profession (CISP-93). La situation dans la profession fait référence au type de contrat de travail explicite ou implicite que la personne a avec d'autres personnes ou organisations. Les critères de base utilisés pour définir les catégories de la classification sont le type de risque économique et le type d'autorité que les titulaires d'un emploi ont ou auront sur les établissements et les autres travailleurs. Cette série fait partie des estimations du BIT et est harmonisée pour tenir compte des différences entre les données nationales, la portée de la couverture, les méthodologies de collecte et de tabulation, et de facteurs spécifiques aux pays. Les données pour 1991-2016 sont des estimations tandis que les données pour 2017-2021 sont des projections. La base de données a été mise à jour en Novembre 2017. Pour plus d'informations, consultez la description de l'indicateur et le document méthodologique sur les estimations et projections du BIT (en anglais).

Cette série fait partie des estimations du BIT et est harmonisée pour tenir compte des différences entre les données nationales, la portée de la couverture, les méthodologies de collecte et de tabulation, et de facteurs spécifiques aux pays. Les données pour 1991-2016 sont des estimations tandis que les données pour 2017-2021 sont des projections. La base de données a été mise à jour en Novembre 2017. Pour plus d'informations, consultez le document méthodologique sur les estimations et projections du BIT (en anglais).

'Statistics on high-tech industry and knowledge-intensive services' (sometimes referred to as simply 'high-tech statistics') comprise economic, employment and science, technology and innovation (STI) data describing manufacturing and services industries or products traded broken down by technological intensity. The domain uses various other domains and sources of  Eurostat's official statistics (CIS, COMEXT, HRST, LFS, PATENT, R&D and SBS) and its coverage is therefore dependent on these other primary sources. Two main approaches are used in the domain to identify technology-intensity: the sectoral approach and the product approach. A third approach is used for data on high-tech and biotechnology patents aggregated on the basis of the International Patent Classification (IPC) 8th edition (see summary table in Annex 1 for which approach is used by each type of data). The sectoral approach: The sectoral approach is an aggregation of the manufacturing industries according to technological intensity (R&D expenditure/value added) and based on the Statistical classification of economic activities in the European Community (NACE)  at 2-digit level. The level of R&D intensity served as a criterion of classification of economic sectors into high-technology, medium high-technology, medium low-technology and low-technology industries. Services are mainly aggregated into knowledge-intensive services (KIS) and less knowledge-intensive services (LKIS) based on the share of tertiary educated persons at NACE 2-digit level. The sectoral approach is used for all indicators except data on high-tech trade and patents. Note that due to the revision of the NACE from NACE Rev. 1.1 to NACE Rev. 2 the definition of high-technology industries and knowledge-intensive services has changed in 2008. For high-tech statistics it means that two different definitions (one according NACE Rev. 1.1 and one according NACE Rev. 2) are used in parallel and the data according to both NACE versions are presented in separated tables depending on the data availability. For example as the LFS provides the results both by NACE Rev. 1.1 and NACE Rev. 2, all the table using this source have been duplicated to present the results by NACE Rev. 2 from 2008. For more details, see both definitions of high-tech sectors under Annexes section. Within the sectoral approach, a second classification was created , named Knowledge Intensive Activities KIA) and based on the share of tertiary educated people in each sectors of industries and services according to NACE at 2-digit level and for all EU28 Member States. A threshold was applied to judge sectors as knowledge intensive. In contrast to first sectoral approach mixing two methodologies, one for manufacturing industries and one for services, the KIA classification is based on one methodology for all the sectors of industries and services covering even public sector activities. The aggregations in use are Total Knowledge Intensive Activities (KIA) and Knowledge Intensive Activities in Business Industries (KIABI). Both classifications are made according to NACE Rev. 1.1 and NACE Rev. 2 at 2- digit level. Note that due to revision of the NACE Rev.1.1 to NACE Rev. 2 the list of Knowledge Intensive Activities has changed as well, the two definitions are used in parallel and the data are shown in two separate tables. NACE Rev.2 collection includes data starting from 2008 reference year. For more details please see the definitions under Annexes section. The product approach: The product approach was created to complement the sectoral approach and it is used for data on high-tech trade. The product list is based on the calculations of R&D intensity by groups of products (R&D expenditure/total sales). The groups classified as high-technology products are aggregated on the basis of the Standard International Trade Classification (SITC). The initial definition was built based on SITC Rev.3 and served to compile the high-tech product aggregates until 2007. With the implementation in 2007 of the new version of SITC Rev.4, the definition of high-tech groups was revised and adapted according to new classification. Starting from 2007 the Eurostat presents the trade data for high-tech groups aggregated based on the SITC Rev.4. . For more details, see definition of high-tech products under Annexes section. High-tech patents: High-tech patents are defined according to another approach. The groups classified as high-tech patents are aggregated on the basis of the International Patent Classification (IPC 8th edition). Biotechnology patents are also aggregated on the basis of the IPC 8th edition. For more details, see the aggregation list of high-tech and biotechnology patents under Annexes section. The high-tech domain also comprises the sub-domain Venture Capital Investments: data are provided by INVEST Europe (formerly named the European Private Equity and Venture Capital Association EVCA). More details are available in the Eurostat metadata under Venture capital investments. Please note that for paragraphs where no metadata for regional data has been specified, the regional metadata is identical to the metadata provided for the national data.

The Human Resources in Science and Technology (HRST) domain provides data on stocks and flows (where flows in turn are divided into job-to-job mobility and education inflows). Stocks and flows are the main statistics for HRST. Their methodologies interlink and are therefore presented together in one single metadata-file. This metadata-file is duplicated in the structure of Eurostat's online database, while statistics for stocks and flows are found in separate folders. Several breakdowns are available for stocks and flows indicators: sex, age, region, sector of economic activity, occupation, educational attainment, fields of education, although not all combinations are possible. The data on stocks and job-to-job mobility are obtained from the European Union Labour Force Survey (EU LFS). The National Statistical Institutes are responsible for conducting the surveys and forwarding the results to Eurostat. The data on education inflows are obtained from Eurostat's Education database and in turn obtained via the UNESCO/OECD/Eurostat questionnaire on education. The National Statistical Institutes are responsible for conducting the surveys, compiling the results and forwarding the results to Eurostat. Please note that for paragraphs where no metadata for regional data has been specified, the regional metadata is identical to the metadata provided for the national data.

The Human Resources in Science and Technology (HRST) domain provides data on stocks and flows (where flows in turn are divided into job-to-job mobility and education inflows). Stocks and flows are the main statistics for HRST. Their methodologies interlink and are therefore presented together in one single metadata-file. This metadata-file is duplicated in the structure of Eurostat's online database, while statistics for stocks and flows are found in separate folders. Several breakdowns are available for stocks and flows indicators: sex, age, region, sector of economic activity, occupation, educational attainment, fields of education, although not all combinations are possible. The data on stocks and job-to-job mobility are obtained from the European Union Labour Force Survey (EU LFS). The National Statistical Institutes are responsible for conducting the surveys and forwarding the results to Eurostat. The data on education inflows are obtained from Eurostat's Education database and in turn obtained via the UNESCO/OECD/Eurostat questionnaire on education. The National Statistical Institutes are responsible for conducting the surveys, compiling the results and forwarding the results to Eurostat. Please note that for paragraphs where no metadata for regional data has been specified, the regional metadata is identical to the metadata provided for the national data.

The section 'LFS series - detailed quarterly survey results' reports detailed quarterly results going beyond the EU-LFS main aggregates, which have a separate data domain and some methodological differences. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.

The section 'LFS series - detailed annual survey results' reports annual results from the EU-LFS. While LFS is a quarterly survey, it is also possible to produce annual results. There are several ways of doing it, see section '18.5 Data compilation' below for details. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metadata. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.

The section 'LFS series - detailed annual survey results' reports annual results from the EU-LFS. While LFS is a quarterly survey, it is also possible to produce annual results. There are several ways of doing it, see section '18.5 Data compilation' below for details. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metadata. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.

The section 'LFS series - detailed quarterly survey results' reports detailed quarterly results going beyond the EU-LFS main aggregates, which have a separate data domain and some methodological differences. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.

The annual Business demography data collection covers variables which explain the characteristics and demography of the business population. The methodology allows for the production of data on enterprise births (and deaths), that is, enterprise creations (cessations) that amount to the creation (dissolution) of a combination of production factors and where no other enterprises are involved. In other words, enterprises created or closed solely as a result of e.g. restructuring, merger or break-up are not considered. The data are drawn from business registers, although some countries improve the availability of data on employment and turnover by integrating other sources. Until 2010 reference year the harmonised data collection is carried out to satisfy the requirements for the Structural Indicators, used for monitoring progress of the Lisbon process, regarding business births, deaths and survival. Currently, business demography delivers key information for policy decision-making and for the indicators to support the Europe 2020 strategy. It also provides key data for the joint OECD-Eurostat "Entrepreneurship Indicators Programme". In summary, the collected indicators are as follows:Population of active enterprisesNumber of enterprise birthsNumber of enterprise survivals up to five yearsNumber of enterprise deathsRelated variables on employmentDerived indicators such as birth rates, death rates, survival rates and employment sharesAn additional set of indicators on high-growth enterprises and 'gazelles' (high-growth enterprises that are up to five years old) The complete list of the basic variables, delivered from the data providers (National Statistical Institutes) and the derived indicators, calculated by Eurostat, is attached in the Annexes of this document (see Business demography indicators).  Geographically EU Member States and EFTA countries are covered. In practice not all Member States have participated in the first harmonised data collection exercises. The methodology laid down in the Eurostat-OECD Manual on Business Demography Statistics  is followed closely by most of the countries (see Country specific notes in the Annexes).

Eurostat Dataset Id:bd_9fh_sz_cl_r2 The data category covers a group of variables which explain the characteristics and demography of the business population. The methodology allows for the production of data on enterprise births (and deaths), that is, enterprise creations (cessations) that amount to the creation (dissolution) of a combination of production factors and where no other enterprises are involved. In other words, enterprises created or closed solely as a result of e.g. restructuring, merger or break-up are not included in this data. Until 2010 reference year the harmonised data collection is carried out to satisfy the requirements for the Structural Indicators, used for monitoring progress of the Lisbon process, regarding business births, deaths and survival. It also provides key data for the joint OECD-Eurostat "Entrepreneurship Indicators Programme". In summary, the collected indicators are as follows:Population of active enterprisesNumber of enterprise birthsNumber of enterprise survivals up to five yearsNumber of enterprise deathsRelated variables on employmentDerived indicators such as birth rates, death rates, survival rates and employment sharesAn additional set of indicators on high-growth enterprises and 'gazelles' (high-growth enterprises that are up to five years old) The data are drawn from business registers, although some individual countries improve the availability or freshness of data on employment and turnover by integrating other sources. Geographically EU Member States and EFTA countries are covered. In practice not all Member States have participated in the first harmonised data collection exercises.

Regional accounts are a regional specification of the national accounts and therefore based on the same concepts and definitions as national accounts (see domain nama10). The main specific regional issues are addressed in chapter 13 of ESA2010, but not practically specified. For practical rules and recommendations on sources and methods see the publication "Manual on regional accounts methods": http://ec.europa.eu/eurostat/en/web/products-manuals-and-guidelines/-/KS-GQ-13-001 . Gross domestic product (GDP) at market prices is the final result of the production activity of resident producer units. It can be defined in three ways: 1. Output approach GDP is the sum of gross value added of the various institutional sectors or the various industries plus taxes and less subsidies on products (which are not allocated to sectors and industries). It is also the balancing item in the total economy production account. 2. Expenditure approach GDP is the sum of final uses of goods and services by resident institutional units (final consumption expenditure and gross capital formation), plus exports and minus imports of goods and services. At regional level the expenditure approach cannot be used in the EU, because there is no data on regional exports and imports.  3. Income approach GDP is the sum of uses in the total economy generation of income account: compensation of employees plus gross operating surplus and mixed income plus taxes on products less subsidies plus consumption of fixed capital. The different measures for the regional GDP are absolute figures in € and Purchasing Power Standards (PPS), figures per inhabitant and relative data compared to the EU28 average.

The 'LFS main indicators' section presents a selection of the main statistics on the labour market. They encompass indicators of activity employment and unemployment. Those indicators are based on the results of the European Labour Force Survey (EU-LFS), in few cases integrated with data sources like national accounts employment or registered unemployment. As a result of the application of adjustments, corrections and reconciliation of EU Labour Force Survey (EU-LFS), 'LFS main indicators' is the most complete and reliable collection of employment and unemployment data available in the sub-domain ' Employment and unemployment'. The EU-LFS data used for 'LFS main indicators' are, where necessary, adjusted and enriched in various ways, in accordance with the specificities of an indicator.  The most common adjustments cover: - correction of the main breaks in the LFS series - estimation of the missing values, (i.e. in case of missing quarters, annual results and EU aggregates are estimated using adjusted quarterly national labour force survey data or interpolations of the EU Labour Force Survey data with reference to the available quarter(s)) - reconciliations of the LFS data with other sources, mainly National Accounts (for Employment growth and activity branches) and national statistics on monthly unemployment (for Harmonised unemployment series). - for a number of indicators (employment, activity, unemployment, supplementary indicators) seasonally adjusted data are available Those adjustments may produce some differences between data published under 'LFS main indicators' and 'LFS series - Detailed survey results', particularly for back data. For the most recent years these two series converge, due to the implementation of a continuous quarterly survey and the improved quality of the data. This page focuses on the particularities of 'LFS main indicators' in general. There are special pages for indicators 'employment growth', 'population in jobless households', 'average exit age of labour market' and 'education indicators: life-long learning, early school leavers and youth education attainment level. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.

National accounts are a coherent set of macroeconomic indicators, which provide an overall picture of the economic situation and are widely used for economic analysis and forecasting, policy design and policy making. The data presented in this collection are the results of a pilot exercise on the sharing selected main GDP aggregates, population and employment data collected by different international organisations. It wasconducted by the Task Force in International Data Collection (TFIDC) which was established by the  Inter-Agency Group on Economic and Financial Statistics (IAG).  The goal of this pilot is to develop a set of commonly shared principles and working arrangements for data cooperation that could be implemented by the international agencies. The data sets are an experimental exercise to present national accounts data form various countries across the globe in one coherent folder, but users should be aware that these data are collected and validated by different organisations and not fully harmonised from a methodological point of view.  The domain consists of the following collections:

The 'LFS main indicators' section presents a selection of the main statistics on the labour market. They encompass indicators of activity employment and unemployment. Those indicators are based on the results of the European Labour Force Survey (EU-LFS), in few cases integrated with data sources like national accounts employment or registered unemployment. As a result of the application of adjustments, corrections and reconciliation of EU Labour Force Survey (EU-LFS), 'LFS main indicators' is the most complete and reliable collection of employment and unemployment data available in the sub-domain ' Employment and unemployment'. The EU-LFS data used for 'LFS main indicators' are, where necessary, adjusted and enriched in various ways, in accordance with the specificities of an indicator.  The most common adjustments cover: - correction of the main breaks in the LFS series - estimation of the missing values, (i.e. in case of missing quarters, annual results and EU aggregates are estimated using adjusted quarterly national labour force survey data or interpolations of the EU Labour Force Survey data with reference to the available quarter(s)) - reconciliations of the LFS data with other sources, mainly National Accounts (for Employment growth and activity branches) and national statistics on monthly unemployment (for Harmonised unemployment series). - for a number of indicators (employment, activity, unemployment, supplementary indicators) seasonally adjusted data are available Those adjustments may produce some differences between data published under 'LFS main indicators' and 'LFS series - Detailed survey results', particularly for back data. For the most recent years these two series converge, due to the implementation of a continuous quarterly survey and the improved quality of the data. This page focuses on the particularities of 'LFS main indicators' in general. There are special pages for indicators 'employment growth', 'population in jobless households', 'average exit age of labour market' and 'education indicators: life-long learning, early school leavers and youth education attainment level. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.

Employment consists of both employees and self-employed, who are engaged in some productive activity that falls within the production boundary of the system (ESA 2010, 11.11). Employment covers employees and self-employed working for production units resident on the economic territory (i.e. the domestic employment concept). Employment is measured in number of persons without distinction according to full-time or part-time work. The following countries provide employment data seasonally adjusted, without calendar adjustment: CZ, GR, FR, MT, PL, PT, SK and CH. The remaining countries provide employment data seasonally and calendar adjusted.

The section 'LFS series - detailed annual survey results' reports annual results from the EU-LFS. While LFS is a quarterly survey, it is also possible to produce annual results. There are several ways of doing it, see section '20.5 Data compilation' below for details. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.

The source for the regional labour market information down to NUTS level 2 is the EU Labour Force Survey (EU-LFS). This is a quarterly household sample survey conducted in all Member States of the EU and in EFTA and Candidate countries.  The EU-LFS survey follows the definitions and recommendations of the International Labour Organisation (ILO). To achieve further harmonisation, the Member States also adhere to common principles when formulating questionnaires. The LFS' target population is made up of all persons in private households aged 15 and over. For more information see the EU Labour Force Survey (lfsi_esms, see paragraph 21.1.).  The EU-LFS is designed to give accurate quarterly information at national level as well as annual information at NUTS 2 regional level and the compilation of these figures is well specified in the regulation. Microdata including the NUTS 2 level codes are provided by all the participating countries with a good degree of geographical comparability, which allows the production and dissemination of a complete set of comparable indicators for this territorial level. At present the transmission of the regional labour market data at NUTS 3 level has no legal basis. However many countries transmit NUTS 3 figures to Eurostat on a voluntary basis, under the understanding that they are not for publication with such detail, but for aggregation in few categories per country, i.e., metropolitan regions and urban-rural typology. Most of the NUTS 3 data are based on the LFS while some countries transmit data based on registers, administrative data, small area estimation and other reliable sources.

The Fisheries Committee (COFI) from the Trade and Agriculture Directorate (TAD) collects, on an annual basis from all its participating countries, data on landings, aquaculture production, fleet, employment in the fisheries sector, and government financial transfers. Data are collected from Fisheries Ministries, National Statistics Offices and other institution designated as an official data source. Concepts Classifications Data are collected by the OECD using the methodologies established by the Coordinating Working Party on Fishery Statistics (CWP) (www.fao.org/fishery/cwp/search/en). This inter-agency body, created in 1960 to develop common procedures and standards for the collation of fisheries statistics, provides technical advice on fishery statistical matters. Its handbook of Fishery Statistical Standards comprises definitions of the various concepts used in fishery statistics, with the exception of Government Financial Transfers which is unique to the OECD. All other statistics are based on the CWP definitions. The OECD, a partner with the CWP, additionally collects information on values for its landings and records the breakdown between the types of landings (i.e. landings in domestic ports, landings in foreign ports) data series which are not collected by the FAO. While a number of countries cover landings in a similar fashion, the same does not hold true for capacity (feet/meters, GRT/engine powers), or for employment for which both Full-time equivalents or numbers of people are used. The OECD therefore does not duplicate FAO statistics but requests complementary information to feed its analytical work.

Eurostat's database covers: 1) Production and trade in roundwood and wood products, including primary and secondary products; 2) Economic data on forestry and logging, including employment data; 3) Sustainable forest management, comprising forest resources (assets) and environmental data. The main types of primary forest products included in (1) are: roundwood, sawnwood, wood-based panels, pulp, and paper and paperboard. Secondary products include further processed wood and paper products. These products are presented in greater detail; definitions are available. All of the data are compiled from the Joint Forest Sector Questionnaire (JFSQ), except for table (e), which is directly extracted from Eurostat's international trade database COMEXT (HS/CN Chapter 44). The tables in (1) cover details of the following topics: - Roundwood removals and production by type of wood and assortment (a) - Roundwood production by type of ownership (b) - Production and trade in roundwood, fuelwood and other basic products (c) - Trade in industrial roundwood by assortment and species (d) - Tropical wood imports to the EU from Chapter 44 of the Harmonised System (e) - Production and trade in sawnwood, panels and other primary products (f) - Sawnwood trade by species (g) - Production and trade in pulp and paper & paperboard (h) - Trade in secondary wood and paper products (i) Data in (2) include the output, intermediate consumption, gross value added, fixed capital consumption, gross fixed capital formation and different measures of income of forestry and logging. The data are in current basic prices and are compatible with National Accounts. They are collected as part of Intergrated environmental and economic accounting for forests (IEEAF), which also covers labour input in annual work units (AWU). Under (2), two separate tables cover the number of employees of forestry and logging, the manufacture of wood and products of wood and cork, and the manufacture of paper and paper products, as estimated from the Labour Force Survey results. There are two separate tables because of the change in the EU's classification of economic activities from NACE Rev. 1.1 to NACE Rev. 2 in 2008. More detailed information on wood products and accounting, including definitions and questionnaires, can be found on our open-access communication platform under the interest group 'Forestry statistics and accounts'. Data in (3) are not collected by Eurostat, but by the FAO, UNECE, Forest Euope, the European Commission's departments for Environment and the Joint Research Centre. They include forest area, wood volume, defoliation on sample plots, fires and areas with protective functions.

Sport employment statistics are derived from data on employment based on the results of the European Labour Force Survey (EU-LFS). Employment in sport statistics aim at investigating on the dimension of the contribution of sport employment to the overall employment. The EU-LFS is the main source of information about the situation and trends on the labour market in the European Union . The methodology for the design and development of sport employment statistics is based on the one proposed by the final report of the European Statistical System Network on Culture (ESS-Net Culture Report 2012) which takes into account two reference classifications: the NACE classification (‘Nomenclature générale des Activités économiques dans les Communautés Européennes’) which classifies the main economic  activitiesthe ISCO classification (‘International Standard Classification of Occupations’) which classifies occupations. Results from the EU-LFS allow characterizing employment in sport by different variables such as gender, age, educational attainment by cross-tabulating ISCO and NACE selected sport codes.

Sport employment statistics are derived from data on employment based on the results of the European Labour Force Survey (EU-LFS). Employment in sport statistics aim at investigating on the dimension of the contribution of sport employment to the overall employment. The EU-LFS is the main source of information about the situation and trends on the labour market in the European Union . The methodology for the design and development of sport employment statistics is based on the one proposed by the final report of the European Statistical System Network on Culture (ESS-Net Culture Report 2012) which takes into account two reference classifications: the NACE classification (‘Nomenclature générale des Activités économiques dans les Communautés Européennes’) which classifies the main economic  activitiesthe ISCO classification (‘International Standard Classification of Occupations’) which classifies occupations. Results from the EU-LFS allow characterizing employment in sport by different variables such as gender, age, educational attainment by cross-tabulating ISCO and NACE selected sport codes.

Sport employment statistics are derived from data on employment based on the results of the European Labour Force Survey (EU-LFS). Employment in sport statistics aim at investigating on the dimension of the contribution of sport employment to the overall employment. The EU-LFS is the main source of information about the situation and trends on the labour market in the European Union . The methodology for the design and development of sport employment statistics is based on the one proposed by the final report of the European Statistical System Network on Culture (ESS-Net Culture Report 2012) which takes into account two reference classifications: the NACE classification (‘Nomenclature générale des Activités économiques dans les Communautés Européennes’) which classifies the main economic  activitiesthe ISCO classification (‘International Standard Classification of Occupations’) which classifies occupations. Results from the EU-LFS allow characterizing employment in sport by different variables such as gender, age, educational attainment by cross-tabulating ISCO and NACE selected sport codes.

'Statistics on high-tech industry and knowledge-intensive services' (sometimes referred to as simply 'high-tech statistics') comprise economic, employment and science, technology and innovation (STI) data describing manufacturing and services industries or products traded broken down by technological intensity. The domain uses various other domains and sources of  Eurostat's official statistics (CIS, COMEXT, HRST, LFS, PATENT, R&D and SBS) and its coverage is therefore dependent on these other primary sources. Two main approaches are used in the domain to identify technology-intensity: the sectoral approach and the product approach. A third approach is used for data on high-tech and biotechnology patents aggregated on the basis of the International Patent Classification (IPC) 8th edition (see summary table in Annex 1 for which approach is used by each type of data). The sectoral approach: The sectoral approach is an aggregation of the manufacturing industries according to technological intensity (R&D expenditure/value added) and based on the Statistical classification of economic activities in the European Community (NACE) at 2-digit level. The level of R&D intensity served as a criterion of classification of economic sectors into high-technology, medium high-technology, medium low-technology and low-technology industries. Services are mainly aggregated into knowledge-intensive services (KIS) and less knowledge-intensive services (LKIS) based on the share of tertiary educated persons at NACE 2-digit level. The sectoral approach is used for all indicators except data on high-tech trade and patents. Note that due to the revision of the NACE from NACE Rev. 1.1 to NACE Rev. 2 the definition of high-technology industries and knowledge-intensive services has changed in 2008. For high-tech statistics it means that two different definitions (one according NACE Rev. 1.1 and one according NACE Rev. 2) are used in parallel and the data according to both NACE versions are presented in separated tables depending on the data availability. For example as the LFS provides the results both by NACE Rev. 1.1 and NACE Rev. 2, all the table using this source have been duplicated to present the results by NACE Rev. 2 from 2008. For more details, see both definitions of high-tech sectors in Annex 2 and 3. Within the sectoral approach, a second classification was created, named Knowledge Intensive Activities KIA) and based on the share of tertiary educated people in each sectors of industries and services according to NACE at 2-digit level and for all EU28 Member States. A threshold was applied to judge sectors as knowledge intensive. In contrast to first sectoral approach mixing two methodologies, one for manufacturing industries and one for services, the KIA classification is based on one methodology for all the sectors of industries and services covering even public sector activities. The aggregations in use are Total Knowledge Intensive Activities (KIA) and Knowledge Intensive Activities in Business Industries (KIABI). Both classifications are made according to NACE Rev. 1.1 and NACE Rev. 2 at 2- digit level. Note that due to revision of the NACE Rev.1.1 to NACE Rev. 2 the list of Knowledge Intensive Activities has changed as well, the two definitions are used in parallel and the data are shown in two separate tables. NACE Rev.2 collection includes data starting from 2008 reference year. For more details please see the definitions in Annex 7 and 8. The product approach: The product approach was created to complement the sectoral approach and it is used for data on high-tech trade. The product list is based on the calculations of R&D intensity by groups of products (R&D expenditure/total sales). The groups classified as high-technology products are aggregated on the basis of the Standard International Trade Classification (SITC). The initial definition was built based on SITC Rev.3 and served to compile the high-tech product aggregates until 2007. With the implementation in 2007 of the new version of SITC Rev.4, the definition of high-tech groups was revised and adapted according to new classification. Starting from 2007 the Eurostat presents the trade data for high-tech groups aggregated based on the SITC Rev.4. For more details, see definition of high-tech products in Annex 4 and 5. High-tech patents: High-tech patents are defined according to another approach. The groups classified as high-tech patents are aggregated on the basis of the International Patent Classification (IPC 8th edition). Biotechnology patents are also aggregated on the basis of the IPC 8th edition. For more details, see the aggregation list of high-tech and biotechnology patents in Annex 6. The high-tech domain also comprises the sub-domain Venture Capital Investments: data are provided by INVEST Europe (formerly named the European Private Equity and Venture Capital Association EVCA). More details are available in the Eurostat metadata under Venture capital investments. Please note that for paragraphs where no metadata for regional data has been specified, the regional metadata is identical to the metadata provided for the national data.

'Statistics on high-tech industry and knowledge-intensive services' (sometimes referred to as simply 'high-tech statistics') comprise economic, employment and science, technology and innovation (STI) data describing manufacturing and services industries or products traded broken down by technological intensity. The domain uses various other domains and sources of  Eurostat's official statistics (CIS, COMEXT, HRST, LFS, PATENT, R&D and SBS) and its coverage is therefore dependent on these other primary sources. Two main approaches are used in the domain to identify technology-intensity: the sectoral approach and the product approach. A third approach is used for data on high-tech and biotechnology patents aggregated on the basis of the International Patent Classification (IPC) 8th edition (see summary table in Annex 1 for which approach is used by each type of data). The sectoral approach: The sectoral approach is an aggregation of the manufacturing industries according to technological intensity (R&D expenditure/value added) and based on the Statistical classification of economic activities in the European Community (NACE) at 2-digit level. The level of R&D intensity served as a criterion of classification of economic sectors into high-technology, medium high-technology, medium low-technology and low-technology industries. Services are mainly aggregated into knowledge-intensive services (KIS) and less knowledge-intensive services (LKIS) based on the share of tertiary educated persons at NACE 2-digit level. The sectoral approach is used for all indicators except data on high-tech trade and patents. Note that due to the revision of the NACE from NACE Rev. 1.1 to NACE Rev. 2 the definition of high-technology industries and knowledge-intensive services has changed in 2008. For high-tech statistics it means that two different definitions (one according NACE Rev. 1.1 and one according NACE Rev. 2) are used in parallel and the data according to both NACE versions are presented in separated tables depending on the data availability. For example as the LFS provides the results both by NACE Rev. 1.1 and NACE Rev. 2, all the table using this source have been duplicated to present the results by NACE Rev. 2 from 2008. For more details, see both definitions of high-tech sectors in Annex 2 and 3. Within the sectoral approach, a second classification was created, named Knowledge Intensive Activities KIA) and based on the share of tertiary educated people in each sectors of industries and services according to NACE at 2-digit level and for all EU28 Member States. A threshold was applied to judge sectors as knowledge intensive. In contrast to first sectoral approach mixing two methodologies, one for manufacturing industries and one for services, the KIA classification is based on one methodology for all the sectors of industries and services covering even public sector activities. The aggregations in use are Total Knowledge Intensive Activities (KIA) and Knowledge Intensive Activities in Business Industries (KIABI). Both classifications are made according to NACE Rev. 1.1 and NACE Rev. 2 at 2- digit level. Note that due to revision of the NACE Rev.1.1 to NACE Rev. 2 the list of Knowledge Intensive Activities has changed as well, the two definitions are used in parallel and the data are shown in two separate tables. NACE Rev.2 collection includes data starting from 2008 reference year. For more details please see the definitions in Annex 7 and 8. The product approach: The product approach was created to complement the sectoral approach and it is used for data on high-tech trade. The product list is based on the calculations of R&D intensity by groups of products (R&D expenditure/total sales). The groups classified as high-technology products are aggregated on the basis of the Standard International Trade Classification (SITC). The initial definition was built based on SITC Rev.3 and served to compile the high-tech product aggregates until 2007. With the implementation in 2007 of the new version of SITC Rev.4, the definition of high-tech groups was revised and adapted according to new classification. Starting from 2007 the Eurostat presents the trade data for high-tech groups aggregated based on the SITC Rev.4. For more details, see definition of high-tech products in Annex 4 and 5. High-tech patents: High-tech patents are defined according to another approach. The groups classified as high-tech patents are aggregated on the basis of the International Patent Classification (IPC 8th edition). Biotechnology patents are also aggregated on the basis of the IPC 8th edition. For more details, see the aggregation list of high-tech and biotechnology patents in Annex 6. The high-tech domain also comprises the sub-domain Venture Capital Investments: data are provided by INVEST Europe (formerly named the European Private Equity and Venture Capital Association EVCA). More details are available in the Eurostat metadata under Venture capital investments. Please note that for paragraphs where no metadata for regional data has been specified, the regional metadata is identical to the metadata provided for the national data.

'Statistics on high-tech industry and knowledge-intensive services' (sometimes referred to as simply 'high-tech statistics') comprise economic, employment and science, technology and innovation (STI) data describing manufacturing and services industries or products traded broken down by technological intensity. The domain uses various other domains and sources of  Eurostat's official statistics (CIS, COMEXT, HRST, LFS, PATENT, R&D and SBS) and its coverage is therefore dependent on these other primary sources. Two main approaches are used in the domain to identify technology-intensity: the sectoral approach and the product approach. A third approach is used for data on high-tech and biotechnology patents aggregated on the basis of the International Patent Classification (IPC) 8th edition (see summary table in Annex 1 for which approach is used by each type of data). The sectoral approach: The sectoral approach is an aggregation of the manufacturing industries according to technological intensity (R&D expenditure/value added) and based on the Statistical classification of economic activities in the European Community (NACE) at 2-digit level. The level of R&D intensity served as a criterion of classification of economic sectors into high-technology, medium high-technology, medium low-technology and low-technology industries. Services are mainly aggregated into knowledge-intensive services (KIS) and less knowledge-intensive services (LKIS) based on the share of tertiary educated persons at NACE 2-digit level. The sectoral approach is used for all indicators except data on high-tech trade and patents. Note that due to the revision of the NACE from NACE Rev. 1.1 to NACE Rev. 2 the definition of high-technology industries and knowledge-intensive services has changed in 2008. For high-tech statistics it means that two different definitions (one according NACE Rev. 1.1 and one according NACE Rev. 2) are used in parallel and the data according to both NACE versions are presented in separated tables depending on the data availability. For example as the LFS provides the results both by NACE Rev. 1.1 and NACE Rev. 2, all the table using this source have been duplicated to present the results by NACE Rev. 2 from 2008. For more details, see both definitions of high-tech sectors in Annex 2 and 3. Within the sectoral approach, a second classification was created, named Knowledge Intensive Activities KIA) and based on the share of tertiary educated people in each sectors of industries and services according to NACE at 2-digit level and for all EU28 Member States. A threshold was applied to judge sectors as knowledge intensive. In contrast to first sectoral approach mixing two methodologies, one for manufacturing industries and one for services, the KIA classification is based on one methodology for all the sectors of industries and services covering even public sector activities. The aggregations in use are Total Knowledge Intensive Activities (KIA) and Knowledge Intensive Activities in Business Industries (KIABI). Both classifications are made according to NACE Rev. 1.1 and NACE Rev. 2 at 2- digit level. Note that due to revision of the NACE Rev.1.1 to NACE Rev. 2 the list of Knowledge Intensive Activities has changed as well, the two definitions are used in parallel and the data are shown in two separate tables. NACE Rev.2 collection includes data starting from 2008 reference year. For more details please see the definitions in Annex 7 and 8. The product approach: The product approach was created to complement the sectoral approach and it is used for data on high-tech trade. The product list is based on the calculations of R&D intensity by groups of products (R&D expenditure/total sales). The groups classified as high-technology products are aggregated on the basis of the Standard International Trade Classification (SITC). The initial definition was built based on SITC Rev.3 and served to compile the high-tech product aggregates until 2007. With the implementation in 2007 of the new version of SITC Rev.4, the definition of high-tech groups was revised and adapted according to new classification. Starting from 2007 the Eurostat presents the trade data for high-tech groups aggregated based on the SITC Rev.4. For more details, see definition of high-tech products in Annex 4 and 5. High-tech patents: High-tech patents are defined according to another approach. The groups classified as high-tech patents are aggregated on the basis of the International Patent Classification (IPC 8th edition). Biotechnology patents are also aggregated on the basis of the IPC 8th edition. For more details, see the aggregation list of high-tech and biotechnology patents in Annex 6. The high-tech domain also comprises the sub-domain Venture Capital Investments: data are provided by INVEST Europe (formerly named the European Private Equity and Venture Capital Association EVCA). More details are available in the Eurostat metadata under Venture capital investments. Please note that for paragraphs where no metadata for regional data has been specified, the regional metadata is identical to the metadata provided for the national data.

'Statistics on high-tech industry and knowledge-intensive services' (sometimes referred to as simply 'high-tech statistics') comprise economic, employment and science, technology and innovation (STI) data describing manufacturing and services industries or products traded broken down by technological intensity. The domain uses various other domains and sources of  Eurostat's official statistics (CIS, COMEXT, HRST, LFS, PATENT, R&D and SBS) and its coverage is therefore dependent on these other primary sources. Two main approaches are used in the domain to identify technology-intensity: the sectoral approach and the product approach. A third approach is used for data on high-tech and biotechnology patents aggregated on the basis of the International Patent Classification (IPC) 8th edition (see summary table in Annex 1 for which approach is used by each type of data). The sectoral approach: The sectoral approach is an aggregation of the manufacturing industries according to technological intensity (R&D expenditure/value added) and based on the Statistical classification of economic activities in the European Community (NACE) at 2-digit level. The level of R&D intensity served as a criterion of classification of economic sectors into high-technology, medium high-technology, medium low-technology and low-technology industries. Services are mainly aggregated into knowledge-intensive services (KIS) and less knowledge-intensive services (LKIS) based on the share of tertiary educated persons at NACE 2-digit level. The sectoral approach is used for all indicators except data on high-tech trade and patents. Note that due to the revision of the NACE from NACE Rev. 1.1 to NACE Rev. 2 the definition of high-technology industries and knowledge-intensive services has changed in 2008. For high-tech statistics it means that two different definitions (one according NACE Rev. 1.1 and one according NACE Rev. 2) are used in parallel and the data according to both NACE versions are presented in separated tables depending on the data availability. For example as the LFS provides the results both by NACE Rev. 1.1 and NACE Rev. 2, all the table using this source have been duplicated to present the results by NACE Rev. 2 from 2008. For more details, see both definitions of high-tech sectors in Annex 2 and 3. Within the sectoral approach, a second classification was created, named Knowledge Intensive Activities KIA) and based on the share of tertiary educated people in each sectors of industries and services according to NACE at 2-digit level and for all EU28 Member States. A threshold was applied to judge sectors as knowledge intensive. In contrast to first sectoral approach mixing two methodologies, one for manufacturing industries and one for services, the KIA classification is based on one methodology for all the sectors of industries and services covering even public sector activities. The aggregations in use are Total Knowledge Intensive Activities (KIA) and Knowledge Intensive Activities in Business Industries (KIABI). Both classifications are made according to NACE Rev. 1.1 and NACE Rev. 2 at 2- digit level. Note that due to revision of the NACE Rev.1.1 to NACE Rev. 2 the list of Knowledge Intensive Activities has changed as well, the two definitions are used in parallel and the data are shown in two separate tables. NACE Rev.2 collection includes data starting from 2008 reference year. For more details please see the definitions in Annex 7 and 8. The product approach: The product approach was created to complement the sectoral approach and it is used for data on high-tech trade. The product list is based on the calculations of R&D intensity by groups of products (R&D expenditure/total sales). The groups classified as high-technology products are aggregated on the basis of the Standard International Trade Classification (SITC). The initial definition was built based on SITC Rev.3 and served to compile the high-tech product aggregates until 2007. With the implementation in 2007 of the new version of SITC Rev.4, the definition of high-tech groups was revised and adapted according to new classification. Starting from 2007 the Eurostat presents the trade data for high-tech groups aggregated based on the SITC Rev.4. For more details, see definition of high-tech products in Annex 4 and 5. High-tech patents: High-tech patents are defined according to another approach. The groups classified as high-tech patents are aggregated on the basis of the International Patent Classification (IPC 8th edition). Biotechnology patents are also aggregated on the basis of the IPC 8th edition. For more details, see the aggregation list of high-tech and biotechnology patents in Annex 6. The high-tech domain also comprises the sub-domain Venture Capital Investments: data are provided by INVEST Europe (formerly named the European Private Equity and Venture Capital Association EVCA). More details are available in the Eurostat metadata under Venture capital investments. Please note that for paragraphs where no metadata for regional data has been specified, the regional metadata is identical to the metadata provided for the national data.

Regional (NUTS level 2) employment rate of the age group 15-64 represents employed persons aged 15-64 as a percentage of the population of the same age group. The indicator is based on the EU Labour Force Survey. The survey covers the entire population living in private households and excludes those in collective households such as boarding houses, halls of residence and hospitals. The employed persons are those aged 15-64, who during the reference week did any work for pay, profit or family gain for at least one hour, or were not at work but had a job or business from which they were temporarily absent.

The source for the regional labour market information down to NUTS level 2 is the EU Labour Force Survey (EU-LFS). This is a quarterly household sample survey conducted in all Member States of the EU and in EFTA and Candidate countries.  The EU-LFS survey follows the definitions and recommendations of the International Labour Organisation (ILO). To achieve further harmonisation, the Member States also adhere to common principles when formulating questionnaires. The LFS' target population is made up of all persons in private households aged 15 and over. For more information see the EU Labour Force Survey (lfsi_esms, see paragraph 21.1.).  The EU-LFS is designed to give accurate quarterly information at national level as well as annual information at NUTS 2 regional level and the compilation of these figures is well specified in the regulation. Microdata including the NUTS 2 level codes are provided by all the participating countries with a good degree of geographical comparability, which allows the production and dissemination of a complete set of comparable indicators for this territorial level. At present the transmission of the regional labour market data at NUTS 3 level has no legal basis. However many countries transmit NUTS 3 figures to Eurostat on a voluntary basis, under the understanding that they are not for publication with such detail, but for aggregation in few categories per country, i.e., metropolitan regions and urban-rural typology. Most of the NUTS 3 data are based on the LFS while some countries transmit data based on registers, administrative data, small area estimation and other reliable sources.

Persons in employment are those who, during the reference week, did any work for pay or profit, or were not working but had a job from which they were temporarily absent. Anyone who receives a wage for on-the-job training that involves the production of goods or services is also considered as being in employment. Self-employed and family workers are also included. Employment is measured in number of persons without distinction according to full-time or part-time work. The data are expressed in 1000 persons. The quarterly data are not seasonally adjusted. Data are sourced from National accounts data. The ESA 2010 distinguishes two employment concepts depending on the geographical coverage: resident persons in employment (i.e. the national scope of employment) and employment in resident production units irrespective of the place of residence of the employed person (i.e. domestic scope). The table presents total employment, according to the domestic concept.

6.1. Reference area

The energy balance is the most complete statistical accounting of energy products and their flow in the economy. The energy balance allows users to see the total amount of energy extracted from the environment, traded, transformed and used by different types of end-users. It also allows seeing the relative contribution of each energy carrier (fuel, product). The energy balance allows studying the overall domestic energy market and monitoring impacts of energy policies. The energy balance offers a complete view on the energy situation of a country in a compact format, such as on energy consumption of the whole economy and of individual sectors. The energy balance presents all statistically significant energy products (fuels) of a country and their production, transformation and consumption by different type of economic actors (industry, transport, etc.). Therefore, an energy balance is the natural starting point to study the energy sector. Annual data collection cover in principle the EU Member States, EFTA, EU candidate countries, and potential candidate countries. Time series starts mostly in year 1990. All data in energy balances are presented in terajoules, kilotonnes of oil equivalent and gigawatt hours.

BGR provides the biennial energy study which covers data and developments concerning German and Global energy supplies. In addition the study also provides information on the global generation of renewable energies, including geothermal energy and hydrogen. 

The indicator measures the total amount of energy tax revenue in millions of euro for all NACE activities plus households, non-residents and not allocated. This shows the size that the energy taxes take in absolute (currency) terms, to complement the share of energy taxes paid by paying sector. Energy taxes are one of the four tax categories that make up environmental taxes (the other three being pollution taxes, resource taxes (excluding taxes on oil and gas extraction) and transport taxes). As per environmental taxes, the energy tax base is a physical unit of something that has a proven specific negative impact on the environment, but it is restricted to certain items that are concerning the energy sector, which also includes CO2 taxes.

The indicator measures the percentage of energy taxes that are raised against seven paying sectors as a proportion of the total amount of tax revenue raised from energy taxes. The seven sectors are: Households, Industry (except Construction), Construction, Wholesale and retail trade and repair of motor vehicles and motorcycles, Transportation and storage, Services (except wholesale and retail trade, transportation and storage), and Agriculture, forestry and fishing. Energy taxes are one of the four tax categories that make up environmental taxes (the other three being pollution taxes, resource taxes (excluding taxes on oil and gas extraction) and transport taxes). As per environmental taxes, the energy tax base is a physical unit of something that has a proven specific negative impact on the environment, but it is restricted to certain items that are concerning the energy sector, which also includes CO2 taxes.

The focus of this domain is on the European Neighbourhood Policy (ENP) countries in Eastern Europe, namely Armenia (AM), Azerbaijan (AZ), Belarus (BY), Georgia (GE), Moldova (MD) and Ukraine (UA). An extensive range of indicators is presented in this domain, including indicators from almost every theme covered by European statistics. Only annual data are published in this domain.

Eurostat Dataset Id:enpr_inisoc The domain focuses on the Eastern European Neighbourhood Policy countries (ENP): Armenia (AM), Azerbaijan (AZ), Belarus (BY), Georgia (GE), Moldova (MD) and the Ukraine (UA). Data are provided for 200 to 300 indicators.

Eurostat Dataset Id:enpr_scienc The domain focuses on the Eastern European Neighbourhood Policy countries (ENP): Armenia (AM), Azerbaijan (AZ), Belarus (BY), Georgia (GE), Moldova (MD) and the Ukraine (UA). Data are provided for 200 to 300 indicators.

1: Most surveys were administered using the Enterprise Surveys Global Methodology as outlined in the Methodology page, while some others did not strictly adhere to the Enterprise Surveys Global Methodology. For example, for surveys which do not follow the Global Methodology, the Universe under consideration may have consisted of only manufacturing firms or the questionnaire used may have been different from the standard global questionnaire. Data users should exercise caution when comparing raw data and point estimates between surveys that did and did not adhere to the Enterprise Surveys Global Methodology. For surveys which did not adhere to the Global Methodology plus Afghanistan 2008, any inference from one of these surveys is representative only for the data sample itself. 2: Regional and "all countries" averages of indicators are computed by taking a simple average of country-level point estimates. For each economy, only the latest available year of survey data is used in this computation. Only surveys, posted during the years 2009-2017, and adhering to the Enterprise Surveys Global Methodology are used to compute these regional and "all countries" averages. 3: Descriptions of firm subgroup levels, e.g. how the ex post groupings are constructed, are provided in the Indicator Descriptions (PDF, 710KB) document. 4: Statistics derived from less than or equal to five firms are displayed with an "n.a." to maintain confidentiality and should be distinguished from ".." which indicates missing values. Also note for three growth-related indicators under the "Performance" topic, these indicators are not computed when they are derived from less than 30 firms. 5: Standard errors are labeled "n.c.", meaning not computed, for the following:    1) indicators for all surveys that were not conducted using the Enterprise Surveys Global Methodology and    2) for indicator breakdowns by ex post groupings: exporter or ownership type, and gender of the top manager.

Data cited at: Wendling, Z. A., Emerson, J. W., Esty, D. C., Levy, M. A., de Sherbinin, A., et al. (2018). 2018 Environmental Performance Index. New Haven, CT: Yale Center for Environmental Law & Policy. https://epi.yale.edu/   The Environmental Performance Index (EPI) is constructed through the calculation and aggregation of 20 indicators reflecting national-level environmental data. These indicators are combined into nine issue categories, each of which fit under one of two overarching objectives. The two objectives that provide the overarching structure of the EPI are Environmental Health and Ecosystem Vitality. Environmental Health measures the protection of human health from environmental harm. Ecosystem Vitality measures ecosystem protection and resource management. These two objectives are further divided into nine issue categories that span high-priority environmental policy issues, including air quality, forests, fisheries, and climate and energy, among others. The issue categories are extensive but not comprehensive. Underlying the nine issue categories are 20 indicators calculated from country-level data and statistics. After more than 15 years of work on environmental performance measurement and six iterations of the EPI, global data are still lacking on a number of key environmental issues. These include: freshwater quality, toxic chemical exposures, municipal solid waste management, nuclear safety, wetlands loss, agricultural soil quality and degradation, recycling rates, adaptation, vulnerability, and resiliency to climate change, desertification.

The indicator is presented in two ways. Firstly, as the proportion of environmental tax revenues in total revenues from all taxes and social contributions. This evidences the different taxation splits that Member States have between environment and other factors such as labour and capital. Secondly, the indicator is presented as the proportion of environmental tax revenues in Gross Domestic Product (GDP). This allows a comparison of environmental taxation between Member States taking into account the size of the different national economies.

Definition of an environmental tax (Regulation (EU) N° 691/2011) An environmental tax is a tax whose tax base is a physical unit (or a proxy of a physical unit) of something that has a proven, specific negative impact on the environment, and which is identified in ESA2010 as a tax. Eurostat collects data on environmental tax revenue (by tax category - energy, transport, pollution and resource taxes) broken down by economic activities (tax payers) using the NACE classification for production activities plus households and non-residents. Eurostat with the European Commission's Directorate General for Taxation and Customs Union also produces annually an analysis of each tax listed in the national accounts of European countries including: - assigning an economic function to each tax; - attributing an environmental code to the environmental taxes (E for Energy, T for Transport, P for Pollution, RS for Resource). These function and environmental codes are reported by countries in their national tax list (NTL) and are validated by Eurostat. Efforts are made to ensure full consistency of the data on environmental taxes by economic activities and revenue data based on the national tax lists even if some discrepancies remain for some countries.

Animal production statistics cover three main sub-domains based on three pieces of relevant legislation and related gentlemen’s agreements. Livestock and meat statistics are collected under Regulation (EC) No 1165/2008. They cover meat production, as activity of slaughterhouses (monthly) and as other slaughtering (annual), meat production (gross indigenous production) forecast (semi-annual or quarterly), livestock statistics, including regional statistics. A quality report is also collected every third year.Milk and milk product statistics are collected under Decision 97/80/EC implementing Directive 96/16/EC. They cover farm production and utilisation of milk (annual), collection (monthly for cows’ milk) and production activity by dairies (annual) and statistics on the structure of dairies (every third year). An annual methodological report is also collected.Statistics on eggs for hatching and farmyard poultry chicks are collected under Regulation (EC) No 617/2008, implementing Regulation (EC) No 1234/2007 (Single CMO Regulation). They cover statistics on the structure (annual) and the activity (monthly) of hatcheries as well as reports on the external trade of chicks. European Economic Area countries (EEA, Iceland, Liechtenstein and Norway) are requested to provide milk statistics, with the exception of those related to home consumption, as stated in Annex XXI of the EEA Agreement. As Iceland is now a candidate country and Liechtenstein is exempted in the Agreement, only Norway is concerned. The Agreement between the European Community and the Swiss Confederation on cooperation in the field of statistics states that Switzerland must provide Eurostat with national milk statistics. It has been amended in 2013 for covering also some livestock and meat statistics. The same statistics are requested from the candidate countries as acquis communautaire. Further data about the same topics refer to repealed legal acts or agreements. The tables on animal product supply balance sheets (apro_mk_bal, apro_mt_bal and apro_ec_bal), statistics on the structure of rearing (apro_mt_str) and the number of laying hens (apro_ec_lshen) are therefore no longer updated. The same applies to some variables (external trade of animals and meat), periods (surveys in April or August) or items (number of horses) included in other tables. The statistical tables disseminated by Eurostat are organised into three groups of tables on Agricultural products (apro), i.e. Milk and milk products (apro_mk), Livestock and meat (apro_mt) and Poultry farming (apro_ec). This last label covers statistics on hatcheries and on trade in chicks. The regional animal production statistics collected on livestock (agr_r_animal) and on cows’ milk production on farms (agr_r_milk_pr) are disseminated separately. Due to the change in the legal basis or in the methodology, the time series may be broken. This is indicated by a flag in the tables. The detailed content of each table and the reference to its legal definition is provided in the table below. Table 3.1: Data tables disseminated regarding animal production statistics <

The annual Eurostat's collections on population are structured as follows:

International trade in goods statistics are an important data source for many public and private sector decision-makers at international, European Union and national level. For example, at the European Union level, external trade data are extensively used for multilateral and bilateral negotiations within the framework of the common commercial policy, to define and implement anti-dumping policy, to evaluate the progress of the Single Market and many other policies. Moreover, they constitute an essential source for the compilation of balance of payments statistics and national accounts. International trade in goods statistics cover both extra- and intra-EU trade: Extra-EU trade statistics cover the trading of goods between Member States and a non-member countries. Intra-EU trade statistics cover the trading of goods between Member States. "Goods" means all movable property including electricity. Detailed and aggregated data are published for the Euro area, the European Union and for each Member State separately. Main components: Data record the monthly trade between Member States in terms of arrivals and dispatches of goods as well as the monthly trade in terms of imports and exports between Member States and non-member countries. However, in publications only the term “exports” for all outward flows and “imports” for all inward flows are applied for both intra-EU trade and extra-EU trade. Extra-EU trade imports and exports are recorded in the Member State where the goods are placed under the customs procedures. Extra-EU trade statistics do not record goods in transit, goods placed into customs warehouses or goods for temporary admission. Data sources: The statistical information is mainly provided by the traders on the basis of Customs (extra-EU) and Intrastat (intra-EU) declarations. Data are collected by the competent national authorities of the Member States and compiled according to a harmonised methodology established by EU regulations before transmission to Eurostat. Classification systems: - Product classification: For detailed data, products are disseminated according to the Combined Nomenclature (CN8), which first six digit codes coincide with the Harmonized Commodity Description and Coding System (HS), products are disseminated as well according to the Standard International Trade Classification (SITC) and the Broad Economic Categories (BEC). - Country classification: The Geonomenclature is used for classifying reporting countries and trading partners. Nomenclatures and correspondence tables are available at the Eurostat’s classification server RAMON. The following basic information is provided by Eurostat: - reporting country, - reference period, - trade flow, - product, - trading partner, - mode of transport. Detailed data are disseminated according to the Combined Nomenclature (HS2, HS4, HS6 and CN8 levels) for the following indicators: - trade value (in Euro), - trade quantity in 100 kg, - trade quantity in supplementary units (published for some goods according to the Combined Nomenclature). Aggregated data cover both short and long term indicators. Short term indicators are disseminated according to major SITC and BEC groups for the following indicators: - gross and seasonally adjusted trade value (in million Euro), - unit-value indices, - gross and seasonally adjusted volume indices, - growth rates of trade values and indices. Adjustments are applied by the Member States to compensate the impact of exemption thresholds, which release the information providers from statistical formalities, as well as, to take into account the late or not response of the providers. In addition, Eurostat applies seasonal adjustments to aggregated time series.

A yield curve (which is known as the term structure of interest rates) represents the relationship between market remuneration (interest) rates and the remaining time to maturity of debt securities. The zero coupon yield curves and their corresponding time series are calculated using "AAA-rated" euro area central government bonds, i.e. debt securities with the most favourable credit risk assessment. They represent the yields to maturity of hypothetical zero coupon bonds. Source: European Central Bank.

 A yield curve, also known as term structure of interest rates, represents the relationship between market remuneration (interest) rates and the remaining time to maturity of debt securities. The information content of a yield curve reflects the asset pricing process on financial markets. When buying and selling bonds, investors include their expectations of  future inflation, real interest rates and their assessment of risks. An investor calculates the price of a bond by discounting the expected future cash flows (coupon payments and/or redemption). The European Central Bank estimates zero-coupon yield curves for the euro area and also derives forward and par yield curves. A zero coupon bond is a bond that pays no coupon and is sold at a discount from its face value. The zero coupon curve represents the yield to maturity of hypothetical zero coupon bonds, since they are not directly observable in the market for a wide range of maturities. They must therfore be estimated from existing zero coupon bonds and fixed coupon bond prices or yields.  The forward curve shows the short-term (instantaneous) interest rate for future periods implied in the yield curve. The par yield reflects hypothetical yields, namely the interest rates the bonds would have yielded had they been priced at par (i.e. at 100). An outlier removal mechanism is applied to bonds that have passed the selection criteria described in 11.1. Bonds are removed if their yields deviate by more than twice the standard deviation from the average yield in the same maturity bracket. Afterwards, the same procedure is repeated. 

A yield curve, also known as term structure of interest rates, represents the relationship between market remuneration (interest) rates and the remaining time to maturity of debt securities. The information content of a yield curve reflects the asset pricing process on financial markets. When buying and selling bonds, investors include their expectations of  future inflation, real interest rates and their assessment of risks. An investor calculates the price of a bond by discounting the expected future cash flows (coupon payments and/or redemption). ECB estimates zero-coupon yield curves for the euro area and also derives forward and par yield curves. A zero coupon bond is a bond that pays no cupon and is sold at a discount from its face value. The zero coupon curve represents the yield to maturity of hypothetical zero coupon bonds, since they are not directly observable in the market for a wide range of maturities. They must therfore be estimatedfrom existing zero coupon bonds and fixed coupon bond prices or yields.  The forward curve shows the short-term (instantaneous) interest rate for future periods implied in the yield curve. The par yield reflects hypothetical yields, namely the interest rates the bonds would have yielded had they been priced at par (i.e. at 100).

 A yield curve, also known as term structure of interest rates, represents the relationship between market remuneration (interest) rates and the remaining time to maturity of debt securities. The information content of a yield curve reflects the asset pricing process on financial markets. When buying and selling bonds, investors include their expectations of  future inflation, real interest rates and their assessment of risks. An investor calculates the price of a bond by discounting the expected future cash flows (coupon payments and/or redemption). The European Central Bank estimates zero-coupon yield curves for the euro area and also derives forward and par yield curves. A zero coupon bond is a bond that pays no coupon and is sold at a discount from its face value. The zero coupon curve represents the yield to maturity of hypothetical zero coupon bonds, since they are not directly observable in the market for a wide range of maturities. They must therfore be estimated from existing zero coupon bonds and fixed coupon bond prices or yields.  The forward curve shows the short-term (instantaneous) interest rate for future periods implied in the yield curve. The par yield reflects hypothetical yields, namely the interest rates the bonds would have yielded had they been priced at par (i.e. at 100). An outlier removal mechanism is applied to bonds that have passed the selection criteria described in 11.1. Bonds are removed if their yields deviate by more than twice the standard deviation from the average yield in the same maturity bracket. Afterwards, the same procedure is repeated. 

Dataset includes European Economic economic forecast releases from Autumn 2017 through Winter 2023.

Eurostat Dataset Id:ei_bsci_m_r2 Six qualitative surveys are conducted on a monthly basis in the following areas: manufacturing industry, construction, consumers, retail trade, services and financial services. Some additional questions are asked on a quarterly basis in the surveys in industry, services, financial services, construction and among consumers. In addition, a survey is conducted twice a year on Investment in the manufacturing sector. The domain consists of a selection for variables from the following type of survey: Industry monthly questions for: production, employment expectations, order-book levels, stocks of finished products and selling price. Industry quarterly questions for:production capacity, order-books, new orders, export expectations, capacity utilization, Competitive position and factors limiting the production. Construction monthly questions for: trend of activity, order books, employment expectations, price expectations and factors limiting building activity. Construction quarterly questions for: operating time ensured by current backlog. Retail sales monthly questions for: business situation, stocks of goods, orders placed with suppliers and firm's employment. Services monthly questions for: business climate, evolution of demand, evolution of employment and selling prices. Services quarterly question for: factors limiting their business Consumer monthly questions for: financial situation, general economic situation, price trends, unemployment, major purchases and savings. Consumer quarterly questions for: intention to buy a car, purchase or build a home, home improvements. Financial services monthly questions for: business situation, evolution of demand and employment Financial services quarterly questions for: operating income, operating expenses, profitability of the company, capital expenditure and competitive position Monthly Confidence Indicators are computed for industry, services, construction, retail trade, consumers (at country level, EU and euro area level) and financial services (EU and euro area). An Economic Sentiment indicator (ESI) is calculated based on a selection of questions from industry, services, construction, retail trade and consumers at country level and aggregate level (EU and euro area). A monthly Euro-zone Business Climate Indicator is also available for industry. The data are published: as balance i.e. the difference between positive and negative answers (in percentage points of total answers)as indexas confidence indicators (arithmetic average of balances),at current level of capacity utilization (percentage)estimated months of production assured by orders (number of months)Unadjusted (NSA) and seasonally adjusted (SA)

Expenditure on social protection contain: social benefits, which consist of transfers, in cash or in kind, to households and individuals to relieve them of the burden of a defined set of risks or needs; administration costs, which represent the costs charged to the scheme for its management and administration; other expenditure, which consists of miscellaneous expenditure by social protection schemes (payment of property income and other). It is calculated in current prices.

Expenditure on social protection contain: social benefits, which consist of transfers, in cash or in kind, to households and individuals to relieve them of the burden of a defined set of risks or needs; administration costs, which represent the costs charged to the scheme for its management and administration; other expenditure, which consist of miscellaneous expenditure by social protection schemes (payment of property income and other).

Note: Data is no longer being updated on source: https://www.ers.usda.gov/topics/international-markets-us-trade/international-consumer-and-food-industry-trends/#data The Dataset contain data on expenditures on food (including nonalcoholic beverages), alcoholic beverages, and tobacco as a share of consumer expenditures on all goods and services for 86 countries. The tables also contain data on per capita consumer expenditures on goods and services, as well as per capita food expenditures for these countries. Data is available for the 104 countries for which this type of information is currently available in the source database, Euromonitor International. All expenditure data are in current U.S. dollars.

Imports are recorded by country of origin, exports by country of destination; quantities are regarded as imported or exported when they have crossed the political boundaries of the country, whether customs clearance has taken place or not.  The concept of the physical movement of flows applies. The monthly imports and exports data collections cover following energy commodities:solid  fuels (hard coal only)oil and petroleum productsgaselectricity      For solid fuels, crude oil and NGLs, imports and exports are reported by country of ultimate origin, respectively destination, while natural gas, refinery products and feedstocks as well as electricity are reported as coming from the country of last consignment. Quantities of crude oil and petroleum products imported or exported under processing agreements (i.e. refining on account) should be included. However, transit trade, international marine and aviation bunkers are excluded from this data set. Please note that this is different from the definition of natural gas trade in the annual gas questionnaire and from the definition in the previous monthly oil and gas questionnaire (before January 2013). For further information, please consult the Energy Statistics Regulation (Regulation (EC) 1099/2008 on energy statistics) and the reporting instructions. Monthly imports and exports of energy commodities cover the full spectrum of the 28 Member States of the European Union, EFTA States, candidate countries and potential candidates. Â

This table presents annual statistics on international trade in services of individual economies by some 100 service categories for trade-partner “World total”. It further contains aggregated figures for various groups of economies for selected principal service categories. The data presented are the result of the common work of UNCTAD and the World Trade Organization (WTO).

ANNUAL Annual data on quantities for crude oil, petroleum products, natural gas and manufactures gases, electricity and derived heat, solid fossil fuels, renewables and wastes covering the full spectrum of the energy sector from supply through transformation to final consumption oby sector and fuel type. Also, annual imports and exports data of various energy carriers by country of origin and destination, as well as infrastructure information. Data on annual statistics are collected by standard questionnaires according to Annex B of the Regulation (EC) No 1099/2008 of the European Parliament and of the Council of 22 October 2008 on energy statistics   MONTHLY The monthly energy data collections cover the most important energy commodities: Crude oil & Petroleum productsNatural gasSolid fuelsElectricity For each of the above mentioned commodities the inflowing data are delivered by the reporting countries to Eurostat via separate dedicated questionnaires. Data on monthly statistics are collected by standard questionnaires according to Annex C of the Regulation (EC) No 1099/2008 of the European Parliament and of the Council of 22 October 2008 on energy statistics   SHORT-TERM MONTHLY Short-term monthly energy data collections cover the most important energy commodities: Oil & petroleum productsNatural gasElectricity Short-term monthly data provides information on main flows (quantities) on the supply side. Data on monthly short term statistics are collected by standard questionnaires according to Annex D of the Regulation (EC) No 1099/2008 of the European Parliament and of the Council of 22 October 2008 on energy statistics

ANNUAL Annual data on quantities for crude oil, petroleum products, natural gas and manufactures gases, electricity and derived heat, solid fossil fuels, renewables and wastes covering the full spectrum of the energy sector from supply through transformation to final consumption oby sector and fuel type. Also, annual imports and exports data of various energy carriers by country of origin and destination, as well as infrastructure information. Data on annual statistics are collected by standard questionnaires according to Annex B of the Regulation (EC) No 1099/2008 of the European Parliament and of the Council of 22 October 2008 on energy statistics   MONTHLY The monthly energy data collections cover the most important energy commodities: Crude oil & Petroleum productsNatural gasSolid fuelsElectricity For each of the above mentioned commodities the inflowing data are delivered by the reporting countries to Eurostat via separate dedicated questionnaires. Data on monthly statistics are collected by standard questionnaires according to Annex C of the Regulation (EC) No 1099/2008 of the European Parliament and of the Council of 22 October 2008 on energy statistics   SHORT-TERM MONTHLY Short-term monthly energy data collections cover the most important energy commodities: Oil & petroleum productsNatural gasElectricity Short-term monthly data provides information on main flows (quantities) on the supply side. Data on monthly short term statistics are collected by standard questionnaires according to Annex D of the Regulation (EC) No 1099/2008 of the European Parliament and of the Council of 22 October 2008 on energy statistics

Dans le but de promouvoir la comparabilité internationale, les statistiques présentées sur ILOSTAT sont basées sur des définitions internationales standard dans la mesure du possible et peuvent différer des chiffres nationaux officiels. Cette série est basée sur les définitions de la 13e CIST. Pour la comparabilité des séries chronologiques, elle inclut les pays qui ont mis en >uvre les normes de la 19e CIST, pour lesquels des données sont également disponibles dans la base de données Statistiques du travail -- 19e CIST (WORK). L'écart de salaires entre hommes et femmes est non ajusté et est calculé comme la différence entre les gains moyens des hommes et ceux des femmes, exprimée en pourcentage des gains moyens des hommes. Cet indicateur fournit une mesure de la différence relative entre les gains des hommes et ceux des femmes. Les données ventilées par profession sont présentées conformément à la version plus récente de la Classification Internationale Type des Professions (CITP) disponible. Les données peuvent avoir été reclassées à partir de classifications nationales, qui peuvent ne pas être strictement comparables à la CITP. Pour plus d'informations, reportez-vous à la description de la base de données Indicateurs d'égalité des sexes et de non-discrimination (GEND).

The dataset includes data on gross and net production indices for various food and agriculture aggregates expressed in both totals and per capita.

Note: Ex: <0.1,<0.2,<0.3 considered as 0.1,0.2,0.3 values  

The Price domain of FAOSTAT contains data on prices received by farmers (called Producer prices) for primary crops, live animals, livestock primary products as collected at the point of initial sale (prices paid at the farm-gate). Data are provided for over 160 countries and for some 200 commodities. The Price domain provides price data in three units: i) Local Currency Units (LCU) ii) Standard Local Currency (SLC) iii) US Dollars.

The food and agricultural trade dataset is collected, processed and disseminated by FAO according to the standard International Merchandise Trade Statistics Methodology. The data is mainly provided by UNSD, Eurostat, and other national authorities as needed. This source data is checked for outliers, trade partner data is used for non-reporting countries or missing cells, and data on food aid is added to take into account total cross-border trade flows. The trade database includes the following variables: export quantity, export value, import quantity and import value. The trade database includes all food and agricultural products imported/exported annually by all the countries in the world.   Classification system: HS 2012 converted into FAO Commodity List (also known as FCL, which is a classification based on the item tree approach used for the compilation of SUA/FBS).   Sector coverage: The dataset contains all food and agricultural products imported and exported during the reference year by country. In addition to the individual country data, other item and country aggregates are disseminated. The processed trade data is essential for the compilation of Supply/Utilization Accounts (SUA) and Food Balance Sheets (FBS).   Statistical concepts and definitions:Quantity of food and agricultural exports: Export quantity is defined by the IMTS as the physical quantity of domestic origin or manufactured products shipped out of the country. It includes re-exports. According to the FAO methodology, the quantity of food and agricultural exports included in the FAOSTAT database is expressed in terms of weight (tonnes) for all commodities except for live animals which are expressed in units (heads); poultry, rabbits, pigeons and other birds are expressed in thousand units. As a general rule, trade quantity refers to net weight, excluding any sort of container.  Value of agricultural exports: Value of agricultural exports are expressed in thousand US dollars in the FAOSTAT database. Export values are reported as FOB  (free on board—that is, the value of the goods plus the value of the services performed to deliver the goods to the border of the exporting country). Quantity of food and agricultural imports: Import quantity represents the physical quantity of the products imported for domestic consumption or processing shipped into a country. It includes re-imports. According to the FAO methodology, the quantity of food and agricultural imports included in the FAOSTAT database is expressed in terms of weight (tonnes) for all commodities except for live animals which are expressed in units (heads); poultry, rabbits, pigeons and other birds are expressed in thousand units. As a general rule, trade quantity refers to net weight, excluding any sort of container. It includes also food aid quantities, where relevant.  Value of agricultural imports: Value of agricultural imports are expressed in thousand US dollars in the FAOSTAT database. Import values are reported as CIF (cost insurance and freight—that is, the value of the goods, plus the value of the services performed to deliver goods to the border of the exporting country, plus the value of the services performed to deliver the good from the border of the exporting country to the border of the importing country). Statistical unit:All crops and livestock products registered by the customs office in the country. In case of non-customs trade data, the observation unit is the trade operator. For example, within intra-EU trade statistics, this unit is any taxable person carrying out intra-EU trade. For more information, see the IMTS compiler manual, edition 2012. Statistical population:All trade data on food and agricultural products, including livestock, are compiled by all customs offices in the country. For intra-EU trade, the statistical population is all trade operators recording trade transactions over a certain threshold. Total merchandise import/export value is also included.

Value of gross production has been compiled by multiplying gross production in physical terms by output prices at farm gate. Thus, value of production measures production in monetary terms at the farm gate level. Since intermediate uses within the agricultural sector (seed and feed) have not been subtracted from production data, this value of production aggregate refers to the notion of "gross production". Value of gross production is provided in both current and constant terms and is expressed in US dollars and Standard Local Currency (SLC). The current value of production measures value in the prices relating to the period being measured. Thus, it represents the market value of food and agricultural products at the time they were produced. Knowing this figure is helpful in understanding exactly what was happening within a given economy at that point in time. Often, this information can help explain economic trends that emerged in later periods and why they took place. Value of production in constant terms is derived using the average prices of a selected year or years, known as the base period. Constant price series can be used to show how the quantity or volume of products has changed, and are often referred to as volume measures. The ratio of the current and constant price series gives a measure of price movements. US dollar figures for value of gross production are converted from local currencies using official exchange rates as prevailing in the respective years. The SLC of a country is the local currency prevailing in the latest year. Expressing data series in one uniform currency is useful because it avoids the influence of revaluation in local currency, if any, on value of production

The Fertilizers by Product dataset contains information on product amounts for the Production, Trade, Agriculture Use and Other Uses of chemical and mineral fertilizers products, over the time series 2002-present. The fertilizer statistics data are validated separately for a set of over thirty individual products. Both straight and compound fertilizers are included.

Assessment of Fertilizer Use by Crop at the Global Level 2015. Fully understanding the contribution of the different crop types to fertilizer use at national, regional and global levels is a prerequisite to the development of sound fertilizer demand forecasts. IFA gathers information on fertilizer use by crop in the main fertilizer-consuming countries. IFA’s country estimates currently cover 28 countries (considering the European Union (EU-28) as a single country). These countries account together for 94% of world fertilizer consumption.

cThe dataset contains data on Import and Export Value (expressed in 1000 USD) for a selected list of fertilizers, from 1961 on wards. Country and country aggregate data are available. The fertilizers covered are: Nitrogenous fertilizers; Phosphate fertilizers; Potash fertilizers; Fertilizers Manufactured, nes; Fertilizers, Organic; Natural Phosphates; Natural Potassic Salts; Natural Sodium Nitrate.

Monthly updates of FIFA World Football Men's Ranking 

Data in this domain constitute only a small part of the entire National Accounts data range available from Eurostat. Annual and quarterly national accounts are compiled in accordance with the European System of Accounts - ESA 2010 as defined in Annex B of the Council Regulation (EU) No 549/2013 of the European Parliament and of the Council of 21 May 2013. The previous European System of Accounts, ESA95, was reviewed to bring national accounts in the European Union, in line with new economic environment, advances in methodological research and needs of users and the updated national accounts framework at the international level, the SNA 2008. The revisions are reflected in an updated Regulation of the European Parliament and of the Council on the European system of national and regional accounts in the European Union of 2010 (ESA 2010). The associated transmission programme is also updated and data transmissions in accordance with ESA 2010 are compulsory from September 2014 onwards. Further information on the transition from ESA 95 to ESA 2010 is presented on the Eurostat website. The annual data of this domain consists of the following collections: 1. Main GDP aggregates: main components from the output, expenditure and income side. nama_10_gdp: GDP and main components (output, expenditure and income) The quarterly data of this domain consists of the following collections 1. Main GDP aggregates, main components from the output, expenditure and income side, expenditure breakdowns by industry and assets. namq_10_ma: Main GDP aggregatesnamq_10_gdp: GDP and main components (output, expenditure and income)namq_10_fcs: Final consumption aggregates by durabilitynamq_10_exi: Exports and imports by Member States of the EU/third countries 2. Breakdowns of GDP aggregates and employment data by main industries and asset classes. namq_10_bbr: Basic breakdowns main GDP aggregates and employment (by industry and assets)namq_10_a10: Gross value added and income by A*10 industrynamq_10_an6: Gross fixed capital formation by AN_F6 asset typenamq_10_a10_e: Employment by A*10 industry breakdowns Geographical entities covered are the European Union, the euro area, EU Member States, Candidate Countries, EFTA countries, US, Japan and possibly other countries on an ad-hoc basis. Data sources: National Statistical Institutes.

Final consumption expenditure by general government includes the value of goods and services purchased or produced by general government and directly supplied to private households for consumption purposes.

Private consumption expenditure consists of expenditure incurred for the direct satisfaction of individual or collective needs by private households or non-profit institutions serving households (such as religious societies, sports and other clubs, political parties, etc.).

Private consumption expenditure consists of expenditure incurred for the direct satisfaction of individual or collective needs by private households or non-profit institutions serving households (such as religious societies, sports and other clubs, political parties, etc.).

Private consumption expenditure consists of expenditure incurred for the direct satisfaction of individual or collective needs by private households or non-profit institutions serving households (such as religious societies, sports and other clubs, political parties, etc.). The data are presented in % of GDP and million units of national currency.

Final consumption expenditure (ESA 2010, 3.94) consists of expenditure incurred by resident institutional units on goods or services that are used for the direct satisfaction of individual needs or wants or the collective needs of members of the community. The consumption expenditure may take place on the domestic territory or abroad. Private final consumption expenditure includes households' (ESA 2010, 2.118) and NPISH's final consumption expenditure. Non Profit Institutions Serving Households (NPISH, ESA 2010, 2.129) are separate legal entities serving households. They include for example trade unions, professional societies, political parties, churches, charities, sports clubs etc. Values are seasonally adjusted (SA). The ESA 2010 (European System of Accounts) regulation may be referred to for more specific explanations on methodology.

National accounts are a coherent and consistent set of macroeconomic indicators, which provide an overall picture of the economic situation and are widely used for economic analysis and forecasting, policy design and policy making. Eurostat publishes annual and quarterly national accounts, annual and quarterly sector accounts as well as supply, use and input-output tables, which are each presented with associated metadata. Even though consistency checks are a major aspect of data validation, temporary (usually limited) inconsistencies between datasets may occur, mainly due to vintage effects. Annual national accounts are compiled in accordance with the European System of Accounts - ESA 2010 as defined in Annex B of the Council Regulation (EU) No 549/2013 of the European Parliament and of the Council of 21 May 2013.   The previous European System of Accounts, ESA95, was reviewed to bring national accounts in the European Union, in line with new economic environment, advances in methodological research and needs of users and the updated national accounts framework at the international level, the SNA 2008. The revisions are reflected in an updated Regulation of the European Parliament and of the Council on the European system of national and regional accounts in the European Union of 2010 (ESA 2010). The associated transmission programme is also updated and data transmissions in accordance with ESA 2010 are compulsory from September 2014 onwards. Further information (including actual communications) is presented on the Eurostat website. The domain consists of the following collections:   1. Main GDP aggregates: main components from the output, expenditure and income side, expenditure breakdowns by durability and exports and imports by origin. <

Final decision on appeal means a decision granted at the final instance of administrative/judicial asylum procedure and which results from the appeal lodged by the asylum seeker rejected in the preceding stage of the procedure.