Germany

The 3P Anti-trafficking Policy Index evaluates governmental anti-trafficking efforts in the three main policy dimensions (3Ps), based on the requirements prescribed by the United Nations Protocol to Prevent, Suppress and Punish Trafficking in Persons, especially Women and Children (2000).   The three main policy dimensions (3Ps) are:Prosecution of perpetrators of human traffickingPrevention of human traffickingProtection of the victims of human trafficking Each of the 3P areas is evaluated on a 5-point scale and each index is aggregated to the overall 3P Anti-trafficking Index as the  sum (score 3-15).Prosecution Index Score: 1 (no compliance) - 5 (full compliance)Prevention Index Score: 1 (no compliance) - 5 (full compliance)Protection Index Score: 1 (no compliance) - 5 (full compliance)3P Anti-trafficking Policy Index Score: 3 (no compliance for any of the three areas) - 15 (full compliance for all of the three areas) The 3P Anti-trafficking Policy Index is available for each country and each year and currently includes up to 189 countries for the preiod from 2000 to 2015.

Post Secondary Education of United States, 2015

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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 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.

Eurostat Dataset Id:demo_r_mdthrt The regional demographic statistics provides annual data on population, vital events (live births and deaths), total and land areas of the regions and key demographic indicators for regions and statistical regions at NUTS2 and NUTS3 levels for 35 countries: each EU-27 Member State, Acceding, Candidate and EFTA countries. The completeness of the tables depends on the availability of data received from the responsible national statistical institutes (NSIs).  The label of each table indicates the lowest NUTS level for which data are available; for the upper NUTS levels data are included as well. Starting with March 2013, demographic statistics at regional level reflect the new NUTS-2010 classification for EU-27 Member States and the new statistical regions for Croatia. Countries affected by the NUTS-2010 changes are expected to transmit to Eurostat the time series for the new regional breakdown. As a general approach, the regions with no data available are not listed in the tables. For a calendar year T, the deadline of the regional demographic data collection is 15 December, and data included have a different degree of detail for regions at NUTS2 and NUTS3 levels: NUTS2 level - high level of data detail:Population by sex and single year of age at 1st January: years T and T-1Live births by single year of age and year of birth of the mother: year T-1 Deaths by sex and single years of age and year of birth: year T-1  NUTS3 level - low level of data detail:Surface area in km2 at 1st January (total area including inland waters and land area): year TPopulation by sex and broad age groups at 1st January, namely for 0-14 (0 up to 14 years), 15-64 (15 up to 64 years) and 65+ (persons of 65 years and older): years T and T-1 Live births and deaths (total number of demographic events): year T-1  Tables are updated mainly during March of the next year (T+1), but also along the year whenever revised data are sent by the official data providers. Demographic indicators at regional level are computed by Eurostat using a harmonised methodology and common concepts for all regions of all countries, namely:average population on 1st January (in thousands), population density;demographic balance and crude rates (population change, natural change, net migration including statistical adjustments, crude birth rate, crude death rate, crude rate of population change, crude rate of natural change, crude rate of net migration (including statistical adjustments));age-specific-fertility rates and Total Fertility Rates;life tables that include age-specific-mortality-rates and life expectancy at given exact age;infant mortality and crude rate of infant mortality. At national level a larger number of demographic indicators are computed, as more detailed demographic data are collected only at this level.   

Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. The series is part of the ILO estimates and is harmonized to account for differences in national data and scope of coverage, collection and tabulation methodologies as well as for other country-specific factors. For more information, refer to the ILO estimates and projections methodological note.

The Agri-environmental Indicators—Land domain provides information on the annual evolution of the distribution of agricultural and forest areas, and their sub-components, including irrigated areas, at national, regional and global levels.

This dataset is used to report the tying status of bilateral ODA commitments. Members have agreed that administrative costs and technical co-operation expenditure should be disregarded in assessing the percentages of tied, partially untied and untied aid. These items have not been included in the data reported in this data set.

The air transport regional data have been calculated using data collected at the airport level in the frame of Commission Regulation (EC) No 1358/2003. They are aggregated at regional level (NUTS 1 and NUTS 2) and also at national level (NUTS0), excluding double counting within each region.

Due to the nature of transport, a spatial reference is built into most legal acts dealing with transport statistics. In a few cases, these sources can be directly used for the derivation of regional transport indicators, while other indicators are collected on a voluntary basis. This is the case of the regional transport data collection in which both data types are used. Three types of regional data can be distinguished depending on their source: two are based on data collections performed on the basis of legal acts (the Maritime and Aviation data) and one is a voluntary data collection (infrastructures, vehicles and road accidents). Regional data collected on voluntary basis: The current regional data collection taking place on a voluntary basis comprises a set of transport indicators at NUTS 0, 1 and 2[1] levels for the road, railways, inland waterways (infrastructure), vehicle stocks, road accidents. The information collected is then disseminated in Eurostat dissemination database (Eurobase) under “General and regional statistics/Regional statistics by NUTS classification/Regional transport statistics” theme and also mirrored under “Transport/Multimodal data/Regional transport statistics” theme. Annual data collection for infrastructure, vehicle stocks and road accidents was launched at the beginning of 2002 covering both Member States and Candidate countries. Since 2007, it includes EFTA countries as well. Regional data are collected directly from the countries using a questionnaire: data on transport infrastructure, vehicle stocks and road accidents (collected previously – till 2006 – on Excel sheets and then – till 2012 – using on-line questionnaires). Currently regional datasets are provided via EDAMIS application. For the voluntary data collection via EDAMIS portal, the definitions from the 4th edition of the Illustrated Glossary for Transport Statistics (jointly elaborated by Eurostat, ECMT, UNECE) were proposed and countries should use them when transmitting data. Regional data based on legal acts: For the collection based on legal acts, regional data are actually derived from the information provided by the participating countries in the frame of the legal data collections at port and airport level. Maritime transport data collection is legally based – Directive 2009/42/EC of the European Parliament and of the Council of 6 May 2009 on statistical returns in respect of carriage of goods and passengers by sea (OJ L141 of 6.6.2009, page 29), which is the recast of the original Council Directive 95/64/EC of 8 December 1995. The air transport regional data have been calculated using data collected at the airport level in the frame of draft Council and Parliament Regulation (Regulation (EC) No 437/2003 of the European Parliament and of the Council of 27 February 2003 on statistical returns in respect of the carriage of passengers, freight and mail by air). Within the collection Maritime transport - data aggregated at standard regional levels (NUTS) there are two tables: Maritime transport of passengers at regional level (new methodology);Maritime transport of freight at regional level (new methodology). Within the collection Air transport - data aggregated at standard regional levels (NUTS) there are two tables: Air transport of passengers at regional levelAir transport of freight at regional level The tables present the evolution of the number of passengers carried (if not available passengers on board) and the volume of freight and mail loaded or unloaded (if not available freight and mail on board) to/from the NUTS regions (level 2, 1 and 0) since 1999. The data is presented at annual level. Before the legal act on air transport statistics was introduced (2003 with 3 years transitional period), air transport statistics have been collected using the statistical questionnaire (voluntary basis). Some countries provided figures for passenger transport taking into account “passengers on board” and some “passenger carried”. Until 2007 reference year, the disseminated numbers of passengers aggregated at regional level are actually a mixture of passengers on board and passengers carried data. The air transport regional data have been calculated using data collected at the airport level in the frame of the regulatory data collection on air transport. Only airports with more than 150 000 passenger units serviced annually are taken into account when aggregating the data at regional levels, because they provide statistics detailed enough to solve the problem of double counting. For each aggregate it is necessary to start at the airport level in order to identify the mirror declarations, i.e. the airport routes for which both airports report the volume, since these constitute the routes where the problem of double counting occurs. When calculating the total volume in such cases, only the departure declarations of the concerned airports have been taken into account. The problem of the double counting only appears for the calculation of the total passengers but not for the total arrivals (respectively total departures), which corresponds to the sum of the arrivals (respectively departures) at each domestic airport. For the tables presenting maritime data at regional level the same aggregation method (exclusion of double counting) is applied taking into account main ports only. Only for these ports (handling more than one million tonnes of goods or recording more than 200 000 passenger movements annually ) the detailed statistics allow such aggregation. For some Member States (up to 1998 data) and for others that joint the EU more recently (up to 2002) transport flows through ports and airports had been collected, via questionnaire. Because of the difference in the methodologies applied, the data for air and maritime transport at regional level up to 2002 reference year are available in separate tablesor some Member States (up to 1998 data) and for others that joint the EU more recently (up to 2002) transport flows through ports and airports had been collected, via questionnaire. Because of the difference in the methodologies applied, the data for air and maritime transport at regional level up to 2002 reference year are avilable in separate tables (listed below) and are no longer updated: Maritime transport of passengers by NUTS 2 regions (questionnaire) (tran_r_mapa_om).Maritime transport of freight by NUTS 2 regions (questionnaire) (tran_r_mago_om).Air transport of passengers by NUTS 2 regions (questionnaire) (tran_r_avpa_om).Air transport of freight by NUTS 2 regions (questionnaire) (tran_r_avgo_om). [1] Regulation (EC) No 1059/2003 of the European Parliament and of the Council of 26 May 2003 on the establishment of a common classification of territorial units for statistics (NUTS) and its amendments. More information on NUTS classification can be found under the following link.

Eurostat Dataset Id:tran_r_avgo_om Due to the nature of transport, a spatial reference is built into most legal acts dealing with transport statistics. In a few cases, these sources can be directly used for the derivation of regional transport indicators, while other indicators are collected on a voluntary basis. This is the case of the regional transport data collection in which both data types are used. Three types of regional data can be distinguished depending on their source: two are based on data collections performed on the basis of legal acts (the Maritime and Aviation data) and one is a voluntary data collection (infrastructures, vehicles and road accidents). Regional data collected on voluntary basis: The current regional data collection taking place on a voluntary basis comprises a set of transport indicators at NUTS 0, 1 and 2[1] levels for the road, railways, inland waterways (infrastructure), vehicle stocks, road accidents. The information collected is then disseminated in Eurostat dissemination database (Eurobase) under “General and regional statistics/Regional statistics/Regional transport statistics” theme and also mirrored under “Transport/Regional transport statistics” theme. Annual data collection for infrastructure, vehicle stocks and road accidents was launched at the beginning of 2002 covering both Member States and Candidate countries. Since 2007, it includes EFTA countries as well. Regional data are collected directly from the countries using a questionnaire: data on transport infrastructure, vehicle stocks and road accidents (collected previously – till 2006 – on Excel sheets and then – till 2012- using on-line questionnaires). Currently regional datasets are provided via eDAMIS application. For the voluntary data collection via eDAMIS portal, the definitions from the 4th edition of the Glossary for transport statistics(jointly elaborated by Eurostat, ECMT, UNECE) were proposed and countries should use them when transmitting data. Regional data based on legal acts: For the collection based on legal acts, regional data are actually derived from the information provided by the participating countries in the frame of the legal data collections at port and airport level. Maritime transport data collection is legally based – Directive 2009/42/EC of the European Parliament and of the Council of 6 May 2009 on statistical returns in respect of carriage of goods and passengers by sea (OJ L141 of 6.6.2009, page 29), which is the recast of the original Council Directive 95/64/EC of 8 December 1995. Within the collection Maritime transport - data aggregated at standard regional levels (NUTS) there are two tables:Maritime transport of passengers at regional level (new methodology);Maritime transport of freight at regional level (new methodology).   Within the collection Air transport - data aggregated at standard regional levels (NUTS) there are two tables:Air transport of passengers at regional levelAir transport of freight at regional level   The tables present the evolution of the number of passengers carried (if not available passengers on board) and the volume of freight and mail loaded or unloaded (if not available freight and mail on board) to/from the NUTS regions (level 2, 1 and 0) since 1999. The data is presented at annual level. The air transport regional data have been calculated using data collected at the airport level in the frame of draft Council and Parliament Regulation (Regulation 95/C 325/08 on statistical returns in respect of carriage of passenger, freight and mail by air). [1] Regulation (EC) No 1059/2003 of the European Parliament and of the Council of 26 May 2003 on the establishment of a common classification of territorial units for statistics (NUTS) and its amendments. More information on NUTS clasiffication can be found under the following link.

The air transport regional data have been calculated using data collected at the airport level in the frame of Commission Regulation (EC) No 1358/2003. They are aggregated at regional level (NUTS 1 and NUTS 2) and also at national level (NUTS0), excluding double counting within each region.

Due to the nature of transport, a spatial reference is built into most legal acts dealing with transport statistics. In a few cases, these sources can be directly used for the derivation of regional transport indicators, while other indicators are collected on a voluntary basis. This is the case of the regional transport data collection in which both data types are used. Three types of regional data can be distinguished depending on their source: two are based on data collections performed on the basis of legal acts (the Maritime and Aviation data) and one is a voluntary data collection (infrastructures, vehicles and road accidents). Regional data collected on voluntary basis: The current regional data collection taking place on a voluntary basis comprises a set of transport indicators at NUTS 0, 1 and 2[1] levels for the road, railways, inland waterways (infrastructure), vehicle stocks, road accidents. The information collected is then disseminated in Eurostat dissemination database (Eurobase) under “General and regional statistics/Regional statistics by NUTS classification/Regional transport statistics” theme and also mirrored under “Transport/Multimodal data/Regional transport statistics” theme. Annual data collection for infrastructure, vehicle stocks and road accidents was launched at the beginning of 2002 covering both Member States and Candidate countries. Since 2007, it includes EFTA countries as well. Regional data are collected directly from the countries using a questionnaire: data on transport infrastructure, vehicle stocks and road accidents (collected previously – till 2006 – on Excel sheets and then – till 2012 – using on-line questionnaires). Currently regional datasets are provided via EDAMIS application. For the voluntary data collection via EDAMIS portal, the definitions from the 4th edition of the Illustrated Glossary for Transport Statistics (jointly elaborated by Eurostat, ECMT, UNECE) were proposed and countries should use them when transmitting data. Regional data based on legal acts: For the collection based on legal acts, regional data are actually derived from the information provided by the participating countries in the frame of the legal data collections at port and airport level. Maritime transport data collection is legally based – Directive 2009/42/EC of the European Parliament and of the Council of 6 May 2009 on statistical returns in respect of carriage of goods and passengers by sea (OJ L141 of 6.6.2009, page 29), which is the recast of the original Council Directive 95/64/EC of 8 December 1995. The air transport regional data have been calculated using data collected at the airport level in the frame of draft Council and Parliament Regulation (Regulation (EC) No 437/2003 of the European Parliament and of the Council of 27 February 2003 on statistical returns in respect of the carriage of passengers, freight and mail by air). Within the collection Maritime transport - data aggregated at standard regional levels (NUTS) there are two tables: Maritime transport of passengers at regional level (new methodology);Maritime transport of freight at regional level (new methodology). Within the collection Air transport - data aggregated at standard regional levels (NUTS) there are two tables: Air transport of passengers at regional levelAir transport of freight at regional level The tables present the evolution of the number of passengers carried (if not available passengers on board) and the volume of freight and mail loaded or unloaded (if not available freight and mail on board) to/from the NUTS regions (level 2, 1 and 0) since 1999. The data is presented at annual level. Before the legal act on air transport statistics was introduced (2003 with 3 years transitional period), air transport statistics have been collected using the statistical questionnaire (voluntary basis). Some countries provided figures for passenger transport taking into account “passengers on board” and some “passenger carried”. Until 2007 reference year, the disseminated numbers of passengers aggregated at regional level are actually a mixture of passengers on board and passengers carried data. The air transport regional data have been calculated using data collected at the airport level in the frame of the regulatory data collection on air transport. Only airports with more than 150 000 passenger units serviced annually are taken into account when aggregating the data at regional levels, because they provide statistics detailed enough to solve the problem of double counting. For each aggregate it is necessary to start at the airport level in order to identify the mirror declarations, i.e. the airport routes for which both airports report the volume, since these constitute the routes where the problem of double counting occurs. When calculating the total volume in such cases, only the departure declarations of the concerned airports have been taken into account. The problem of the double counting only appears for the calculation of the total passengers but not for the total arrivals (respectively total departures), which corresponds to the sum of the arrivals (respectively departures) at each domestic airport. For the tables presenting maritime data at regional level the same aggregation method (exclusion of double counting) is applied taking into account main ports only. Only for these ports (handling more than one million tonnes of goods or recording more than 200 000 passenger movements annually ) the detailed statistics allow such aggregation. For some Member States (up to 1998 data) and for others that joint the EU more recently (up to 2002) transport flows through ports and airports had been collected, via questionnaire. Because of the difference in the methodologies applied, the data for air and maritime transport at regional level up to 2002 reference year are available in separate tablesor some Member States (up to 1998 data) and for others that joint the EU more recently (up to 2002) transport flows through ports and airports had been collected, via questionnaire. Because of the difference in the methodologies applied, the data for air and maritime transport at regional level up to 2002 reference year are avilable in separate tables (listed below) and are no longer updated: Maritime transport of passengers by NUTS 2 regions (questionnaire) (tran_r_mapa_om).Maritime transport of freight by NUTS 2 regions (questionnaire) (tran_r_mago_om).Air transport of passengers by NUTS 2 regions (questionnaire) (tran_r_avpa_om).Air transport of freight by NUTS 2 regions (questionnaire) (tran_r_avgo_om). [1] Regulation (EC) No 1059/2003 of the European Parliament and of the Council of 26 May 2003 on the establishment of a common classification of territorial units for statistics (NUTS) and its amendments. More information on NUTS classification can be found under the following link.

Eurostat Dataset Id:tran_r_avpa_om Due to the nature of transport, a spatial reference is built into most legal acts dealing with transport statistics. In a few cases, these sources can be directly used for the derivation of regional transport indicators, while other indicators are collected on a voluntary basis. This is the case of the regional transport data collection in which both data types are used. Three types of regional data can be distinguished depending on their source: two are based on data collections performed on the basis of legal acts (the Maritime and Aviation data) and one is a voluntary data collection (infrastructures, vehicles and road accidents). Regional data collected on voluntary basis: The current regional data collection taking place on a voluntary basis comprises a set of transport indicators at NUTS 0, 1 and 2[1] levels for the road, railways, inland waterways (infrastructure), vehicle stocks, road accidents. The information collected is then disseminated in Eurostat dissemination database (Eurobase) under “General and regional statistics/Regional statistics/Regional transport statistics” theme and also mirrored under “Transport/Regional transport statistics” theme. Annual data collection for infrastructure, vehicle stocks and road accidents was launched at the beginning of 2002 covering both Member States and Candidate countries. Since 2007, it includes EFTA countries as well. Regional data are collected directly from the countries using a questionnaire: data on transport infrastructure, vehicle stocks and road accidents (collected previously – till 2006 – on Excel sheets and then – till 2012- using on-line questionnaires). Currently regional datasets are provided via eDAMIS application. For the voluntary data collection via eDAMIS portal, the definitions from the 4th edition of the Glossary for transport statistics(jointly elaborated by Eurostat, ECMT, UNECE) were proposed and countries should use them when transmitting data. Regional data based on legal acts: For the collection based on legal acts, regional data are actually derived from the information provided by the participating countries in the frame of the legal data collections at port and airport level. Maritime transport data collection is legally based – Directive 2009/42/EC of the European Parliament and of the Council of 6 May 2009 on statistical returns in respect of carriage of goods and passengers by sea (OJ L141 of 6.6.2009, page 29), which is the recast of the original Council Directive 95/64/EC of 8 December 1995. Within the collection Maritime transport - data aggregated at standard regional levels (NUTS) there are two tables: Maritime transport of passengers at regional level (new methodology);Maritime transport of freight at regional level (new methodology). Within the collection Air transport - data aggregated at standard regional levels (NUTS) there are two tables: Air transport of passengers at regional levelAir transport of freight at regional level The tables present the evolution of the number of passengers carried (if not available passengers on board) and the volume of freight and mail loaded or unloaded (if not available freight and mail on board) to/from the NUTS regions (level 2, 1 and 0) since 1999. The data is presented at annual level. The air transport regional data have been calculated using data collected at the airport level in the frame of draft Council and Parliament Regulation (Regulation 95/C 325/08 on statistical returns in respect of carriage of passenger, freight and mail by air). [1] Regulation (EC) No 1059/2003 of the European Parliament and of the Council of 26 May 2003 on the establishment of a common classification of territorial units for statistics (NUTS) and its amendments. More information on NUTS clasiffication can be found under the following link.

General aviation operations are defined by the FAA based Source: FAA Operations Network (OPSNET) on the traffic operations counted in the OPSNET. Air Traffic Control data shows federal, non-federal, and military through 2005, while 2006 through 2011 are FAA and contract.    TRANSLATE with xEnglishArabicHebrewPolishBulgarianHindiPortugueseCatalanHmong DawRomanianChinese SimplifiedHungarianRussianChinese TraditionalIndonesianSlovakCzechItalianSlovenianDanishJapaneseSpanishDutchKlingonSwedishEnglishKoreanThaiEstonianLatvianTurkishFinnishLithuanianUkrainianFrenchMalayUrduGermanMalteseVietnameseGreekNorwegianWelshHaitian CreolePersian  TRANSLATE with COPY THE URL BELOW BackEMBED THE SNIPPET BELOW IN YOUR SITEEnable collaborative features and customize widget: Bing Webmaster PortalBack

Causes of Death data refer to the underlying cause which - according to the World Health Organisation (WHO) - is the disease or injury which initiated the train of morbid events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury"."

Data source(s) used: Crimes reported to the Judicial authorities by the State Police, Carabinieri and Guardia di Finanza: Are processed the data on felonies and people who were reported by police to the court Other data characteristics: Data referring to social demographic characteristics of alleged offenders could not coincide with data on reports because of the different timing of extraction from police forces database.The sum of the crimes by province could not coincide with the total of the region, and that of the regions with the total Italy, because of the missed precise statement, for some crimes, of the place where they have been committed (or of the region of the committed crime but not of the province).

The domain EUROFARM (ef) contains information (statistical tables) on structure of agricultural holdings collected through agricultural structure surveys. The data of the domain have been organised into two collections:Results of the farm structure surveys contains data from 1990 onwards based on the 1988 legislation andResults of the farm structure surveys based on the legislation from 2008. This collection contains the data from the 2010 Census onwards as well as the data for the surveys 2007 and 2005. Farm Structure Survey data are used to collect information on agricultural holdings in the Member States at different geographic levels (Member States, regions, districts) and over periods (follow up the changes in agricultural sector), thus provide a base for decision making in the Common Agricultural Policy. Two kinds of Farm Structure Survey (FSS) are carried out by Member States:a basic survey (full scope Agricultural Census - AC) every 10 years,several sample based intermediate surveys between them. However for certain characteristics the Member States may use sample base for every survey. The calendar for the surveys to be held in all Member States is agreed by the Agricultural Statistics Committee of the European Commission. For a given survey year, Member States have to conduct their surveys within the agreed time-frame, thus all the data are as comparable as possible. The FSS are organised in all Member States on a harmonised base. Whereas the characteristics are based on community legislation, the same data are available for all countries in case of each survey. The data on individual agricultural holdings are collected by all Member States and sent to Eurostat. The aggregated results are disseminated through statistical tables. The variables are arranged into groups:general overview with key variables,and other specialized groups containing detailed data onland uselivestockfarm labour forcerural development issues as well as management and practices. The 2010 survey additionally provides the results of the survey on agricultural production methods (SAPM). The scope of the survey is agriculture, while the survey unit is the agricultural holding (farm). Data for basic surveys are available in a three-level geographical breakdown of the whole country, the regions and the district; while data for intermediate surveys are only available upon the two-levels of country and regions. Since FSS 1999/2000 information about local farm location is collected in most countries, so that the data can also be disseminated by NUTS and are robust regarding the changes in the NUTS definition. The FSS 2009/2010 information is inline with the NUTS 2010 classification: Regulation (EU) No 31/2011 amending the NUTS classification from January 2003. The Eurofarm domain does not cover the whole territory only the land covered by the agricultural holdings. So the land use data without link with other farm characteristics should be downloaded by the user from the relevant domain. Specific national data about crops, animals or agricultural labour force can be found in other domains, without link between the other information at farm level. For a comprehensive description of the domain, please consult detailed structure (See annex at the bottom of the page). Regional Data Data for basic surveys are available in a three-level geographical breakdown of the whole country, the regions and the district; while data for intermediate surveys are only available upon the two-levels of country and regions. Since FSS 1999/2000 information about local farm location is collected in most countries, so that the data can also be disseminated by NUTS classification and are robust regarding the changes in the NUTS definition. The FSS 2009/2010 information is inline with the NUTS 2010 classification: Regulation (EU) No 31/2001 amending the NUTS classification from January 2012. 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 domain EUROFARM (ef) contains information (statistical tables) on structure of agricultural holdings collected through agricultural structure surveys. The data of the domain have been organised into two collections:Results of the farm structure surveys contains data from 1990 onwards based on the 1988 legislation andResults of the farm structure surveys based on the legislation from 2008. This collection contains the data from the 2010 Census onwards as well as the data for the surveys 2007 and 2005. Farm Structure Survey data are used to collect information on agricultural holdings in the Member States at different geographic levels (Member States, regions, districts) and over periods (follow up the changes in agricultural sector), thus provide a base for decision making in the Common Agricultural Policy. Two kinds of Farm Structure Survey (FSS) are carried out by Member States:a basic survey (full scope Agricultural Census - AC) every 10 years,several sample based intermediate surveys between them. However for certain characteristics the Member States may use sample base for every survey. The calendar for the surveys to be held in all Member States is agreed by the Agricultural Statistics Committee of the European Commission. For a given survey year, Member States have to conduct their surveys within the agreed time-frame, thus all the data are as comparable as possible. The FSS are organised in all Member States on a harmonised base. Whereas the characteristics are based on community legislation, the same data are available for all countries in case of each survey. The data on individual agricultural holdings are collected by all Member States and sent to Eurostat. The aggregated results are disseminated through statistical tables. The variables are arranged into groups:general overview with key variables,and other specialized groups containing detailed data onland uselivestockfarm labour forcerural development issues as well as management and practices. The 2010 survey additionally provides the results of the survey on agricultural production methods (SAPM). The scope of the survey is agriculture, while the survey unit is the agricultural holding (farm). Data for basic surveys are available in a three-level geographical breakdown of the whole country, the regions and the district; while data for intermediate surveys are only available upon the two-levels of country and regions. Since FSS 1999/2000 information about local farm location is collected in most countries, so that the data can also be disseminated by NUTS and are robust regarding the changes in the NUTS definition. The FSS 2009/2010 information is inline with the NUTS 2010 classification: Regulation (EU) No 31/2011 amending the NUTS classification from January 2003. The Eurofarm domain does not cover the whole territory only the land covered by the agricultural holdings. So the land use data without link with other farm characteristics should be downloaded by the user from the relevant domain. Specific national data about crops, animals or agricultural labour force can be found in other domains, without link between the other information at farm level. For a comprehensive description of the domain, please consult detailed structure (See annex at the bottom of the page). Regional Data Data for basic surveys are available in a three-level geographical breakdown of the whole country, the regions and the district; while data for intermediate surveys are only available upon the two-levels of country and regions. Since FSS 1999/2000 information about local farm location is collected in most countries, so that the data can also be disseminated by NUTS classification and are robust regarding the changes in the NUTS definition. The FSS 2009/2010 information is inline with the NUTS 2010 classification: Regulation (EU) No 31/2001 amending the NUTS classification from January 2012. 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.

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

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

Eurostat Dataset Id:demo_r_d3avg The regional demographic statistics provides annual data on population, vital events (live births and deaths), total and land areas of the regions and key demographic indicators for regions and statistical regions at NUTS2 and NUTS3 levels for 35 countries: each EU-27 Member State, Acceding, Candidate and EFTA countries. The completeness of the tables depends on the availability of data received from the responsible national statistical institutes (NSIs).  The label of each table indicates the lowest NUTS level for which data are available; for the upper NUTS levels data are included as well. Starting with March 2013, demographic statistics at regional level reflect the new NUTS-2010 classification for EU-27 Member States and the new statistical regions for Croatia. Countries affected by the NUTS-2010 changes are expected to transmit to Eurostat the time series for the new regional breakdown. As a general approach, the regions with no data available are not listed in the tables. For a calendar year T, the deadline of the regional demographic data collection is 15 December, and data included have a different degree of detail for regions at NUTS2 and NUTS3 levels: NUTS2 level - high level of data detail: Population by sex and single year of age at 1st January: years T and T-1Live births by single year of age and year of birth of the mother: year T-1 Deaths by sex and single years of age and year of birth: year T-1  NUTS3 level - low level of data detail: Surface area in km2 at 1st January (total area including inland waters and land area): year TPopulation by sex and broad age groups at 1st January, namely for 0-14 (0 up to 14 years), 15-64 (15 up to 64 years) and 65+ (persons of 65 years and older): years T and T-1 Live births and deaths (total number of demographic events): year T-1  Tables are updated mainly during March of the next year (T+1), but also along the year whenever revised data are sent by the official data providers. Demographic indicators at regional level are computed by Eurostat using a harmonised methodology and common concepts for all regions of all countries, namely: average population on 1st January (in thousands), population density;demographic balance and crude rates (population change, natural change, net migration including statistical adjustments, crude birth rate, crude death rate, crude rate of population change, crude rate of natural change, crude rate of net migration (including statistical adjustments));age-specific-fertility rates and Total Fertility Rates;life tables that include age-specific-mortality-rates and life expectancy at given exact age;infant mortality and crude rate of infant mortality. At national level a larger number of demographic indicators are computed, as more detailed demographic data are collected only at this level.Â

Eurostat Dataset Id:earn_ses10_rbns The Structure of Earnings Survey (SES) is a 4-yearly survey which provides EU-wide harmonised structural data on gross earnings, hours paid and annual days of paid holiday leave, which are collected every four years under Council Regulation (EC) No 530/1999 concerning structural statistics on earnings and on labour costs, and Commission Regulation (EC) No 1738/2005 amending Regulation (EC) No 1916/2000 as regards the definition and transmission of information on the structure of earnings. The objective of this legislation is so that National Statistical Institutes (NSIs) provide accurate and harmonised data on earnings in EU Member States and other countries for policy-making and research purposes. The SES 2010 provides detailed and comparable information on relationships between the level of hourly, monthly and annual remuneration, personal characteristics of employees (sex, age, occupation, length of service, highest educational level attained, etc.) and their employer (economic activity, size and economic control of the enterprise). Regional data is also available for some countries and regional metadata is identical to that provided for national data.

Total Surface Area (TSA)  – Total Surface Area is defined as the area of any given statistical area and includes land area and inland waters (lakes, rivers etc.). The sub-national areas (e.g. LAU and NUTS areas) defined by statistical and/or administrative boundaries are the building blocks for calculating both concepts. By definition Total Surface Area does not cover areas that are not statistical areas. Total Land Area (TLA) is defined as TSA excluding lakes, rivers, transitional and coastal waters. Mountainous regions, glaciers, forests, wetlands and other tempoarily or permanently uninhabitable regions are included in TLA. Both TSA and TLA are provided per Member State and for all statistical units from NUTS level 1 to NUTS level 3. TSA and TLA are the denominator in area based indicators, such as population density. Both datasets have the same reference date as the current valid NUTS classification (2013). A more generalised version (scale 1: 1 000 000) of the NUTS areas than used for the calculation of TSA and TLA can be downloaded from the Eurostat website http://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/nuts

The domain VIT contains the results of surveys of areas under vines in statistical tables. The data of the domain have been organised into two collections: - vit_an contains all available data from annual intermediate surveys. These surveys collect only data on areas under vines of wine grape varieties.  - vit_bs includes the data collected via basic surveys every ten years. The scope of these surveys is the area under all type of vines. Surveys on vineyards are used to collect information on vines and wine production in the Member States at different geographic levels (Member States and regions) and over time (follow up the changes); thus they provide basic information in framework of the common market organisation of wine. The intermediate annual surveys  cover the area under vines of wine grape varieties in the holdings and relate to changes which have taken place in that area during the preceding wine-growing year. Information regarding the following characteristics is available: - area under vines for wine grape varieties, - area under vines grubbed or no longer cultivated, - area under vines replanted, - area under vines newly planted. All of these are broken down according to their normal use for the production of - quality wines psr (produced in specific regions), - other wines (including wines compulsorily intended for the manufacture of certain potable spirits obtained from wine with a registered designation of origin). The areas under vines cultivated with wine grape varieties are also broken down into yield classes based upon the potential and (optionally) effective yield per hectare (hl/ha of grape must or wine).   The basic surveys cover all holdings having a cultivated area under vines normally intended for the production for sale of grapes, grape must, wine or vegetative propagation material for vines. Information regarding the following characteristics is available for each unit: - agricultural area in use, - area under vines cultivated; broken down according to its normal production into: area under wine grape varieties (separate the quality wines produced in special regions  and the other wines),area under table grape varieties,area planted with root-stock for future grafting,area cultivated solely for production of vegetative propagation material for vines (subdivided into nurseries and parent vines for root-stock areas),area under grapes intended for drying (raisins). The areas under vines cultivated with wine grape varieties as recorded in the basic surveys are broken down into yield classes based upon the potential and (optionally) effective yield per hectare (hl/ha of grape must or wine). Data are available for different territorial breakdowns for each country (wine-growing regions). Data is available for the following EU Member States: Germany, Greece, Spain, France, Italy, Luxembourg, Austria, Portugal, United Kingdom.

Eurostat Dataset Id:agr_r_crops

Ecuador: Arrival and Departure Statistics

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.

Eurostat Dataset Id:migr_r_2arr The data comprises series of internal (in the country) regional migration on NUTS2 level during the reference year. Data on arrivals and departures due to internal migration are disaggregated by NUTS2 region of arrival/departure, sex and single year age. Data on migration by region of origin and destination (excluding intra-regional migration) are presented separately for each country for which such data are available. The tables are in the form of matrix (NUTS2 region of origin and destination) excluding intra-refional migration, disaggregated by sex. The data source is administrative records or national surveys. The completeness of the tables depends largely on the availability of data from the relevant national statistical institutes.

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 persons with an equivalised disposable income below the risk-of-poverty threshold, which is set at 60 % 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 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.

Eurostat Dataset Id:migr_r_2at The data comprises series of internal (in the country) regional migration on NUTS2 level during the reference year. Data on arrivals and departures due to internal migration are disaggregated by NUTS2 region of arrival/departure, sex and single year age. Data on migration by region of origin and destination (excluding intra-regional migration) are presented separately for each country for which such data are available. The tables are in the form of matrix (NUTS2 region of origin and destination) excluding intra-refional migration, disaggregated by sex. The data source is administrative records or national surveys. The completeness of the tables depends largely on the availability of data from the relevant national statistical institutes.

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.

Eurostat Dataset Id:nama_r_e3popgdp 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 1995 (Council Regulation 2223/96). Annex B of the Regulation consists of a comprehensive list of the variables to be transmitted for Community purposes within specified time limits. This transmission programme has been updated by Regulation (EC) N° 1392/2007 of the European Parliament and of the Council. Meanwhile, the ESA95 has been 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: GDP and main aggregates. The data are recorded at current and constant prices and include the corresponding implicit price indices. Final consumption aggregates, including the split into household and government consumption. The data are recorded at current and constant prices and include the corresponding implicit price indices. Income, saving and net lending / net borrowing at current prices. Disposable income is also shown in real terms. Exports and imports by Member States of the EU/third countries. The data are recorded at current and constant prices and include the corresponding implicit price indices. Breakdowns of gross value added, compensation of employees, wages and salaries, operating surplus, employment (domestic scope), gross fixed capital formation (GFCF) and fixed assets and other main aggregates by industry; investment by products and household final consumption expenditure by consumption purposes (COICOP). The data are recorded at current and constant prices and include the corresponding implicit price indices. Auxiliary indicators: Population and employment national data, purchasing power parities, contributions to GDP growth, labour productivity, unit labour cost and GDP per capita. 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. The data are published: - in ECU/euro, in national currencies (including euro converted from former national currencies using the irrevocably fixed rate for all years) and in Purchasing Power Standards (PPS); - at current prices and in volume terms; - Population and employment are measured in persons. Employment is also measured in total hours worked. Data sources: National Statistical Institutes

Labour cost statistics provide a comprehensive and detailed picture of the level, structure and short-term development of labour costs in the different sectors of economic activity in the European Union and certain other countries. All statistics are based on a harmonised definition of labour costs. Structural information on labour costs is collected through four-yearly Labour Cost Surveys (LCS), which provides details on the level and structure of labour cost data, hours worked and hours paid. LCS results are available for the reference years 2000, 2004, 2008 and 2012. All EU Member States together with Norway and Iceland (2004 onwards), Turkey and Macedonia (2008), as well as Serbia (2012) participated in the LCS. As far as available data and confidentiality rules permit, all variables and proportions are further broken down by enterprise size category, economic activity and region (for larger countries only). The data are collected by the National Statistical Institutes in most cases on the basis of stratified random samples of enterprises or local units, restricted in most countries to units with at least 10 employees. The stratification is based on economic activity, size category and region (where appropriate). Regional metadata is identical to the metadata provided for national data. Some countries also complement the survey results with administrative data. Monetary variables are expressed in EUR, national currencies (for non-euro-area countries) and Purchasing Power Standards (PPS). Labour costs are quoted in total per year, per month and per hour, as well as per capita and per full-time equivalents (FTE). Information on staff, hours worked and hours paid is quoted in aggregate and separately for full- and part-time employees.

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 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.

Sugar Data of United States

Eurostat Dataset Id:migr_r_2be The data comprises series of internal (in the country) regional migration on NUTS2 level during the reference year. Data on arrivals and departures due to internal migration are disaggregated by NUTS2 region of arrival/departure, sex and single year age. Data on migration by region of origin and destination (excluding intra-regional migration) are presented separately for each country for which such data are available. The tables are in the form of matrix (NUTS2 region of origin and destination) excluding intra-refional migration, disaggregated by sex. The data source is administrative records or national surveys. The completeness of the tables depends largely on the availability of data from the relevant national statistical institutes.

There is more to life than the cold numbers of GDP and economic statistics. This dataset contains the 2018 data of the Better Life Index which allows you to compare well-being across countries as well as measuring well-being, based on 11 topics the OECD has identified as essential, in the areas of material living conditions and quality of life. Abstract: Your Better Life Index aims to involve citizens in the debate on measuring the well-being of societies, and to empower them to become more informed and engaged in the policy-making process that shapes all our lives. Each of the 11 topics of the Index is currently based on one to three indicators. Within each topic, the indicators are averaged with equal weights. The indicators have been chosen on the basis of a number of statistical criteria such as relevance (face-validity, depth, policy relevance) and data quality (predictive validity, coverage, timeliness, cross-country comparability etc.) and in consultation with OECD member countries. These indicators are good measures of the concepts of well-being, in particular in the context of a country comparative exercise. Other indicators will gradually be added to each topic.  Notes: Data cannot be compared between different editions of the Better Life Index. For more information on change over time, please contact [email protected].

Eurostat Dataset Id:migr_r_2bg The data comprises series of internal (in the country) regional migration on NUTS2 level during the reference year. Data on arrivals and departures due to internal migration are disaggregated by NUTS2 region of arrival/departure, sex and single year age. Data on migration by region of origin and destination (excluding intra-regional migration) are presented separately for each country for which such data are available. The tables are in the form of matrix (NUTS2 region of origin and destination) excluding intra-refional migration, disaggregated by sex. The data source is administrative records or national surveys. The completeness of the tables depends largely on the availability of data from the relevant national statistical institutes.

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."

United States Cancer Statistics (USCS)

Data on causes of death (COD) provide information on mortality patterns and form a major element of public health information. COD data refer to the underlying cause which - according to the World Health Organisation (WHO) - is "the disease or injury which initiated the train of morbid events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury". Causes of death are classified by the 86 causes of the "European shortlist" of causes of death. This shortlist is based on the International Statistical Classification of Diseases and Related Health Problems (ICD). COD data are derived from death certificates. The medical certification of death is an obligation in all Member States. Countries code the information provided in the medical certificate of cause of death into ICD codes according to the rules specified in the ICD. Data are broken down by sex, 5-year age groups, cause of death and by residency and country of occurrence. For stillbirths and neonatal deaths additional breakdows might include age of mother. Data are available for EU-28, Iceland, Norway, Liechtenstein, Switzerland, Serbia and Turkey. Regional data (NUTS level 2) are available for most of the countries. Annual national data are provided in absolute number, crude death rates and standardised death rates. At regional level (NUTS level 2) the same is provided in form of 3 years averages. Annual crude death rates are also available at NUTS level 2.

Data on causes of death (COD) provide information on mortality patterns and form a major element of public health information. COD data refer to the underlying cause which - according to the World Health Organisation (WHO) - is "the disease or injury which initiated the train of morbid events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury". Causes of death are classified by the 86 causes of the "European shortlist" of causes of death. This shortlist is based on the International Statistical Classification of Diseases and Related Health Problems (ICD). COD data are derived from death certificates. The medical certification of death is an obligation in all Member States. Countries code the information provided in the medical certificate of cause of death into ICD codes according to the rules specified in the ICD. Data are broken down by sex, 5-year age groups, cause of death and by residency and country of occurrence. For stillbirths and neonatal deaths additional breakdows might include age of mother. Data are available for EU-28, the former Yugoslav Republic of Macedonia, Albania, Iceland, Norway, Liechtenstein and Switzerland. Regional data (NUTS level 2) are available for most of the countries. Annual national data are provided in absolute number, crude death rates and standardised death rates. At regional level (NUTS level 2) the same is provided in form of 3 years averages. Annual crude death rates are also available at NUTS level 2.

Data on causes of death (COD) provide information on mortality patterns and form a major element of public health information. COD data refer to the underlying cause which - according to the World Health Organisation (WHO) - is "the disease or injury which initiated the train of morbid events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury". Causes of death are classified by the 86 causes of the "European shortlist" of causes of death. This shortlist is based on the International Statistical Classification of Diseases and Related Health Problems (ICD). COD data are derived from death certificates. The medical certification of death is an obligation in all Member States. Countries code the information provided in the medical certificate of cause of death into ICD codes according to the rules specified in the ICD. Data are broken down by sex, 5-year age groups, cause of death and by residency and country of occurrence. For stillbirths and neonatal deaths additional breakdows might include age of mother. Data are available for EU-28, Iceland, Norway, Liechtenstein, Switzerland, Serbia and Turkey. Regional data (NUTS level 2) are available for most of the countries. Annual national data are provided in absolute number, crude death rates and standardised death rates. At regional level (NUTS level 2) the same is provided in form of 3 years averages. Annual crude death rates are also available at NUTS level 2.

Data on causes of death (COD) provide information on mortality patterns and form a major element of public health information. COD data refer to the underlying cause which - according to the World Health Organisation (WHO) - is "the disease or injury which initiated the train of morbid events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury". Causes of death are classified by the 86 causes of the "European shortlist" of causes of death. This shortlist is based on the International Statistical Classification of Diseases and Related Health Problems (ICD). COD data are derived from death certificates. The medical certification of death is an obligation in all Member States. Countries code the information provided in the medical certificate of cause of death into ICD codes according to the rules specified in the ICD. Data are broken down by sex, 5-year age groups, cause of death and by residency and country of occurrence. For stillbirths and neonatal deaths additional breakdows might include age of mother. Data are available for EU-28, Iceland, Norway, Liechtenstein, Switzerland, Serbia and Turkey. Regional data (NUTS level 2) are available for most of the countries. Annual national data are provided in absolute number, crude death rates and standardised death rates. At regional level (NUTS level 2) the same is provided in form of 3 years averages. Annual crude death rates are also available at NUTS level 2.

Data on causes of death (COD) provide information on mortality patterns and form a major element of public health information. COD data refer to the underlying cause which - according to the World Health Organisation (WHO) - is "the disease or injury which initiated the train of morbid events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury". Causes of death are classified by the 86 causes of the "European shortlist" of causes of death. This shortlist is based on the International Statistical Classification of Diseases and Related Health Problems (ICD). COD data are derived from death certificates. The medical certification of death is an obligation in all Member States. Countries code the information provided in the medical certificate of cause of death into ICD codes according to the rules specified in the ICD. Data are broken down by sex, 5-year age groups, cause of death and by residency and country of occurrence. For stillbirths and neonatal deaths additional breakdows might include age of mother. Data are available for EU-28, Iceland, Norway, Liechtenstein, Switzerland, Serbia and Turkey. Regional data (NUTS level 2) are available for most of the countries. Annual national data are provided in absolute number, crude death rates and standardised death rates. At regional level (NUTS level 2) the same is provided in form of 3 years averages. Annual crude death rates are also available at NUTS level 2.

Data on causes of death (COD) provide information on mortality patterns and form a major element of public health information. COD data refer to the underlying cause which - according to the World Health Organisation (WHO) - is "the disease or injury which initiated the train of morbid events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury". Causes of death are classified by the 86 causes of the "European shortlist" of causes of death. This shortlist is based on the International Statistical Classification of Diseases and Related Health Problems (ICD). COD data are derived from death certificates. The medical certification of death is an obligation in all Member States. Countries code the information provided in the medical certificate of cause of death into ICD codes according to the rules specified in the ICD. Data are broken down by sex, 5-year age groups, cause of death and by residency and country of occurrence. For stillbirths and neonatal deaths additional breakdows might include age of mother. Data are available for EU-28, Iceland, Norway, Liechtenstein, Switzerland, Serbia and Turkey. Regional data (NUTS level 2) are available for most of the countries. Annual national data are provided in absolute number, crude death rates and standardised death rates. At regional level (NUTS level 2) the same is provided in form of 3 years averages. Annual crude death rates are also available at NUTS level 2.

Notes: Eurostat Hierarchy: General and regional statistics > Population and social conditions > Health (health) > Public health (hlth) > Causes of death (hlth_cdeath).

Data on causes of death (COD) provide information on mortality patterns and form a major element of public health information. COD data refer to the underlying cause which - according to the World Health Organisation (WHO) - is "the disease or injury which initiated the train of morbid events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury". Causes of death are classified by the 86 causes of the "European shortlist" of causes of death. This shortlist is based on the International Statistical Classification of Diseases and Related Health Problems (ICD). COD data are derived from death certificates. The medical certification of death is an obligation in all Member States. Countries code the information provided in the medical certificate of cause of death into ICD codes according to the rules specified in the ICD. Data are broken down by sex, 5-year age groups, cause of death and by residency and country of occurrence. For stillbirths and neonatal deaths additional breakdows might include age of mother. Data are available for EU-28, the former Yugoslav Republic of Macedonia, Albania, Iceland, Norway, Liechtenstein and Switzerland. Regional data (NUTS level 2) are available for most of the countries. Annual national data are provided in absolute number, crude death rates and standardised death rates. At regional level (NUTS level 2) the same is provided in form of 3 years averages. Annual crude death rates are also available at NUTS level 2.

Data on causes of death (COD) provide information on mortality patterns and form a major element of public health information. COD data refer to the underlying cause which - according to the World Health Organisation (WHO) - is "the disease or injury which initiated the train of morbid events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury". Causes of death are classified by the 86 causes of the "European shortlist" of causes of death. This shortlist is based on the International Statistical Classification of Diseases and Related Health Problems (ICD). COD data are derived from death certificates. The medical certification of death is an obligation in all Member States. Countries code the information provided in the medical certificate of cause of death into ICD codes according to the rules specified in the ICD. Data are broken down by sex, 5-year age groups, cause of death and by residency and country of occurrence. For stillbirths and neonatal deaths additional breakdows might include age of mother. Data are available for EU-28, Iceland, Norway, Liechtenstein, Switzerland, Serbia and Turkey. Regional data (NUTS level 2) are available for most of the countries. Annual national data are provided in absolute number, crude death rates and standardised death rates. At regional level (NUTS level 2) the same is provided in form of 3 years averages. Annual crude death rates are also available at NUTS level 2.

Data on causes of death (COD) provide information on mortality patterns and form a major element of public health information. COD data refer to the underlying cause which - according to the World Health Organisation (WHO) - is "the disease or injury which initiated the train of morbid events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury". Causes of death are classified by the 86 causes of the "European shortlist" of causes of death. This shortlist is based on the International Statistical Classification of Diseases and Related Health Problems (ICD). COD data are derived from death certificates. The medical certification of death is an obligation in all Member States. Countries code the information provided in the medical certificate of cause of death into ICD codes according to the rules specified in the ICD. Data are broken down by sex, 5-year age groups, cause of death and by residency and country of occurrence. For stillbirths and neonatal deaths additional breakdows might include age of mother. Data are available for EU-28, Iceland, Norway, Liechtenstein, Switzerland, Serbia and Turkey. Regional data (NUTS level 2) are available for most of the countries. Annual national data are provided in absolute number, crude death rates and standardised death rates. At regional level (NUTS level 2) the same is provided in form of 3 years averages. Annual crude death rates are also available at NUTS level 2.

Data on causes of death (COD) provide information on mortality patterns and form a major element of public health information. COD data refer to the underlying cause which - according to the World Health Organisation (WHO) - is "the disease or injury which initiated the train of morbid events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury". Causes of death are classified by the 86 causes of the "European shortlist" of causes of death. This shortlist is based on the International Statistical Classification of Diseases and Related Health Problems (ICD). COD data are derived from death certificates. The medical certification of death is an obligation in all Member States. Countries code the information provided in the medical certificate of cause of death into ICD codes according to the rules specified in the ICD. Data are broken down by sex, 5-year age groups, cause of death and by residency and country of occurrence. For stillbirths and neonatal deaths additional breakdows might include age of mother. Data are available for EU-28, Iceland, Norway, Liechtenstein, Switzerland, Serbia and Turkey. Regional data (NUTS level 2) are available for most of the countries. Annual national data are provided in absolute number, crude death rates and standardised death rates. At regional level (NUTS level 2) the same is provided in form of 3 years averages. Annual crude death rates are also available at NUTS level 2.

Data on causes of death (COD) provide information on mortality patterns and form a major element of public health information. COD data refer to the underlying cause which - according to the World Health Organisation (WHO) - is "the disease or injury which initiated the train of morbid events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury". Causes of death are classified by the 86 causes of the "European shortlist" of causes of death. This shortlist is based on the International Statistical Classification of Diseases and Related Health Problems (ICD). COD data are derived from death certificates. The medical certification of death is an obligation in all Member States. Countries code the information provided in the medical certificate of cause of death into ICD codes according to the rules specified in the ICD. Data are broken down by sex, 5-year age groups, cause of death and by residency and country of occurrence. For stillbirths and neonatal deaths additional breakdows might include age of mother. Data are available for EU-28, Iceland, Norway, Liechtenstein, Switzerland, Serbia and Turkey. Regional data (NUTS level 2) are available for most of the countries. Annual national data are provided in absolute number, crude death rates and standardised death rates. At regional level (NUTS level 2) the same is provided in form of 3 years averages. Annual crude death rates are also available at NUTS level 2.

The structure of agricultural holdings (collected through farm structure surveys - FSS) is presented at different geographical levels and over periods. The information follows up the changes in the agricultural sector and provides a basis for decision-making in the Common Agricultural Policy (CAP) and other European Union policies. The survey unit is the agricultural holding (farm). The data on individual agricultural holdings are collected by all Member States, Iceland, Norway and Switzerland and sent to Eurostat.  The Former Yugoslav Republic of Macedonia, Montenegro and Serbia have also provided data for some years. The aggregated results are disseminated through statistical tables. The FSS surveys are organised in all countries in a harmonised way. For a given survey year, countries have to conduct their surveys within the agreed time-frame. Whereas the characteristics are based on Community legislation, the same data are available for all countries in case of each survey. Thus all the data are as comparable as possible.

Chance for Success Index Indicators, United States

... 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.

Data cited at: Statistics Finland http://www.stat.fi/index_en.html Publication: 030 -- Citizenship by sex, by region and municipality in 1990 to 2017 http://pxnet2.stat.fi/PXWeb/pxweb/en/StatFin/StatFin__vrm__vaerak/statfin_vaerak_pxt_030.px License: http://creativecommons.org/licenses/by/4.0/ Concepts and definitions Description Quality descriptionThese statistics apply the regional division of 1 January 2018 to the whole time series.Population statistics from 1750 to 2000 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)AreaFor reasons of privacy protection, cells with less than 10 cases of citizenship, country of birth, background country or language by municipality have been marked with two dots. Continent sums have not been hidden in municipality data nor have regional data concerning individual languages or countries.CitizenshipIf a person has two nationalities and one of them is Finnish, he/she will be included in statistics as a Finnish national. The used classification of continents is the classification of Eurostat, where Cyprus and Turkey belong to Europe. Citizens of non-autonomous states are summed under the mother country.CitizenshipCzech RepublicCzech Republic + Former CzechoslovakiaSudanSudan + Former Sudan

Climate Change Knowledge Portal, Temperature and Rainfall: Historical Data (CRU Observed)

Meteorology and Hydrology Statistics of Lao PDR. This data set contains information of The statistics yearbook 2017 is compiled and published by Lao statistics Bureau, Ministry of Planning and Investment. Lao statistics Bureau of expressing gratitude provincial statistics Centre, line ministries involved, ministry-equivalent agencies and other parties to contribute to compile statistical data Economic, Social and Natural Resources and Environment cooperation throughout, Lao Statistics Bureau welcome the proposal, comments and feedback to guide the improvement this magazines better. Meanwhile, the Lao Statistics Bureau express thanks to highly among those interested in using information and statistics to Lao Statistics Bureau.

Common Core Data For Universe surveys, School District (LEA) The primary purposes of the Local Education Agency (School District) Universe Survey are:to provide a complete listing of every education agency in the United States responsible for providing free public elementary/secondary instruction or education support services;to provide basic information about all education agencies and the students for whose education the agencies are responsible.  

Intellectual property refers broadly to the creations of the human mind. Intellectual property rights protect the interests of creators by giving them property rights over their creations. Designs constitute means by which creators seek protection for their industrial property. Designs reflect the non-technological innovation in every sector of economic life, including services. In this context, indicators based on Design data can provide a link between innovation and the market. A design is the outward appearance of a product or part of it, resulting from the lines, contours, colours, shape, texture, materials and/or its ornamentation. The design or shape of a product can be synonymous with the branding and image of a company and can become an asset with increasing monetary value. This domain provides users with data concerning Community Designs. Community Designs refer to design protections throughout the European Union, which covers 28 countries. The Office for Harmonization in the Internal Market (EUIPO) is the official office of the European Union for the registration of Community Trade marks and Designs. A registered Community design (RCD) is an exclusive right that covers the outward appearance of a product or part of it. The fact that the right is registered confers on the design great certainty should infringement occur. An RCD initially has a life of five years from the filing date and can be renewed in blocks of five years up to a maximum of 25 years. Applicants may market a design for up to 12 months before filing for an RCD without destroying its novelty (Source: EUIPO).

Intellectual property refers broadly to the creations of the human mind. Intellectual property rights protect the interests of creators by giving them property rights over their creations. Designs constitute means by which creators seek protection for their industrial property. Designs reflect the non-technological innovation in every sector of economic life, including services. In this context, indicators based on Design data can provide a link between innovation and the market. A design is the outward appearance of a product or part of it, resulting from the lines, contours, colours, shape, texture, materials and/or its ornamentation. The design or shape of a product can be synonymous with the branding and image of a company and can become an asset with increasing monetary value. This domain provides users with data concerning Community Designs. Community Designs refer to design protections throughout the European Union, which covers 28 countries. The Office for Harmonization in the Internal Market (EUIPO) is the official office of the European Union for the registration of Community Trade marks and Designs. A registered Community design (RCD) is an exclusive right that covers the outward appearance of a product or part of it. The fact that the right is registered confers on the design great certainty should infringement occur. An RCD initially has a life of five years from the filing date and can be renewed in blocks of five years up to a maximum of 25 years. Applicants may market a design for up to 12 months before filing for an RCD without destroying its novelty (Source: EUIPO).

Intellectual property refers broadly to the creations of the human mind. Intellectual property rights protect the interests of creators by giving them property rights over their creations. Designs constitute means by which creators seek protection for their industrial property. Designs reflect the non-technological innovation in every sector of economic life, including services. In this context, indicators based on Design data can provide a link between innovation and the market. A design is the outward appearance of a product or part of it, resulting from the lines, contours, colours, shape, texture, materials and/or its ornamentation. The design or shape of a product can be synonymous with the branding and image of a company and can become an asset with increasing monetary value. This domain provides users with data concerning Community Designs. Community Designs refer to design protections throughout the European Union, which covers 28 countries. The Office for Harmonization in the Internal Market (EUIPO) is the official office of the European Union for the registration of Community Trade marks and Designs. A registered Community design (RCD) is an exclusive right that covers the outward appearance of a product or part of it. The fact that the right is registered confers on the design great certainty should infringement occur. An RCD initially has a life of five years from the filing date and can be renewed in blocks of five years up to a maximum of 25 years. Applicants may market a design for up to 12 months before filing for an RCD without destroying its novelty (Source: EUIPO).

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.

Classification: Monthly datasets may mix codes from multiple HS revisions and are provided as is except for standardization of trade flow and partner information, as well as conversion to US dollars. Note: The original dataset has been modified to include World as 'Reporter'.

Both ethanol and biodiesel are classified under the HS-6 digit categories that also contain other products. Biodiesel is an industrial product (as it is produced through a chemical process called transesterification) and classified under HS code 382490 - products, preparations and residual products of the chemical or allied industries not elsewhere specified. Ethanol is classified as an agriculture product under HS code 2207, which covers un-denatured (HS 2207 10) and denatured alcohol (HS 2207 20).

Consumer Price Index of UAE

Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. The series is part of the ILO estimates and is harmonized to account for differences in national data and scope of coverage, collection and tabulation methodologies as well as for other country-specific factors. For more information, refer to the ILO estimates and projections methodological note.

The 2011 Population and Housing Census marks a milestone in census exercises in Europe. For the first time, European legislation defined in detail a set of harmonised high-quality data from the population and housing censuses conducted in the EU Member States. As a result, the data from the 2011 round of censuses offer exceptional flexibility to cross-tabulate different variables and to provide geographically detailed data. EU Member States have developed different methods to produce these census data.  The national differences reflect the specific national situations in terms of data source availability, as well as the administrative practices and traditions of that country. The EU census legislation respects this diversity. The Regulation of the European Parliament and of the Council on population and housing censuses (Regulation (EC) No 763/2008) is focussed on output harmonisation rather than input harmonisation. Member States are free to assess for themselves how to conduct their 2011 censuses and which data sources, methods and technology should be applied given the national context. This gives the Member States flexibility, in line with the principles of subsidiarity and efficiency, and with the competences of the statistical institutes in the Member States. However, certain important conditions must be met in order to achieve the objective of comparability of census data from different Member States and to assess the data quality: Regulation (EC) No 1201/20092 contains definitions and technical specifications for the census topics (variables) and their breakdowns that are required to achieve Europe-wide comparability. The specifications are based closely on international recommendations and have been designed to provide the best possible information value. The census topics include geographic, demographic, economic and educational characteristics of persons, international and internal migration characteristics as well as household, family and housing characteristics. Regulation (EU) No 519/2010 requires the data outputs that Member States transmit to the Eurostat to comply with a defined programme of statistical data (tabulation) and with set rules concerning the replacement of statistical data. The content of the EU census programme serves major policy needs of the European Union. Regionally, there is a strong focus on the NUTS 2 level. The data requirements are adapted to the level of regional detail. The Regulation does not require transmission of any data that the Member States consider to be confidential. The statistical data must be completed by metadata that will facilitate interpretation of the numerical data, including country-specific definitions plus information on the data sources and on methodological issues. This is necessary in order to achieve the transparency that is a condition for valid interpretation of the data. Users of output-harmonised census data from the EU Member States need to have detailed information on the quality of the censuses and their results. Regulation (EU) No 1151/2010) therefore requires transmission of a quality report containing a systematic description of the data sources used for census purposes in the Member States and of the quality of the census results produced from these sources. A comparably structured quality report for all EU Member States will support the exchange of experience from the 2011 round and become a reference for future development of census methodology (EU legislation on the 2011 Population and Housing Censuses - Explanatory Notes ). In order to ensure proper transmission of the data and metadata and provide user-friendly access to this information, a common technical format is set for transmission for all Member States and for the Commission (Eurostat). The Regulation therefore requires the data to be transmitted in a harmonised structure and in the internationally established SDMX format from every Member State. In order to achieve this harmonised transmission, a new system has been developed – the CENSUS HUB. The Census Hub is a conceptually new system used for the dissemination of the 2011 Census. It is based on the concept of data sharing, where a group of partners (Eurostat on one hand and National Statistical Institutes on the other) agree to provide access to their data according to standard processes, formats and technologies. The Census Hub is a readily-accessible system that provided the following functions: • Data providers (the NSIs) can make data available directly from their systems through a querying system. In parallel, • Data users browse the hub to define a dataset of interest via the above structural metadata and retrieve the dataset from the NSIs. From the data management point of view, the hub is based on agreed hypercubes (data-sets in the form of multi-dimensional aggregations). The hypercubes are not sent to the central system. Instead the following process operates: 1. a user defines a dataset through the web interface of the central hub and requests it; 2. the central hub translates the user request in one or more queries and sends them to the related NSIs’ systems; 3. NSIs’ systems process the query and send the result to the central hub in a standard format; 4. the central hub puts together all the results sent by the NSI systems and presents them in a user-specified format.

Cost of health consulting services, diagnostics services and clinical procedures in major cities/towns and the public and private healthcare services points in each location.

Data cited at NUMBEO Numbeo is the world’s largest database of user-contributed data about cities and countries worldwide. Numbeo provides current and timely information on world living conditions including cost of living, housing indicators, health care, traffic, crime and pollution. For more information please check http://www.numbeo.com/cost-of-living/rankings_by_country.jsp   About dataset: These indices are relative to New York City (NYC). Which means that for New York City, each index should be 100(%). If another city has, for example, rent index of 120, it means rents in average in that city are 20% more expensive than in New York City. If a city has rent index of 70, that means in the average in that city rents are 30% less expensive than in New York City. Cost of Living Index (Excl. Rent) is a relative indicator of consumer goods price, including groceries, restaurants, transportation and utilities. Cost of Living Index doesn't include accommodation expenses such as rent or mortgage. If a city has a Cost of Living Index of 120, it means Numbeo estimates it is 20% more expensive than New York (excluding rent). Rent Index is estimation of prices of renting apartments in the city compared to New York City. If Rent index is 80, Numbeo estimates that price for renting in that city is 80% of price in New York. Groceries Index is an estimation of grocery prices in the city compared to New York City. To calculate this section, Numbeo uses "Markets"section of each city. Restaurants Index is a comparison of prices of meals and drinks in restaurants and bars compared to NYC. Cost of Living Plus Rent Index is an estimation of consumer goods prices including rent in the city comparing to New York City. Local Purchasing Power shows relative purchasing power in buying goods and services in a given city for the average wage in that city. If domestic purchasing power is 40, this means that the inhabitants of that city with the average salary can afford to buy 60% less typical goods and services than New York City residents with an average salary.

Citation: Damodaran, Aswath, Equity Risk Premiums (ERP): Determinants, Estimation and Implications – The 2016 Edition (March 5, 2016). Available at SSRN: https://ssrn.com/abstract=2742186 or http://dx.doi.org/10.2139/ssrn.2742186   This dataset summarizes the latest bond ratings and appropriate default spreads for different countries. While you can use these numbers as rough estimates of country risk premiums, you may want to modify the premia to reflect the additional risk of equity markets. To estimate the long term country equity risk premium, I start with a default spread, which I obtain in one of two ways: (1) I use the local currency sovereign rating (from Moody's: www.moodys.com) and estimate the default spread for that rating (based upon traded country bonds) over a default free government bond rate. For countries without a Moody's rating but with an S&P rating, I use the Moody's equivalent of the S&P rating. To get the default spreads by sovereign rating, I use the CDS spreads and compute the average CDS spread by rating. Using that number as a basis, I extrapolate for those ratings for which I have no CDS spreads. (2) I start with the CDS spread for the country, if one is available and subtract out the US CDS spread, since my mature market premium is derived from the US market. That difference becomes the country spread. For the few countries that have CDS spreads that are lower than the US, I will get a negative number. You can add just this default spread to the mature market premium to arrive at the total equity risk premium. I add an additional step. In the short term especially, the equity country risk premium is likely to be greater than the country's default spread. You can estimate an adjusted country risk premium by multiplying the default spread by the relative equity market volatility for that market (Std dev in country equity market/Std dev in country bond). I have used the emerging market average of 1.12 (estimated by comparing a emerging market equity index to an emerging market government/public bond index) to estimate country risk premium.I have added this to my estimated risk premium of 5.08% for mature markets (obtained by looking at the implied premium for the S&P 500) to get the total risk premium. Notes:  The year of publication has been considered as per publication date. For example, data published on 2018-Jan considered as 2018, similarly 2019-Jan as 2019    

The data in the table concerning country of birth were corrected on 22 May 2019.For some with country of birth data recorded as 'unknown' (2,404 people), some other foreign country has been specified as the country of birth.Description of statistic Quality descriptions Concepts and definitionsStarting 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) ---RegionThese statistics apply the regional division of 1 January 2019 to the whole time series.Country of birthCountries (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. Sudan = Sudan + Former Sudan --- 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.

LGA2011 based data for Country Of Birth Of Person by Sex, Time Series Profiles Table t08, for 2011 Census ABS Census Homepage

SA1 based data for by Sex, Basic Community Profile Table B09, for 2011 Census ABS Census Homepage

Australia/State/SA4/SA3/SA2 based data for Country Of Birth Of Person by Sex, Time Series Profiles Table t08, for 2011 Census ABS Census Homepage

Australia/State/SA4/SA3/SA2 based data for Year of Arrival in Australia, Basic Community Profile Table B10, for 2011 Census ABS Census Homepage

LGA2011 based data for Year of Arrival in Australia, Basic Community Profile Table B10, for 2011 Census ABS Census Homepage

SA1 based data for Year of Arrival in Australia, Basic Community Profile Table B10, for 2011 Census ABS Census Homepage

Note: CPA data for 2018 and 2019 are projections from the 2016 Survey on Forward Spending Plans. Country Programmable Aid (CPA), outlined in our Development Brief  and also known as “core” aid, is the portion of aid donors programme for individual countries, and over which partner countries could have a significant say. CPA is much closer than ODA to capturing the flows of aid that goes to the partner country, and has been proven in several studies to be a good proxy of aid recorded at country level. CPA was developed in 2007 in close collaboration with DAC members. It is derived on the basis of DAC statistics and was retroactively calculated from 2000 onwards

Data cited at: Numbeo Methodology: The Index has been calculated twice per year by considering the latest 36 months. A). Beginning of the Year and B). Mid Year Crime Index is an estimation of the overall level of crime in a given city or a country. We consider crime levels lower than 20 as very low, crime levels between 20 and 40 as being low, crime levels between 40 and 60 as being moderate, crime levels between 60 and 80 as being high and finally crime levels higher than 80 as being very high. Safety index is, on the other way, quite the opposite of crime index. If the city has a high safety index, it is considered very safe.

Eurostat Dataset Id:migr_r_2hr The data comprises series of internal (in the country) regional migration on NUTS2 level during the reference year. Data on arrivals and departures due to internal migration are disaggregated by NUTS2 region of arrival/departure, sex and single year age. Data on migration by region of origin and destination (excluding intra-regional migration) are presented separately for each country for which such data are available. The tables are in the form of matrix (NUTS2 region of origin and destination) excluding intra-refional migration, disaggregated by sex. The data source is administrative records or national surveys. The completeness of the tables depends largely on the availability of data from the relevant national statistical institutes.

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.

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

Eurostat Dataset Id:migr_r_2cz The data comprises series of internal (in the country) regional migration on NUTS2 level during the reference year. Data on arrivals and departures due to internal migration are disaggregated by NUTS2 region of arrival/departure, sex and single year age. Data on migration by region of origin and destination (excluding intra-regional migration) are presented separately for each country for which such data are available. The tables are in the form of matrix (NUTS2 region of origin and destination) excluding intra-refional migration, disaggregated by sex. The data source is administrative records or national surveys. The completeness of the tables depends largely on the availability of data from the relevant national statistical institutes.

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/

Causes of Death data refer to the underlying cause which - according to the World Health Organisation (WHO) - is the disease or injury which initiated the train of morbid events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury". Death due to accidents refer to all kind of accident (transport, drowning, fire, ...).

Causes of Death data refer to the underlying cause which - according to the World Health Organisation (WHO) - is the disease or injury which initiated the train of morbid events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury". Death due to cancer refer to all death caused by a malignant neoplasm."

Causes of Death data refer to the underlying cause which - according to the World Health Organisation (WHO) - is the disease or injury which initiated the train of morbid events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury". Death due to ischaemic heart diseases refer to all death caused by reduced blood supply to the heart. Most of these deaths are due to 'heart attack'.

Causes of Death data refer to the underlying cause which - according to the World Health Organisation (WHO) - is the disease or injury which initiated the train of morbid events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury". Death due to transport accidents refer to all kind of transport (road: car, pedestrian, cyclist, ..; water; air; ...).

"Death" means the permanent disappearance of all evidence of life at any time after live birth has taken place (post-natal cessation of vital functions without capability of resuscitation).

Eurostat Dataset Id:migr_r_2dk The data comprises series of internal (in the country) regional migration on NUTS2 level during the reference year. Data on arrivals and departures due to internal migration are disaggregated by NUTS2 region of arrival/departure, sex and single year age. Data on migration by region of origin and destination (excluding intra-regional migration) are presented separately for each country for which such data are available. The tables are in the form of matrix (NUTS2 region of origin and destination) excluding intra-refional migration, disaggregated by sex. The data source is administrative records or national surveys. The completeness of the tables depends largely on the availability of data from the relevant national statistical institutes.

Data on dentists should refer to those “immediately serving patients”, i.e. dentists who have direct contact with patients as consumers of health care services. In the context of comparing health care services across Member States, Eurostat considers that this is the concept which best describes the availability of health care resources. However, Member States use different concepts when they report the number of health care professionals. Therefore for some countries the data might refer to dentists ‘licensed to practice’ (i.e. successfully graduated dentists irrespective whether they see patients or not) or they might include dentists who work in their profession but do not see patients (i.e. they work in research, administration etc.). Please have a look in the annexes of the metadata to see for which concept these data refer to for each country.

Eurostat Dataset Id:migr_r_2dep The data comprises series of internal (in the country) regional migration on NUTS2 level during the reference year. Data on arrivals and departures due to internal migration are disaggregated by NUTS2 region of arrival/departure, sex and single year age. Data on migration by region of origin and destination (excluding intra-regional migration) are presented separately for each country for which such data are available. The tables are in the form of matrix (NUTS2 region of origin and destination) excluding intra-refional migration, disaggregated by sex. The data source is administrative records or national surveys. The completeness of the tables depends largely on the availability of data from the relevant national statistical institutes.

Direct Investment by Country measures the value of direct investment in the United States by overseas investors and U.S. investment in other countries. This Dataset provides data for a large set of countries broken down by major industry.

Note: Data for the European Union (EU) reflect the EU membership during the reference period. In 1994, the EU was comprised of Belgium, Denmark, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, and the United Kingdom. Beginning with 1995, Austria, Finland, and Sweden were included. Beginning with second quarter 2004, Cyprus, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Slovakia, and Slovenia were included. Bulgaria and Romania were included beginning with first quarter 2007 and Croatia was included beginning with third quarter 2013. The United Kingdom was excluded beginning with first quarter 2020.

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-LFS (Statistics Explained) webpage. 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 by territorial typologies, i.e. urban-rural, metropolitan, coastal, mountain, borders and island 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 dispersion of regional GDP (at NUTS level 2 and 3) is measured by the sum of the absolute differences between regional and national GDP per inhabitant, weighted with the share of population and expressed in percent of the national GDP per inhabitant. The indicator is calculated from regional GDP figures based on the European System of Accounts (ESA95).

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 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.

The aim of this section is to provide comparable statistics and indicators on education in the 27 Member States of the European Union, at the regional level NUTS 2. In order to facilitate comparison between countries, data from each Member State are allocated to the various education levels of the International Standard Classification of Education (ISCED), UNESCO 1997.

Census round 2011 The tables presented cover the total dwellings for 33 countries.The "traditional" census, with enumeration based on questionnaires through door-to-door visits - with interviews of respondents by enumerators or self-compilation of the forms by the respondents - and manual data entry by operators;The "Register based" census which enumerate population on the basis of administrative sources of information. Data collection is based on the use of registers (inhabitants' registers, registers of buildings and dwellings, geographical co-ordinates, school registers, social security, tax, business and company registers). In addition, countries that produce their population statistics from population-register information automatically seem to follow the de jure population concept. Indeed, it must at least be assumed that population registers include only residents who habitually live in the country;The "mixed" census, the third possible census method based on a combination of statistical inquiries and sources. In this case enumeration is always carried out on specific topics or on a sample of the population, and is combined with existing regular statistical surveys, registers, lists, or ad hoc organised activities. (See R 763/2008 Article 4) Census round 2001 The tables presented cover the total dwellings for 31 countries. In the census round 2001 four ways of collecting census data were used, namely: - the traditional method of using census questionnaires (exhaustive census); - the method of using registers and/or other administrative sources; - a combination of registers and/or other administrative sources and - surveys (complete enumerations or sample surveys). Census round 1991 The tables presented in the census 1990/1991 round cover the total dwellings for 19 countries. Five main topics are covered: structure of population, active population, education level, households and dwellings. The level of completeness of the tables depends largely on the availability of data at the respective national statistical institutes.

Early-Childhood Education of United States

The indicator is defined as the percentage of the population aged 18-24 with at most lower secondary education and who were not in further education or training during the last four weeks preceding the survey. Lower secondary education refers to ISCED (International Standard Classification of Education) 2011 level 0-2 for data from 2014 onwards and to ISCED 1997 level 0-3C short for data up to 2013. The indicator is based on the EU Labour Force Survey.

The aim of this section is to provide comparable statistics and indicators on education in the 27 Member States of the European Union, at the regional level NUTS 2. In order to facilitate comparison between countries, data from each Member State are allocated to the various education levels of the International Standard Classification of Education (ISCED), UNESCO 1997.

Contains annual data series on water consumption, irrigated area, solar water and space heating area, countries overpumping aquifers and water deficits for the countries and regions through the time period from 1961 to 2013.

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 are detailed data on the value of output (measured in both producer prices and basic prices), intermediate consumption, subsidies and taxes, consumption of fixed capital, rent and interest, capital formation etc. The values are available in both current prices and constant prices. Agricultural Labour Input (ALI) statistics and Unit Values (UV) are an integrated part of the overall concept of the EAA. The EAA are a satellite account of the European System of Accounts (ESA), 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 calculations of national EAA, in accordance with EC Regulations. Eurostat is responsible for the EU aggregations. Regional data The EAA are also compiled at regional level (NUTS2), but only in values at current prices. The agricultural labour input data and unit values, however, are not available at regional levels. Please note that for paragraphs where no metadata for regional data has been specified, the regional metadata is identical to the metadata provided for national data. Frequency of data collection for data under Regulation (EC) 138/2004 and gentlemen's agreement l

This data set contains information of The statistics yearbook 2017 is compiled and published by Lao statistics Bureau, Ministry of Planning and Investment. Lao statistics Bureau of expressing gratitude provincial statistics Centre, line ministries involved, ministry-equivalent agencies and other parties to contribute to compile statistical data Economic, Social and Natural Resources and Environment cooperation throughout, Lao Statistics Bureau welcome the proposal, comments and feedback to guide the improvement this magazines better. Meanwhile, the Lao Statistics Bureau express thanks to highly among those interested in using information and statistics to Lao Statistics Bureau. TRANSLATE with xEnglishArabicHebrewPolishBulgarianHindiPortugueseCatalanHmong DawRomanianChinese SimplifiedHungarianRussianChinese TraditionalIndonesianSlovakCzechItalianSlovenianDanishJapaneseSpanishDutchKlingonSwedishEnglishKoreanThaiEstonianLatvianTurkishFinnishLithuanianUkrainianFrenchMalayUrduGermanMalteseVietnameseGreekNorwegianWelshHaitian CreolePersian  TRANSLATE with COPY THE URL BELOW BackEMBED THE SNIPPET BELOW IN YOUR SITEEnable collaborative features and customize widget: Bing Webmaster PortalBack

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 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 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 OECD, in cooperation with the EU, has developed a harmonised definition of urban areas which overcomes previous limitations linked to administrative definitions (OECD, 2012). According to this definition an urban area is a functional economic unit characterised by densely inhabited “city core” and “commuting zone” whose labour market is highly integrated with the core. The Metropolitan database provides indicators of 649 OECD metropolitan areas identified in 33 OECD countries and the functional urban areas of Colombia. Comparable values of population, GDP, employment, and other indicators are presented.

Education Accountability of United States, 2012

Education Achievement United States

This data set contains information of The statistics yearbook 2017 is compiled and published by Lao statistics Bureau, Ministry of Planning and Investment. Lao statistics Bureau of expressing gratitude provincial statistics Centre, line ministries involved, ministry-equivalent agencies and other parties to contribute to compile statistical data Economic, Social and Natural Resources and Environment cooperation throughout, Lao Statistics Bureau welcome the proposal, comments and feedback to guide the improvement this magazines better. Meanwhile, the Lao Statistics Bureau express thanks to highly among those interested in using information and statistics to Lao Statistics Bureau.

Eurostat Dataset Id:educ_regind The aim of this section is to provide comparable statistics and indicators on education in the 27 Member States of the European Union, at the regional level NUTS 2. In order to facilitate comparison between countries, data from each Member State are allocated to the various education levels of the International Standard Classification of Education (ISCED), UNESCO 1997.

Data cited at: The World Bank https://datacatalog.worldbank.org/ Topic:Education Statistics Publication: https://datacatalog.worldbank.org/dataset/education-statistics License: http://creativecommons.org/licenses/by/4.0/   The World Bank EdStats All Indicator Query holds over 4,000 internationally comparable indicators that describe education access, progression, completion, literacy, teachers, population, and expenditures. The indicators cover the education cycle from pre-primary to vocational and tertiary education.

Education Technology United States, 2007

The tables presented in the topic of active population cover the total population for 31 countries (for more information on received tables and geographic coverage, see "2001 Census Round - Tables Received" in the Annex at the bottom of the page). The level of completeness of the tables depends largely on the availability of data at the respective national statistical institutes. There are four ways of collecting census data, namely: - the traditional method of using census questionnaires (exhaustive census); - the method of using registers and/or other administrative sources; - a combination of registers and/or other administrative sources and - surveys (complete enumerations or sample surveys). Other methods (other mixed census or micro-census) can be used as well. Details for the method employed by each country are provided in "2001 Census Method"in the Annex at the bottom of the page. In the same table you can find the dates on which the census was carried out in each country.

Eurostat Dataset Id:cens_01reisco The tables presented in the topic of educational level cover the total population for 31 countries (for more information on received tables and geographic coverage, see "2001 Census Round - Tables Received" in the Annex at the bottom of the page). The level of completeness of the tables depends largely on the availability of data at the respective national statistical institutes. There are four ways of collecting census data, namely: - the traditional method of using census questionnaires (exhaustive census); - the method of using registers and/or other administrative sources; - a combination of registers and/or other administrative sources and - surveys (complete enumerations or sample surveys). Other methods (other mixed census or micro-census) can be used as well. Details for the method employed by each country are provided in "2001 Census Method"in the Annex at the bottom of the page. In the same table you can find the dates on which the census was carried out in each country.

Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. The series is part of the ILO estimates and is harmonized to account for differences in national data and scope of coverage, collection and tabulation methodologies as well as for other country-specific factors. For more information, refer to the ILO estimates and projections methodological note.

Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. The series is part of the ILO estimates and is harmonized to account for differences in national data and scope of coverage, collection and tabulation methodologies as well as for other country-specific factors. For more information, refer to the ILO estimates and projections methodological note.

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.

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 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, the United Kingdom, 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-LFS (Statistics Explained) webpage. 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 by territorial typologies, i.e. urban-rural, metropolitan, coastal, mountain, borders and island 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.

Employees and income

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 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 data shows the employment in high-tech sectors (code HTC) as a percentage of total employment. The data are aggregated according to the sectoral approach at NACE Rev.2 on 2-digit level and is oriented on the ratio of highly qualified working in these areas.

'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.

'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.

Regional (NUTS level 2) employment rate of the age group 20-64 represents employed persons aged 20-64 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 20-64, 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.

Regional (NUTS level 2) employment rate of the age group 55-64 represents employed persons aged 55-64 as a percentage of the population of the same age group. Employed persons are those 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.

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 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 aim of this section is to provide comparable statistics and indicators on education in the 27 Member States of the European Union, at the regional level NUTS 2. In order to facilitate comparison between countries, data from each Member State are allocated to the various education levels of the International Standard Classification of Education (ISCED), UNESCO 1997.

Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. The series is part of the ILO estimates and is harmonized to account for differences in national data and scope of coverage, collection and tabulation methodologies as well as for other country-specific factors. For more information, refer to the ILO estimates and projections methodological note.

Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. The employment-to-population ratio expresses the number of persons who are employed as a percent of the total working age population. The series is part of the ILO modelled estimates and is harmonized to account for differences in national data and scope of coverage, collection and tabulation methodologies as well as for other country-specific factors. For more information, refer to the ILOSTAT pages on concepts and definitions and ILO modelled estimates and projections.

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. 

Eurostat Dataset Id:env_rpep

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.

Eurostat Dataset Id:env_ac_exp4r2 Data show environmental protection expenditure (EPE). Environmental protection includes all activities directly aimed at the prevention, reduction and elimination of pollution or any other degradation of the environment. Data on regional EPE were collected from the European countries for the first time in 2010 through the Eurostat Questionnaire on Regional Environmental Data Collection (REQ) based on a Gentlemen's Agreement. The scope of environmental protection is defined according to the Classification of Environmental Protection Activities (CEPA 2000), which distinguishes nine environmental domains: protection of ambient air and climate; wastewater management; waste management; protection and remediation of soil, groundwater and surface water; noise and vibration abatement; protection of biodiversity and landscape; protection against radiation; research and development and other environmental protection activities. The data cover three economic sectors (public sector, specialised producers and industry), one economic variable (total environmental protection expenditure) and the nine environmental domains mentioned above. Data are published for years 2000-2009.

General and regional statistics > Regional environment and energy statistics > Other regional environment statistics > Environmental protection expenditure by NUTS 2 regions (NACE Rev. 2).

Estimated average scores and percent distribution of 15-year-old students, science, by proficiency level, Programme for International Student Assessment (PISA), Canada, provinces and participating countries, Council of Ministers of Education Canada (CMEC). This table is included in Section C: Elementary-secondary education: Student achievement of the Pan Canadian Education Indicators Program (PCEIP). PCEIP draws from a wide variety of data sources to provide information on the school-age population, elementary, secondary and postsecondary education, transitions, education finance and labour market outcomes. The program presents indicators for all of Canada, the provinces, the territories, as well as selected international comparisons and comparisons over time. PCEIP is an ongoing initiative of the Canadian Education Statistics Council, a partnership between Statistics Canada and the Council of Ministers of Education, Canada that provides a set of statistical measures on education systems in Canada.

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

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.

Data on soil erosion are published under agreement with data provider - Joint Research Centre of the European Commission (JRC – Ispra), one of the partners of the Memorandum of Understanding for cooperation on the development of Agri-environmental indicators. Soil erosion by water is one of the most widespread forms of soil degradation in Europe. Since it is difficult to measure at large scales, soil erosion models are a crucial estimation tool at regional, national and European levels. This dataset represents the soil erosion rates by water estimated on the basis of the Revised Universal Soil Loss Equation (RUSLE) empirical computer model in tonnes per ha of EU territory per year (t ha-1 yr-1), in EU-28 Member States for NUTS 3 level administrative areas. Note that Eurostat is not the producer of these data, only re-publishes them. For more information please consult the Eurostat Statistics Explained article Agri-environmental indicator – Soil erosion.

Intellectual property refers broadly to the creations of the human mind. Intellectual property rights protect the interests of creators by giving them property rights over their creations. Trade marks constitute means by which creators seek protection for their industrial property. Trade marks reflect the non-technological innovation in every sector of economic life, including services. In this context, indicators based on Trade mark data can provide a link between innovation and the market. Trade marks such as words or figurative marks are an essential part of the “identity” of goods and services. They help deliver brand recognition, in logos for example, and play an important role in marketing and communication. It is possible to register a variety of Trade marks including words, other graphical representations, and even sounds. Rights owners have a choice of obtaining protection on a country-by-country basis, or using international systems. This domain provides users with data concerning European Union Trade marks. European Union Trade marks refer to trade mark protections throughout the European Union, which covers 28 countries. The European Union Intellectual Property Office (EUIPO) is the official office of the European Union for the registration of European Union Trade marks and Designs. A European Union Trade mark is an exclusive right that protects distinctive signs, valid across the EU, registered directly with EUIPO in Alicante in accordance with the conditions specified in the EUTM Regulations (Source: EUIPO).

This dataset contains the exchange rate of 1USD to their country local currency.

Data source(s) used: The source of the cross-country indicators is OECD: "Education at a glance". For further details please see the publication at the link www.oecd.org. The source of the final consumption expenditure by general government on education and training by region is Istat: regional economic accounts.

The Export of Value Added (EVA) dataset illustrates the strength of economy- wide linkages. It provides data on how value added structures and services linkages to trade have evolved over time. Thanks to repeated updating of the GTAP dataset, we have data for both cross border linkages in recent years, and how these have changed since the early 1990s. This serves as the basis for the database, which builds on Christen, Francois, and Hoekman (2012) and Francois, Manchin, and Tomberger (2012). We work with a panel of global input-output data (a set of global social accounting matrices spanning intermittent years from 1992 to 2011) that covers not only key OECD economies, but also a range of developing countries as well. Sector_GMatrix:  This matrix contains the total domestic value added based on linkages. Depending whether rows or columns are considered its sum corresponds to forward (row) or backward (colunn) linkages. Thus reading a row for a given sector (sector presented on the y-axis) provides information about how much this sector went into each sector (on the x-axis) as inputs DomVAshare: This vector denotes the domestic share of value added of gross value of output per sector. GXshare: Denotes the share of each sector in total exports per country based on the gross value of exports. DXshare: Denotes the share of each sector’s exports of total exports per country based on direct value added, ignoring linkages. VXsharefwd: Denotes the total value added in exports based on forward linkages per sector and country. VXsharebwd: Denotes the total value added in exports based on backward linkages. It is obtained by taking the column-sums of matrix H.

Exports by Country for Senegal

Air pollution is considered one of the most pressing environmental and health issues across OECD countries and beyond. According to the World Health Organisation (WHO), exposure to fine particulate matter (PM2.5) has potentially the most significant adverse effects on health compared to other pollutants. PM2.5 can be inhaled and cause serious health problems including both respiratory and cardiovascular disease, having its most severe effects on children and elderly people. Exposure to PM2.5 has been shown to considerably increase the risk of heart disease and stroke in particular. For these reasons, population exposure to (outdoor or ambient) PM2.5 has been identified as an OECD Green Growth headline indicator. The underlying PM2.5 concentrations estimates are taken from van Donkelaar et al. (2016). They have been derived using satellite observations and a chemical transport model, calibrated to global ground-based measurements using Geographically Weighted Regression at 0.01° resolution. The underlying population data, Gridded Population of the World, version 4 (GPWv4) are taken from the Socioeconomic Data and Applications Center (SEDAC) at the NASA. The underlying boundary geometries are taken from the Global Administrative Unit Layers (GAUL) developed by the FAO, and the OECD Territorial Classification, when available. The current version of the database presents much more variation with respect to the previous one. The reason is that the underlying concentration estimates previously included smoothed multi-year averages and interpolations; while in the current version annual concentration estimates are used. Establishing trends of pollution exposure should be done with care, especially at smaller output areas, as their inputs (e.g. underlying data and models) can change from year to year. We recommend using a 3-year moving average for visualisation.

 Data cited at:  CBS StatLine databank https://opendata.cbs.nl/statline/portal.html?_la=en&_catalog=CBS Publication: Netherlands: External Migration Statistics https://opendata.cbs.nl/portal.html?_la=en&_catalog=CBS&tableId=03742eng&_theme=1088 License: http://creativecommons.org/licenses/by/4.0/   Immigration and emigration and the administrative corrections in the Netherlands by country of birth, sex, age and marital status

If a search in the StatBank does not return any result, this does not necessarily mean that there is no trade or that the country code is not valid in the particular period. A zero (0) could also imply that the figures are confidential or that the value is less than half of the unit used. From 2006 on the following countries had their belonging to continent changed - Cyprus from Asia to Europe, and Armenia, Georgia, Kyrgyzstan, Kazakhstan, Tajikistan, Turkmenistan and Uzbekistan from Europe to Asia. The total for Trade are/continent includes this change - while in the selections of countries in the pull-down menu for European/Asian countries it is the present classification that will be shown for the whole time period (Cypros belonging to Europe and the other countries to Asia). Statistics Norway do not publish figures for the trade region previously Comecon after 2011. For more information, see About the statisticsMonthly figures are released on the 15th of the month after the observation period (the previous month), or the first subsequent working day. These figures are preliminary. Corresponding yearly figures are published together with the monthly figures for December. With regard to the current year, all the monthly figures are updated in every publication. Final figures for the preceding year are released twice. For the first time in May the following year, while the corrected final figures are published in May one year later.countryEUCroatia is included in the trade with the EU from 2014 on.Palestine (2013-)Previously: West Bank/Gaza Stripe (2001-2012)

If a search in the StatBank does not return any result, this does not necessarily mean that there is no trade or that the country code is not valid in the particular period. A zero (0) could also imply that the figures are confidential or that the value is less than half of the unit used. From 2006 on the following countries had their belonging to continent changed - Cyprus from Asia to Europe, and Armenia, Georgia, Kyrgyzstan, Kazakhstan, Tajikistan, Turkmenistan and Uzbekistan from Europe to Asia. The total for Trade are/continent includes this change - while in the selections of countries in the pull-down menu for European/Asian countries it is the present classification that will be shown for the whole time period (Cypros belonging to Europe and the other countries to Asia). Statistics Norway do not publish figures for the trade region previously Comecon after 2011. For more information, see About the statisticsMonthly figures are released on the 15th of the month after the observation period (the previous month), or the first subsequent working day. These figures are preliminary. Corresponding yearly figures are published together with the monthly figures for December. With regard to the current year, all the monthly figures are updated in every publication. Final figures for the preceding year are released twice. For the first time in May the following year, while the corrected final figures are published in May one year later.countryEUCroatia is included in the trade with the EU from 2014 on.Palestine (2013-)Previously: West Bank/Gaza Stripe (2001-2012)

Methodological explanationsSymbols usedSource: State Statistical Office, Year 2018M12, preliminary data

Methodological explanationsSymbols usedSource: State Statistical OfficeYear2018preliminary data

The 2011 Population and Housing Census marks a milestone in census exercises in Europe. For the first time, European legislation defined in detail a set of harmonised high-quality data from the population and housing censuses conducted in the EU Member States. As a result, the data from the 2011 round of censuses offer exceptional flexibility to cross-tabulate different variables and to provide geographically detailed data. EU Member States have developed different methods to produce these census data.  The national differences reflect the specific national situations in terms of data source availability, as well as the administrative practices and traditions of that country. The EU census legislation respects this diversity. The Regulation of the European Parliament and of the Council on population and housing censuses (Regulation (EC) No 763/2008) is focussed on output harmonisation rather than input harmonisation. Member States are free to assess for themselves how to conduct their 2011 censuses and which data sources, methods and technology should be applied given the national context. This gives the Member States flexibility, in line with the principles of subsidiarity and efficiency, and with the competences of the statistical institutes in the Member States. However, certain important conditions must be met in order to achieve the objective of comparability of census data from different Member States and to assess the data quality: Regulation (EC) No 1201/20092 contains definitions and technical specifications for the census topics (variables) and their breakdowns that are required to achieve Europe-wide comparability. The specifications are based closely on international recommendations and have been designed to provide the best possible information value. The census topics include geographic, demographic, economic and educational characteristics of persons, international and internal migration characteristics as well as household, family and housing characteristics. Regulation (EU) No 519/2010 requires the data outputs that Member States transmit to the Eurostat to comply with a defined programme of statistical data (tabulation) and with set rules concerning the replacement of statistical data. The content of the EU census programme serves major policy needs of the European Union. Regionally, there is a strong focus on the NUTS 2 level. The data requirements are adapted to the level of regional detail. The Regulation does not require transmission of any data that the Member States consider to be confidential. The statistical data must be completed by metadata that will facilitate interpretation of the numerical data, including country-specific definitions plus information on the data sources and on methodological issues. This is necessary in order to achieve the transparency that is a condition for valid interpretation of the data. Users of output-harmonised census data from the EU Member States need to have detailed information on the quality of the censuses and their results. Regulation (EU) No 1151/2010) therefore requires transmission of a quality report containing a systematic description of the data sources used for census purposes in the Member States and of the quality of the census results produced from these sources. A comparably structured quality report for all EU Member States will support the exchange of experience from the 2011 round and become a reference for future development of census methodology (EU legislation on the 2011 Population and Housing Censuses - Explanatory Notes ). In order to ensure proper transmission of the data and metadata and provide user-friendly access to this information, a common technical format is set for transmission for all Member States and for the Commission (Eurostat). The Regulation therefore requires the data to be transmitted in a harmonised structure and in the internationally established SDMX format from every Member State. In order to achieve this harmonised transmission, a new system has been developed – the CENSUS HUB. The Census Hub is a conceptually new system used for the dissemination of the 2011 Census. It is based on the concept of data sharing, where a group of partners (Eurostat on one hand and National Statistical Institutes on the other) agree to provide access to their data according to standard processes, formats and technologies. The Census Hub is a readily-accessible system that provided the following functions: • Data providers (the NSIs) can make data available directly from their systems through a querying system. In parallel, • Data users browse the hub to define a dataset of interest via the above structural metadata and retrieve the dataset from the NSIs. From the data management point of view, the hub is based on agreed hypercubes (data-sets in the form of multi-dimensional aggregations). The hypercubes are not sent to the central system. Instead the following process operates: 1. a user defines a dataset through the web interface of the central hub and requests it; 2. the central hub translates the user request in one or more queries and sends them to the related NSIs’ systems; 3. NSIs’ systems process the query and send the result to the central hub in a standard format; 4. the central hub puts together all the results sent by the NSI systems and presents them in a user-specified format.

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.

The domain EUROFARM (ef) contains information (statistical tables) on structure of agricultural holdings collected through agricultural structure surveys. The data of the domain have been organised into two collections: Results of the farm structure surveys contains data from 1990 onwards based on the 1988 legislation andResults of the farm structure surveys based on the legislation from 2008. This collection contains the data from the 2010 Census onwards as well as the data for the surveys 2007 and 2005. Farm Structure Survey data are used to collect information on agricultural holdings in the Member States at different geographic levels (Member States, regions, districts) and over periods (follow up the changes in agricultural sector), thus provide a base for decision making in the Common Agricultural Policy. Two kinds of Farm Structure Survey (FSS) are carried out by Member States: a basic survey (full scope Agricultural Census - AC) every 10 years,several sample based intermediate surveys between them. However for certain characteristics the Member States may use sample base for every survey. The calendar for the surveys to be held in all Member States is agreed by the Agricultural Statistics Committee of the European Commission. For a given survey year, Member States have to conduct their surveys within the agreed time-frame, thus all the data are as comparable as possible. The FSS are organised in all Member States on a harmonised base. Whereas the characteristics are based on community legislation, the same data are available for all countries in case of each survey. The data on individual agricultural holdings are collected by all Member States and sent to Eurostat. The aggregated results are disseminated through statistical tables. The variables are arranged into groups: general overview with key variables,and other specialized groups containing detailed data onland uselivestockfarm labour forcerural development issues as well as management and practices. The 2010 survey additionally provides the results of the survey on agricultural production methods (SAPM). The scope of the survey is agriculture, while the survey unit is the agricultural holding (farm). Data for basic surveys are available in a three-level geographical breakdown of the whole country, the regions and the district; while data for intermediate surveys are only available upon the two-levels of country and regions. Since FSS 1999/2000 information about local farm location is collected in most countries, so that the data can also be disseminated by NUTS and are robust regarding the changes in the NUTS definition. The FSS 2009/2010 information is inline with the NUTS 2010 classification: Regulation (EU) No 31/2011 amending the NUTS classification from January 2003. The Eurofarm domain does not cover the whole territory only the land covered by the agricultural holdings. So the land use data without link with other farm characteristics should be downloaded by the user from the relevant domain. Specific national data about crops, animals or agricultural labour force can be found in other domains, without link between the other information at farm level. For a comprehensive description of the domain, please consult detailed structure (See annex at the bottom of the page). Regional Data Data for basic surveys are available in a three-level geographical breakdown of the whole country, the regions and the district; while data for intermediate surveys are only available upon the two-levels of country and regions. Since FSS 1999/2000 information about local farm location is collected in most countries, so that the data can also be disseminated by NUTS classification and are robust regarding the changes in the NUTS definition. The FSS 2009/2010 information is inline with the NUTS 2010 classification: Regulation (EU) No 31/2001 amending the NUTS classification from January 2012. 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.

Eurostat Dataset Id:ef_lu_ovcropaa The domain EUROFARM (ef) contains information (statistical tables) on structure of agricultural holdings collected through agricultural structure surveys. The data of the domain have been organised into two collections: Results of the farm structure surveys contains data from 1990 onwards Standard Gross Margin (SGM) and Standard Output (SO) coefficients. Farm Structure Survey data are used to collect information on agricultural holdings in the Member States at different geographic levels (Member States, regions, districts) and over periods (follow up the changes in agricultural sector), thus provide a base for decision making in the Common Agricultural Policy. Two kinds of Farm Structure Survey (FSS) are carried out by Member States: a basic survey (full scope Agricultural Census - AC) every 10 years, several sample based intermediate surveys between them. However for certain characteristics the Member States may use sample base for every survey. The calendar for the surveys to be held in all Member States is agreed by the Agricultural Statistics Committee of the European Commission. For a given survey year, Member States have to conduct their surveys within the agreed time-frame, thus all the data are as comparable as possible. The FSS are organised in all Member States on a harmonised base. Whereas the characteristics are based on community legislation, the same data are available for all countries in case of each survey. The data on individual agricultural holdings are collected by all Member States and sent to Eurostat. The aggregated results are disseminated through statistical tables. The variables are arranged into groups: one general overview with the key variables, and other specialized groups containing detailed data on land use livestock special interest topics: farm labour force, rural development issues as well as management and practices. The scope of the survey is agriculture, while the survey unit is the agricultural holding (farm). Data for basic surveys are available in a three-level geographical breakdown of the whole country, the regions and the district; while data for intermediate surveys are only available upon the two-levels of country and regions. Since FSS 1999/2000 information about local farm location is collected in most countries, so that the data can also be disseminated by NUTS and are robust regarding the changes in the NUTS definition. The FSS 2009/2010 information is inline with the NUTS 2010 classification: Regulation (EU) No 31/2001 amending the NUTS classification from January 2012. The Eurofarm domain does not cover the whole territory only the land covered by the agricultural holdings. So the land use data without link with other farm characteristics should be downloaded by the user from the relevant domain. Specific national data about crops, animals or agricultural labour force can be found in other domains, without link between the other information at farm level. For a comprehensive description of the domain, please consult detailed structure. Regional Data Data for basic surveys are available in a three-level geographical breakdown of the whole country, the regions and the district; while data for intermediate surveys are only available upon the two-levels of country and regions. Since FSS 1999/2000 information about local farm location is collected in most countries, so that the data can also be disseminated by NUTS classification and are robust regarding the changes in the NUTS definition. The FSS 2009/2010 information is inline with the NUTS 2010 classification: Regulation (EU) No 31/2001 amending the NUTS classification from January 2012. 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.

An occupational injury is defined as any personal injury, disease or death resulting from an occupational accident; The case is fatal where death occurred within one year of the day of the accident. Data provided refers to new fatal occupational injuries per 100'000 in reference group coverage. For more information, refer to our resources on methods.

The FDI Regulatory Restrictiveness Index (FDI Index) measures statutory restrictions on foreign direct investment across 22 economic sectors. It gauges the restrictiveness of a country’s FDI rules by looking at the four main types of restrictions on FDI: 1) Foreign equity limitations; 2) Discriminatory screening or approval mechanisms; 3) Restrictions on the employment of foreigners as key personnel and 4) Other operational restrictions, e.g. restrictions on branching and on capital repatriation or on land ownership by foreign-owend enterprises. Restrictions are evaluated on a 0 (open) to 1 (closed) scale. The overall restrictiveness index is the average of sectoral scores. The discriminatory nature of measures, i.e. when they apply to foreign investors only, is the central criterion for scoring a measure. State ownership and state monopolies, to the extent they are not discriminatory towards foreigners, are not scored. The FDI Index is not a full measure of a country’s investment climate. A range of other factors come into play, including how FDI rules are implemented. Entry barriers can also arise for other reasons, including state ownership in key sectors. A country’s ability to attract FDI will be affected by others factors such as the size of its market, the extent of its integration with neighbours and even geography among other. Nonetheless, FDI rules can be a critical determinant of a country’s attractiveness to foreign investors.

Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. The labour force comprises all persons of working age who furnish the supply of labour for the production of goods and services during a specified time-reference period. It refers to the sum of all persons of working age who are employed and those who are unemployed. The working-age population is commonly defined as persons aged 15 years and older, but this varies from country to country. The series is part of the ILO estimates and is harmonized to account for differences in national data and scope of coverage, collection and tabulation methodologies as well as for other country-specific factors. For more information, refer to the ILO estimates and projections methodological note.

Data on causes of death (COD) provide information on mortality patterns and form a major element of public health information. COD data refer to the underlying cause which - according to the World Health Organisation (WHO) - is "the disease or injury which initiated the train of morbid events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury". Causes of death are classified by the 86 causes of the "European shortlist" of causes of death. This shortlist is based on the International Statistical Classification of Diseases and Related Health Problems (ICD). COD data are derived from death certificates. The medical certification of death is an obligation in all Member States. Countries code the information provided in the medical certificate of cause of death into ICD codes according to the rules specified in the ICD. Data are broken down by sex, 5-year age groups, cause of death and by residency and country of occurrence. For stillbirths and neonatal deaths additional breakdows might include age of mother. Data are available for EU-28, the former Yugoslav Republic of Macedonia, Albania, Iceland, Norway, Liechtenstein and Switzerland. Regional data (NUTS level 2) are available for most of the countries. Annual national data are provided in absolute number, crude death rates and standardised death rates. At regional level (NUTS level 2) the same is provided in form of 3 years averages. Annual crude death rates are also available at NUTS level 2.

FHIR: Fast Healthcare Interoperability Resources HRR: Healthcare Referral Region

Data source(s) used: Data source(s) usedThe source of data is OECD (PISA - Programme for international student assessment). The PISA survey aims to evaluate education systems every 3 years by assessing 15-years-olds' competencies in the key subjects: reading, mathematicas and science. The first Italian survey was in 2000 and it was conducted by Invalsi and the source is OECD/Invalsi- Pisa.

Data cited at: tax justice network - https://fsi.taxjustice.net/en/introduction/fsi-results;  License term - https://fsi.taxjustice.net/en/introduction/copyright-license     Jurisdiction Note for Year 2020: Footnote 1: The territories marked in dark color are Overseas Territories (OTs) and Crown Dependencies (CDs) where the Queen is head of state; powers to appoint key government officials rest with the British Crown; laws must be approved in London; and the UK government holds various other powers (see here for more details: www.financialsecrecyindex.com/PDF/UnitedKingdom.pdf). Territories marked in light color are British Commonwealth territories which are not OTs or CDs but whose final court of appeal is the Judicial Committee of the Privy Council in London (see here for more details: http://www.taxjustice.net/cms/upload/pdf/Privy_Council_and_Secrecy_Scores.pdf). Footnote 6: For jurisdictions marked with 2, we provide special narrative reports exploring the history and politics of their offshore sectors. You can read and download these reports by clicking on the country name.

Eurostat Dataset Id:migr_r_2fi The data comprises series of internal (in the country) regional migration on NUTS2 level during the reference year. Data on arrivals and departures due to internal migration are disaggregated by NUTS2 region of arrival/departure, sex and single year age. Data on migration by region of origin and destination (excluding intra-regional migration) are presented separately for each country for which such data are available. The tables are in the form of matrix (NUTS2 region of origin and destination) excluding intra-refional migration, disaggregated by sex. The data source is administrative records or national surveys. The completeness of the tables depends largely on the availability of data from the relevant national statistical institutes.

Data cited at: Statistics Finland http://www.stat.fi/index_en.html Publication: 004 -- International trade in services by region, 1 000 000 euros http://pxnet2.stat.fi/PXWeb/pxweb/en/StatFin/StatFin__kan__tpulk/statfin_tpulk_pxt_004.px License: http://creativecommons.org/licenses/by/4.0/ The statistics on international trade in goods and services cover international trade in balance of payments terms on the quarterly level. The statistics form a link for goods trade in customs and balance of payments terms, describe the breakdown of quarterly trade in services, and indicate the total exports of goods and services by area. . = Category not applicable. .. = Data not available or too uncertain for presentation, or subject to secrecy. Description of statistics Concepts and definitionsRegion Region and statesYear Year.Data Import The value of imports, 1 000 000 euros.Export The value of exports, 1 000 000 euros.

Data cited at: Statistics Finland http://www.stat.fi/index_en.html Publication: 008 -- Nationality according to age and sex by region in 1990 to 2017 http://pxnet2.stat.fi/PXWeb/pxweb/en/StatFin/StatFin__vrm__vaerak/statfin_vaerak_pxt_008.px License: http://creativecommons.org/licenses/by/4.0/ Concepts and definitions Description Quality description These statistics apply the regional division of 1 January 2018 to the whole time series. Population statistics from 1750 to 2000 have been digitised into PDF format in the National Library's Doria service. Publications on Population structure in Doria (in Finnish) Publications on Vital statistics in Doria (in Finnish) Publications on Population censuses in Doria (in Finnish)Nationality If a person has two nationalities and one of them is Finnish, he/she will be included in statistics as a Finnish national. The used classification of continents is the classification of Eurostat, where Cyprus and Turkey belong to Europe. Citizens of non-autonomous states are summed under the mother country.Nationality Czech Republic Czech Republic + Former CzechoslovakiaSudan Sudan + Former Sudan

Data cited at: Statistics Finland  http://www.stat.fi/index_en.html Publication: 009 -- Finnish citizens with dual nationality by age and second nationality in 2000 to 2017 http://pxnet2.stat.fi/PXWeb/pxweb/en/StatFin/StatFin__vrm__vaerak/statfin_vaerak_pxt_009.px License: http://creativecommons.org/licenses/by/4.0/ Concepts and definitions Description Quality description Population statistics from 1750 to 2000 have been digitized into PDF format in the National Library's Doria service. Publications on Population structure in Doria (in Finnish) Publications on Vital statistics in Doria (in Finnish) Publications on Population censuses in Doria (in Finnish) second nationality If a person has two nationalities and one of them is Finnish, he/she will be included in statistics as a Finnish national. second nationality   Czech Republic Czech Republic + Former Czechoslovakia Sudan Sudan + Former Sudan

This database contains capture production statistics by country or territory, species item, and FAO Major Fishing Area.

 

The activities of multinational enterprises statistics available here provide a picture of the overall activities of US affiliates of foreign parents and contain a wide variety of indicators of their financial structure and operations. These statistics cover items that are needed in analyzing the characteristics, performance, and economic impact of MNEs, and are obtained from mandatory surveys of US affiliates of foreign parents conducted by BEA.

The Community concept applies for all cross-border movements of goods, including purchases (imports) and sales (re-exports) in Belgium between non-resident businesses.

The national concept refers solely to cross-border movements of goods involving a resident business as counter party.

International Merchandise Trade Statistics

Data cited at: https://data.gov.om/OMFRTRD2016 this Data set covers the statistical indicators illustrating the development of trade between Oman and other countries, and classification of  merchandise exports, re-exports and merchandise imports by commodity group, nature of materials, their final utilization and port of entry.  It includes also a table on of the balance of payments estimates.   The commodity classification used in the presentation of foreign trade data is the Hormonised System, which has been adopted in Oman since 1987, in addition to the SITC Revision (4) for international comparison.  Commodity values are estimated in Rial Omani on the basis of the (C.I.F.) value for imports (i.e. the cost, insurance and freight of goods to the custom points in Oman) and (F.O.B.) for exports and re-exports.

Freedom Status obtained by Combined Average of the Political Rights and Civil Liberties Ratings (Freedom Rating) Range 1-7; 1-2.5 Free; 3-5: Partly Free and 5.5-7: Not Free The score for the A. Electoral Process subcategory The score for the B. Political Pluralism and Participation subcategory The score for the C. Functioning of Government subcategory The score for the Political Rights category The score for the D. Freedom of Expression and Belief subcategory The score for the E. Associational and Organizational Rights subcategory The score for the F. Rule of Law subcategory The score for the G. Personal Autonomy and Individual Rights subcategory The score for the Civil Liberties category Total aggregate Score for all categories  

Variables converted from character to numeric as follow:Variables under consideration are top 3 vars i.e. Status, print and Broadcast 1 = Free (F) 2 = Partly Free (PF) 3 = Not Free (NF) Under source it values are present like: "F" , "PF" and "NF"  Note:- Date range has been considered as follow: Jan.1981-Aug.1982 is considered as 1982 Aug.1982-Nov.1983 is considered as 1983 Nov.1983-Nov.1984 is considered as 1984 Nov.1984-Nov.1985 is considered as 1985 Nov.1985-Nov.1986 is considered as 1986 Nov.1986-Nov.1987 is considered as 1987   About Freedom of the press: Freedom of the Press, an annual report on media independence around the world which assesses the degree of print, broadcast, and digital media freedom in 199 countries and territories. Published since 1980, it provides numerical scores and country narratives evaluating the legal environment for the media, political pressures that influence reporting, and economic factors that affect access to news and information. Freedom of the Press is the most comprehensive data set available on global media freedom and serves as a key resource for policymakers, international institutions, journalists, activists, and scholars worldwide.

Eurostat Dataset Id:env_watres_r2 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.

Data include pension funds per the OECD classification by type of pension plans and by type of pension funds. All types of plans are included (occupational and personal, mandatory and voluntary). The OECD classification considers both funded and book reserved pension plans that are workplace-based (occupational pension plans) or accessed directly in retail markets (personal pension plans). Both mandatory and voluntary arrangements are included. The data include plans where benefits are paid by a private sector entity (classified as private pension plans by the OECD) as well as those paid by a funded public sector entity. A full description of the OECD classification can be found at:http://www.oecd.org/dataoecd/0/49/38356329.pdf. Pension funds include also some personal pension arrangements like the Individual Retirement Accounts (IRAs) in the United States as well as funds for government workers. The coverage of the statistics follows the regulatory and supervisory framework. All authorised pension funds are therefore normally covered by the Global Pension Statistics exercise. Assets pertaining to reserve funds in social security systems are excluded.

The Global Burden of Disease Study 2015 (GBD 2015), coordinated by the Institute for Health Metrics and Evaluation (IHME), estimated the burden of diseases, injuries, and risk factors at the global, regional, national, territorial, and, for a subset of countries, subnational level. This dataset measures progress towards the Millennium Development Goal 5 (MDG 5) target of a 75% reduction in the maternal mortality ratio between 1990 and 2015. Maternal mortality ratio estimates for 21 regions, 195 countries and territories and 4 United Kingdom subnational units for 1990-2015 (quinquennial) are available by age and cause from the GBD Results Tool. Files available in this record include tables published in The Lancet in October 2016 in "Global, regional, and national levels of maternal mortality, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015.

Data cited at: Global Database of Events, Language, and Tone   The GDELT Event Database records over 300 categories of physical activities around the world, from riots and protests to peace appeals and diplomatic exchanges, georeferenced to the city or mountain top, across the entire planet dating back to January 1, 1979 and updated every 15 minutes. Essentially it takes a sentence like "The United States criticized Russia yesterday for deploying its troops in Crimea, in which a recent clash with its soldiers left 10 civilians injured" and transforms this blurb of unstructured text into three structured database entries, recording US CRITICIZES RUSSIA, RUSSIA TROOP-DEPLOY UKRAINE (CRIMEA), and RUSSIA MATERIAL-CONFLICT CIVILIANS (CRIMEA). Nearly 60 attributes are captured for each event, including the approximate location of the action and those involved. This translates the textual descriptions of world events captured in the news media into codified entries in a grand "global spreadsheet."

Macro Economic indicators for Afghanistan 

Data cited at: The World Bank https://datacatalog.worldbank.org/ Topic: Gender Statistics Publication: https://datacatalog.worldbank.org/dataset/gender-statistics License: http://creativecommons.org/licenses/by/4.0/

The GID-DB is a database providing researchers and policymakers with key data on gender-based discrimination in social institutions. This data helps analyse women’s empowerment and understand gender gaps in other key areas of development.Covering 180 countries and territories, the GID-DB contains comprehensive information on legal, cultural and traditional practices that discriminate against women and girls.

Bilateral ODA commitments by purpose. Data cover the years 2005 to 2009. Amounts are expressed in USD million. The sectoral distribution of bilateral ODA commitments refers to the economic sector of destination (i.e. the specific area of the recipient's economic or social structure whose development is, or is intended to be fostered by the aid), rather than to the type of goods or services provided. These are aggregates of individual projects notified under the Creditor Reporting System, supplemented by reporting on the sectoral distribution of technical co-operation, and on actual disbursements of food and emergency aid.

For latest data, please visit here: Federal Statistical Office of Germany-  https://knoema.com/atlas/sources/Federal-Statistical-Office-of-Germany Eurostat - https://knoema.com/atlas/sources/Eurostat  

Revenue of the overall public budget 2014 and 2017

This database contains statistics on production volume and value by species, country or area, fishing area and culture environment

Data cited at: The World Bank https://datacatalog.worldbank.org/ Topic: Global Bilateral Migration Database Publication: https://datacatalog.worldbank.org/dataset/global-bilateral-migration-database License: http://creativecommons.org/licenses/by/4.0/   Global Bilateral Migration Database: Global matrices of bilateral migrant stocks spanning the period 1960-2000, disaggregated by gender and based primarily on the foreign-born concept are presented. Over one thousand census and population register records are combined to construct decennial matrices corresponding to the last five completed census rounds. For the first time, a comprehensive picture of bilateral global migration over the last half of the twentieth century emerges. The data reveal that the global migrant stock increased from 92 to 165 million between 1960 and 2000. South-North migration is the fastest growing component of international migration in both absolute and relative terms. The United States remains the most important migrant destination in the world, home to one fifth of the world’s migrants and the top destination for migrants from no less than sixty sending countries. Migration to Western Europe remains largely from elsewhere in Europe. The oil-rich Persian Gulf countries emerge as important destinations for migrants from the Middle East, North Africa and South and South-East Asia. Finally, although the global migrant stock is still predominantly male, the proportion of women increased noticeably between 1960 and 2000.

The Global Burden of Disease Study 2015 (GBD 2015), coordinated by the Institute for Health Metrics and Evaluation (IHME), estimated the burden of diseases, injuries, and risk factors at the global, regional, national, territorial, and, for a subset of countries, subnational level. As part of this study, estimates for obesity and overweight prevalence and the disease burden attributable to high body mass index (BMI) were produced by sex, age group, and year for 195 countries and territories. Estimates for high BMI-attributable deaths, DALYs, and other measures (1990-2015) are available from the GBD Results Tool. Files available in this record include obesity and overweight prevalence estimates for 1980-2015. Study results were published in The New England Journal of Medicine in June 2017 in "Health Effects of Overweight and Obesity in 195 Countries over 25 Years."

This data set provides meaningful forums for exchanging up-to-date best practices to address current issues, impacting cities, and to develop global awareness among the next generation through cross-national interactive educational programs designed to enhance their ability to act as global citizens

This database contains statistics on the annual production of fishery commodities and imports and exports of fishery commodities by country and commodities in terms of volume and value from 1976.

The GEM Adult Population Survey (APS) measures the level and nature of entrepreneurial activity around the world. It is administered to a representative national sample of at least 2000 respondents. The Global Entrepreneurship Monitor is the world's foremost study of entrepreneurship. Through a vast, centrally coordinated, internationally executed data collection effort, GEM is able to provide high quality information, comprehensive reports and interesting stories, to enhance the understanding of the entrepreneurial phenomenon.

Data cited at:  GEM National Expert Survey The GEM National Expert Survey (NES) monitors the factors that are believed to have a significant impact on entrepreneurship, known as the Entrepreneurial Framework Conditions (EFCs). It is administered to a minimum of 36 carefully chosen 'experts' in each country. The Global Entrepreneurship Monitor is the world's foremost study of entrepreneurship. Through a vast, centrally coordinated, internationally executed data collection effort, GEM is able to provide high quality information, comprehensive reports and interesting stories, to enhance the understanding of the entrepreneurial phenomenon.

Data cited at: The World Bank https://datacatalog.worldbank.org/ Topic: Global Financial Development Publication: https://datacatalog.worldbank.org/dataset/global-financial-development License: http://creativecommons.org/licenses/by/4.0/   The Global Financial Development Database is an extensive dataset of financial system characteristics for 206 economies. The database includes measures of (1) size of financial institutions and markets (financial depth), (2) degree to which individuals can and do use financial services (access), (3) efficiency of financial intermediaries and markets in intermediating resources and facilitating financial transactions (efficiency), and (4) stability of financial institutions and markets (stability).For a complete description of the dataset and a discussion of the underlying literature, see: Martin Cihak; Asli Demirguc-Kunt; Erik Feyen; and Ross Levine, 2012. "Benchmarking Financial Systems Around the World." World Bank Policy Research Working Paper 6175, World Bank, Washington, D.C.

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems.  The dataset help us to know about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

Contains global production statistics (capture and aquaculture). This database contains the volume of aquatic species caught by country or area, by species items, by FAO major fishing areas, and year, for all commercial, industrial, recreational and subsistence purposes. The harvest from maricultural, aquaculture and other kinds of fish farming is also included

DescriptionThe Global Forest Resources Assessment 2015 (FRA 2015) is the most comprehensive assessment of forests and forestry to date - not only in terms of the number of countries and people involved - but also in terms of scope. It examines the current status and recent trends for about 90 variables covering the extent, condition, uses and values of forests and other wooded land, with the aim of assessing all benefits from forest resources. Information has been collated from 233 countries and territories for four points in time: 1990, 2000, 2005 and 2010. The results are presented according to the seven thematic elements of sustainable forest management. FAO worked closely with countries and specialists in the design and implementation of FRA 2010 - through regular contact, expert consultations, training for national correspondents and ten regional and subregional workshops. More than 900 contributors were involved, including 178 officially nominated national correspondents and their teams. The outcome is better data, a transparent reporting process and enhanced national capacity in developing countries for data analysis and reporting. The final report of FRA 2010 was published at the start of the latest biennial meeting of the FAO' Committee on Forestry and World Forest Week, in Rome.

Citation: Global Health Observatory (GHO) Data: https://www.who.int/gho/en/: World Health Organization; 2019. License: CC BY-NC-SA 3.0 IGO   The GHO data provides access to indicators on priority health topics including mortality and burden of diseases, the Millennium Development Goals (child nutrition, child health, maternal and reproductive health, immunization, HIV/AIDS, tuberculosis, malaria, neglected diseases, water and sanitation), non communicable diseases and risk factors, epidemic-prone diseases, health systems, environmental health, violence and injuries, equity among others.

Global Internal Displacement Database (GIDD) aims to provide comprehensive information on internal displacement worldwide. It covers all countries and territories for which IDMC has obtained data on situations of internal displacement, and provides data on situations of internal displacement associated with conflict and generalized violence (2014-2015), displacement associated with sudden-onset natural hazard-related disasters (2008-2015).

FAO - GLEAM Global greenhouse gas emissions from livestock summary data (2017)

GPSS data (Accounts & Access, retail payment transactions and RTGS transactions – volumes and values). The World Bank’s Global Payment Systems Survey (GPSS) surveys national and regional central banks and monetary authorities on the status of payment systems. The GPSS is the only global survey that combines quantitative and qualitative measures of payment system development and covers all aspects of national payment systems – from infrastructure and the legal and regulatory environment to technological and business model innovations, international remittances, and oversight framework. The GPSS aims to take an accurate snapshot of payment systems worldwide to obtain information on payment system reforms and the factors which hinder and/or facilitate them in order to help guide policy-dialogue at the international and national levels, and World Bank Group technical assistance. In April 2007, the World Bank launched the first Global Payment Systems Survey among national central banks to collect information on the situation of national payment and securities settlement systems worldwide and provide a payment systems snapshot of both advanced and emerging economies in order to identify main issues that should guide the agenda of authorities, multilateral and market players in the field over the next few years.

Data cited at: Institute for Economics and Peace retrieved from Vision of Humanity  The Global Peace Index 2022

Data cited at:  Institute for Economics & Peace and available from http://visionofhumanity.org/reports Note: The Year of data has been considered as year of publication, for example 2020 report is considered as year 2020.   The Global Terrorism Index (GTI) is a comprehensive study which accounts for the direct and indirect impact of terrorism in 163 countries in terms of its effect on lives lost, injuries, property damage and the psychological aftereffects of terrorism. This study covers 99.6 per cent of the world's population. It aggregates the most authoritative data source on terrorism today, the Global Terrorism Database (GTD) collated by the National Consortium for the Study of Terrorism and Responses to Terrorism (START) into a composite score in order to provide an ordinal ranking of nations on the negative impact of terrorism. The GTD is unique in that it consists of systematically and comprehensively coded data on domestic as well as international terrorist incidents and now includes more than 140,000 cases.  

  

National Accounts of Costa Rica

Graduation Indicators of United States, 2013

The OECD Green Growth database contains selected indicators for monitoring progress towards green growth to support policy making and inform the public at large. The database synthesises data and indicators across a wide range of domains including a range of OECD databases as well as external data sources. The database covers OECD member and accession countries, key partners (including Brazil, China, India, Indonesia and South Africa) and other selected non-OECD countries.

Data cited at: CAIT, retrieved from Climate Watch Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2019, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report.

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.

Eurostat Dataset Id:nama_r_e2gdp Gross domestic product - GDP at market prices - is the final result of the production activity of resident producer units (ESA 1995, 8.89). 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 expediture approach is not 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, taxes on production, less subsidies, gross operating surplus and mixed income of the total economy. 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 EU27 average.

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 EU Member States average.

Eurostat Dataset Id:nama_r_e3gdp 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 1995 (Council Regulation 2223/96). Annex B of the Regulation consists of a comprehensive list of the variables to be transmitted for Community purposes within specified time limits. This transmission programme has been updated by Regulation (EC) N° 1392/2007 of the European Parliament and of the Council. Meanwhile, the ESA95 has been 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: GDP and main aggregates. The data are recorded at current and constant prices and include the corresponding implicit price indices. Final consumption aggregates, including the split into household and government consumption. The data are recorded at current and constant prices and include the corresponding implicit price indices. Income, saving and net lending / net borrowing at current prices. Disposable income is also shown in real terms. Exports and imports by Member States of the EU/third countries. The data are recorded at current and constant prices and include the corresponding implicit price indices. Breakdowns of gross value added, compensation of employees, wages and salaries, operating surplus, employment (domestic scope), gross fixed capital formation (GFCF) and fixed assets and other main aggregates by industry; investment by products and household final consumption expenditure by consumption purposes (COICOP). The data are recorded at current and constant prices and include the corresponding implicit price indices. Auxiliary indicators: Population and employment national data, purchasing power parities, contributions to GDP growth, labour productivity, unit labour cost and GDP per capita. 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. The data are published: - in ECU/euro, in national currencies (including euro converted from former national currencies using the irrevocably fixed rate for all years) and in Purchasing Power Standards (PPS); - at current prices and in volume terms; - Population and employment are measured in persons. Employment is also measured in total hours worked. Data sources: National Statistical Institutes

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.

Eurostat Dataset Id:nama_r_e2gfcfr2 Branch accounts include data on gross value added, compensation of employees, gross fixed capital formation, total employment and number of employees. The legal base for the collection of branch accounts data is the European System of Accounts ESA95. The ESA95 data are sent to Eurostat by the National Statistical Institutes. The units for these variables are: Millions of national currency and millions of Euro for gross value added, compensation of employees and gross fixed capital formation. 1000 persons for total employment and number of employees at NUTS level 3 1000 hours worked for total employment and number of employees at NUTS level 2 Geographical coverage comprises all EU Member States and some Candidate countries down to Nuts 3 level (Nuts = "Nomenclature of territorial units for statistics" - see Eurostat's classification server "RAMON") for the variables gross value added, total employment and number of employees. Compensation of employees, employment in hours worked and gross fixed capital formation are only collected down to Nuts 2 level. For further information about sources and collection methods in the Member States, please refer to National Statistical Institutes (select Services - Links).

The Gross Nutrient Balance provides an insight into the links between the use of agricultural nutrients, their losses to the environment, and the sustainable use of soil nutrients resources. It consists of the Gross Nitrogen Balance and the Gross Phosphorus Balance and is intended to be an indicator of the potential threat of surplus or deficit of two important soil and plant nutrients in agricultural land. It shows the link between agricultural activities and the environmental impact, identifying the factors determining the nutrients surplus or deficit and the trends over time. Nitrogen (N) and Phosphorus (P) are key elements for plants to grow. A persistent deficit of these nutrients can lead in the long term to soil degradation and erosion. When N and P are however persistently applied in excess, they can cause surface and groundwater (including drinking water) pollution and eutrophication. The Gross Nitrogen Balance also includes Nitrogenous Emissions from livestock production and the application of manure and fertilizers. These nitrogenous emissions include: - Ammonia (NH3) contributing to acidification, eutrophication and atmospheric particulate pollution), and - Nitrous oxide (N2O), a potent greenhouse gas contributing to global warming. The gross nutrient balance is calculated as the balance between inputs and outputs of nutrients to the agricultural soil. A balance per hectare is also presented. The Inputs are: -         Consumption of Fertilizers, -         Gross Input of Manure, and -         Other Inputs. The Outputs are: -         Removal of nutrients with the harvest of Crops, -         Removal of nutrients through the harvest and grazing of Fodder, and -         Crop Residues removed from the field.    The data presented in the table are calculated from basic data from various data sources multiplied with coefficients to derive the nutrient content. The basic data used include the consumption of inorganic and other organic fertilizers (excluding manure) (tonnes), livestock population (1000 heads), manure imports, withdrawals and stock changes (tonnes), crop and fodder production (tonnes), crop residues removed from the field (tonnes), use of seeds and planting materials planted in the soil (tonnes), area of leguminous crops (1000 ha), area of arable land, land under permanent crops and permanent grassland (1000 ha). Countries may have used different types of data sources for these data. For instance some countries use estimates of the livestock population based on data from the Livestock Surveys or they have used other data sources like national registers on livestock. Data sources that are used and are available in Eurostat include:  Crop Production Statistics (production and landuse), Livestock Statistics (livestock numbers), Farm Structure Survey (livestock numbers). Countries have estimated coefficients based on measurements, scientific research, expert judgment, default values etc.

The indicator balance represents the total potential threat to the environment of nitrogen and phosphorous surplus or deficit in agricultural soils. A lack of nitrogen or phosphorous may cause degradation in soil fertility and erosion, while an excess may cause surface and groundwater (including drinking water) pollution and eutrophication. Manure and fertiliser introduce nitrogen and phosphorous to the soil while harvesting of crops, removal of residues and runoff remove nitrogen and phosphorous from the soil. Nitrogen and phosphorous balance surpluses are monitored for the purposes of the Water Framework Directive and nitrogen for the Nitrates Directive. The data comes from multiple sources including the consumption of fertilisers, livestock population, crop production and areas of various types of crops. The land types included are arable land, permanent crops and permanent grassland. The unit of measure used is kg of nutrient per hectare of this land.

The indicator measures the potential surplus or deficit of nitrogen and phosphorous in agricultural soils. A lack of nitrogen or phosphorous may lead to degradation in soil fertility, while an excess may cause surface and groundwater (including drinking water) pollution and eutrophication. Ideally, the input/output of nutrition to the soil should be balanced. Nutrient inputs consist of the amount of nitrogen/phosphorus applied via mineral fertilizers and animal manure as well as nitrogen fixation by legumes, deposition from the air, and some other minor sources. Nitrogen/phosphorus output is contained in the harvested crops, or grass and crops eaten by livestock (escape of nitrogen to the atmosphere, e.g. as N2O, is not taken into account for calculation of the indicator). The land types included in Utilised Agricultural Area (UAA) are arable land, permanent crops and permanent grassland.

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.

Eurostat Dataset Id:nama_r_e3vab95r2 Branch accounts include data on gross value added, compensation of employees, gross fixed capital formation, total employment and number of employees. The legal base for the collection of branch accounts data is the European System of Accounts ESA95. The ESA95 data are sent to Eurostat by the National Statistical Institutes. The units for these variables are: Millions of national currency and millions of Euro for gross value added, compensation of employees and gross fixed capital formation. 1000 persons for total employment and number of employees at NUTS level 3 1000 hours worked for total employment and number of employees at NUTS level 2 Geographical coverage comprises all EU Member States and some Candidate countries down to Nuts 3 level (Nuts = "Nomenclature of territorial units for statistics" - see Eurostat's classification server "RAMON") for the variables gross value added, total employment and number of employees. Compensation of employees, employment in hours worked and gross fixed capital formation are only collected down to Nuts 2 level. For further information about sources and collection methods in the Member States, please refer to National Statistical Institutes (select Services - Links).

Productivity is a key driver of economic growth and changes in living standards. Labour productivity growth implies a higher level of output for unit of labour input (hours worked or persons employed). This can be achieved if more capital is used in production or through improved overall efficiency with which labour and capital are used together, i.e., higher multifactor productivity growth (MFP). Productivity is also a key driver of international competitiveness, e.g. as measured by Unit Labour Costs (ULC).   The OECD Productivity Database aims at providing users with the most comprehensive and the latest productivity estimates. The update cycle is on a rolling basis, i.e. each variable in the dataset is made publicly available as soon as it is updated in the sources databases. However, some time lag may arise which affects individual series and/or countries for two reasons: first, hours worked data from the OECD Employment Outlook are typically updated less frequently than the OECD Annual National Accounts Database; second, source data for capital services are typically available in annual national accounts later than source data for labour productivity and ULCs.   Note to users: The OECD Productivity Database accounts for the methodological changes in national accounts' statistics, such as the implementation of the System of National Accounts 2008 (2008 SNA) and the implementation of the international industrial classification ISIC Rev.4. These changes had an impact on output, labour and capital measurement. For Chile, China, Colombia, India, Japan, Turkey and the Russian Federation the indicators are in line with the System of National Accounts 1993 (1993 SNA); for all other countries, the indicators presented are based on the 2008 SNA

A System of Health Accounts 2011 provides an updated and systematic description of the financial flows related to the consumption of health care goods and services. As demands for information increase and more countries implement and institutionalise health accounts according to the system, the data produced are expected to be more comparable, more detailed and more policy relevant. It builds on the original OECD Manual, published in 2000 to create a single global framework for producing health expenditure accounts that can help track resource flows from sources to uses. It is the result of a collaborative effort between the OECD, WHO and the European Commission, and sets out in more detail the boundaries, the definitions and the concepts – responding to health care systems around the globe – from the simplest to the more complicated. The accounting framework is organised around a tri-axial system for the recording of health care expenditure, namely classifications of the functions of health care (ICHA-HC), health care provision (ICHA-HP), and financing schemes (ICHA-HF).

Health Nutrition and Population Statistics database provides key health, nutrition and population statistics gathered from a variety of international and national sources. Themes include global surgery, health financing, HIV/AIDS, immunization, infectious diseases, medical resources and usage, noncommunicable diseases, nutrition, population dynamics, reproductive health, universal health coverage, and water and sanitation.

This dataset presents HNP data by wealth quintile since 1990s to present. It covers more than 70 indicators, including childhood diseases and interventions, nutrition, sexual and reproductive health, mortality, and other determinants of health, for more than 90 low- and middle-income countries. The data sources are Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS).

Non-expenditure health care data provide information on institutions providing health care in countries, on resources used and on output produced in the framework of health care provision. Data on health care form a major element of public health information as they describe the capacities available for different types of health care provision as well as potential 'bottlenecks' observed. The quantity and quality of health care services provided and the work sharing established between the different institutions are a subject of ongoing debate in all countries. Sustainability - continuously providing the necessary monetary and personal resources needed - and meeting the challenges of ageing societies are the primary perspectives used when analysing and using the data. The output-related data ('activities') refer to contacts between patients and the health care system, and to the treatment thereby received. Data are available for hospital discharges of in-patients and day cases, average length of stay of in-patients and medical procedures performed in hospitals. Annual national and regional data are provided in absolute numbers and in population-standardised rates (per 100 000 inhabitants). Wherever applicable, the definitions and classifications of the System of Health Accounts (SHA) are followed, e.g. International Classification for Health Accounts - Providers of health care (ICHA-HP). For hospital discharges, the International Shortlist for Hospital Morbidity Tabulation (ISHMT) is used. Health care data on activities are largely based on administrative data sources in the countries. Therefore, they reflect the country-specific way of organising health care and may not always be completely comparable.

This data set contains information of The statistics yearbook 2017 is compiled and published by Lao statistics Bureau, Ministry of Planning and Investment. Lao statistics Bureau of expressing gratitude provincial statistics Centre, line ministries involved, ministry-equivalent agencies and other parties to contribute to compile statistical data Economic, Social and Natural Resources and Environment cooperation throughout, Lao Statistics Bureau welcome the proposal, comments and feedback to guide the improvement this magazines better. Meanwhile, the Lao Statistics Bureau express thanks to highly among those interested in using information and statistics to Lao Statistics Bureau. TRANSLATE with xEnglishArabicHebrewPolishBulgarianHindiPortugueseCatalanHmong DawRomanianChinese SimplifiedHungarianRussianChinese TraditionalIndonesianSlovakCzechItalianSlovenianDanishJapaneseSpanishDutchKlingonSwedishEnglishKoreanThaiEstonianLatvianTurkishFinnishLithuanianUkrainianFrenchMalayUrduGermanMalteseVietnameseGreekNorwegianWelshHaitian CreolePersian  TRANSLATE with COPY THE URL BELOW BackEMBED THE SNIPPET BELOW IN YOUR SITEEnable collaborative features and customize widget: Bing Webmaster PortalBack

Global Burden of Disease Study 2016 (GBD 2016) Healthcare Access and Quality Index Based on Amenable Mortality 1990–2016. Global Burden of Disease Study 2016 (GBD 2016) estimates were used in an analysis of personal healthcare access and quality for 195 countries and territories, as well as selected subnational locations, over time. This dataset includes the following global, regional, national, and selected subnational estimates for 1990-2016: age-standardized risk-standardized death rates from 24 non-cancer causes considered amenable to healthcare; age-standardized mortality-to-incidence ratios for 8 cancers considered amenable to healthcare; and the Healthcare Access and Quality (HAQ) Index and individual scores for each of the 32 causes on a scale of 0 to 100. Code used to produce the estimates is also included. Results were published in The Lancet in May 2018 in "Measuring performance on the Healthcare Access and Quality Index for 195 countries and territories and selected subnational locations: a systematic analysis from the Global Burden of Disease Study 2016

Eurostat Dataset Id:nrg_esdgr_a Consumption of energy depends strongly on weather conditions. If the temperature decreases below a certain value, "heating threshold", more energy is consumed due to increased need for space heating. Taking this into account, Eurostat launched a project aiming at the development and implementation of a common method for the climatic correction of final energy consumption for space heating purposes in the 27 Member States of the European Union. Temperature corrected energy consumption data help interpretating energy consumption trends.

The data refers to the ratio of patent applications made directly to the European Patent Office (EPO) or via the Patent Cooperation Treaty and designating the EPO (Euro-PCT), in the field of high-technology patents per million inhabitants of a region. The definition of high-technology patents uses specific subclasses of the International Patent Classification (IPC) as defined in the trilateral statistical report of the EPO, JPO and USPTO.

Historical Statistics on Population, GDP and Per Capita GDP for 1-2008 AD period. Copyright Angus Maddison.

Non-expenditure health care data provide information on institutions providing health care in countries, on resources used and on output produced in the framework of health care provision. Data on health care form a major element of public health information as they describe the capacities available for different types of health care provision as well as potential 'bottlenecks' observed. The quantity and quality of health care services provided and the work sharing established between the different institutions are a subject of ongoing debate in all countries. Sustainability - continuously providing the necessary monetary and personal resources needed - and meeting the challenges of ageing societies are the primary perspectives used when analysing and using the data. The output-related data ('activities') refer to contacts between patients and the health care system, and to the treatment thereby received. Data are available for hospital discharges of in-patients and day cases, average length of stay of in-patients and medical procedures performed in hospitals. Annual national and regional data are provided in absolute numbers and in population-standardised rates (per 100 000 inhabitants). Wherever applicable, the definitions and classifications of the System of Health Accounts (SHA) are followed, e.g. International Classification for Health Accounts - Providers of health care (ICHA-HP). For hospital discharges, the International Shortlist for Hospital Morbidity Tabulation (ISHMT) is used. Health care data on activities are largely based on administrative data sources in the countries. Therefore, they reflect the country-specific way of organising health care and may not always be completely comparable.

In December 2022, IHME paused its COVID-19 modeling. IHME has developed projections for total and daily deaths, daily infections and testing, hospital resource use, and social distancing due to COVID-19 for a number of countries. Forecasts at the subnational level are included for select countries. The projections for total deaths, daily deaths, and daily infections and testing each include a reference scenario: Current projection, which assumes social distancing mandates are re-imposed for 6 weeks whenever daily deaths reach 8 per million (0.8 per 100k). They also include two additional scenarios: Mandates easing, which reflects continued easing of social distancing mandates, and mandates are not re-imposed; and Universal Masks, which reflects 95% mask usage in public in every location. Hospital resource use forecasts are based on the Current projection scenario. Social distancing forecasts are based on the Mandates easing scenario. These projections are produced with a model that incorporates data on observed COVID-19 deaths, hospitalizations, and cases, information about social distancing and other protective measures, mobility, and other factors. They include uncertainty intervals and are being updated daily with new data. These forecasts were developed in order to provide hospitals, policy makers, and the public with crucial information about how expected need aligns with existing resources, so that cities and countries can best prepare. Dataset contains Observed and Projected data

The availability of broadband is measured by the percentage of households that are connectable to an exchange that has been converted to support xDSL-technology, to a cable network upgraded for internet traffic, or to other broadband technologies.

The access of households to internet is measured as percentage of households where any member of the household has the possibility to access the internet from home.

Data cited at: https://mauritius.opendataforafrica.org/HDIM2016

Human resources in science and technology (HRST) as a share of the active population in the age group 15-74 at the regional NUTS 2 level. The data shows the active population in the age group 15-74 that is classified as HRST (i.e. having successfully completed an education at the third level or being employed in science and technology) as a percentage of total active population aged 15-74. HRST are measured mainly using the concepts and definitions laid down in the Canberra Manual, OECD, Paris, 1995.

Data cited at: Financial Tracking Service

Eurostat Dataset Id:migr_r_2hu The data comprises series of internal (in the country) regional migration on NUTS2 level during the reference year. Data on arrivals and departures due to internal migration are disaggregated by NUTS2 region of arrival/departure, sex and single year age. Data on migration by region of origin and destination (excluding intra-regional migration) are presented separately for each country for which such data are available. The tables are in the form of matrix (NUTS2 region of origin and destination) excluding intra-refional migration, disaggregated by sex. The data source is administrative records or national surveys. The completeness of the tables depends largely on the availability of data from the relevant national statistical institutes.