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Georgia

  • President:Giorgi Margvelashvili
  • Prime Minister:Mamuka Bakhtadze
  • Capital city:Tbilisi (official/seat of executive government and president), Kutaisi (legislative)
  • Languages:Georgian (official) 87.6%, Azeri 6.2%, Armenian 3.9%, Russian 1.2%, other 1%
  • Government
  • National statistics office
  • Population, persons:3,717,100 (2017)
  • Area, sq km:69,490 (2017)
  • GDP per capita, US$:4,078 (2017)
  • GDP, billion current US$:15.2 (2017)
  • GINI index:36.5 (2016)
  • Ease of Doing Business rank:9 (2017)
All datasets:  3 A B C D E F G H I J K L M N O P Q R S T U V W Y Z
  • 3
    • October 2016
      Source: Philipps-University of Marburg, Empirical Institutional Economics
      Uploaded by: Knoema
      Accessed On: 07 December, 2016
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      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.
  • A
    • April 2017
      Source: North American Electric Reliability Corporation
      Uploaded by: Olga Bikeeva
      Accessed On: 04 July, 2017
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      This Wide-Area Perspective on the August 21, 2017 Total Solar Eclipse document was created in order to evaluate potential reliability consequences of the total solar eclipse on the BPS. This assessment focuses specifically on impacts of system loading and potential reliability implications when an area experiences a large reduction of distributed energy resource (DER) capacity due to a total solar eclipse. Ramping is a specific concern for areas with large amounts of variable generation in their resource mix. The areas that have ramping issues will need further evaluation by the Regions and include areas that are not in the direct path of the eclipse. An example of such an area is California, where the transmission (utility) installed nameplate capacity for solar generation is 11,444 MW. Some areas in Northern California are projected to experience up to 95 percent of the obscuration of the Sun from the August 21, 2017 eclipse.
    • October 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 20 October, 2018
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      Not applicable
    • July 2016
      Source: Knoema
      Uploaded by: Knoema
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      Accuracy of annual economic forecasts of international organisations - European Commission, IMF, OECD, World Bank, UN LINK
    • November 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 27 November, 2018
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      This indicator aims to capture the share of persons in the labour force protected through a contributory pension scheme (with benefits guaranteed but not currently being received). It provides information about the proportion of the labour force that will receive an old age pension once reaching pensionable age. This right to income security in old age is guaranteed by the prior payment of premiums or contributions, i.e. before the occurrence of the insured contingency.
    • September 2014
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 31 August, 2018
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      This indicator aims to capture the share of persons in the labour force protected through a contributory pension scheme (with benefits guaranteed but not currently being received). It provides information about the proportion of the labour force that will receive an old age pension once reaching pensionable age. This right to income security in old age is guaranteed by the prior payment of premiums or contributions, i.e. before the occurrence of the insured contingency.
    • September 2014
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 31 August, 2018
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      This indicator aims to capture the share of persons of working age protected through a contributory pension scheme (with benefits guaranteed but not currently being received). It provides information about the proportion of the working-age population that will receive an old age pension once reaching pensionable age. This right to income security in old age is guaranteed by the prior payment of premiums or contributions, i.e. before the occurrence of the insured contingency.
    • November 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 27 November, 2018
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      This indicator aims to capture the share of persons of working age protected through a contributory pension scheme (with benefits guaranteed but not currently being received). It provides information about the proportion of the working-age population that will receive an old age pension once reaching pensionable age. This right to income security in old age is guaranteed by the prior payment of premiums or contributions, i.e. before the occurrence of the insured contingency.
    • October 2018
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 21 November, 2018
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      Source: UNECE Statistical Database, compiled from national and international (Eurostat, UN Statistics Division Demographic Yearbook, WHO European health for all database and UNICEF TransMONEE) official sources. Definition: Adolescent fertility covers live births to women aged 15-19. A live birth is the complete expulsion or extraction from its mother of a product of conception, irrespective of the duration of pregnancy, which after such separation breathes or shows any other evidence of life such as beating of the heart, pulsation of the umbilical cord or definite movement of voluntary muscles, whether or not the umbilical cord has been cut or the placenta is attached. The adolescent fertility rate is the number of live births to women aged 15-19 per 1000 women aged 15-19. General note: Data on live births come from registers, unless otherwise specified. The adolescent fertility rate is computed by UNECE secretariat. .. - data not available Country: Albania Data refer to age group 0-19. Country: Armenia Data do not cover infants born alive with less than 28 weeks gestation, less than 1000 grams in weight and 35 centimeters in length, who die within seven days of birth. Data refer to age group 0-19. Country: Azerbaijan Data do not cover infants born alive with less than 28 weeks gestation, less than 1000 grams in weight and 35 centimeters in length, who die within seven days of birth. Data refer to age group 0-19. Country: Belarus Data do not cover infants born alive with less than 28 weeks gestation, less than 1000 grams in weight and 35 centimeters in length, who die within seven days of birth. Data refer to age group 0-19. Country: Bosnia and Herzegovina 1995 : data refer to 1996. Country: Canada Data include Canadian residents temporarily in the United States, but exclude United States residents temporarily in Canada. Country: Cyprus Data cover only the area controlled by the Republic of Cyprus. Country: Estonia Data refer to age group 0-19. Country: Finland Data include nationals temporarily outside the country. Country: Georgia Data do not cover infants born alive with less than 28 weeks gestation, less than 1000 grams in weight and 35 centimeters in length, who die within seven days of birth. From 1995 : data do not cover Abkhazia and South Ossetia (Tshinvali). 1980-2003 : data refer to age group 15-20. Country: Germany 1980-1990 : data cover only West Germany (Federal Republic of Germany). From 1995 : data refer to reunified Germany, i.e. include the ex-German Democratic Republic (East Germany). Country: Ireland Data are tabulated by date of registration (rather than occurrence) and refer to births registered within one year of occurrence. 2005-2006 : provisional data. Country: Israel Data cover East Jerusalem and Israeli residents in certain other territories under occupation by Israeli military forces since June 1967. 1980 : data refer to age group 0-19. Country: Kazakhstan Data do not cover infants born alive with less than 28 weeks gestation, less than 1000 grams in weight and 35 centimeters in length, who die within seven days of birth. Data refer to age group 0-19. Country: Kyrgyzstan 1980-2003 : data do not cover infants born alive with less than 28 weeks gestation, less than 1000 grams in weight and 35 centimeters in length, who die within seven days of birth. Country: Latvia Data refer to age group 0-19. Country: Malta Data refer to age group 0-19. Country: Netherlands Data refer to age group 0-19. Country: Norway Age classification is based on year of birth of mother rather than the exact age of mother at birth of child. Country: Poland 1980 : data refer to age group 0-19. Country: Portugal Data refer to resident mothers. Country: Russian Federation Data do not cover infants born alive with less than 28 weeks gestation, less than 1000 grams in weight and 35 centimeters in length, who die within seven days of birth. Data refer to age group 0-19. Country: Serbia Data do not cover Kosovo and Metohija. Data are tabulated by date of registration (rather than occurrence). Country: Turkey 1980-2000: data source is population censuses. From 2001: data are from administrative source. Country: Turkmenistan Data do not cover infants born alive with less than 28 weeks gestation, less than 1000 grams in weight and 35 centimeters in length, who die within seven days of birth. Data refer to age group 0-19. Country: Ukraine Data do not cover infants born alive with less than 28 weeks gestation, less than 1000 grams in weight and 35 centimeters in length, who die within seven days of birth. 2000 : data refer to 1998. 1990 : data refer to age group 0-19. Country: United Kingdom Data are tabulated by date of occurrence for England and Wales and by date of registration for Northern Ireland and Scotland. Country: United States 2000 : data refer to 1999. Country: Uzbekistan Data refer to age group 18-19.
    • February 2016
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 22 November, 2018
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      To view the original national data please open the questionnaires. Source: Joint Forest Europe / UNECE / FAO Questionnaire on Pan-European Indicators for Sustainable Forest Management. Country: Russian Federation The source of the data of Russian Federation is the National Report for the Joint Forest Europe / UNECE / FAO reporting on quantitative pan-European indicators 2011.
    • February 2016
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 22 November, 2018
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      To view the original national data please open the questionnaires. Source: Joint Forest Europe / UNECE / FAO Questionnaire on Pan-European Indicators for Sustainable Forest Management. Country: Russian Federation The source of the data of Russian Federation is the National Report for the Joint Forest Europe / UNECE / FAO reporting on quantitative pan-European indicators 2011.
    • December 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 04 December, 2018
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      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.
    • June 2018
      Source: National Statistics Office of Georgia
      Uploaded by: Knoema
      Accessed On: 12 July, 2018
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    • May 2013
      Source: Food and Agriculture Organization
      Uploaded by: Knoema
      Accessed On: 29 July, 2015
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    • November 2018
      Source: Food and Agriculture Organization
      Uploaded by: Knoema
      Accessed On: 07 December, 2018
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      The data describe the average use of chemical and mineral fertilizers per area of cropland (arable land and permanent crops) at national, regional, and global level in a time series from 2002 to 2014The data describe the average use of chemical and mineral fertilizers per area of cropland (arable land and permanent crops) at national, regional, and global level in a time series from 2002 to 2015
    • November 2018
      Source: Food and Agriculture Organization
      Uploaded by: Knoema
      Accessed On: 07 December, 2018
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      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.
    • February 2018
      Source: Food and Agriculture Organization
      Uploaded by: Knoema
      Accessed On: 08 March, 2018
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      The Livestock Patterns domain of the FAOSTAT Agri-Environmental Indicators contains data on livestock numbers, shares of major livestock species and livestock densities in the agricultural area. Values are calculated using Livestock Units (LSU), which facilitate aggregating information for different livestock types. Data are available by country, with global coverage, for the period 1961–2014. This methodology applies the LSU coefficients reported in the "Guidelines for the preparation of livestock sector reviews" (FAO, 2011). From this publication, LSU coefficients are computed by livestock type and by country. The reference unit used for the calculation of livestock units (=1 LSU) is the grazing equivalent of one adult dairy cow producing 3000 kg of milk annually, fed without additional concentrated foodstuffs. FAOSTAT agri-environmental indicators on livestock patterns closely follow the structure of the indicators in EUROSTAT.
    • May 2013
      Source: Food and Agriculture Organization
      Uploaded by: Knoema
      Accessed On: 04 December, 2018
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    • May 2013
      Source: Food and Agriculture Organization
      Uploaded by: Knoema
      Accessed On: 04 December, 2018
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    • October 2018
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 19 November, 2018
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      commitment is a firm written obligation by a government or official agency, backed by the appropriation or availability of the necessary funds, to provide resources of a specified amount under specified financial terms and conditions and for specified purposes for the benefit of a recipient country or a multilateral agency. Members unable to comply with this definition should explain the definition that they use. -- Commitments are considered to be made at the date a loan or grant agreement is signed or the obligation is otherwise made known to the recipient (e.g. in the case of budgetary allocations to overseas territories, the final vote of the budget should be taken as the date of commitment). For certain special expenditures, e.g. emergency aid, the date of disbursement may be taken as the date of commitment. -- Bilateral commitments comprise new commitments and additions to earlier commitments, excluding any commitments cancelled during the same year. Cancellations and reductions in the year reported on of commitments made in earlier years are reported in the CRS, but not in the DAC questionnaire. -- In contrast to bilateral commitments, commitments of capital subscriptions, grants and loans to multilateral agencies should show the sum of amounts which are expected to be disbursed before the end of the next year and amounts disbursed in the year reported on but not previously reported as a commitment. For capital subscriptions in the form of notes payable at sight, enter the expected amount of deposits of such notes as the amount committed.
    • July 2018
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 06 July, 2018
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      Destination of Official Development Assistance Disbursements. Geographical breakdown by donor, recipient and for some types of aid (e.g. grant, loan, technical co-operation) on a disbursement basis (i.e. actual expenditures). The data cover flows from bilateral and multilateral donors which focus on flows from DAC member countries and the EU Institutions.
    • December 2017
      Source: The General Aviation Manufacturers Association (GAMA)
      Uploaded by: Knoema
      Accessed On: 28 May, 2018
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      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.
    • June 2013
      Source: World Bank
      Uploaded by: Knoema
      Accessed On: 21 November, 2014
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      This dataset includes combined and standardized Gini data from eight original sources: Luxembourg Income Study (LIS), Socio-Economic Database for Latin America (SEDLAC), Survey of Living Conditions (SILC) by Eurostat, World Income Distribution (WYD; the full data set is available here), World Bank Europe and Central Asia dataset, World Institute for Development Research (WIDER), World Bank Povcal, and Ginis from individual long-term inequality studies (just introduced in this version).
    • January 2017
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 21 November, 2018
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      Source: UNECE Statistical Database, compiled from national official sources Definition: An ambassador is a diplomatic official accredited to a foreign sovereign or government, or to an international organisation, to serve as the official representative of his or her own country. In everyday usage it applies to the top ranking government representative stationed in a foreign country. .. - data not available Country: Belarus Including consuls genaral Country: Cyprus Reference period (2008): data refer to 2009 Country: Cyprus Territorial change (2006 onward): Government controlled area only. Country: Finland Reference period (2013): situation in March 3, 2014 Country: Georgia Territorial change (1995 onward): Data do not cover Abkhazia AR and Tskhinvali Region. Country: Iceland Data refers to number at end of year. Country: Kazakhstan 1990: data refer to 1992-1994; 1995: data refer to 1999. Country: Latvia Change in definition (1995 - 2012): Data refer to Ambassadors, Ambassadors-at-large, Consuls General, Vice Consuls. Country: Montenegro 2008: data refer to 2009. Country: Slovakia Reference period (2015): Data refer to October 20, 2015. Data refer to heads of Diplomatic missions of the Slovak Republic (Ambassadors, Charge d?affaires, Consul General etc.) Country: Spain 2013 data correspond to 24 January 2014. 2015 data correspond to 15 July 2015. Country: Switzerland Change in definition (1980 - onwards): Data include only heads of missions, i.e. exclude collaborators with ambassador title.
    • February 2016
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 22 November, 2018
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      To view the original national data please open the questionnaires. Source: Joint Forest Europe / UNECE / FAO Questionnaire on Pan-European Indicators for Sustainable Forest Management. Country: Russian Federation The source of the data of Russian Federation is the National Report for the Joint Forest Europe / UNECE / FAO reporting on quantitative pan-European indicators 2011.
    • December 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 03 December, 2018
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      This indicator conveys the annual growth rates of labour productivity. Labour productivity represents the total volume of output (measured in terms of Gross Domestic Product, GDP) produced per unit of labour (measured in terms of the number of employed persons) during a given time reference period. The indicator allows data users to assess GDP-to-labour input levels and growth rates over time, thus providing general information about the efficiency and quality of human capital in the production process for a given economic and social context, including other complementary inputs and innovations used in production. For further information, see the SDG Indicators Metadata Repository or ILOSTAT’s indicator description.
    • August 2018
      Source: Food and Agriculture Organization
      Uploaded by: Knoema
      Accessed On: 19 November, 2018
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      AQUASTAT is FAO's global information system on water and agriculture, developed by the Land and Water Division. The main mandate of the program is to collect, analyze and disseminate information on water resources, water uses, and agricultural water management with an emphasis on countries in Africa, Asia, Latin America and the Caribbean. This allows interested users to find comprehensive and regularly updated information at global, regional, and national levels.
    • January 2014
      Source: World Resources Institute
      Uploaded by: Knoema
      Accessed On: 07 December, 2015
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      This dataset shows countries and river basins' average exposure to five of Aqueduct's water risk indicators: baseline water stress, interannual variability, seasonal variability, flood occurrence, and drought severity. Risk exposure scores are available for every country (except Greenland and Antarctica), the 100 most populous river basins, and the 100 largest river basins by area. Scores are also available for all industrial, agricultural, and domestic users' average exposure to each indicator in each country and river basin. Citation: Gassert, F., P. Reig, T. Luo, and A. Maddocks. 2013. “Aqueduct country and river basin rankings: a weighted aggregation of spatially distinct hydrological indicators.” Working paper. Washington, DC: World Resources Institute, November 2013. Available online at http://wri.org/publication/aqueduct-country-river-basin-rankings.
    • February 2016
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 21 November, 2018
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      To view the original national data please open the questionnaires. Source: Joint Forest Europe / UNECE / FAO Questionnaire on Pan-European Indicators for Sustainable Forest Management. Country: Russian Federation The source of the data of Russian Federation is the National Report for the Joint Forest Europe / UNECE / FAO reporting on quantitative pan-European indicators 2011.
    • March 2016
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 21 November, 2018
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      To view the original national data please open the questionnaires. Source: Joint Forest Europe / UNECE / FAO Questionnaire on Pan-European Indicators for Sustainable Forest Management. Country: Russian Federation The source of the data of Russian Federation is the National Report for the Joint Forest Europe / UNECE / FAO reporting on quantitative pan-European indicators 2011.
    • March 2018
      Source: Stockholm International Peace Research Institute
      Uploaded by: Knoema
      Accessed On: 14 November, 2018
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      The SIPRI Arms Transfers Database contains information on all transfers of major conventional weapons from 1950 to the most recent full calendar year. It is a unique resource for researchers, policy-makers and analysts, the media and civil society interested in monitoring and measuring the international flow of major conventional arms. For more information, see http://www.sipri.org/databases/armstransfers/sources-and-methods/
    • October 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 19 November, 2018
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      Average Electricity, Gas and Spark Spread prices for industrial consumers - bi-annual data (from 2007 onwards) - Data upto 2018 H1   Note: The Average value for Electricity, Gas and Spark Spread Prices are calculated from below datasets. "Spark Spread" = Avg.Electricity (-) Avg.Gas Electricity: https://knoema.com/nrg_pc_205-20160316/ Gas: https://knoema.com/nrg_pc_203/
    • July 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 17 July, 2018
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      This table presents data on average monthly earnings converted to a common currency. Data in U.S. dollars are converted from local currency using exchange rates, while data in constant 2011 U.S. dollars are converted using 2011 purchasing power parities (PPPs)   Dataset splitted into below datasets:-   Local Currency (Total) - https://knoema.com/EAR_TEAR_NOC_NB   Local Currency (Men) - https://knoema.com/EAR_MEAR_NOC_NB   Local Currency (Women) - https://knoema.com/EAR_FEAR_NOC_NB   Constant 2011 PPP $ (Total) - https://knoema.com/EAR_4MPT_NOC_NB   Constant 2011 PPP $ (Men) - https://knoema.com/EAR_4MPM_NOC_NB   Constant 2011 PPP $ (Women) - https://knoema.com/EAR_4MPW_NOC_NB
    • November 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 27 November, 2018
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    • November 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 27 November, 2018
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      The concept of earnings, as applied in wages statistics, relates to gross remuneration in cash and in kind paid to employees, as a rule at regular intervals, for time worked or work done together with remuneration for time not worked, such as annual vacation, other type of paid leave or holidays. This indicator is presented in terms of the average monthly earnings per employee, in local currency.
    • November 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 27 November, 2018
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    • November 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 27 November, 2018
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      The concept of earnings, as applied in wages statistics, relates to gross remuneration in cash and in kind paid to employees, as a rule at regular intervals, for time worked or work done together with remuneration for time not worked, such as annual vacation, other type of paid leave or holidays. This indicator is presented in terms of the average monthly earnings per employee, in local currency, for men.
    • November 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 27 November, 2018
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    • November 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 27 November, 2018
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      The concept of earnings, as applied in wages statistics, relates to gross remuneration in cash and in kind paid to employees, as a rule at regular intervals, for time worked or work done together with remuneration for time not worked, such as annual vacation, other type of paid leave or holidays. This indicator is presented in terms of the average monthly earnings per employee, in local currency, for women.
  • B
    • November 2018
      Source: Information & eGovernment Authority of Bahrain
      Uploaded by: Knoema
      Accessed On: 26 November, 2018
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    • November 2018
      Source: International Monetary Fund
      Uploaded by: Knoema
      Accessed On: 12 December, 2018
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      BOPSY Global Tables aggregate country data by major balance of payments components and by international investment position (IIP) data for (i) Net IIP and (ii) Total Assets and Total Liabilities. Data for countries, country groups, and the world are provided. In addition to data reported by countries as shown in BOPSY, balance of payments data are provided for international organizations in BOPSY Global Tables. The BOPSY Global Tables include, in addition to reported data, data derived in a few instances indirectly from published sources.
    • January 2018
      Source: Bertelsmann Stiftung
      Uploaded by: Knoema
      Accessed On: 19 April, 2018
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      The Bertelsmann Stiftung’s Transformation Index (BTI) analyzes and evaluates the quality of democracy, a market economy and political management in 128 developing and transition countries. It measures successes and setbacks on the path toward a democracy based on the rule of law and a market economy flanked by sociopolitical safeguards. Within this framework, the BTI publishes two rankings, the Status Index and the Management Index. Countries are further categorized on the basis of these status index and management rankings/scores. For instance, countries are categorized in to 5 groups – viz; 5 or failed, 4 or very limited, 3 or limited, 2 or advanced, and 1 or highly advanced—based on their status index score of 1 to 10. A country with a high score, 8.5 and above, is categorized as highly advanced. A country with a low score, below 4, is categorized as failed. A country is categorized as ‘very limited’ if it has a status index score between 4 and 5.5. A score between 5.5 and 7 means the country is categorized as ‘limited’ and a country is categorized as ‘advanced’ for a score between 7.1 and 8.5. On the basis of the democratic status ranking, countries are further categorized as 5 or ‘hard - line autocracies,’ 4 or ‘moderate autocracies,’ 3 or ‘highly defective democracies,’ 2 or ‘defective democracies,’ and 1 or ‘democracies in consolidation.’ A country with a democratic status ranking below 4 is categorized as a hard line autocracy. A democratic status score between 4 and 5 means that the country is part of the ‘moderate autocracy’ group. A country is grouped as a ‘highly defective democracy’ for a score between 5 and 6. A country is recognized as a ‘defective democracy’ for a score between 6 and 8, and a score of 8 and above earns a country the status of a ‘democracy in consolidation.’ Countries are also categorized in to 5 groups based on their market economy status ranking. The countries are categorized as ‘rudimentary’ or group 5, ‘poorly functioning’ or group 4, ‘functional flaws’ or group 3, ‘functioning’ or group 2, and ‘developed’ or group 1. A country is recognized as a member of the ‘developed’ group with a market economy status ranking/score of 8 and above. A country is grouped as ‘functioning’ if it has a score between 7 and 8. A market economy status ranking between 5 and 7 means the country is categorized to group 3 or the ‘functional flaws’ group. A score between 3 and 5 means that the country is ‘poorly functioning’ and a score below 3 means the country enjoys a ‘rudimentary’ status. Based on the management index ranking, countries are categorized as 5 or failed, 4 or weak, 3 or moderate, 2 or good, and1 or very good. A country is categorized as ‘very good’ for a score of 7 and above. It is categorized as ‘good’ for a score between 5.6 and 7, and as ‘moderate’ for a score between 4.4 and 5.5. A score between 3 and 4.3 means a country is categorized as ‘weak,’ and a score below 3 means the categorization of a country as ‘failed.’ Countries are ranked between 1 and 10 on the basis of the level of difficulty they face. The level of difficulty is further categorized as 5 or negligible, 4 or minor, 3 or moderate, 2 or substantial, and 1 or massive. A score of 8.5 and above means the categorization of the country’s level of difficulty as ‘massive, and a score below 2.5 means the categorization of the level of difficulty faced by the country as ‘negligible.’ The level of difficulty score of 2.5 to 4.4 means a country faces a ‘minor’ level of difficulty and a score between 4.5 and 6.4 means the level of difficulty faced by a country is ‘moderate.’ A country with a score of 6.5 to 8.4 faces a ‘substantial’ level of difficulty.
    • January 2018
      Source: National Statistics Bureau, Bhutan
      Uploaded by: Knoema
      Accessed On: 29 January, 2018
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    • April 2017
      Source: Aliance for Biking & Walking
      Uploaded by: Knoema
      Accessed On: 20 April, 2017
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      In conjunction with the Centers for Disease Control and Prevention's Healthy Community Design Initiative, the Alliance publishes the biennial Benchmarking Report to collect and analyze data on bicycling and walking in all 50 states, the 52 largest U.S. cities, and a select number of midsized cities. The Report combines original research with over 20 government data sources to compile data on bicycling and walking levels and demographics, safety, funding, policies, infrastructure, education, public health indicators, and economic impacts. It's an essential go-to resource for public officials, advocates, decision makers, and researchers.
    • April 2014
      Source: United Nations Conference on Trade and Development
      Uploaded by: Knoema
      Accessed On: 08 February, 2016
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      UNCTAD's Bilateral FDI Statistics provides up-to-date and systematic FDI data for 206 economies around the world, covering inflows (table 1), outflows (table 2), inward stock (table 3) and outward stock (table 4) by region and economy. Data are in principle collected from national sources. In order to cover the entire world, where data are not available from national sources, data from partner countries (mirror data) as well as from other international organizations have also been used.
    • April 2018
      Source: World Bank
      Uploaded by: Knoema
      Accessed On: 14 November, 2018
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      This data set provides a snapshot of migration and remittances for all countries, regions and income groups of the world, compiled from available data from various sources
    • November 2018
      Source: United Nations COMTRADE
      Uploaded by: Knoema
      Accessed On: 30 November, 2018
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      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).
    • October 2018
      Source: Bank for International Settlements-BIS
      Uploaded by: Knoema
      Accessed On: 24 October, 2018
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      The consolidated banking statistics (CBS) measure international banking activity from a nationality perspective, focusing on the country where the banking group's parent is headquartered. While residence-based data such as the locational banking statistics indicate where positions are booked, they do not always identify where underlying decisions are made. This is because banking offices in one country may operate within a business model decided by the group's controlling parent, which may be headquartered in another country. The CBS capture the worldwide claims of banking groups based in reporting countries and exclude intragroup positions, similar to the consolidation approach followed by banking supervisors. The CBS provide several different measures of banking groups' country risk exposures, on either an immediate counterparty or an ultimate risk basis. The most appropriate exposure measure depends on the issue being analysed. The benchmark measure in the CBS is foreign claims, which capture credit to borrowers outside a banking group's home country.   Measure for all Combinations - Amounts Outstanding / Stocks   Note: Under "Reporting country" they have removed "Euro Area".  
    • September 2018
      Source: Bank for International Settlements-BIS
      Uploaded by: Knoema
      Accessed On: 16 October, 2018
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      Below Parameters are common for all combinations : Frequency - Quarterly Measure -Amounts Outstanding / Stocks CBS Bank Type - Domestic Banks CBS Reporting Basis - Immediate Counterparty Basis Balance Sheet Position - Total Claims Type of Instruments - All Instruments Remaining Maturity - All Maturities Currency Type of Booking Location - All Currencies Counterparty Sector - All Sectors
    • April 2017
      Source: Bloom Consulting
      Uploaded by: Knoema
      Accessed On: 24 May, 2017
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      Bloom Consulting was founded in 2003 as a Nation Branding consultancy. Our Headquarters are located in Madrid, with offices in Lisbon and São Paulo. Bloom Consulting has been interviewed by The Economist, Forbes and CNN . According to Country Branding Central www.countrybrandingwiki.org, our CEO José Filipe Torres, a recurrent lecturer in Universities such as Harvard, is considered one of the top 3 international experts in the field of Nation Branding, Region and City Branding, providing advisory for the OECD. In addition, Bloom Consulting publishes the Bloom Consulting Country Brand Ranking © annually for both Trade and Tourism, to extensively analyze the brand performance of 193 countries and territories worldwide and the Digital Country Index - Measuring the Brand appeal of countries and territories in the Digital World.
    • August 2018
      Source: U.S. Centers for Disease Control and Prevention
      Uploaded by: Knoema
      Accessed On: 27 August, 2018
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      Percentages are weighted to population characteristics. Data are not available if it did not meet BRFSS stability requirements.For more information on these requirements, as well as risk factors and calculated variables, see the Technical Documents and Survey Data for a specific year - http://www.cdc.gov/brfss/annual_data/annual_data.htm.Recommended citation: Centers for Disease Control and Prevention (CDC). Behavioral Risk Factor Surveillance System. Atlanta, Georgia: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, [appropriate year].
    • August 2018
      Source: U.S. Centers for Disease Control and Prevention
      Uploaded by: Knoema
      Accessed On: 27 August, 2018
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      Percentages are weighted to population characteristics. Data are not available if it did not meet BRFSS stability requirements.For more information on these requirements, as well as risk factors and calculated variables, see the Technical Documents and Survey Data for a specific year - http://www.cdc.gov/brfss/annual_data/annual_data.htm.Recommended citation: Centers for Disease Control and Prevention (CDC). Behavioral Risk Factor Surveillance System. Atlanta, Georgia: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, [appropriate year].
    • August 2018
      Source: U.S. Centers for Disease Control and Prevention
      Uploaded by: Knoema
      Accessed On: 27 August, 2018
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      The 2011 BRFSS data reflects a change in weighting methodology (raking) and the addition of cell phone only respondents. Shifts in observed prevalence from 2010 to 2011 for BRFSS measures will likely reflect the new methods of measuring risk factors, rather than true trends in risk-factor prevalence. A break in trend lines after 2010 is used to reflect this change in methodolgy. Percentages are weighted to population characteristics. Data are not available if it did not meet BRFSS stability requirements.For more information on these requirements, as well as risk factors and calculated variables, see the Technical Documents and Survey Data for a specific year - http://www.cdc.gov/brfss/annual_data/annual_data.htm.Recommended citation: Centers for Disease Control and Prevention (CDC). Behavioral Risk Factor Surveillance System. Atlanta, Georgia: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, [appropriate year].
    • September 2018
      Source: World Bank
      Uploaded by: Knoema
      Accessed On: 26 October, 2018
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      The World Bank Jobs Statistics Over 150 indicators on labor-related topics, covering over 200 economies from 1990 to present.
    • February 2012
      Source: Federal State Statistics Service, Russia
      Uploaded by: Knoema
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      Внешняя торговля товарами Российской Федерации по странам партнерам, 1995-2011
  • C
    • October 2017
      Source: World Resources Institute
      Uploaded by: Knoema
      Accessed On: 06 August, 2018
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      Data Citation: CAIT Climate Data Explorer. 2017. Washington, DC: World Resources Institute. Available online at: http://cait.wri.org   CAIT data carries a Creative Commons Attribution-NonCommercial 4.0 International license   CAIT Historic allows for easy access, analysis and visualization of the latest available international greenhouse gas emissions data. It includes information for 186 countries, 50 U.S. states, 6 gases, multiple economic sectors, and 160 years - carbon dioxide emissions for 1850-2012 and multi-sector greenhouse gas emission for 1990-2012.
    • December 2018
      Source: Government of Canada
      Uploaded by: Knoema
      Accessed On: 12 December, 2018
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      This dataset is updated with data obtained from Statistics Canada and the U.S. Census Bureau. Current data June 2018. 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
    • December 2018
      Source: Statistics Canada
      Uploaded by: Knoema
      Accessed On: 12 December, 2018
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      For the location "Puerto Rico" data is available from 1990.
    • May 2018
      Source: China Association of Automobile Manufacturers
      Uploaded by: Shakthi Krishnan
      Accessed On: 13 September, 2018
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      World: Car Sales by Country 2017
    • March 2016
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 21 November, 2018
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      To view the original national data please open the questionnaires. Source: Joint Forest Europe / UNECE / FAO Questionnaire on Pan-European Indicators for Sustainable Forest Management. Country: Russian Federation The source of the data of Russian Federation is the National Report for the Joint Forest Europe / UNECE / FAO reporting on quantitative pan-European indicators 2011.
    • September 2018
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 21 November, 2018
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      Source: UNECE Transport Division Database. Definitions:National rail transport : Rail transport between two places (a place of loading/embarkment and a place of unloading/disembarkment) located in the same country irrespective of the country in which the railway vehicles were registered. It may involve transit through a second country. International rail transport : Rail transport between two places (a place of loading/embarkment and a place of unloading/disembarkment) in two different countries. It may involve transit through one or more additional countries. Goods carried by rail : Any goods moved by rail vehicles. This includes all packaging and equipment, such as containers, swap-bodies or pallets as well as road goods vehicles carried by rail. Tonne-kilometre by rail : Unit of measure of goods transport which represents the transport of one tonne of goods by rail over a distance of one kilometre. Goods loaded : Goods placed on a rail vehicle and dispatched by rail. Unlike in road and inland waterway transport, transshipments from one rail vehicle to another and change of tractive vehicle are not regarded as loading after unloading. Goods unloaded : Goods taken off a rail vehicle after transport by rail. Unlike in road and inland waterway transport, transshipments from one rail vehicle to another and change of tractive vehicle are not regarded as unloading before reloading. International - loaded Goods having left the country by rail (other than goods in transit by rail throughout) : Goods loaded on a reporting railway network and transported by rail to be unloaded in a foreign country. Wagons loaded on a railway network and carried by ferry to a foreign network are included. International - unloaded Goods having entered the country by rail (other than goods in transit by rail throughout) : Goods loaded on a foreign railway network and transported by rail on the reporting railway network for unloading in the country of this reporting network. Wagons loaded on a foreign railway network and carried by ferry to the reporting network are included. Goods in transit by rail throughout : Goods loaded on a foreign railway network for a destination on a foreign railway network which are transported on the reporting railway network. Wagons entering and/or leaving the reporting network by ferry are included. Please note that country footnotes are not always in alphabetical order. .. - data not available Country: Croatia Until 2012 international transport includes goods partly transported by railway and partly by another mode of transport. Since 2013 this kind of goods have been included in national transport. Country: Estonia ''Goods in transit by rail'' includes transition between rail and maritime transport in ports. Country: Slovenia Prior to 2004 data are based on transport of goods as to origin and destination. From 2004 on data are based on journeys, which means that the transport of goods is observed as to the place of loading and the place of unloading to/from a rail vehicle Country: Spain Refers to Renfe and ADIF only Country: Sweden ''Locomotives'' includes railcars. Country: United States Includes only Class I freight railroads.
    • November 2018
      Source: Institute for Health Metrics and Evaluation
      Uploaded by: Knoema
      Accessed On: 05 December, 2018
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      The Global Burden of Disease Study 2017 (GBD 2017), 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.
    • November 2016
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 21 November, 2018
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      Source: UNECE Statistical Database, compiled from national official sources. Definition:The Central Bank is the institution which is charged with regulating the amount of the money supply in a country, the availability and cost of credit, and the foreign exchange value of its currency. The boards of Central Banks are the decision making bodies. General note: Data on any fixed date of the year. .. - data not available Country: Bosnia and Herzegovina Data refer to: Governor and members of Governing Board. Country: Croatia Additional information (2013): Since 2013, Central Bank has 8 (instead of previously 14) board members. Country: Cyprus Reference period (2011): data refer to 2012. Country: Cyprus Government controlled area only. Country: Czechia Reference period (2008): Data refer to June - July. Country: Georgia Territorial change (2000 onward): Data do not cover Abkhazia AR and Tskhinvali Region. Country: Germany Additional information (1990): The structure of the Deutsche Bundesbank and the maximum number of members of the decision making body was reorganized in 1992. Country: Germany Additional information (2002): The structure of the Deutsche Bundesbank and the maximum number of members of the decision making body was reorganized in 2002. Country: Hungary Change in definition (1995 onward): Data refer to President and deputy presidents. Country: Iceland Change in definition (1980 onward): Data refer to Board of governors. Country: Kazakhstan 1990: data refer to 1993. Country: Latvia Additional information (1995 - 2013): The Bank of Latvia is administered by the Council of the Bank and the Board of the Bank. Country: Latvia Change in definition (1995 - 2013): Data refer to the Council of the Bank. Country: Portugal Banco de Portugal is included. Country: Slovakia 2015 data refer to 20 November 2015. Country: Sweden Change in nomenclature from ISCO-88 to ISCO-08 between 2013 and 2014. Country: Switzerland Reference period: as of 1st January
    • November 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 23 November, 2018
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      These metadata refer to the annual population data under Population / Demography domain in Eurostat's Dissemination data tree. Eurostat carries on annual demography data collections with the aim of collecting from the National Statistical Institutes detailed data on population, vital events, marriages and divorces. These data are validated, processed and disseminated. Further on, Eurostat uses the collected detailed data to compute and disseminate demographic indicators at country level, at regional level and at EU level, by applying harmonized methods of calculation. The demography data collections are done on voluntary basis and the completeness of information depends on the availability of data reported by the National Statistical Institutes. The first demography data collection of each year, named Rapid, is carried out in April-May (deadline 15 May). Within this data collection the first results on the main demographic developments in the previous year (T-1) and the population on 1st January of the current year (T) are collected from the National Statistical Institutes. A second annual data collection, Joint Demography data collection, is carried out in cooperation with United Nation Statistical Division (UNSD) in the summer of each year, having the deadline 15 September. Within this data collection Eurostat collects from the National Statistical Institutes detailed data on the demographic events (births, deaths, marriages and divorces) of the previous year (T-1) and the population on 1st January of the current year (T), broken down by sex, age and other characteristics. The Nowcast Demography data collection is carried out in October-November (deadline 15 November). The monthly time series on births, deaths, immigrants and emigrants available from the beginning of current year (T) are collected, with the purpose of producing by the end of the current year (T) a forecast on 1st January population of the following year (T+1). The Regional Demography data collection is carried out in November-December (deadline 15 December). It is based on the regional breakdown of the countries agreed at EU level using the latest version of the Nomenclature of Territorial Units for Statistics (NUTS) and of the Statistical regions for the EFTA and Candidate countries. Within this data collection Eurostat collects from the National Statistical Institutes data by NUTS level 1, 2 and 3 for the vital events taking place in the previous year (T-1) and the population figures on 1st January of the current year (T). Any updates sent by the National Statistical Institutes in-between data collections are validated, processed and disseminated in Eurostat's online database as soon as possible. The European aggregates and the demographic indicators are updated accordingly. Please note:The tables presenting population on 1 January figures by various breakdowns may display variations in the total population for some countries at a given moment in time. This may occur due to one of the following reasons: - The timing of the transmission to Eurostat of the population data for various breakdown may lead to different population on 1 January figures displayed in different population tables at a given moment in time. - The transmission to Eurostat of the post-census population revisions (following the 2011 population Censuses) is expected to be done by the national statistical offices gradually for the population breakdowns. The time series of populations between the previous census taking place in the country and 2011 will be revised by end 2013 by some of the countries, taking into account Eurostat’s recommendation. The following countries have transmitted to Eurostat post-2011 Census population revisions, broken down by age and sex, by autumn of 2013, which are reflected in the tables ‘Demographic balance and crude rates (demo_gind)’, ‘Population on 1 January by age and sex (demo_pjan)’, ‘Population on 1 January by five years age groups and sex (demo_pjangroup)’ and ‘Population on 1 January by broad age group and sex (demo_pjanbroad)’: BG 2007-2011; CZ 2001-2011; EE 2000-2011; IE 2007-2011; EL 2011; ES 2002-2011; HR 2001-2011; CY 2003-2011; LV 2001-2011; LT 2001-2011; MT 2006-2011; AT 2008-2011; PT 1992-2011; RO 2002-2011; SK 2002-2011; UK 2002-2011 (not including post-2011 Census data for Scotland); ME 2010-2011; RS 2011. As regards the the population data for the year 2012 and after, for most of the countries these take into account the results of the latest population census (held in 2011). IT 2012-2013 and DE 2012-2013 reported only the total post-2011 Census populations which are published in the table ‘Demographic balance and crude rates (demo_gind)’. The breakdown by age and sex will follow later on. - The succession of the annual demography data collections described above, which collect and update population breakdowns at different moment during the calendar year. - The calendar of the national statistical offices for producing and releasing population broken down by various topics, respectively the timings when data are transmitted to Eurostat. The most updated data on total population on 1st January and on the total number of live births and deaths may be found in the table 'Demographic balance and crude rates (demo_gind)' of the online 'Database by theme'. This table includes the latest updates (or revised data) on total population, births and deaths reported by the countries, while the detailed breakdowns by various characteristics included in the rest of the tables of the Demography domain (and also for Population by citizenship and by country of birth) may be transmitted to Eurostat at a subsequent date.
    • January 2017
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 21 November, 2018
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      Source: UNECE Statistical Database, compiled from national and international (UNICEF TransMONEE) official sources. Definition: Child-care refers to formal child-care arrangements, public or private, such as group care in child-care centres (creche) or registered childminders based in their own homes looking after two or more children. Child-care refers to children at youngest age (typically children aged under 3); pre-primary schools are excluded. Enrolment in child-care centres: Number of children aged under 3 enrolled in child-care centres per 100 children of the same age group. Data normally refer to beginning of the school-year. Availability of places in child-care centres: Ratio of the number of places available for children aged under 3 in child-care centres per 100 children of the same age group. Data refer to beginning of the school-year. General note: depending on the organization of education and child-care centers in countries, data may be available for age groups different from under 3 years. Such differences and other deviations from the above definitions are specified in country notes. .. - data not available Country: Austria Change in definition (1995 - 2012): Data include centre-based institutions and exclude home-based arrangements. Country: Austria Reference period (1995 - 2012): Age calculation as of 31 August, the beginning of school year. Country: Belgium Change in definition (1990 - 2012): Data refer to children aged 0-2.5 years Country: Belgium Reference period (2008 - 2009): Data refer to children enrolled on October 2008 Country: Belgium Territorial change (1990 - 2012): Data cover only the French community of Belgium Country: Bulgaria Reference period (1980 - 2012): Data refer to end of calendar year. E.g. 1980-1981 refers to 31.12.1980. Country: Croatia Additional information (2011 - 2012): Census 2011 data are used for children of the corresponding age. Country: Croatia Data refers to children aged 6 months to 2 years. Country: Cyprus Data refer to the Government controlled area only. Country: Cyprus Data only include enrolmemts in child care centres, exclude child care provided by registered childminders. Country: Denmark Reference period (2004): As of 2004, reference month changed from March to September. Country: Estonia Change in definition (1995 - 2007): Data refer to children aged 1?2 years. Country: Estonia Change in definition (2008 onward): Data refer to children aged 0-2 years. Country: Estonia Reference period (1995 - 2008): Data refer to middle of the school year, i.e. end of calendar year. Country: Estonia Reference period (2009 onward): Data refer to beginning of school year. Country: Finland Change in definition (2000 - 2012): Data refer to end of calendar year. Country: Finland The data include full- and part-time care in day care centres and families Country: France Data cover only Metropolitan France. Child care refers to child care centers and registered childminders based in their own homes. The data exclude pre-primary school, kindergartens, unregistered childminders and childminders working at home. Available places are here counted regardless of the age of the children actually using them : all of them are theorically available for 0-2 years old but some of them are in practice used for children aged 3 or more. Country: Georgia Change in definition (2008 - 2009): Data cover only child care organizations and refer to december. Country: Georgia Territorial change (2000 onward): Data do not cover Abkhazia AR and Tskhinvali Region. Country: Germany Break in methodology (1990): Average calculated for Germany Country: Germany Reference period (1990): Data refer to 21.12.1991. Country: Germany Reference period (1995): Data refer to 1994. Country: Germany Reference period (2000): Data on places refer to 31.12.1998. Country: Germany Children in day care are included starting with reference year 2012/2013 according to definition of ISCED Level 010 in ISCED 2011. Country: Hungary Change in definition (1990 - 2007): Data for available places refer to all children enrolled including children aged 3+ years. Data referred only to nurseries, from 2008 day care and child minding are also included. Country: Hungary Reference period (1990 onward): Data refer to 31 May of each year Country: Iceland Change in definition (1990 - 2012): Data refer to children aged 0-2 years in formal child-care arrangements and with registered private child-minders. Country: Israel Data are from registers. Country: Italy Change in definition (1980 - 2003): Data refer to formal child-care arrangements in public centres. Country: Italy Change in definition (2004 - 2012): Data refer to formal child-care arrangements, public or private. Country: Kazakhstan Change in definition (2001 - 2012): Data refer to children aged 0-2 years enrolled in permanent pre-primary organizations functioning at least 10 months per year. Data do not cover other types of existing organizations such as seasonal kindergartens etc. Country: Kyrgyzstan Reference period (1990 - 2012): Data refer to the end of the year. Country: Lithuania Data refer to children aged 1-2 years. Data refer to end of calendar year Country: Moldova, Republic of Data exclude the territory of the Transnistria and municipality of Bender. Data for indicator ''Places available in child-care centres per 100 children'' refers to 0-6 group of age. Country: Montenegro Change in definition (2000 - 2012): Data refer to children aged 0-2 years enrolled in pre-primary public organizations. Country: Netherlands Data refer to children aged 0-4 years Country: Netherlands 1995-1996 data refer to 1996. 2000-2001 data refer to 2000, 2002-2003 data refer to 2002 etc. Country: Norway Data refer to end of calendar year. i.e. 2000/2001 data refer to December 2000. Country: Poland From 2000 onwards, data concern health care facility: nurseries and nursery wards of nursery schools. Since 2011, the data also apply to children’s club which are a new form of childcare. Country: Poland Reference period (from 2000 onwards): The data in the two-year period refers to the end of the calendar year mentioned in the range as earlier Country: Portugal Data refer to calendar year Country: Portugal Data cover mainland only. Country: Romania Break in methodlogy (2002): From 2002, reference population is the resident population Country: Romania Break in methodology (2010): data refer to formal child-care in public and private sector. Starting 2010 data refer to children aged 0 to less than 3 years. The reference population is the population aged 0-2 years. However in enrolled population also includes children aged 3 years and over. From 2014 data compiled according to ISCED 2011. Country: Romania Change in definition (1990 - 2012): Data refer to formal child-care in public and private sector. Country: Romania Reference period (1990 - 2012): Data refer to calendar year. i.e. data for 2009-2010 refer to 2009. Country: Romania Reference period (2010): Data refer to calendar year. i.e. data for 2009-2010 refer to 2009. Data refer to calendar year. i.e. data for 2010-2011 refer to 2010. Country: Russian Federation Reference period (2000 - 2012): Data are given at the end of the year. Country: Serbia Territorial change (2000 - 2012): The Statistical Office of the Republic of Serbia has no available data on the AP Kosovo and Metohija. Country: Sweden Change in definition (1980 - onwards): Data refer to children aged 1-2 years due to longer parental leave which allows most children aged 0-1 years to be with their parents. Country: Sweden Reference period (2000): Before 2000/2001: data as of 31 December. From 2001: data as of 15 December. Country: Switzerland Data refer to children from 0 to less than 4 years. Country: Tajikistan Change in definition (2000 - 2012): Data refer to children aged 0-3 years. Country: Tajikistan Reference period (2006 - 2007): Data refer to end of calendar year Country: Ukraine Reference period (1990 - 2014): Data refer to calendar year. For all years, data refer to children aged 0-2. Country: United Kingdom Change in definition (2010 - 2012): Childcare includes: Day nursery, Playgroup or Preschool, and Childminders. Childminders look after at least one child for more than 2 hours in any day Country: United Kingdom Reference period (2010 - 2012): Figures do not relate to the beginning of the school year but to a term-time reference week. The Survey is not carried out at the same point each year Country: United Kingdom Territorial change (2010 - 2012): Figures relate to England only and not the whole of the UK Country: United States Change in definition (1995 - 2012): Data refer to civilian, non-institutionalized population. Data refer to children enrolled in an organized care facility which includes day care centers, nursery, preschools, Federal Head Start programs, and kindergarten, grade school. Country: United States Reference period (2000): Data refer to 1999.
    • October 2014
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 22 November, 2018
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      General note on the UNECE MDG Database: The database aims to show the official national estimates of MDG-indicators used for monitoring progress towards the Millennium Development Goals. Data is shown alongside official international estimates of MDG-indicators (as published on the official United Nations site for the MDG Indicators: http://unstats.un.org/unsd/mdg). Besides the international MDG-indicators, other indicators and disaggregates that are relevant for the UNECE-region are included. At present, the tables include data from the latest official MDG-report of each country. Currently, data from official dedicated MDG-websites and previous official national MDG-reports are being added. Additionally, more detailed metadata is being added to the footnotes. Additional indicators might be added if they are used generally across the region. Please note that some indicators are also available in the Gender Statistics Database of UNECE. Figures might differ due to the use of different sources. Definition of the indicators: Explanations on the indicators are listed below. Deviations from the standard definitions provided here are specified in the country-specific footnotes. Indicator Under five mortality rate per 1,000 live births Definition: The under-five mortality rate (U5MR) is the probability of a child born in a specified year dying before reaching the age of five if subject to current age-specific mortality rates. Infant mortality rate (0-1 year) per 1,000 live births Definition: The infant mortality rate (IMR) is the probability of a child born in a specified year dying before reaching the age of one, if subject to current age-specific mortality rates. Children 1 year old immunized against measles, (%) Definition: The proportion of 1 year-old children immunized against measles is the percentage of children under one year of age who have received at least one dose of measles-containing vaccine. Breast-fed under 6 months (%) Definition: Number of children under the age of 6 months that are breast-fed as a percentage of all children under the age of 6 months. Perinatal mortality rate Definition: Number of stillbirths (or fetal deaths) and deaths in the first week of life (or early neonatal deaths) per 1,000 total births (live and still births). The perinatal period commences at 22 completed weeks (154 days) of gestation and ends seven. This indicator is not monitored in The official United Nations site for the MDG Indicators. Indicator: Under five mortality rate per 1,000 live births , Country: Albania National Series Reference: 1990 to 1993: MDG Report 2002; 1994 to 1999: MDG Report 2004; 2000: MDG Progress Report 2010; 2001: MDG Report 2004; 2002 to 2009: MDG Progress Report 2010; Definition: 1994 to 1999: Per 1,000 children under the age of five; 2001: Per 1,000 children under the age of five; Note: 2000: NSO: 18.1; Source in Reference: 1990 to 1993: IPH; 1994 to 2001: NSO; 2002 to 2008: Min. of Health; 2009: NSO; Primary Source in Reference: 2000: DHS 2000; 2002 to 2008: Administrative data; 2009: DHS 2008-2009; Indicator: Infant mortality rate (0-1 year) per 1,000 live births , Country: Albania National Series Reference: 1990 to 1993: MDG Report 2002; 1994 to 1999: MDG Report 2004; 2000: MDG Progress Report 2010; 2001: MDG Report 2004; 2002 to 2009: MDG Progress Report 2010; Note: 2000: NSO: 16.0; Source in Reference: 1990 to 1993: IPH; 1994 to 2001: NSO; 2002 to 2008: Min. of Health; 2009: NSO; Primary Source in Reference: 2000: DHS 2000; 2002 to 2008: Administrative data; 2009: DHS 2008-2009; Indicator: Children 1 year old immunized against measles, (%) , Country: Albania National Series Reference: 1991 to 2000: MDG Report 2002; 2001: MDG Report 2004; 2002 to 2009: MDG Progress Report 2010; Source in Reference: 1991 to 2000: IPH; 2001: NSO; 2002 to 2009: Min. of Health; Primary Source in Reference: 2002 to 2009: Administrative data; Indicator: Under five mortality rate per 1,000 live births , Country: Armenia National Series Reference: 1990: MDG Progress Report 2005-2009; 1996: ArmeniaInfo at: http://www.armdevinfo.am/ (accessed: 15 June 2011); 1998 to 1999: MDG Progress Report 2005-2009; 2000 to 2009: ArmeniaInfo at: http://www.armdevinfo.am/ (accessed: 15 June 2011); 2010: ArmeniaInfo (http://www.armdevinfo.am/) 2012-05-12; 2011 to 2012: Armenia MDGs Indicators (http://www.armstat.am/) 06/02/2014; Definition: 2010: Per 1,000 children under the age of five; Note: 2001 to 2005: DHS 2005: 30 (2001-2005); 2010: DHS 2010: 16; Reference period: 1998: 1996-2000; Source in Reference: 1996: Min. of Justice; 1998: NSO; 2000 to 2010: Min. of Justice; 2011 to 2012: NSO; Primary Source in Reference: 1990: Administrative data; 1998: DHS 2000; 1999: Administrative data; Indicator: Infant mortality rate (0-1 year) per 1,000 live births , Country: Armenia National Series Reference: 1988 to 1990: MDG Progress Report 2005-2009; 1996: ArmeniaInfo at: http://www.armdevinfo.am/ (accessed: 15 June 2011); 1998 to 1999: MDG Progress Report 2005-2009; 2000 to 2009: ArmeniaInfo at: http://www.armdevinfo.am/ (accessed: 15 June 2011); 2010: ArmeniaInfo (http://www.armdevinfo.am/) 2012-05-12; 2011 to 2012: Armenia MDGs Indicators (http://www.armstat.am/) 06/02/2014; Note: 2001 to 2005: DHS 2005: 26 (2001-2005); 2010: DHS 2010: 13; Reference period: 1988: 1986-1990; 1998: 1996-2000; Source in Reference: 1988: NSO; 1996: Min. of Justice; 1998: NSO; 2000 to 2010: Min. of Justice; 2011 to 2012: NSO; Primary Source in Reference: 1988: DHS 2000; 1990: Administrative data; 1998: DHS 2000; 1999: Administrative data; 2011 to 2012: Administrative data; Indicator: Children 1 year old immunized against measles, (%) , Country: Armenia National Series Reference: 1990: MDG Progress Report 2005-2009; 1996: ArmeniaInfo at: http://www.armdevinfo.am/ (accessed: 15 June 2011); 1999: MDG Progress Report 2005-2009; 2000 to 2003: ArmeniaInfo at: http://www.armdevinfo.am/ (accessed: 15 June 2011); 2004: MDG Progress Report 2005-2009; 2005 to 2006: ArmeniaInfo at: http://www.armdevinfo.am/ (accessed: 15 June 2011); 2007 to 2008: MDG Progress Report 2005-2009; 2009: ArmeniaInfo at: http://www.armdevinfo.am/ (accessed: 15 June 2011); 2010: ArmeniaInfo (http://www.armdevinfo.am/) 2012-05-12; 2011 to 2012: Armenia MDGs Indicators (http://www.armstat.am/) 06/02/2014; Definition: 1990 to 2009: Under two-years old; Source in Reference: 1990 to 2009: Min. of Health; 2010: NSO / Min. of Health; 2011 to 2012: NSO; Primary Source in Reference: 1990: Administrative data; 1999: Administrative data; 2004: Administrative data; 2007 to 2008: Administrative data; 2011 to 2012: Administrative data; Indicator: Under five mortality rate per 1,000 live births , Country: Azerbaijan National Series Reference: 1990 to 2012: NSO MDG data; Note: 1999: RHS 1996-2000: 88.4; Source in Reference: 1990 to 2012: NSO; Indicator: Infant mortality rate (0-1 year) per 1,000 live births , Country: Azerbaijan National Series Reference: 1990 to 2012: NSO MDG data; Note: 1999: RHS 1996-2000: 74.4; Source in Reference: 1990 to 2012: NSO; Indicator: Children 1 year old immunized against measles, (%) , Country: Azerbaijan National Series Reference: 1990 to 2012: NSO MDG data; Note: 2003 to 2012: Combined vaccination against measles, rubella, epidemic parotiditis; 2000: MICS 2000: 9.4 (under 4 months); 2006: DHS 2006: 74.4; Source in Reference: 1990 to 2002: NSO; 2003 to 2012: Min. of Health; Indicator: Under five mortality rate per 1,000 live births , Country: Belarus National Series Reference: 1990 to 1999: MDG Progress 2005; 2000 to 2009: MDG progress 2010; 2010 to 2011: MDG Report 2012; Indicator: Infant mortality rate (0-1 year) per 1,000 live births , Country: Belarus National Series Reference: 1990 to 1999: MDG Progress 2005; 2000 to 2009: MDG progress 2010; 2010 to 2011: MDG Report 2012; Indicator: Children 1 year old immunized against measles, (%) , Country: Belarus National Series Reference: 1990 to 1999: MDG Progress 2005; 2000 to 2009: MDG progress 2010; 2010 to 2011: MDG Report 2012; Indicator: Under five mortality rate per 1,000 live births , Country: Bosnia and Herzegovina National Series Reference: 2000 to 2011: MDG Report 2013; Note: 2000: UN Inter-agency Group for Child Mortality Estimation; 2008 to 2011: UN Inter-agency Group for Child Mortality Estimation; Source in Reference: 2000: UN Inter-agency Group for Child Mortality Estimation; 2007: NSO (BHAS); 2008 to 2011: UN Inter-agency Group for Child Mortality Estimation; Indicator: Infant mortality rate (0-1 year) per 1,000 live births , Country: Bosnia and Herzegovina National Series Reference: 2000 to 2012: MDG Report 2013; Source in Reference: 2000 to 2012: NSO (BHAS); Indicator: Children 1 year old immunized against measles, (%) , Country: Bosnia and Herzegovina National Series Reference: 2000 to 2009: MDG progress report 2010; 2011: MDG Report 2013; Note: 2007 to 2009: Only for the territory of the Federation of Bosnia and Herzegovina; Reference period: 2011: 2011/12; Source in Reference: 2000 to 2001: FBiH PHI, RS HP Fund, FBiH SI; 2007 to 2009: FBiH Public Health Institute; Primary Source in Reference: 2007 to 2009: Administrative data; 2011: MICS 2011-12; Indicator: Breast-fed under 6 months (%) , Country: Bosnia and Herzegovina National Series Reference: 2000 to 2006: MDG progress report 2010; 2011: MDG Report 2013; Reference period: 2011: 2011/12; Source in Reference: 2000: FBiH PHI, RS HP Fund, FBiH SI; Primary Source in Reference: 2006: MICS 2006; 2011: MICS 2011-12; Indicator: Under five mortality rate per 1,000 live births , Country: Bulgaria National Series Reference: 2001 to 2007: MDG report 2010; Source in Reference: 2001 to 2007: National Health Information Center / NSO; Indicator: Infant mortality rate (0-1 year) per 1,000 live births , Country: Bulgaria National Series Reference: 2001 to 2007: MDG report 2010; Source in Reference: 2001 to 2007: National Health Information Center / NSO; Indicator: Perinatal mortality rate , Country: Bulgaria National Series Reference: 2001 to 2007: MDG report 2010; Definition: 2001 to 2007: After 28 weeks of gestation; Source in Reference: 2001 to 2007: National Health Information Center / NSO; Indicator: Infant mortality rate (0-1 year) per 1,000 live births , Country: Croatia National Series Reference: 1990 to 2002: MDG Report 2004; 2004: MDG Progress Report 2005; Note: 1998 to 2002: To mothers who had lived in Croatia for longer than the period of one year; Indicator: Perinatal mortality rate , Country: Croatia National Series Reference: 2002 to 2005: MDG Progress Report 2005; Definition: 2002 to 2005: birth weight >500g; Indicator: Under five mortality rate per 1,000 live births , Country: Czechia National Series Reference: 2002: MDG report 2004; Source in Reference: 2002: Health Yearbook of the Czech Republic 2001; Indicator: Infant mortality rate (0-1 year) per 1,000 live births , Country: Czechia National Series Reference: 1990 to 2002: MDG report 2004; Source in Reference: 1990 to 2002: Health Yearbook of the Czech Republic 2001; Indicator: Perinatal mortality rate , Country: Czechia National Series Reference: 1990 to 2002: MDG report 2004; Definition: 1990 to 2002: After 28 weeks of gestation; Source in Reference: 2000 to 2002: Health Yearbook of the Czech Republic 2001; Indicator: Under five mortality rate per 1,000 live births , Country: Georgia National Series Reference: 2000 to 2004: MDG Progress Report 2004-2005; Definition: 2000 to 2001: Number of deaths below age five per 1,000 live births in a calendar year.; Note: 2000 to 2004: Official statistics; Source in Reference: 2000 to 2004: National Center for Disease Control and Medical Statistics; Indicator: Infant mortality rate (0-1 year) per 1,000 live births , Country: Georgia National Series Reference: 2000 to 2004: MDG Progress Report 2004-2005; Note: 2000 to 2004: Official statistics; Source in Reference: 2000 to 2004: National Center for Disease Control and Medical Statistics; Indicator: Children 1 year old immunized against measles, (%) , Country: Georgia National Series Reference: 2000 to 2004: MDG Progress Report 2004-2005; Definition: 2000 to 2004: Under two-years old; Source in Reference: 2000: National Center for Disease Control and Medical Statistics; Indicator: Under five mortality rate per 1,000 live births , Country: Hungary National Series Reference: 1990 to 2001: MDG report 2004; Indicator: Infant mortality rate (0-1 year) per 1,000 live births , Country: Hungary National Series Reference: 1990 to 2002: MDG report 2004; Source in Reference: 1990 to 2002: NSO; Primary Source in Reference: 1990 to 2002: Hungarian Health Database 1985-2001; Indicator: Under five mortality rate per 1,000 live births , Country: Kazakhstan National Series Reference: 1987 to 1999: MDG in Kazakhstan 2005; 2000 to 2005: Poverty assessment in Kazakhstan: current status and prospects for development; 2006 to 2008: MDG Report 2010; 2009 to 2012: Poverty assessment in Kazakhstan: current status and prospects for development; Definition: 1990 to 1999: Excluding pregnancies that terminate at less than 28 weeks of gestation, and newborns weighing less than 1000 grams at the time of birth, shorter than 35 cm, or alive for less than seven days.; Note: 1990 to 1994: DHS 1995: 56.7; 1995 to 1999: DHS 1999: 71.4; 2006: MICS 2006: 36.3; Reference period: 1990 to 1994: 1989-1994; 1995 to 1999: 1995-1999; Source in Reference: 1990 to 1999: TransMonee; 2000 to 2005: NSO; 2006 to 2008: Min. of Healthcare; 2009 to 2012: NSO; Primary Source in Reference: 2006 to 2008: Administrative data; Indicator: Infant mortality rate (0-1 year) per 1,000 live births , Country: Kazakhstan National Series Reference: 1987 to 1999: MDG in Kazakhstan 2005; 2000 to 2001: Poverty assessment in Kazakhstan: current status and prospects for development; 2002: MDG in Kazakhstan 2005; 2003 to 2005: Poverty assessment in Kazakhstan: current status and prospects for development; 2006 to 2007: MDG Report 2010; 2008 to 2012: Poverty assessment in Kazakhstan: current status and prospects for development; Definition: 1990 to 1999: Excluding pregnancies that terminate at less than 28 weeks of gestation, and newborns weighing less than 1000 grams at the time of birth, shorter than 35 cm, or alive for less than seven days.; 2002: Excluding pregnancies that terminate at less than 28 weeks of gestation, and newborns weighing less than 1000 grams at the time of birth, shorter than 35 cm, or alive for less than seven days.; Note: 1990 to 1994: DHS 1995: 49.7; 1995 to 1999: DHS 1999: 61.9; Reference period: 1990 to 1993: 1989-1994; 1994 to 1999: 1995-1999; Source in Reference: 1990 to 1999: Min. of Healthcare; 2000 to 2001: NSO; 2002: Min. of Healthcare; 2003 to 2005: NSO; 2006 to 2007: Min. of Healthcare; 2008 to 2012: NSO; Primary Source in Reference: 2006 to 2007: Administrative data; Indicator: Children 1 year old immunized against measles, (%) , Country: Kazakhstan National Series Reference: 1995: MDG in Kazakhstan 2002; 2000 to 2012: Poverty assessment in Kazakhstan: current status and prospects for development; Source in Reference: 1995: Min. of Healthcare; 2000: NSO; 2001 to 2012: Min. of Health; Indicator: Breast-fed under 6 months (%) , Country: Kazakhstan National Series Reference: 1995 to 2006: MDG Report 2010; Definition: 1995 to 2006: Under 3 months; Source in Reference: 2002: Tazhibayev Sh., Sharmanov T., Ergalieva A., Dolmatova O., Mukasheva O., Seidakhmetova A., Kushenova R. ‘Promotion of Lactation Amenorrhea Method Intervention Trial, Kazakhstan’. Population Council, Frontiers in Reproductive Health 2004; Primary Source in Reference: 1999: DHS 1999; Indicator: Perinatal mortality rate , Country: Kazakhstan National Series Reference: 2008: MDG Report 2010; Definition: 2008: After 22 weeks of gestation; Indicator: Under five mortality rate per 1,000 live births , Country: Kyrgyzstan National Series Reference: 1990 to 1999: NSO MDG database as on 2014-07-08; 2000 to 2009: MDG Progress Report 2010; 2010 to 2012: NSO MDG database as on 2014-07-08; Definition: 1990 to 1999: Excluding pregnancies that terminates at less than 28 weeks of gestation; Source in Reference: 1990 to 2010: NSO; Indicator: Infant mortality rate (0-1 year) per 1,000 live births , Country: Kyrgyzstan National Series Reference: 1990 to 1999: NSO MDG database as on 2014-07-08; 2000 to 2009: MDG Progress Report 2010; 2010 to 2012: NSO MDG database as on 2014-07-08; Definition: 1990 to 1999: Excluding pregnancies that terminates at less than 28 weeks of gestation; Source in Reference: 1990 to 1999: NSO / Min. of Health; 2000 to 2009: NSO; 2010: NSO / Min. of Health; Indicator: Children 1 year old immunized against measles, (%) , Country: Kyrgyzstan National Series Reference: 1990 to 1999: NSO MDG database as on 2014-07-08; 2000 to 2009: MDG Progress Report 2010; 2010 to 2012: NSO MDG database as on 2014-07-08; Source in Reference: 1990 to 1999: NSO / Min. of Health; 2000 to 2009: NSO; 2010: NSO / Min. of Health; Indicator: Under five mortality rate per 1,000 live births , Country: Latvia National Series Reference: 1990 to 2003: MDG Report 2005; Definition: 1990 to 2003: Per 1,000 children under the age of five; Source in Reference: 1990 to 2003: NSO / Min. of Health; Indicator: Infant mortality rate (0-1 year) per 1,000 live births , Country: Latvia National Series Reference: 1996 to 2003: MDG Report 2005; Source in Reference: 1996 to 2003: NSO / Min. of Health; Indicator: Perinatal mortality rate , Country: Latvia National Series Reference: 1980 to 2003: MDG Report 2005; Definition: 1980 to 2003: After 28 weeks of gestation; Source in Reference: 1980 to 2003: NSO / Min. of Health; Indicator: Under five mortality rate per 1,000 live births , Country: Lithuania National Series Reference: 1990 to 2001: MDG Assessment 2002; Definition: 1990 to 2001: Including live births at least 500 grams weight and 22 weeks gestation; Indicator: Infant mortality rate (0-1 year) per 1,000 live births , Country: Lithuania National Series Reference: 1990 to 2001: MDG Assessment 2002; Definition: 1990 to 1991: Excluding pregnancies that terminate at less than 28 weeks of gestation, and newborns weighing less than 1000 grams at the time of birth, shorter than 35 cm, or alive for less than seven days.; 1992 to 2001: Excluding live births weighting less than 500 grams and less than 22 weeks of gestation; Indicator: Children 1 year old immunized against measles, (%) , Country: Lithuania National Series Reference: 2000: MDG Assessment 2002; Indicator: Under five mortality rate per 1,000 live births , Country: Moldova, Republic of National Series Reference: 2000 to 2010: Statbank of the National Bureau of Statistics of the Republic of Moldova as on 08-08-2012; 2011 to 2012: Moldova Statbank (http://statbank.statistica.md) 11-11-2013; Definition: 2000 to 2007: Number of deaths below age five per 1,000 live births. Excluding live births weighting less than 1,000 grams and less than 30 weeks of gestation; 2008 to 2010: Number of deaths below age five per 1,000 live births. Excluding live births weighting less than 500 grams and less than 22 weeks of gestation; 2011 to 2012: Number of deaths below age five per 1,000 live births. Excluding live births weighting less than 1,000 grams and less than 30 weeks of gestation; Note: 2000 to 2012: Information is presented without the data from the left side of the river Nistru and municipality Bender.; Source in Reference: 2000 to 2012: Central Election Commission; Indicator: Infant mortality rate (0-1 year) per 1,000 live births , Country: Moldova, Republic of National Series Reference: 2000 to 2010: Statbank of the National Bureau of Statistics of the Republic of Moldova as on 08-08-2012; 2011 to 2012: Moldova Statbank (http://statbank.statistica.md) 11-11-2013; Definition: 2000 to 2007: Excluding live births weighting less than 1,000 grams and less than 30 weeks of gestation; 2008 to 2010: Excluding live births weighting less than 500 grams and less than 22 weeks of gestation; 2011 to 2012: Excluding live births weighting less than 1,000 grams and less than 30 weeks of gestation; Note: 2000 to 2010: Deaths in a given calendar year divided by the size of their birth cohort.; 2000 to 2012: Information is presented without the data from the left side of the river Nistru and municipality Bender.; Source in Reference: 2000 to 2012: Min. of Health / NSO; Indicator: Children 1 year old immunized against measles, (%) , Country: Moldova, Republic of National Series Reference: 2000 to 2005: Statbank of the National Bureau of Statistics of the Republic of Moldova as on 08-08-2012; 2006 to 2012: Third MDG Report 2013; Definition: 2000 to 2012: Under two-years old; Note: 2000 to 2005: Information is presented without the data from the left side of the river Nistru and municipality Bender.; Source in Reference: 2000 to 2005: Min. of Health / NSO; 2006 to 2012: National Centre for Public Health; Indicator: Breast-fed under 6 months (%) , Country: Moldova, Republic of National Series Reference: 2008: MDG Report 2010; Source in Reference: 2008: National Perinatal Program 2008; Indicator: Perinatal mortality rate , Country: Moldova, Republic of National Series Reference: 1990 to 2009: MDG Report 2010; Definition: 1990 to 2009: After 28 weeks of gestation; Indicator: Under five mortality rate per 1,000 live births , Country: Montenegro National Series Reference: 1990 to 2000: MDG report 2005; 2004 to 2008: MDG Report 2010; 2009 to 2011: MDG Report 2013; Source in Reference: 1990 to 2011: NSO; Indicator: Infant mortality rate (0-1 year) per 1,000 live births , Country: Montenegro National Series Reference: 1990 to 2000: MDG report 2005; 2004 to 2008: MDG Report 2010; 2009 to 2011: MDG Report 2013; Source in Reference: 1990 to 2011: NSO; Indicator: Children 1 year old immunized against measles, (%) , Country: Montenegro National Series Reference: 1990 to 2000: MDG report 2005; 2004 to 2008: MDG Report 2010; 2009 to 2011: MDG Report 2013; Source in Reference: 1990 to 2000: Report on immuzation against infectious diseases in Montenegro; 2004 to 2008: NSO; Indicator: Breast-fed under 6 months (%) , Country: Montenegro National Series Reference: 2009: MDG Report 2010; Source in Reference: 2009: NSO; Indicator: Under five mortality rate per 1,000 live births , Country: Poland National Series Reference: 1990 to 1999: MDG Report 2002; Source in Reference: 1990: NSO; 1991 to 1998: Demographic Yearbook 2000, NSO; 1999: NSO; Indicator: Infant mortality rate (0-1 year) per 1,000 live births , Country: Poland National Series Reference: 1990 to 1999: MDG Report 2002; Source in Reference: 1990 to 1999: Demographic Yearbook 2000, NSO; Indicator: Infant mortality rate (0-1 year) per 1,000 live births , Country: Romania National Series Reference: 1990 to 2000: MDG Report 2003; 2001 to 2009: MDG Report 2010; Source in Reference: 1990 to 2000: Min. of Health; 2001 to 2009: NSO; Indicator: Children 1 year old immunized against measles, (%) , Country: Romania National Series Reference: 2001: MDG Report 2003; Source in Reference: 2001: Min. of Health; Indicator: Under five mortality rate per 1,000 live births , Country: Russian Federation National Series Definition: 2003 to 2008: Excluding pregnancies that terminate at less than 28 weeks of gestation, and newborns weighing less than 1000 grams at the time of birth, shorter than 35 cm, or alive for less than seven days.; Source in Reference: 2003 to 2008: WHO; Indicator: Infant mortality rate (0-1 year) per 1,000 live births , Country: Russian Federation National Series Definition: 2003 to 2009: Excluding pregnancies that terminate at less than 28 weeks of gestation, and newborns weighing less than 1000 grams at the time of birth, shorter than 35 cm, or alive for less than seven days.; Source in Reference: 2003 to 2009: WHO; Indicator: Children 1 year old immunized against measles, (%) , Country: Russian Federation National Series Source in Reference: 2008: WHO; Indicator: Breast-fed under 6 months (%) , Country: Russian Federation National Series Source in Reference: 2008: WHO; Indicator: Under five mortality rate per 1,000 live births , Country: Serbia National Series Reference: 1990 to 1999: MDG Report 2001-2004; 2000: MDG progress report 2009; 2001 to 2002: MDG Report 2001-2004; 2005: MDG report 2006; 2008: MDG progress report 2009; Source in Reference: 1990 to 2002: NSO; 2008: NSO; Indicator: Infant mortality rate (0-1 year) per 1,000 live births , Country: Serbia National Series Reference: 1990 to 1999: MDG Report 2001-2004; 2000: MDG progress report 2009; 2001 to 2002: MDG Report 2001-2004; 2005: MDG report 2006; 2008: MDG progress report 2009; Source in Reference: 1990 to 2002: NSO; 2008: NSO; Indicator: Children 1 year old immunized against measles, (%) , Country: Serbia National Series Reference: 1990 to 1999: MDG Report 2001-2004; 2000: MDG progress report 2009; 2001 to 2002: MDG Report 2001-2004; 2008: MDG progress report 2009; Definition: 1990 to 2008: Under 18 months; Source in Reference: 1990 to 1999: NSO; 2000: National Institute of Public Health Database; 2001 to 2002: NSO; 2008: National Institute of Public Health Database; Indicator: Breast-fed under 6 months (%) , Country: Serbia National Series Reference: 2000 to 2005: MDG progress report 2009; Definition: 2000: Under 4 months; Source in Reference: 2000 to 2005: UNICEF; Primary Source in Reference: 2005: MICS 2005; Indicator: Perinatal mortality rate , Country: Serbia National Series Reference: 1990 to 1999: MDG Report 2001-2004; 2000: MDG progress report 2009; 2001 to 2002: MDG Report 2001-2004; 2005: MDG report 2006; 2008: MDG progress report 2009; Definition: 1990 to 2002: After 28 weeks of gestation; 2005: Gestation period not specified; 2008: After 28 weeks of gestation; Source in Reference: 2000: NSO; 2008: NSO; Indicator: Infant mortality rate (0-1 year) per 1,000 live births , Country: Slovakia National Series Reference: 1990 to 2002: MDG report 2004; Source in Reference: 1990 to 2002: European Health for All Database, WHO; Indicator: Children 1 year old immunized against measles, (%) , Country: Slovakia National Series Reference: 2002: MDG report 2004; Definition: 2002: Under 18 months; Indicator: Under five mortality rate per 1,000 live births , Country: Slovenia National Series Reference: 1990 to 2001: MDG report 2004; Source in Reference: 1990 to 2001: European Health for All Database, WHO - Health Statistics yearbook 2003; Indicator: Infant mortality rate (0-1 year) per 1,000 live births , Country: Slovenia National Series Reference: 1990 to 2001: MDG report 2004; Source in Reference: 1990 to 2001: European Health for All Database, WHO - Health Statistics yearbook 2003; Indicator: Under five mortality rate per 1,000 live births , Country: Tajikistan National Series Reference: 2000: MDG Progress Report 2010; 2003: MDG Needs Assessment 2005; 2005 to 2009: MDG Progress Report 2010; Source in Reference: 2003: UNICEF SOWC; 2007: NSO; Primary Source in Reference: 2000: MICS 2000; 2005: MICS 2005; 2007: LSS 2007; Indicator: Infant mortality rate (0-1 year) per 1,000 live births , Country: Tajikistan National Series Reference: 1990 to 1999: MDG Progress Report 2003; 2000: MDG Progress Report 2010; 2001: MDG Progress Report 2003; 2005 to 2009: MDG Progress Report 2010; Source in Reference: 2001: Republican Center of Medical Statistics; 2007: NSO; Primary Source in Reference: 2000: MICS 2000; 2005: MICS 2005; 2007: LSS 2007; Indicator: Children 1 year old immunized against measles, (%) , Country: Tajikistan National Series Reference: 2001 to 2003: NSO MDG data; 2005 to 2008: MDG Progress Report 2010; Primary Source in Reference: 2001: MICS 2000; 2005: MICS 2005; Indicator: Under five mortality rate per 1,000 live births , Country: The former Yugoslav Republic of Macedonia National Series Reference: 1990: MDG report 2005; 1991 to 1996: MDG progress report 2009; 1997: MDG report 2005; 1998 to 2007: MDG progress report 2009; Note: 2004 to 2007: New Methodology; Source in Reference: 1991 to 1996: NSO; 1998 to 2007: NSO; Indicator: Infant mortality rate (0-1 year) per 1,000 live births , Country: The former Yugoslav Republic of Macedonia National Series Reference: 1990 to 2007: MDG progress report 2009; Note: 2004 to 2007: New Methodology; Source in Reference: 1990 to 2007: NSO; Indicator: Children 1 year old immunized against measles, (%) , Country: The former Yugoslav Republic of Macedonia National Series Reference: 1990 to 2007: MDG progress report 2009; Source in Reference: 1990 to 2007: Republic Institute for Health Protection; Indicator: Breast-fed under 6 months (%) , Country: The former Yugoslav Republic of Macedonia National Series Reference: 2007: MDG progress report 2009; Source in Reference: 2007: UNICEF 2007; Primary Source in Reference: 2007: MICS; Indicator: Under five mortality rate per 1,000 live births , Country: Turkey National Series Reference: 1993 to 2008: MDG Report 2010; Reference period: 1998: 1993-1998; 2003: 1998-2003; Source in Reference: 1993 to 2008: Hacettepe University; Primary Source in Reference: 1993: DHS 1993; 1998: DHS 1998; 2003: DHS 2003; 2008: DHS 2008; Indicator: Infant mortality rate (0-1 year) per 1,000 live births , Country: Turkey National Series Reference: 1993 to 2008: MDG Report 2010; Reference period: 1998: 1993-1998; 2003: 1998-2003; Source in Reference: 1993 to 2008: Hacettepe University; Primary Source in Reference: 1993: DHS 1993; 1998: DHS 1998; 2003: DHS 2003; 2008: DHS 2008; Indicator: Children 1 year old immunized against measles, (%) , Country: Turkey National Series Reference: 1993 to 2009: MDG Report 2010; Source in Reference: 1993 to 2003: Hacettepe University; 2009: Min. of Health; Primary Source in Reference: 1993: DHS 1993; 1998: DHS 1998; 2003: DHS 2003; 2009: Ministry of Health Registry; Indicator: Infant mortality rate (0-1 year) per 1,000 live births , Country: Turkmenistan National Series Reference: 1991 to 2002: MDG Report 2003; Source in Reference: 1991 to 2002: Min. of Health and the Medical Industry; Indicator: Under five mortality rate per 1,000 live births , Country: Ukraine National Series Reference: 1990 to 2000: MDG Report 2005; 2001 to 2009: MDG Report 2010; 2010 to 2012: MDG Report 2013; Definition: 1990 to 2000: Per 1,000 children under the age of five; Source in Reference: 2010 to 2012: NSO; Indicator: Infant mortality rate (0-1 year) per 1,000 live births , Country: Ukraine National Series Reference: 1990: MDG Report 2005; 2000 to 2009: MDG Report 2010; 2010 to 2012: MDG Report 2013; Definition: 1990: Per 1,000 children under 1 years old; Source in Reference: 2000 to 2008: NSO; 2010 to 2012: NSO; Indicator: Children 1 year old immunized against measles, (%) , Country: Ukraine National Series Reference: 2008: MDG Report 2010; Indicator: Under five mortality rate per 1,000 live births , Country: Uzbekistan National Series Reference: 1995 to 2000: MDG Report 2006; Reference period: 1995: 1992-1997; 1998: 1996-2000; 2000: 1998-2002; Source in Reference: 1995: Min. of Health / Institute of Obstetrics and Gynecology; 1998: UNICEF; 2000: Min. of Health / Institute of Obstetrics and Gynecology; Primary Source in Reference: 1995: DHS 1996; 1998: MICS 2000; 2000: Uzbekistan Health Examination Survey 2002; Indicator: Infant mortality rate (0-1 year) per 1,000 live births , Country: Uzbekistan National Series Reference: 1995 to 2000: MDG Report 2006; Reference period: 1995: 1992-1997; 1998: 1996-2000; 2000: 1998-2002; Source in Reference: 1995: Min. of Health / Institute of Obstetrics and Gynecology; 1998: UNICEF; 2000: Min. of Health / Institute of Obstetrics and Gynecology; Primary Source in Reference: 1995: DHS 1996; 1998: MICS 2000; 2000: Uzbekistan Health Examination Survey 2002; Indicator: Children 1 year old immunized against measles, (%) , Country: Uzbekistan National Series Reference: 1996 to 2004: MDG Report 2006; Source in Reference: 1996 to 2004: TransMonee;
    • June 2018
      Source: Statistics Finland
      Uploaded by: Knoema
      Accessed On: 29 November, 2018
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      Concepts and definitionsDescription 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 and vital statistics in Doria (in Finnish) Publications on Population censuses in Doria (in Finnish) Area For 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. Citizenship 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. Citizenship Czech Republic Czech Republic + Former Czechoslovakia Sudan Sudan + Former Sudan
    • January 2016
      Source: World Bank
      Uploaded by: Knoema
      Accessed On: 22 September, 2016
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    • December 2012
      Source: World Bank
      Uploaded by: Knoema
      Accessed On: 05 September, 2016
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    • July 2018
      Source: End Coal
      Uploaded by: Knoema
      Accessed On: 16 July, 2018
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      Coal Power Plants Statistics
    • November 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 27 November, 2018
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    • January 2018
      Source: Food and Agriculture Organization
      Uploaded by: Knoema
      Accessed On: 07 December, 2018
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      Food supply data is some of the most important data in FAOSTAT. In fact, this data is for the basis for estimation of global and national undernourishment assessment, when it is combined with parameters and other data sets. This data has been the foundation of food balance sheets ever since they were first constructed. The data is accessed by both business and governments for economic analysis and policy setting, as well as being used by the academic community.
    • September 2018
      Source: U.S. National Center for Education Statistics
      Uploaded by: Knoema
      Accessed On: 27 September, 2018
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      Program Overview:The Common Core of Data (CCD) nonfiscal surveys consist of data submitted annually to the National Center for Education Statistics (NCES) by state education agencies (SEAs) in the 50 states, the District of Columbia, Puerto Rico, the four U.S. Island Areas (American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin Islands), the Department of Defense Education Activity (DoDEA), and the Bureau of Indian Education (BIE). In order to provide data comparable across states, NCES and representatives of SEAs have worked since the 1950s to develop and accept common data items and definitions. SEAs report school-, agency-, and state-level education data for the CCD through the U.S. Department of Education’s EDFacts collection system. CCD survey staff edits the data to produce the final data file, which NCES uses to construct general-purpose publications and specialized reports. The principal users of CCD nonfiscal data are the federal government; the education research community; state and local government officials, including school boards and LEA administrators; and the general public.   Guidelines for Missing, Not Applicable, and Suppression Code Values: For Numeric Data: -1 denotes missing, not available, or not reported data items. -2 denotes "not applicable" data items. -9 indicates suppressed data because it failed the multi-year edit.        
    • March 2016
      Source: UNESCO Institute for Statistics
      Uploaded by: Knoema
      Accessed On: 22 March, 2016
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    • May 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 18 June, 2018
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      Annual data on quantities for crude oil, oil products, natural gas and manufactures gases, electricity and derived heat, solid fossil fuels,  renewables and wastes covering the full spectrum of the energy sector from supply through transformation to final energy consumption by sector and fuel type. Also, annual imports and exports data of various energy carriers by country of origin and destination, as well as infrastructure information. Annual data collection cover in principle the EU Member States, EFTA, EU candidate countries, and potential candidate countries. Time series starts mostly in year 1990. All data are presented in the form of energy balances.
    • April 2018
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 21 November, 2018
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      Source: UNECE Statistical Database, compiled from national and international (Eurostat) official sources. Definition: Data provided refer to the proportion of persons who used a computer in the last three months preceding the survey over the total population of corresponding sex and age group. A computer is defined as a multi purpose machine, a personal computer, powered by one of the major operating systems, i.e. Macintosh (Apple), Linux or Microsoft (Windows XP, NT or Vista). PDAs (handheld computers or palmtops) are included. Other equipments with embedded computing technologies, e.g. cell phones, TV sets, washing machines and dish washers are not considered as computers. .. - data not available Country: Armenia Additional information (2004 - 2008): Data refer to percentage of persons using computers in households covered in Integrated household living standards survey. Country: Armenia For 2013-2014 data refer to the proportion of persons who used a computer in the last 12 months. Since 2015, to the proportion of persons who used a computer in the last three months. Country: Belarus Refers to computer use in the past 12 months. Country: Israel Change in definition (2002 - 2006): Data refer to population aged 20 and over. Data refer to the proportion of persons who used a computer in the last month. Country: Israel Change in definition (2007 - 2013): Data refer to population aged 20 and over. Country: Moldova, Republic of Change in definition (2009): Data refer to ge groups: 16-29, 30-59, 60-74. Country: Russian Federation Reference period (2013): Data do not refer to equipment such as mobile cellular phones , PDAs ( personal digital assistants) or TVs etc. Country: Serbia Data exclude territory of Kosovo and Metohija Country: United States Change in definition (1990 - 2013): Data do not refer to last 3 months, i.e. not time specific. Data are collected in October.
    • December 2016
      Source: Concordia
      Uploaded by: Knoema
      Accessed On: 28 July, 2017
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      THE CONCORDIA PARTNERSHIP Index (the Index) was developed as a tool for public, private, and nonprofit organizations to identify opportunities to form strategic partnerships and pool resources for the implementation of innovative ideas. The Index ranks countries based on their readiness and need to engage in public-private partnerships (P3s). The inclu- sion of the need indicators sets the Index apart from other indices that measure P3 environ- ments. While the success of a P3 depends on a country’s political and market structures, the Index recognizes that for a P3 to be truly impactful it must address a large-scale need.
    • November 2016
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 21 November, 2018
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      Definition: Constitutional court is the high court that deals primarily with constitutional law. Its main authority is to rule on whether or not laws that are challenged are in fact constitutional.In the case that the country does not have a separate constitutional court, data relates to the institution that has been delegated constitutional judicial authority, usually the supreme court. General note: Reference period - any fixed date of the year. .. - data not available Country: Croatia Additional information (2012 - 2013): The Croatian Constitution regulates that the Constitutional Court of the Republic of Croatia consists of 13 judges.Due to retirement, there are 12 judges left. Country: Cyprus Reference period (2011): data refer to 2012. Country: Cyprus Government controlled area only. Country: Estonia 2015: Figures reported are data as of 30.08.2016. Refers to justices of the Supreme court, not the full composition of the constitutional court. Country: Germany Change in definition (2004 - 2012): Data refer to members of constitutional court, without constitutional courts of the Federal States (Laender). Country: Moldova, Republic of Data exclude the territory of the Transnistria and municipality of Bender Country: Montenegro Reference period (2007): Data is valid only up to September 2007. Country: Netherlands Reference period (2011): Data refer to April 2012. Country: Slovakia Data for 2014 refer to 15 March. Data for 2015 refer to 20 November. Country: Switzerland Change in definition (1980 - 2013): Data refer to members of Federal Supreme Court.
    • July 2018
      Source: National Statistics Office of Georgia
      Uploaded by: Pallavi S
      Accessed On: 08 October, 2018
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    • November 2018
      Source: International Monetary Fund
      Uploaded by: Knoema
      Accessed On: 22 November, 2018
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      Consumer price indexes (CPIs) are index numbers that measure changes in the prices of goods and services purchased or otherwise acquired by households, which households use directly, or indirectly, to satisfy their own needs and wants. In practice, most CPIs are calculated as weighted averages of the percentage price changes for a specified set, or ‘‘basket’’, of consumer products, the weights reflecting their relative importance in household consumption in some period. CPIs are widely used to index pensions and social security benefits. CPIs are also used to index other payments, such as interest payments or rents, or the prices of bonds. CPIs are also commonly used as a proxy for the general rate of inflation, even though they measure only consumer inflation. They are used by some governments or central banks to set inflation targets for purposes of monetary policy. The price data collected for CPI purposes can also be used to compile other indices, such as the price indices used to deflate household consumption expenditures in national accounts, or the purchasing power parities used to compare real levels of consumption in different countries.
    • June 2018
      Source: National Oceanic and Atmospheric Administration
      Uploaded by: Olga Bikeeva
      Accessed On: 29 June, 2018
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      States Affected and Category by States Affected: The impact of the hurricane on individual U.S. states based upon the Saffir-Simpson Hurricane Wind Scale (through the estimate of the maximum sustained [1-min] surface [10 m] winds at each state). TX S-South Texas, TX C-Central Texas, TX N-North Texas, LA-Louisiana, MS-Mississippi, AL-Alabama, FL NW-Northwest Florida, FL SW-Southwest Florida, FL SE-Southeast Florida, FL NE-Northeast Florida, GA-Georgia, SC-South Carolina, NC-North Carolina, VA-Virginia, MD-Maryland, DE-Delaware, NJ-New Jersey, NY-New York, PA-Pennsylvania, CT-Connecticut, RI-Rhode Island, MA-Massachusetts, NH-New Hampshire, ME-Maine. In Texas, south refers to the area from the Mexican border to Corpus Christi; central spans from north of Corpus Christi to Matagorda Bay and north refers to the region from north of Matagorda Bay to the Louisiana border. In Florida, the north-south dividing line is from Cape Canaveral [28.45N] to Tarpon Springs [28.17N]. The dividing line between west-east Florida goes from 82.69W at the north Florida border with Georgia, to Lake Okeechobee and due south along longitude 80.85W.) Occasionally, a hurricane will cause a hurricane impact (estimated maximum sustained surface winds) in an inland state. To differentiate these cases versus coastal hurricane impacts, these inland hurricane strikes are denoted with an "I" prefix before the state abbreviation. States that have been so impacted at least once during this time period include Alabama (IAL), Georgia (IGA), North Carolina (INC), Virginia (IVA), and Pennsylvania (IPA). The Florida peninsula, by the nature of its relatively landmass, is all considered as coastal in this database. Highest U.S. Saffir-Simpson Category: The highest Saffir-Simpson Hurricane Wind Scale impact in the United States based upon estimated maximum sustained (1-min) surface (10 m) winds produced at the coast. ("TS" indicates that the system caused only tropical storm conditions in the United States, though it was a hurricane at landfall. See "&" below.) Central Pressure: The observed or estimated central pressure of the hurricane at landfall. Maximum Winds: Estimated maximum sustained (1-min) surface (10 m) winds to occur along the U. S. coast. Winds are estimated to the nearest 10 kt for the period of 1851 to 1885 and to the nearest 5 kt for the period of 1886 to date. (1 kt = 1.15 mph.) * - Indicates that the hurricane center did not make a U.S. landfall (or substantially weakened before making landfall), but did produce the indicated hurricane-force winds over land. In this case, central pressure is given for the time that the hurricane winds along the coast were the strongest. & - Indicates that the hurricane center did make a direct landfall, but that the strongest winds likely remained offshore. Thus the winds indicated here are lower than in HURDAT. # - Indicates that the hurricane made landfall over Mexico, but also caused sustained hurricane force surface winds in Texas. The strongest winds at landfall impacted Mexico, while the weaker maximum sustained winds indicated here were conditions estimated to occur in Texas. Indicated central pressure given is that at Mexican landfall. Additional Note: Because of the sparseness of towns and cities before 1900 in some coastal locations along the United States, the above list is not complete for all states. Before the Gulf of Mexico and Atlantic coasts became settled, hurricanes may have been underestimated in their intensity or missed completely for small-sized systems (i.e., 2004's Hurricane Charley). The following list provides estimated dates when accurate tropical cyclone records began for specified regions of the United States based upon U.S Census reports and other historical analyses. Years in parenthesis indicate possible starting dates for reliable records before the 1850s that may be available with additional research: Texas-south > 1880, Texas-central > 1851, Texas-north > 1860, Louisiana > 1880, Mississippi > 1851, Alabama < 1851 (1830), Florida-northwest > 1880, Florida-southwest > 1900, Florida-southeast > 1900, Florida-northeast > 1880, Georgia < 1851 (1800), South Carolina < 1851 (1760), North Carolina < 1851 (1760), Virginia < 1851 (1700), Maryland < 1851 (1760), Delaware < 1851 (1700), New Jersey < 1851 (1760), New York < 1851 (1700), Connecticut < 1851 (1660), Rhode Island < 1851 (1760), Massachusetts < 1851 (1660), New Hampshire < 1851 (1660), and Maine < 1851 (1790).
    • December 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 03 December, 2018
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      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.
    • March 2018
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 21 November, 2018
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      Source: UNECE Statistical Database, compiled from national official sources. Definition: Persons convicted are persons found guilty by any legal body duly authorised to do so under national law, whether the conviction was later upheld or not. .. - data not available Country: Austria Break in methodlogy (2000): Significantly reduced number of convictions between 1999 and 2000: the decline is due to diversion which is now applicable to adults in criminal law. Country: Austria Change in definition (1990): Juveniles: data refer to persons aged less than 19. Persons, who were convicted more than once in the indicated year are multiple-counted. Country: Austria Change in definition (1995 - 2001): Juveniles: data refer to persons aged less than 19. Country: Bulgaria Break in methodlogy (2000): Until 1997 data are based on the activity of the regional and district courts on penal trials of general, private and administrative character. Since 1998 the information for the activity of military courts is also included. Country: Bulgaria Break in methodlogy (2012): Since 2012 data include activities of the Special Criminal Court. Country: Canada Found guilty includes guilty of the charged offence, of an included offence, of an attempt of the charged offence, or of an attempt of an included offence. This category also includes cases where an absolute or conditional discharge has been imposed. Data refer to fiscal year (April 1 through March 31 of following year). 1995-2004: data do not cover all provinces and territories. Adult is a person of age 18+ at the time of the offence. Juvenile is a person aged 12 to 17 y.o at the time of the offence. Country: Cyprus Data refer to the Government controlled area only. Country: Cyprus Includes convictions of both serious crimes (in violation of the Penal Code) and minor offences, as well as traffic violations. Country: Czechia Change in definition (2000 - 2012): Data include not only imprisonment but also e.g. fines, ban on activity, etc. Country: Denmark Break in methodlogy (2007): From 1980 to 2006, data refer to all persons with a decision, incl. acquitted and prosecutor dropped. From 2007, data cover only those who are convicted. Country: Estonia Break in methodlogy (1990): Change in laws and methodology. Country: Finland Break in methodlogy (2000): Offences against the Road Traffic Act carrying imprisonment as penalty were transferred to the Penal code on 1 October 1999. Country: France Additional information (1995 - 2002): Amnesties (part of convictions was not registered). Country: France Change in definition (1980 - 2012): Data include DOM-TOM. Country: France Provisional value (2012): Country: Georgia Territorial change (1990 onward): Data do not cover Abkhazia AR and Tskhinvali Region. Country: Germany Territorial change (1980 - 2006): Data refer to former territory of Germany. Country: Greece Change in definition (1990 - 2004): Juveniles: persons aged up to 17 Country: Ireland Change in definition (2000 - 2002): Headline Incidents only being included. Juveniles: 16 years or younger. Country: Israel Change in definition (1980 - 1990): Convicted juvenile offenders are those tried in juvenile courts. Country: Israel Change in definition (2000 - 2012): Convicted juvenile offenders are those tried in juvenile courts. Data on persons charged in criminal trials conducted in courts of first instance, who were sentenced during a given year. Since 2000 classification as adults or as juveniles was based on the following criteria, 1) The offender`age at the time crime was committed. 2)The offender`s age at time of the indictment 3)The type of court in which the trial was held.A juvenile offender is a person who meets two out of the three criteria . All other cases are considered to be adults. Country: Israel Reference period (1980): Data refer to 1981 Country: Israel Reference period (1990): Data refer to 1989 Country: Italy Break in methodlogy (2000): Change in methodology and source Country: Italy Change in definition (1980 - 2011): Data refers to the convicted persons recorded in the Judicial Database Country: Kazakhstan Break in methodlogy (2000): Change of source as of 2000 Country: Moldova, Republic of Data exclude the territory of the Transnistria and municipality of Bender Country: Netherlands Change in definition (1990 - 2012): Data exclude persons with unknown sex and age. Country: Poland Change in definition (1980 - 1990): Juveniles: persons aged up to 16. Country: Poland Change in definition (1995 - 2012): Juveniles: persons aged up to 17. Country: Romania Convictions is equivalent to Persons convicted because there are no data regarding final convictions. Country: Serbia Territorial change (2000 onward): Data exclude territory of Kosovo and Metohija. Country: Slovenia Break in methodology (1995): Change in law. Break in methodology (2013): New amendment to the Criminal Procedure Act enabled the implementation of criminal proceedings and economized trials. This is reflected in the large increase of the number of convicted persons over the previous year. The number of convicted juveniles did not significantly increase during the same period – around 10%. Country: Spain Break in methodlogy (2008): Before 2007: different source and partial coverage. Country: Spain Change in definition (1980 - 2013): Juveniles: persons aged between 14 to 17 years. Country: Spain Change in definition (2000 - 2006): Juveniles: persons aged between 14 to 17 years. Convicted persons are partially reported by sex. Country: Sweden Change in definition (1980 onwards): Data refer to number of convictions. One person can contribute with more than one conviction during a calendar year. Includes attempts, assistance, preparation and conspiracy to commit an offence. Country: Switzerland Additional information (1990 - 1995): Data are not complete (Juvenile convictions are not available) Country: Switzerland Change in definition (1990 - 2012): Only convicted persons for felonies and misdemeanours. Country: Turkey 2005: break in series: introduction of changes in laws. 2009: break in series: change in data compilation method. Data refer to the number of sentence decisions rendered for accused persons at criminal courts in accordance with Turkish Criminal Law and special laws for 2009 and later. Total excludes judicial person, foreign national and unknown sex and age for 2009 and later. Country: Ukraine From 2014 data cover the territories under the government control. Country: United Kingdom Change in definition (2008 - onwards): For total convicted persons, male and female may not add up to total because the sex is not always recorded Country: United Kingdom Territorial change (1980): Data refer to England and Wales only. Country: United States Adults: data represent felony conviction in state and federal courts. 1995: data refer to 1994.
    • March 2018
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 22 November, 2018
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      .. - data not available Source: UNECE Statistical Database, compiled from national official sources. Definition: Conviction is the verdict that results when a court of law finds a defendant guilty of a crime. A serious assault is an injury whereby life could be endangered, including cases of injury involving the use of dangerous instrument. Cases where instruments are used only to threaten are excluded. An assault refers to physical attack against the body of another person, including battery but excluding indecent assault. A homicide is intentional or unintentional killing. Intentional homicide is a death deliberately inflicted on a person by another person, including infanticide.Non-intentional homicide is a death not deliberately inflicted on a person by another person. That includes crime of manslaughter but excludes traffic accidents that result in a death of persons. The distinction between intentional and unintentional homicide differs from country to country, as does the definition of attempted murder. Rape is a sexual intercourse without valid consent. Robbery is a theft of property from a person, overcoming resistance by force or threat of force. Theft is any act of intentionally and unlawfully removing property belonging to another person (or organisation), excluding burglary. Drug crimes are any violation involving the illicit brokerage, cultivation, delivery (on any terms whatsoever), dispatch, dispatch in transit, distribution, extraction, exportation or importation, offering for sale, preparation, production, purchase, manufacture, sale, traffic, transportation, or use of narcotic drugs. General note: Data come from administrative data sources unless otherwise specified. Country: Albania Assault includes article 89, this change includes years 2013-2015. Theft includes all crimes against property and economic sphere, but excludes robbery. Country: Austria Break in methodlogy (2000): Significantly reduced number of convictions between 1999 and 2000: the decline is due to diversion which is now applicable to adults in criminal law. Country: Bulgaria Break in methodlogy (2000): Until 1997 data are based on the activity of the regional and district courts on penal trials of general, private and administrative character. Since 1998 the information for the activity of military courts is also included. Country: Bulgaria Break in methodlogy (2012): Since 2012 data include activities of the Special Criminal Court. Country: Canada Assault includes Level 1 Assault, Criminal Code of Canada, section 266. A common assault has been committed when an individual intentionally applies force or threatens to apply force to another person, without that person's consent. The seriousness of physical injury is what distinguishes this type of assault from other, more serious assaults. Serious assault includes assault with a weapon (Level 2, Criminal Code of Canada, section 267), aggravated assault (Level 3, Criminal Code of Canada, section 268) and other assaults (assaults against police officers, and unlawfully causing bodily harm). Homicide includes first-degree murder, second-degree murder, manslaughter and infanticide. Rape is not a recognized offence in the Criminal Code of Canada. Data reported are sexual assault (level 1), sexual assault with a weapon or bodily harm (level 2) and sexual assault aggravated (level 3). Theft includes theft over and under $5,000 as well as motor vehicle theft. Drug crime includes drug possession, trafficking, production, importing and exporting. Data refer to a fiscal year (April 1 through March 31). Data do not cover all provinces and territories. Data includes persons aged 12 y.o. or older at the time of the offence. Country: Croatia Data refer to adults serving imprisonment sentences. Country: Cyprus Data refer to the Government controlled area only. Country: Cyprus Includes convictions of both serious crimes (in violation of the Penal Code) and minor offences, as well as traffic violations. Country: Denmark Change in definition (1980 - 2012): All persons with a decision, incl. acquitted and prosecutor dropped Assault: Include serious assault and homicide Country: Denmark Only guilty decisions included. Country: Estonia Break in methodlogy (1990 - 1995): Change in laws and methodology. Country: Estonia Change in definition (1990 - 2013): Theft includes burglary. Country: Finland Break in methodology (2000): The Penal Code includes the offences against the Road Traffic Act carrying imprisonment as penalty. Country: Finland Data refer to offences against the Penal Code only. Country: France Additional information (1995 - 2002): Amnesties (part of convictions was not registered). Country: France Change in definition (1990 - 2011): Data are based on different classification of offences. Country: Georgia Territorial change (2000 onward): Data do not cover Abkhazia AR and Tskhinvali Region. Country: Germany Territorial change (1980 - 2006): Data refer to former territory of Germany. Country: Greece Change in definition (1980 - 2010): Number of convictions equals to number of convicted persons (persons found definitively guilty from penal courts). Serious assault excludes fatal body injuries. Country: Iceland Data refer to convictions from the district courts. Country: Ireland 2009: break in series, change in methodology. Country: Israel Reference period (1980): Data refer to 1981 Country: Israel Reference period (1990): Data refer to 1989 Country: Italy Break in methodlogy (2000): Until 2000 data referred to the most serious crime. Series from 2000 to 2011 have been updated according to the new systems and calculating the convinctions instead of the persons convicted. Country: Italy Change in definition (1980 - 2011): Rape: convicted for misdemeanours are not included. Country: Kazakhstan Break in methodlogy (2000): Change of source as of 2000 Country: Kyrgyzstan Change in definition (2000 - onwards): Data are changed concidering the definition of the robbery. Country: Latvia Break in methodlogy (2011): Data include fraud and misappropriation on small scale Country: Latvia Change in definition (2000 - 2012): Data for theft include burglary. Country: Moldova, Republic of Territorial change (2004 onward): Data exclude the territory of the Transnistria and municipality of Bender Country: Montenegro 2001-2006: data refer to convicted adults. From 2007: data refer to convicted adults and juveniles. Assaults include serious assaults. Country: Netherlands Assaults include serious assaults. Data exclude persons with unknown sex. Country: Norway Until 2000: the total does not include convictions for misdemeanours, i.e. ticket fines and prosecutions conditionally dropped are not included. Country: Poland Data refer to adults only. Country: Romania Convictions is equivalent to Persons convicted because there are no data regarding final convictions. Country: Serbia Territorial change (2000 onward): Data exclude territory of Kosovo and Metohija. Country: Slovakia Break in methodlogy (2006): Change in criminal code. Country: Slovenia Break in methodology (1995): Change in law. Break in methodology (2013): New amendment to the Criminal Procedure Act enabled the implementation of criminal proceedings and economized trials. This is reflected in the large increase of the number of convicted persons over the previous year. The number of convicted juveniles did not significantly increase during the same period – around 10%. Country: Spain Break in methodology (2007): change in source, data include only firm convictions. Country: Spain Total could be less than sum of convictions by type as each conviction can include different crimes. Country: Sweden Break in methodlogy (2005): Break in series for convictions of Rape due to changes in legislation for sexual offenses. Country: Sweden Statistics presented refers to conviction decisions laid down by courts (first instance county court convictions) or prosecutors (prosecutor fines or waiver of prosecution). Sub groups for some years do not add up to the main level, due to missing data on gender. Attempt, preparation, being an accomplice, incitement, failure to disclose and failure to prevent offences are included in respective offence category. Drug crime does not include drug trafficking for the years 1995 and 2000. Drug trafficking is included from 2001 onwards. Country: Switzerland Change in definition (1990 - onwards): Only convicted persons for felonies and misdemeanours. Country: Turkey Break in methodlogy (2009): Change in data compilation method. Country: Turkey Change in definition (1990 - 2010): Data includes intentional and non-intentional homicide. Theft includes burglary. Country: Turkey Data refer to the number of sentence decisions rendered for accused persons at criminal courts in accordance with Turkish Criminal Law and special laws for 2009 and later. Total excludes judicial person, foreign national and unknown sex for 2009 and later. Country: Ukraine From 2014 data cover the territories under the government control. Country: United Kingdom Change in definition (2000 - onwards): Serious assault includes attempted murder. Rape includes attempted rape. Country: United Kingdom Change in definition (2008 - onwards): Male and female may not add up to total because sex is not always recorded. Country: United Kingdom Territorial change (2000 - onwards): Data refer to England and Wales. Country: United States Data represent felony convictions in State and Federal Courts. Convictions in juvenile courts are not included. Data do not distinguish between assault and serious assault. 1995: data refers to 1994.
    • February 2018
      Source: International Monetary Fund
      Uploaded by: Knoema
      Accessed On: 12 April, 2018
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      The CDIS database presents detailed data on "inward" direct investment positions (i.e., direct investment into the reporting economy) cross-classified by economy of immediate investor, and data on "outward" direct investment positions (i.e., direct investment abroad by the reporting economy) cross-classified by economy of immediate investment. The CDIS database contains breakdowns of direct investment position data, including, in most instances, separate data on net equity and net debt positions, as well as tables that present "mirror" data (i.e., tables in which data from the reporting economy are shown side-by-side with the data obtained from all other counterpart reporting economies).
    • November 2016
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 21 November, 2018
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      Source: UNECE Statistical Database, compiled from national official sources. Definition: A ministry is a department of a government, led by a minister. A minister (sometimes called secretary) is a politician who holds significant public office in a national cabinet and is entrusted with the management of a division of governmental activities. A cabinet is a body of high-ranking members of government, typically representing the executive branch. Core ministries include: Cabinet of Prime Minister, Ministry of Home Affairs, Ministry for Foreign Affairs, Ministry of Finance, Ministry of Defence, Ministry of Justice. General note: Reference period: any fixed date of the year. .. - data not available Country: Estonia 2015: Data refers to composition after September 14, 2015. 2014: Data refers to composition between November 17, 2014 to April 9, 2015. Country: Georgia Territorial change (2004 onward): Data do not cover Abkhazia AR and Tskhinvali Region. Country: Israel 1990: data refer to average from 1988-1990, 1995: data refer to average from 1992-1995, 2000: data refer to average from 1999-2001. Country: Latvia Reference period (1990): data refer to 1991. Country: Moldova, Republic of Additional information (1980): Data include the territory of the Transnistria and municipality of Bender Country: Moldova, Republic of Additional information (1990): Data include the territory of the Transnistria and municipality of Bender Data exclude the territory of the Transnistria and municipality of Bender Country: Moldova, Republic of Additional information (1995 onward): Data exclude the territory of the Transnistria and municipality of Bender Country: Montenegro Additional information (2006): Ministry for Defense was formed in 2006. Country: Portugal 2008: data refer to 2009. Country: Slovakia Data for 2014 refer to 15 March. Data for 2015 refer to 20 November. Country: Switzerland Change in definition (1980 - onwards): All the 7 ministers in Switzerland are considered as being head of a Core Ministry.
    • February 2018
      Source: Transparency International
      Uploaded by: Pallavi S
      Accessed On: 27 February, 2018
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      Data cited at CORRUPTION PERCEPTIONS INDEX 2017 by Transparency International is licensed under CC-BY-ND 4.0. Global Corruption Barometer is the largest world-wide public opinion survey on corruption. see more at https://www.transparency.org/news/feature/corruption_perceptions_index_2017 Transparency International(TI) defines corruption as the abuse of entrusted power for private gain. This definition encompasses corrupt practices in both the public and private sectors. The Corruption Perceptions Index (CPI) ranks countries according to the perception of corruption in the public sector. The CPI is an aggregate indicator that combines different sources of information about corruption, making it possible to compare countries. The CPI ranks almost 200 countries by their perceived levels of corruption, as determined by expert assessments and opinion surveys.
    • February 2018
      Source: Numbeo
      Uploaded by: Knoema
      Accessed On: 28 February, 2018
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      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.
    • November 2012
      Source: Freedom House
      Uploaded by: Knoema
      Accessed On: 12 December, 2012
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      Countries at the Crossroads is an annual analysis of government performance in 70 strategically important countries worldwide that are at a critical crossroads in determining their political future. The in-depth comparative assessments and quantitative ratings – examining government accountability, civil liberties, rule of law, and anticorruption and transparency efforts – are intended to help international policymakers identify areas of progress, as well as to highlight areas of concern that could be addressed in diplomatic efforts and reform assistance.The Crossroads project has generated far-reaching interest since its inception in 2004. Increased attention to the relationship between competent governance and respect for civil and political rights means that scholars and policymakers require sophisticated tools to help place the performance of various governments in perspective. Crossroads helps ground this analysis by providing indispensable quantitative assessment that allows for comparison over time, as well as detailed narrative reports that provide real-world context.A new edition of Crossroads is published each year, with half the set of countries analyzed in odd years and the other half in even years. Crossroads reports are written and evaluated by some of the most prominent independent experts available for each country.
    • April 2015
      Source: International Monetary Fund
      Uploaded by: Knoema
      Accessed On: 20 August, 2015
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      Global growth is forecast at 3.5 percent in 2015 and 3.8 percent in 2016, with uneven prospects across the main countries and regions of the world. The distribution of risks to near-term global growth has become more balanced relative to the October World Economic Outlook but is still tilted to the downside. The decline in oil prices could boost activity more than expected. Geopolitical tensions continue to pose threats, and risks of disruptive shifts in asset prices remain relevant. In some advanced economies, protracted low inflation or deflation also pose risks to activity. The chapter takes a region-by-region look at the recent development in the world economy and the outlook for 2015, with particular attention to notable development in countries within each region.
    • January 2018
      Source: NYU Stern
      Uploaded by: Knoema
      Accessed On: 24 October, 2018
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      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.   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  
    • March 2018
      Source: Statistics Finland
      Uploaded by: Knoema
      Accessed On: 29 November, 2018
      Select Dataset
      Concepts and definitionsDescription 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) Country of birth The used classification of continents is the classification of Eurostat, where Cyprus and Turkey belong to Europe. Non-autonomous states are summed under their mother country. Country of birth Sudan Sudan + Former Sudan
    • June 2018
      Source: Statistics Finland
      Uploaded by: Knoema
      Accessed On: 29 November, 2018
      Select Dataset
      Concepts and definitionsDescription 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 and vital statistics in Doria (in Finnish) Publications on Population censuses in Doria (in Finnish) Area For 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. Country of birth The used classification of continents is the classification of Eurostat, where Cyprus and Turkey belong to Europe. Non-autonomous states are summed under their mother country. Country of birth Sudan Sudan + Former Sudan
    • December 2018
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 11 December, 2018
      Select Dataset
      Source: UNECE Statistical Database, compiled from national and international official sources. Area data exclude overseas departments and territories. For population footnotes click here. For life expectancy footnotes click here. For fertility rate footnotes click here. For population by marital status footnotes click here. For female members of parliament footnotes click here. For female government ministers footnotes click here. For female central bank board members footnotes click here. For female tertiary students footnotes click here. For economic activity rate footnotes click here. For gender pay gap footnotes click here. For employment growth rate footnotes click here. For unemployment rate footnotes click here. For youth unemployment rate footnotes click here. For employment by economic sector footnotes click here. For economic indicator footnotes click here. For road accident footnotes click here. For total length of motorways footnotes click here. For total length of railway lines footnotes click here. Key indicators in maps .. - data not available Indicator GDP in agriculture (ISIC4 A): output approach, index, 2010=100 If the country has not yet provided data according to ISIC 4, you may find the data according to ISIC 3.1 in more detailed tables under the Economy section of the database. GDP in industry (incl. construction) (ISIC4 B-F): output approach, index, 2010=100 If the country has not yet provided data according to ISIC 4, you may find the data according to ISIC 3.1 in more detailed tables under the Economy section of the database. GDP in services (ISIC4 G-U): output approach, index, 2010=100 If the country has not yet provided data according to ISIC 4, you may find the data according to ISIC 3.1 in more detailed tables under the Economy section of the database. GDP: in agriculture etc. (ISIC4 A), output approach, per cent share of GVA If the country has not yet provided data according to ISIC 4, you may find the data according to ISIC 3.1 in more detailed tables under the Economy section of the database. GDP: in industry etc. (ISIC4 B-E), output approach, per cent share of GVA If the country has not yet provided data according to ISIC 4, you may find the data according to ISIC 3.1 in more detailed tables under the Economy section of the database. GDP: in construction (ISIC4 F), output approach, per cent share of GVA If the country has not yet provided data according to ISIC 4, you may find the data according to ISIC 3.1 in more detailed tables under the Economy section of the database. GDP: in trade, hospitality, transport and communication (ISIC4 G-J), output approach, per cent share of GVA If the country has not yet provided data according to ISIC 4, you may find the data according to ISIC 3.1 in more detailed tables under the Economy section of the database. GDP: in finance and business services (ISIC4 K-N), output approach, per cent share of GVA If the country has not yet provided data according to ISIC 4, you may find the data according to ISIC 3.1 in more detailed tables under the Economy section of the database. GDP: in public administration, education and health (ISIC4 O-Q), output approach, per cent share of GVA If the country has not yet provided data according to ISIC 4, you may find the data according to ISIC 3.1 in more detailed tables under the Economy section of the database. GDP: in other service activities (ISIC4 R-U), output approach, per cent share of GVA If the country has not yet provided data according to ISIC 4, you may find the data according to ISIC 3.1 in more detailed tables under the Economy section of the database. Employment in agriculture, hunting, forestry and fishing (ISIC Rev. 4 A), share of total employment If the country has not yet provided data according to ISIC 4, you may find the data according to ISIC 3.1 in more detailed tables under the Economy section of the database. Employment in industry and energy (ISIC Rev. 4 B-E), share of total employment If the country has not yet provided data according to ISIC 4, you may find the data according to ISIC 3.1 in more detailed tables under the Economy section of the database. Employment in construction (ISIC Rev. 4 F), share of total employment If the country has not yet provided data according to ISIC 4, you may find the data according to ISIC 3.1 in more detailed tables under the Economy section of the database. Employment in trade, hotels, restaurants, transport and communications (ISIC Rev. 4 G-J), share of total employment If the country has not yet provided data according to ISIC 4, you may find the data according to ISIC 3.1 in more detailed tables under the Economy section of the database. Employment in finance, real estate and business services (ISIC Rev. 4 K-N), share of total employment If the country has not yet provided data according to ISIC 4, you may find the data according to ISIC 3.1 in more detailed tables under the Economy section of the database. Employment in public administration, education and health (ISIC Rev. 4 O-Q), share of total employment If the country has not yet provided data according to ISIC 4, you may find the data according to ISIC 3.1 in more detailed tables under the Economy section of the database. Employment in other service activities (ISIC Rev. 4 R-U), share of total employment If the country has not yet provided data according to ISIC 4, you may find the data according to ISIC 3.1 in more detailed tables under the Economy section of the database.
    • June 2018
      Source: World Bank
      Uploaded by: Knoema
      Accessed On: 19 July, 2018
      Select Dataset
      The World Bank's Country Policy and Institutional Assessment is done annually for all its borrowing countries. It has evolved into a set of criteria, which are grouped in four clusters: (a) economic management; (b) structural policies; (c) policies for social inclusion and equity; and (d) public sector management and institutions. The number of criteria, currently sixteen, reflect a balance between ensuring that all key factors that foster pro-poor growth and poverty alleviation are captured, without overly burdening the evaluation process. Ratings for each of the criteria reflect a variety of indicators, observations, and judgments. They focus on the quality of each country's current policies and institutions - which are the main determinant of present aid effectiveness prospects. To fully underscore the importance of the CPIA in the IDA Performance Based Allocations, the overall country score is referred to as the IDA Resource Allocation Index (IRAI)
    • April 2018
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 21 May, 2018
      Select Dataset
      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
    • July 2016
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 29 July, 2016
      Select Dataset
      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
    • December 2015
      Source: International Monetary Fund
      Uploaded by: Knoema
      Accessed On: 18 April, 2016
      Select Dataset
      COFR presents data on fiscal transparency. It provides an overview of fiscal reporting, including whether fiscal data are available for all of the general government, whether the government reports a balance sheet, and whether spending and revenue are reported on a cash or accrual basis. It also derives specific indices of the coverage of public institutions, fiscal flows, and fiscal stocks.
    • October 2018
      Source: Food and Agriculture Organization
      Uploaded by: Knoema
      Accessed On: 02 November, 2018
      Select Dataset
      The Credit to Agriculture dataset provides national data for over 100 countries on the amount of loans provided by the private/commercial banking sector to producers in agriculture, forestry and fisheries, including household producers, cooperatives, and agro-businesses. For some countries, the three sub sectors of agriculture, forestry, and fishing are completely specified. In other cases, complete dis aggregations are not available. The dataset also provides statistics on the total credit to all industries, indicators on the share of credit to agricultural producers, and an agriculture orientation index (the agriculture share of credit, over the agriculture share of GDP).
    • August 2018
      Source: Numbeo
      Uploaded by: Knoema
      Accessed On: 21 August, 2018
      Select Dataset
      Crime Index is an estimation of 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 opposite of crime index. If the city has a high safety index, it is considered very safe. Data cited at: https://www.numbeo.com/crime/rankings.jsp?title=2018.
    • November 2017
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 01 December, 2017
      Select Dataset
      Not applicable
    • March 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 12 March, 2018
      Select Dataset
      Data on marriages and divorces at national level are based on the annual demographic data collections in the field of demography carried out by Eurostat. The completeness of information depends on the availability of data reported by the National Statistical Institutes. The Joint demographic data collection is carried out in cooperation with United Nation Statistical Division (UNSD) in the summer of each year, having the deadline 15 September. During this data collection Eurostat collects from the national statistical institutes detailed data by sex, age and other characteristics for the demographic events (births, deaths, marriages and divorces) of the previous year and the population on 1 January of the current and previous years. More specifically, during year T the following data are collected and disseminated on fertility field: - total number of marriages and divorces - persons getting married during the reference year by previous legal marital status, year T-1 Data can be found under the section Marriages and divorces (demo_nup). The information is updated towards the end of each year based on information collected during the Joint data collection. Moreover, any update sent by the countries in-between data collections are validated, processed and uploaded into Eurostat's demographic database and in Eurostat's free dissemination online database as soon as possible. Aggregates are recalculated accordingly. The data transmitted by the National Statistical Institutes are validated by Eurostat, processed and uploaded into Eurostat's Demographic Database and in Eurostat's free dissemination online database. The data are also disseminated in several thematic and horizontal Eurostat's publications. Data are presented by single country and for aggregates of countries. For EU and Euro Area, only the current and the previous version of the aggregates are published. The currently disseminated aggregates are: EU-27, EU-25, EA-16, and EA-15. Moreover, data is disseminated for the European Economic Area (EEA) and the European Free Trade Association (EFTA). International marriages and divorces Statistics on the number of international marriages and divorces (2000-2007) were collected by Eurostat from national statistical institutes in September 2008. The data were further used by the European Commission for preparing  a proposal for a Council Regulation on the law applicable in divorce and legal separation.  These data collected are available upon request.
    • March 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 12 March, 2018
      Select Dataset
      Data on marriages and divorces at national level are based on the annual demographic data collections in the field of demography carried out by Eurostat. The completeness of information depends on the availability of data reported by the National Statistical Institutes. The Joint demographic data collection is carried out in cooperation with United Nation Statistical Division (UNSD) in the summer of each year, having the deadline 15 September. During this data collection Eurostat collects from the national statistical institutes detailed data by sex, age and other characteristics for the demographic events (births, deaths, marriages and divorces) of the previous year and the population on 1 January of the current and previous years. More specifically, during year T the following data are collected and disseminated on fertility field: - total number of marriages and divorces - persons getting married during the reference year by previous legal marital status, year T-1 Data can be found under the section Marriages and divorces (demo_nup). The information is updated towards the end of each year based on information collected during the Joint data collection. Moreover, any update sent by the countries in-between data collections are validated, processed and uploaded into Eurostat's demographic database and in Eurostat's free dissemination online database as soon as possible. Aggregates are recalculated accordingly. The data transmitted by the National Statistical Institutes are validated by Eurostat, processed and uploaded into Eurostat's Demographic Database and in Eurostat's free dissemination online database. The data are also disseminated in several thematic and horizontal Eurostat's publications. Data are presented by single country and for aggregates of countries. For EU and Euro Area, only the current and the previous version of the aggregates are published. The currently disseminated aggregates are: EU-27, EU-25, EA-16, and EA-15. Moreover, data is disseminated for the European Economic Area (EEA) and the European Free Trade Association (EFTA). International marriages and divorces Statistics on the number of international marriages and divorces (2000-2007) were collected by Eurostat from national statistical institutes in September 2008. The data were further used by the European Commission for preparing  a proposal for a Council Regulation on the law applicable in divorce and legal separation.  These data collected are available upon request.
    • November 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 29 November, 2018
      Select Dataset
      Data on marriages and divorces at national level are transmitted by the National Statistics Institutes on voluntary basis in the context of the annual demographic data collections in the field of demography carried out by Eurostat as follows:
    • March 2017
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 30 March, 2017
      Select Dataset
      These metadata refer to the annual population data under Population / Demography domain in Eurostat's Dissemination data tree. Eurostat carries on annual demography data collections with the aim of collecting from the National Statistical Institutes detailed data on population, vital events, marriages and divorces. These data are validated, processed and disseminated. Further on, Eurostat uses the collected detailed data to compute and disseminate demographic indicators at country level, at regional level and at EU level, by applying harmonized methods of calculation. The demography data collections are done on voluntary basis and the completeness of information depends on the availability of data reported by the National Statistical Institutes. The first demography data collection of each year, named Rapid, is carried out in April-May (deadline 15 May). Within this data collection the first results on the main demographic developments in the previous year (T-1) and the population on 1st January of the current year (T) are collected from the National Statistical Institutes. A second annual data collection, Joint Demography data collection, is carried out in cooperation with United Nation Statistical Division (UNSD) in the summer of each year, having the deadline 15 September. Within this data collection Eurostat collects from the National Statistical Institutes detailed data on the demographic events (births, deaths, marriages and divorces) of the previous year (T-1) and the population on 1st January of the current year (T), broken down by sex, age and other characteristics. The Nowcast Demography data collection is carried out in October-November (deadline 15 November). The monthly time series on births, deaths, immigrants and emigrants available from the beginning of current year (T) are collected, with the purpose of producing by the end of the current year (T) a forecast on 1st January population of the following year (T+1). The Regional Demography data collection is carried out in November-December (deadline 15 December). It is based on the regional breakdown of the countries agreed at EU level using the latest version of the Nomenclature of Territorial Units for Statistics (NUTS) and of the Statistical regions for the EFTA and Candidate countries. Within this data collection Eurostat collects from the National Statistical Institutes data by NUTS level 1, 2 and 3 for the vital events taking place in the previous year (T-1) and the population figures on 1st January of the current year (T). Any updates sent by the National Statistical Institutes in-between data collections are validated, processed and disseminated in Eurostat's online database as soon as possible. The European aggregates and the demographic indicators are updated accordingly. Please note:The tables presenting population on 1 January figures by various breakdowns may display variations in the total population for some countries at a given moment in time. This may occur due to one of the following reasons: - The timing of the transmission to Eurostat of the population data for various breakdown may lead to different population on 1 January figures displayed in different population tables at a given moment in time. - The transmission to Eurostat of the post-census population revisions (following the 2011 population Censuses) is expected to be done by the national statistical offices gradually for the population breakdowns. The time series of populations between the previous census taking place in the country and 2011 will be revised by end 2013 by some of the countries, taking into account Eurostat’s recommendation. The following countries have transmitted to Eurostat post-2011 Census population revisions, broken down by age and sex, by autumn of 2013, which are reflected in the tables ‘Demographic balance and crude rates (demo_gind)’, ‘Population on 1 January by age and sex (demo_pjan)’, ‘Population on 1 January by five years age groups and sex (demo_pjangroup)’ and ‘Population on 1 January by broad age group and sex (demo_pjanbroad)’: BG 2007-2011; CZ 2001-2011; EE 2000-2011; IE 2007-2011; EL 2011; ES 2002-2011; HR 2001-2011; CY 2003-2011; LV 2001-2011; LT 2001-2011; MT 2006-2011; AT 2008-2011; PT 1992-2011; RO 2002-2011; SK 2002-2011; UK 2002-2011 (not including post-2011 Census data for Scotland); ME 2010-2011; RS 2011. As regards the the population data for the year 2012 and after, for most of the countries these take into account the results of the latest population census (held in 2011). IT 2012-2013 and DE 2012-2013 reported only the total post-2011 Census populations which are published in the table ‘Demographic balance and crude rates (demo_gind)’. The breakdown by age and sex will follow later on. - The succession of the annual demography data collections described above, which collect and update population breakdowns at different moment during the calendar year. - The calendar of the national statistical offices for producing and releasing population broken down by various topics, respectively the timings when data are transmitted to Eurostat. The most updated data on total population on 1st January and on the total number of live births and deaths may be found in the table 'Demographic balance and crude rates (demo_gind)' of the online 'Database by theme'. This table includes the latest updates (or revised data) on total population, births and deaths reported by the countries, while the detailed breakdowns by various characteristics included in the rest of the tables of the Demography domain (and also for Population by citizenship and by country of birth) may be transmitted to Eurostat at a subsequent date.
    • March 2017
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 30 March, 2017
      Select Dataset
      These metadata refer to the annual population data under Population / Demography domain in Eurostat's Dissemination data tree. Eurostat carries on annual demography data collections with the aim of collecting from the National Statistical Institutes detailed data on population, vital events, marriages and divorces. These data are validated, processed and disseminated. Further on, Eurostat uses the collected detailed data to compute and disseminate demographic indicators at country level, at regional level and at EU level, by applying harmonized methods of calculation. The demography data collections are done on voluntary basis and the completeness of information depends on the availability of data reported by the National Statistical Institutes. The first demography data collection of each year, named Rapid, is carried out in April-May (deadline 15 May). Within this data collection the first results on the main demographic developments in the previous year (T-1) and the population on 1st January of the current year (T) are collected from the National Statistical Institutes. A second annual data collection, Joint Demography data collection, is carried out in cooperation with United Nation Statistical Division (UNSD) in the summer of each year, having the deadline 15 September. Within this data collection Eurostat collects from the National Statistical Institutes detailed data on the demographic events (births, deaths, marriages and divorces) of the previous year (T-1) and the population on 1st January of the current year (T), broken down by sex, age and other characteristics. The Nowcast Demography data collection is carried out in October-November (deadline 15 November). The monthly time series on births, deaths, immigrants and emigrants available from the beginning of current year (T) are collected, with the purpose of producing by the end of the current year (T) a forecast on 1st January population of the following year (T+1). The Regional Demography data collection is carried out in November-December (deadline 15 December). It is based on the regional breakdown of the countries agreed at EU level using the latest version of the Nomenclature of Territorial Units for Statistics (NUTS) and of the Statistical regions for the EFTA and Candidate countries. Within this data collection Eurostat collects from the National Statistical Institutes data by NUTS level 1, 2 and 3 for the vital events taking place in the previous year (T-1) and the population figures on 1st January of the current year (T). Any updates sent by the National Statistical Institutes in-between data collections are validated, processed and disseminated in Eurostat's online database as soon as possible. The European aggregates and the demographic indicators are updated accordingly. Please note:The tables presenting population on 1 January figures by various breakdowns may display variations in the total population for some countries at a given moment in time. This may occur due to one of the following reasons: - The timing of the transmission to Eurostat of the population data for various breakdown may lead to different population on 1 January figures displayed in different population tables at a given moment in time. - The transmission to Eurostat of the post-census population revisions (following the 2011 population Censuses) is expected to be done by the national statistical offices gradually for the population breakdowns. The time series of populations between the previous census taking place in the country and 2011 will be revised by end 2013 by some of the countries, taking into account Eurostat’s recommendation. The following countries have transmitted to Eurostat post-2011 Census population revisions, broken down by age and sex, by autumn of 2013, which are reflected in the tables ‘Demographic balance and crude rates (demo_gind)’, ‘Population on 1 January by age and sex (demo_pjan)’, ‘Population on 1 January by five years age groups and sex (demo_pjangroup)’ and ‘Population on 1 January by broad age group and sex (demo_pjanbroad)’: BG 2007-2011; CZ 2001-2011; EE 2000-2011; IE 2007-2011; EL 2011; ES 2002-2011; HR 2001-2011; CY 2003-2011; LV 2001-2011; LT 2001-2011; MT 2006-2011; AT 2008-2011; PT 1992-2011; RO 2002-2011; SK 2002-2011; UK 2002-2011 (not including post-2011 Census data for Scotland); ME 2010-2011; RS 2011. As regards the the population data for the year 2012 and after, for most of the countries these take into account the results of the latest population census (held in 2011). IT 2012-2013 and DE 2012-2013 reported only the total post-2011 Census populations which are published in the table ‘Demographic balance and crude rates (demo_gind)’. The breakdown by age and sex will follow later on. - The succession of the annual demography data collections described above, which collect and update population breakdowns at different moment during the calendar year. - The calendar of the national statistical offices for producing and releasing population broken down by various topics, respectively the timings when data are transmitted to Eurostat. The most updated data on total population on 1st January and on the total number of live births and deaths may be found in the table 'Demographic balance and crude rates (demo_gind)' of the online 'Database by theme'. This table includes the latest updates (or revised data) on total population, births and deaths reported by the countries, while the detailed breakdowns by various characteristics included in the rest of the tables of the Demography domain (and also for Population by citizenship and by country of birth) may be transmitted to Eurostat at a subsequent date.
    • December 2017
      Source: Food and Agriculture Organization
      Uploaded by: Knoema
      Accessed On: 05 January, 2018
      Select Dataset
      GHG emissions data from the cultivation of organic soils are those associated with nitrous oxide gas from organic soils under cropland (item: Cropland organic soils) and grassland (item: Grassland organic soils). The FAOSTAT emissions database is computed following Tier 1 IPCC 2006 Guidelines for National GHG Inventories (http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol4.html). GHG emissions are provided by country, region and special groups, with global coverage, relative to the period 1990-present (with annual updates) and with projections for 2030 and 2050, expressed both as Gg N2O and Gg CO2eq, by cropland, grassland and by their aggregation. Implied emission factor for N2O as well activity data (areas) are also provided.
    • May 2018
      Source: United Nations Conference on Trade and Development
      Uploaded by: Knoema
      Accessed On: 28 June, 2018
      Select Dataset
      This table shows exchange rates for currencies used in over 190 world economies presented in a cross rates layout where countries are presented in both rows and columns. National currency per US dollars exchange rates are used to derive explicit exchange rates for each of the countries presented with regard to any other country. Country series are consistent over time: for example, a conversion was made from national currency to Euro for the Euro Zone economies for all years prior to the adoption of Euro.
  • D
    • May 2018
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 21 November, 2018
      Select Dataset
      .. - data not available Source: UNECE Statistical Division Database, compiled from national and international (WHO European health for all database) official sources. Definitions: The (age-) standardized death rate (SDR) is a weighted average of age-specific mortality rates per 100 000 population. The weighting factor is the age distribution of a standard reference population. The standard reference population used is the European standard population as defined by the World Health Organisation (WHO). As method for standardisation, the direct method is applied. As most causes of death vary significantly with age and sex, the use of standardised death rates improves comparability over time and between countries. Death refers to the permanent disappearance of all evidence of life at any time after a live birth has taken place (post-natal cessation of vital functions without capability of resuscitation). This definition therefore excludes foetal deaths. Causes of death (CoD) are all diseases, morbid conditions or injuries that either resulted in or contributed to death, and the circumstances of the accident or violence that produced any such injuries. Symptoms or modes of dying, such as heart failure or asthenia, are not considered to be causes of death for vital statistics purposes. General note:: Diseases and external causes of death are coded differently in different versions of the International Classification of Diseases (ICD). For many diseases it is not possible to identify codes in different classification systems that would correspond precisely to the same disease or groups of diseases. Often the change in the trend of a certain cause-specific mortality rate may be the result of a changing ICD version or national death certification and coding practices, rather than an actual change in the mortality. It should be noted that mortality rates for some countries may be biased due to the under-registration of death cases. The basic principle of selection of the 17 CoD for presentation in the UNECE Gender Database is to include one main SDR for each of the ICD chapters and also to focus on some of the leading CoD across the European Region and some specific causes with high gender differences. ICD versionCountries9.3 - ICD-9 3-digit codes Albania, The former Yugoslav Republic of Macedonia 9.4 - ICD-9 4-digit or mixture of 3- and 4-digit codesGreece9.5 - ICD-9 BTL codes (in most countries actually original ICD-9 codes were used but the data later were converted by WHO into BTL codes) Bosnia and Herzegovina10.1 - ICD-10 mortality tabulation condensed list No1 (103 causes) Armenia, Azerbaijan, Belarus, Kazakhstan, Russian Federation, Ukraine10.3 - ICD-10 3-digit codes Belgium, Bulgaria, Estonia, Georgia, Latvia, Montenegro, Serbia, Slovakia, Slovenia, Uzbekistan10.4 - ICD-10 4-digit or mixture of 3- and 4-digit codes Austria, Canada, Croatia, Cyprus, Czech Republic, Denmark, Finland, France, Germany, Hungary, Iceland, Ireland, Israel, Italy, Kyrgyzstan, Lithuania, Luxembourg, Malta, Netherlands, Norway, Poland, Portugal, Republic of Moldova, Romania, Spain, Sweden, Switzerland, United Kingdom, United States 1.75 - Special tabulation list of 175 causes used in some ex-USSR countries Tajikistan, Turkmenistan Link to International Classification of Diseases 10th Revision Country: Canada Data on accidents include sequelae of transport and other accidents. Data on transport accidents include sequelae of transport accidents. Data on suicide and intentional self-harm include sequelae of intentional self-harm. Country: United States Data on accidents include sequelae of transport and other accidents. Data on transport accidents include sequelae of transport accidents.
    • March 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 18 March, 2018
      Select Dataset
      Eurostat statistics on mortality are based on the annual demographic data collection in the field of demography carried out by Eurostat. The completeness of information depends on the availability of data reported by the National Statistical Institutes. The first demographic data collection of each year (T), named Rapid, is carried out in April-May (deadline 15 May of year T); during this data collection the first results on the main demographic developments in the previous year (T-1) and the population on 1 January of the current year (T) are collected from the National Statistical Institutes. The Joint demographic data collection is carried out in cooperation with United Nation Statistical Division (UNSD) in the summer of each year, having the deadline 15 September. During this data collection Eurostat collects from the National Statistical Institutes detailed data by sex, age and other characteristics for the demographic events (births, deaths, marriages and divorces) of the previous year and the population on 1 January of the current and previous years. The Nowcast demographic data collection is carried out in October-November (deadline 15 November of year T). The monthly time series on births, deaths, immigrants and emigrants available from the beginning of current year (T) are collected, with the purpose of producing a forecast on 1 January population of the following year (T+1). More specifically, during year T the following data are collected and disseminated on mortality field: - Total number of deaths in year (T-1) - Infant mortality by age and sex (T-1) - Late foetal deaths by mother's age (T-1) - Deaths by age, year of birth and sex (T-1) - Deaths by age, sex and educational attainment (ISCED 1997) - Deaths by month, year (T) and (T-1) Based on these information, Eurostat currently computes and disseminates the following mortality indicators: - Crude death rate - Infant mortality rate - Neonatal mortality rate - Early neonatal mortality rate - Late foetal mortality rate - Perinatal mortality rate - Life table - Life expectancy by age and sex - Life expectancy by age, sex and educational attainment (ISCED 1997)  The most recent (aggregated) data on the number of deaths can be found under the Main demographic indicators. This includes also the most recent Eurostat now casts on the main demographic indicators (population, births, deaths and net migration including statistical adjustment). In principle, the table containing the main demographic indicators is updated three times per year, after each of the national data collections. Detailed information on mortality (by age, sex, etc.) can be found under the section Mortality (demo_mor). These disaggregated information are updated towards the end of each year based on information collected during the Joint data collection. Moreover, any update sent by the countries in-between data collections are validated, processed and uploaded into Eurostat's demographic database and in Eurostat's free dissemination online database as soon as possible. The geographical aggregates are recalculated accordingly. The data transmitted by the National Statistical Institutes are validated by Eurostat, processed and uploaded into Eurostat's Demographic Database and in Eurostat's free dissemination online database. The data are also disseminated in several thematic and horizontal Eurostat's publications. Data are presented at national level and for aggregates of countries. For EU and Euro Area, only the current and the previous geographical status are published. The currently disseminated geographical aggregates are: EU-27, EU-25, EA-16, and EA-15. Moreover, data is disseminated for the European Economic Area (EEA) and the European Free Trade Association (EFTA).
    • November 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 29 November, 2018
      Select Dataset
      number - per 1 000 personsDeath means the permanent disappearance of all evidence of life at any time after life birth has taken place (postnatal cessation of vital functions without capability of resuscitation).The crude death rate is the ratio of the number of deaths during the year to the average population in that year. The value is expressed per 1 000 persons.
    • March 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 18 March, 2018
      Select Dataset
      Eurostat statistics on mortality are based on the annual demographic data collection in the field of demography carried out by Eurostat. The completeness of information depends on the availability of data reported by the National Statistical Institutes. The first demographic data collection of each year (T), named Rapid, is carried out in April-May (deadline 15 May of year T); during this data collection the first results on the main demographic developments in the previous year (T-1) and the population on 1 January of the current year (T) are collected from the National Statistical Institutes. The Joint demographic data collection is carried out in cooperation with United Nation Statistical Division (UNSD) in the summer of each year, having the deadline 15 September. During this data collection Eurostat collects from the National Statistical Institutes detailed data by sex, age and other characteristics for the demographic events (births, deaths, marriages and divorces) of the previous year and the population on 1 January of the current and previous years. The Nowcast demographic data collection is carried out in October-November (deadline 15 November of year T). The monthly time series on births, deaths, immigrants and emigrants available from the beginning of current year (T) are collected, with the purpose of producing a forecast on 1 January population of the following year (T+1). More specifically, during year T the following data are collected and disseminated on mortality field: - Total number of deaths in year (T-1) - Infant mortality by age and sex (T-1) - Late foetal deaths by mother's age (T-1) - Deaths by age, year of birth and sex (T-1) - Deaths by age, sex and educational attainment (ISCED 1997) - Deaths by month, year (T) and (T-1) Based on these information, Eurostat currently computes and disseminates the following mortality indicators: - Crude death rate - Infant mortality rate - Neonatal mortality rate - Early neonatal mortality rate - Late foetal mortality rate - Perinatal mortality rate - Life table - Life expectancy by age and sex - Life expectancy by age, sex and educational attainment (ISCED 1997)  The most recent (aggregated) data on the number of deaths can be found under the Main demographic indicators. This includes also the most recent Eurostat now casts on the main demographic indicators (population, births, deaths and net migration including statistical adjustment). In principle, the table containing the main demographic indicators is updated three times per year, after each of the national data collections. Detailed information on mortality (by age, sex, etc.) can be found under the section Mortality (demo_mor). These disaggregated information are updated towards the end of each year based on information collected during the Joint data collection. Moreover, any update sent by the countries in-between data collections are validated, processed and uploaded into Eurostat's demographic database and in Eurostat's free dissemination online database as soon as possible. The geographical aggregates are recalculated accordingly. The data transmitted by the National Statistical Institutes are validated by Eurostat, processed and uploaded into Eurostat's Demographic Database and in Eurostat's free dissemination online database. The data are also disseminated in several thematic and horizontal Eurostat's publications. Data are presented at national level and for aggregates of countries. For EU and Euro Area, only the current and the previous geographical status are published. The currently disseminated geographical aggregates are: EU-27, EU-25, EA-16, and EA-15. Moreover, data is disseminated for the European Economic Area (EEA) and the European Free Trade Association (EFTA).
    • April 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 11 May, 2018
      Select Dataset
      Eurostat statistics on mortality are based on the annual demographic data collection in the field of demography carried out by Eurostat. The completeness of information depends on the availability of data reported by the National Statistical Institutes. The first demographic data collection of each year (T), named Rapid, is carried out in April-May (deadline 15 May of year T); during this data collection the first results on the main demographic developments in the previous year (T-1) and the population on 1 January of the current year (T) are collected from the National Statistical Institutes. The Joint demographic data collection is carried out in cooperation with United Nation Statistical Division (UNSD) in the summer of each year, having the deadline 15 September. During this data collection Eurostat collects from the National Statistical Institutes detailed data by sex, age and other characteristics for the demographic events (births, deaths, marriages and divorces) of the previous year and the population on 1 January of the current and previous years. The Nowcast demographic data collection is carried out in October-November (deadline 15 November of year T). The monthly time series on births, deaths, immigrants and emigrants available from the beginning of current year (T) are collected, with the purpose of producing a forecast on 1 January population of the following year (T+1). More specifically, during year T the following data are collected and disseminated on mortality field: - Total number of deaths in year (T-1) - Infant mortality by age and sex (T-1) - Late foetal deaths by mother's age (T-1) - Deaths by age, year of birth and sex (T-1) - Deaths by age, sex and educational attainment (ISCED 1997) - Deaths by month, year (T) and (T-1) Based on these information, Eurostat currently computes and disseminates the following mortality indicators: - Crude death rate - Infant mortality rate - Neonatal mortality rate - Early neonatal mortality rate - Late foetal mortality rate - Perinatal mortality rate - Life table - Life expectancy by age and sex - Life expectancy by age, sex and educational attainment (ISCED 1997)  The most recent (aggregated) data on the number of deaths can be found under the Main demographic indicators. This includes also the most recent Eurostat now casts on the main demographic indicators (population, births, deaths and net migration including statistical adjustment). In principle, the table containing the main demographic indicators is updated three times per year, after each of the national data collections. Detailed information on mortality (by age, sex, etc.) can be found under the section Mortality (demo_mor). These disaggregated information are updated towards the end of each year based on information collected during the Joint data collection. Moreover, any update sent by the countries in-between data collections are validated, processed and uploaded into Eurostat's demographic database and in Eurostat's free dissemination online database as soon as possible. The geographical aggregates are recalculated accordingly. The data transmitted by the National Statistical Institutes are validated by Eurostat, processed and uploaded into Eurostat's Demographic Database and in Eurostat's free dissemination online database. The data are also disseminated in several thematic and horizontal Eurostat's publications. Data are presented at national level and for aggregates of countries. For EU and Euro Area, only the current and the previous geographical status are published. The currently disseminated geographical aggregates are: EU-27, EU-25, EA-16, and EA-15. Moreover, data is disseminated for the European Economic Area (EEA) and the European Free Trade Association (EFTA).
    • April 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 11 May, 2018
      Select Dataset
      Eurostat statistics on mortality are based on the annual demographic data collection in the field of demography carried out by Eurostat. The completeness of information depends on the availability of data reported by the National Statistical Institutes. The first demographic data collection of each year (T), named Rapid, is carried out in April-May (deadline 15 May of year T); during this data collection the first results on the main demographic developments in the previous year (T-1) and the population on 1 January of the current year (T) are collected from the National Statistical Institutes. The Joint demographic data collection is carried out in cooperation with United Nation Statistical Division (UNSD) in the summer of each year, having the deadline 15 September. During this data collection Eurostat collects from the National Statistical Institutes detailed data by sex, age and other characteristics for the demographic events (births, deaths, marriages and divorces) of the previous year and the population on 1 January of the current and previous years. The Nowcast demographic data collection is carried out in October-November (deadline 15 November of year T). The monthly time series on births, deaths, immigrants and emigrants available from the beginning of current year (T) are collected, with the purpose of producing a forecast on 1 January population of the following year (T+1). More specifically, during year T the following data are collected and disseminated on mortality field: - Total number of deaths in year (T-1) - Infant mortality by age and sex (T-1) - Late foetal deaths by mother's age (T-1) - Deaths by age, year of birth and sex (T-1) - Deaths by age, sex and educational attainment (ISCED 1997) - Deaths by month, year (T) and (T-1) Based on these information, Eurostat currently computes and disseminates the following mortality indicators: - Crude death rate - Infant mortality rate - Neonatal mortality rate - Early neonatal mortality rate - Late foetal mortality rate - Perinatal mortality rate - Life table - Life expectancy by age and sex - Life expectancy by age, sex and educational attainment (ISCED 1997)  The most recent (aggregated) data on the number of deaths can be found under the Main demographic indicators. This includes also the most recent Eurostat now casts on the main demographic indicators (population, births, deaths and net migration including statistical adjustment). In principle, the table containing the main demographic indicators is updated three times per year, after each of the national data collections. Detailed information on mortality (by age, sex, etc.) can be found under the section Mortality (demo_mor). These disaggregated information are updated towards the end of each year based on information collected during the Joint data collection. Moreover, any update sent by the countries in-between data collections are validated, processed and uploaded into Eurostat's demographic database and in Eurostat's free dissemination online database as soon as possible. The geographical aggregates are recalculated accordingly. The data transmitted by the National Statistical Institutes are validated by Eurostat, processed and uploaded into Eurostat's Demographic Database and in Eurostat's free dissemination online database. The data are also disseminated in several thematic and horizontal Eurostat's publications. Data are presented at national level and for aggregates of countries. For EU and Euro Area, only the current and the previous geographical status are published. The currently disseminated geographical aggregates are: EU-27, EU-25, EA-16, and EA-15. Moreover, data is disseminated for the European Economic Area (EEA) and the European Free Trade Association (EFTA).
    • April 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 11 May, 2018
      Select Dataset
      Eurostat statistics on mortality are based on the annual demographic data collection in the field of demography carried out by Eurostat. The completeness of information depends on the availability of data reported by the National Statistical Institutes. The first demographic data collection of each year (T), named Rapid, is carried out in April-May (deadline 15 May of year T); during this data collection the first results on the main demographic developments in the previous year (T-1) and the population on 1 January of the current year (T) are collected from the National Statistical Institutes. The Joint demographic data collection is carried out in cooperation with United Nation Statistical Division (UNSD) in the summer of each year, having the deadline 15 September. During this data collection Eurostat collects from the National Statistical Institutes detailed data by sex, age and other characteristics for the demographic events (births, deaths, marriages and divorces) of the previous year and the population on 1 January of the current and previous years. The Nowcast demographic data collection is carried out in October-November (deadline 15 November of year T). The monthly time series on births, deaths, immigrants and emigrants available from the beginning of current year (T) are collected, with the purpose of producing a forecast on 1 January population of the following year (T+1). More specifically, during year T the following data are collected and disseminated on mortality field: - Total number of deaths in year (T-1) - Infant mortality by age and sex (T-1) - Late foetal deaths by mother's age (T-1) - Deaths by age, year of birth and sex (T-1) - Deaths by age, sex and educational attainment (ISCED 1997) - Deaths by month, year (T) and (T-1) Based on these information, Eurostat currently computes and disseminates the following mortality indicators: - Crude death rate - Infant mortality rate - Neonatal mortality rate - Early neonatal mortality rate - Late foetal mortality rate - Perinatal mortality rate - Life table - Life expectancy by age and sex - Life expectancy by age, sex and educational attainment (ISCED 1997)  The most recent (aggregated) data on the number of deaths can be found under the Main demographic indicators. This includes also the most recent Eurostat now casts on the main demographic indicators (population, births, deaths and net migration including statistical adjustment). In principle, the table containing the main demographic indicators is updated three times per year, after each of the national data collections. Detailed information on mortality (by age, sex, etc.) can be found under the section Mortality (demo_mor). These disaggregated information are updated towards the end of each year based on information collected during the Joint data collection. Moreover, any update sent by the countries in-between data collections are validated, processed and uploaded into Eurostat's demographic database and in Eurostat's free dissemination online database as soon as possible. The geographical aggregates are recalculated accordingly. The data transmitted by the National Statistical Institutes are validated by Eurostat, processed and uploaded into Eurostat's Demographic Database and in Eurostat's free dissemination online database. The data are also disseminated in several thematic and horizontal Eurostat's publications. Data are presented at national level and for aggregates of countries. For EU and Euro Area, only the current and the previous geographical status are published. The currently disseminated geographical aggregates are: EU-27, EU-25, EA-16, and EA-15. Moreover, data is disseminated for the European Economic Area (EEA) and the European Free Trade Association (EFTA).
    • March 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 18 March, 2018
      Select Dataset
      Eurostat statistics on mortality are based on the annual demographic data collection in the field of demography carried out by Eurostat. The completeness of information depends on the availability of data reported by the National Statistical Institutes. The first demographic data collection of each year (T), named Rapid, is carried out in April-May (deadline 15 May of year T); during this data collection the first results on the main demographic developments in the previous year (T-1) and the population on 1 January of the current year (T) are collected from the National Statistical Institutes. The Joint demographic data collection is carried out in cooperation with United Nation Statistical Division (UNSD) in the summer of each year, having the deadline 15 September. During this data collection Eurostat collects from the National Statistical Institutes detailed data by sex, age and other characteristics for the demographic events (births, deaths, marriages and divorces) of the previous year and the population on 1 January of the current and previous years. The Nowcast demographic data collection is carried out in October-November (deadline 15 November of year T). The monthly time series on births, deaths, immigrants and emigrants available from the beginning of current year (T) are collected, with the purpose of producing a forecast on 1 January population of the following year (T+1). More specifically, during year T the following data are collected and disseminated on mortality field: - Total number of deaths in year (T-1) - Infant mortality by age and sex (T-1) - Late foetal deaths by mother's age (T-1) - Deaths by age, year of birth and sex (T-1) - Deaths by age, sex and educational attainment (ISCED 1997) - Deaths by month, year (T) and (T-1) Based on these information, Eurostat currently computes and disseminates the following mortality indicators: - Crude death rate - Infant mortality rate - Neonatal mortality rate - Early neonatal mortality rate - Late foetal mortality rate - Perinatal mortality rate - Life table - Life expectancy by age and sex - Life expectancy by age, sex and educational attainment (ISCED 1997)  The most recent (aggregated) data on the number of deaths can be found under the Main demographic indicators. This includes also the most recent Eurostat now casts on the main demographic indicators (population, births, deaths and net migration including statistical adjustment). In principle, the table containing the main demographic indicators is updated three times per year, after each of the national data collections. Detailed information on mortality (by age, sex, etc.) can be found under the section Mortality (demo_mor). These disaggregated information are updated towards the end of each year based on information collected during the Joint data collection. Moreover, any update sent by the countries in-between data collections are validated, processed and uploaded into Eurostat's demographic database and in Eurostat's free dissemination online database as soon as possible. The geographical aggregates are recalculated accordingly. The data transmitted by the National Statistical Institutes are validated by Eurostat, processed and uploaded into Eurostat's Demographic Database and in Eurostat's free dissemination online database. The data are also disseminated in several thematic and horizontal Eurostat's publications. Data are presented at national level and for aggregates of countries. For EU and Euro Area, only the current and the previous geographical status are published. The currently disseminated geographical aggregates are: EU-27, EU-25, EA-16, and EA-15. Moreover, data is disseminated for the European Economic Area (EEA) and the European Free Trade Association (EFTA).
    • May 2018
      Source: United Nations Statistics Division
      Uploaded by: Knoema
      Accessed On: 04 December, 2018
      Select Dataset
      The United Nations Statistics Division collects, compiles and disseminates official demographic and social statistics on a wide range of topics. Data have been collected since 1948 through a set of questionnaires dispatched annually to over 230 national statistical offices and have been published in the Demographic Yearbook collection. The Demographic Yearbook disseminates statistics on population size and composition, births, deaths, marriage and divorce, as well as respective rates, on an annual basis. The Demographic Yearbook census datasets cover a wide range of additional topics including economic activity, educational attainment, household characteristics, housing characteristics, ethnicity, language, foreign-born and foreign population. The available Population and Housing Censuses' datasets reported to UNSD for the censuses conducted worldwide since 1995, are now available in UNdata. This latest update includes several datasets on international travel and migration inflows and outflows, and on incoming and departing international migrants by several characteristics, as reported by the national authorities to the UN Statistics Division for the reference years 2010 to the present as available.
    • August 2018
      Source: National Statistics Office of Georgia
      Uploaded by: Knoema
      Accessed On: 10 September, 2018
      Select Dataset
    • April 2018
      Source: Institute for Health Metrics and Evaluation
      Uploaded by: Knoema
      Accessed On: 03 May, 2018
      Select Dataset
      Financing Global Health 2016 is the eighth edition of IHME’s annual series on global health spending and health financing. In addition to describing the trends in development assistance for health (DAH), this year’s report features an expanded discussion of domestic spending across low-, middle-, and high-income countries to describe the context in which DAH operates, identify health financing gaps, and support the pursuit of universal health coverage. Also new in Financing Global Health this year are detailed data for the funding of specific program areas within DAH for malaria and more thorough analysis of DAH for health system strengthening. This adds to the existing detailed tracking of DAH by program area for HIV/AIDS, maternal, newborn, and child health, and non-communicable diseases (NCDs). The coverage of domestic health spending builds on data and analyses presented in two papers published this year: “Global Burden of Disease Financing Global Health Collaborator Network. Evolution and patterns of global health financing 1995–2014: development assistance for health, and government, prepaid private, and out-of-pocket health spending in 184 countries,” and “Global Burden of Disease Financing Global Health Collaborator Network. Future and potential spending on health 2015–2040 by government, prepaid private, out-of-pocket, and donor financing for 184 countries.” Both analyses were published in The Lancet in April 2017. More information about these data and methods are found in the online methods annex.
    • February 2016
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 21 November, 2018
      Select Dataset
      To view the original national data please open the questionnaires. Source: Joint Forest Europe / UNECE / FAO Questionnaire on Pan-European Indicators for Sustainable Forest Management. Country: Russian Federation The source of the data of Russian Federation is the National Report for the Joint Forest Europe / UNECE / FAO reporting on quantitative pan-European indicators 2011.
    • May 2007
      Source: International Telecommunication Union
      Uploaded by: Knoema
      Accessed On: 28 May, 2015
      Select Dataset
      The Digital Opportunity Index (DOI) is the only index that includes price data for 181 economies, which is vital in assessing effective market demand. The Digital Opportunity Index (DOI) has been designed to as a tool for tracking progress in bridging the digital divide and the implementa- tion of the outcomes of the World Summit on the Information Society (WSIS). As such, it provides a powerful policy tool for exploring the global and regional trends in infrastructure, opportu- nity and usage that are shaping the Information Society.
    • July 2018
      Source: U.S. Department of Commerce, Bureau of Economic Analysis
      Uploaded by: Knoema
      Accessed On: 10 August, 2018
      Select Dataset
      Direct Investment Position Abroad on a Historical-Cost Basis:  Country Detail by Industry, United States
    • November 2018
      Source: Institute for Health Metrics and Evaluation
      Uploaded by: Knoema
      Accessed On: 03 December, 2018
      Select Dataset
      GBD 2017 - Disability-Adjusted Life Years and Healthy Life Expectancy 1990-2017 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 disability-adjusted life years (DALYs) by cause, age, and sex and healthy life expectancy (HALE) by age and sex are available from the GBD Results Tool for 1990-2016 (quinquennial). Select tables published in The Lancet in September 2017 in "Global, regional, and national disability-adjusted life-years (DALYs) for 333 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016" are also available for download via the “Files” tab above.
    • November 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 27 November, 2018
      Select Dataset
      Discouraged job-seekers refer to those persons of working age who during a specified reference period were without work and available for work, but did not look for work in the recent past for specific reasons (for example, believing that there were no jobs available, believing there were none for which they would qualify, or having given up hope of finding employment). The working age population is commonly defined as persons aged 15 years and older, but this varies from country to country. In addition to using a minimum age threshold, certain countries also apply a maximum age limit.
    • June 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 23 June, 2018
      Select Dataset
      Data on marriages and divorces at national level are based on the annual demographic data collections in the field of demography carried out by Eurostat. The completeness of information depends on the availability of data reported by the National Statistical Institutes. The Joint demographic data collection is carried out in cooperation with United Nation Statistical Division (UNSD) in the summer of each year, having the deadline 15 September. During this data collection Eurostat collects from the national statistical institutes detailed data by sex, age and other characteristics for the demographic events (births, deaths, marriages and divorces) of the previous year and the population on 1 January of the current and previous years. More specifically, during year T the following data are collected and disseminated on fertility field: - total number of marriages and divorces - persons getting married during the reference year by previous legal marital status, year T-1 Data can be found under the section Marriages and divorces (demo_nup). The information is updated towards the end of each year based on information collected during the Joint data collection. Moreover, any update sent by the countries in-between data collections are validated, processed and uploaded into Eurostat's demographic database and in Eurostat's free dissemination online database as soon as possible. Aggregates are recalculated accordingly. The data transmitted by the National Statistical Institutes are validated by Eurostat, processed and uploaded into Eurostat's Demographic Database and in Eurostat's free dissemination online database. The data are also disseminated in several thematic and horizontal Eurostat's publications. Data are presented by single country and for aggregates of countries. For EU and Euro Area, only the current and the previous version of the aggregates are published. The currently disseminated aggregates are: EU-27, EU-25, EA-16, and EA-15. Moreover, data is disseminated for the European Economic Area (EEA) and the European Free Trade Association (EFTA). International marriages and divorces Statistics on the number of international marriages and divorces (2000-2007) were collected by Eurostat from national statistical institutes in September 2008. The data were further used by the European Commission for preparing  a proposal for a Council Regulation on the law applicable in divorce and legal separation.  These data collected are available upon request.
    • September 2012
      Source: Americans for Divorce Reform
      Uploaded by: Knoema
      Select Dataset
      Divorce Indicators across countries
    • September 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 12 September, 2018
      Select Dataset
      Data on marriages and divorces at national level are based on the annual demographic data collections in the field of demography carried out by Eurostat. The completeness of information depends on the availability of data reported by the National Statistical Institutes. The Joint demographic data collection is carried out in cooperation with United Nation Statistical Division (UNSD) in the summer of each year, having the deadline 15 September. During this data collection Eurostat collects from the national statistical institutes detailed data by sex, age and other characteristics for the demographic events (births, deaths, marriages and divorces) of the previous year and the population on 1 January of the current and previous years. More specifically, during year T the following data are collected and disseminated on fertility field: - total number of marriages and divorces - persons getting married during the reference year by previous legal marital status, year T-1 Data can be found under the section Marriages and divorces (demo_nup). The information is updated towards the end of each year based on information collected during the Joint data collection. Moreover, any update sent by the countries in-between data collections are validated, processed and uploaded into Eurostat's demographic database and in Eurostat's free dissemination online database as soon as possible. Aggregates are recalculated accordingly. The data transmitted by the National Statistical Institutes are validated by Eurostat, processed and uploaded into Eurostat's Demographic Database and in Eurostat's free dissemination online database. The data are also disseminated in several thematic and horizontal Eurostat's publications. Data are presented by single country and for aggregates of countries. For EU and Euro Area, only the current and the previous version of the aggregates are published. The currently disseminated aggregates are: EU-27, EU-25, EA-16, and EA-15. Moreover, data is disseminated for the European Economic Area (EEA) and the European Free Trade Association (EFTA). International marriages and divorces Statistics on the number of international marriages and divorces (2000-2007) were collected by Eurostat from national statistical institutes in September 2008. The data were further used by the European Commission for preparing  a proposal for a Council Regulation on the law applicable in divorce and legal separation. These data collected are available upon request.
    • September 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 12 September, 2018
      Select Dataset
      Data on marriages and divorces at national level are based on the annual demographic data collections in the field of demography carried out by Eurostat. The completeness of information depends on the availability of data reported by the National Statistical Institutes. The Joint demographic data collection is carried out in cooperation with United Nation Statistical Division (UNSD) in the summer of each year, having the deadline 15 September. During this data collection Eurostat collects from the national statistical institutes detailed data by sex, age and other characteristics for the demographic events (births, deaths, marriages and divorces) of the previous year and the population on 1 January of the current and previous years. More specifically, during year T the following data are collected and disseminated on fertility field: - total number of marriages and divorces - persons getting married during the reference year by previous legal marital status, year T-1 Data can be found under the section Marriages and divorces (demo_nup). The information is updated towards the end of each year based on information collected during the Joint data collection. Moreover, any update sent by the countries in-between data collections are validated, processed and uploaded into Eurostat's demographic database and in Eurostat's free dissemination online database as soon as possible. Aggregates are recalculated accordingly. The data transmitted by the National Statistical Institutes are validated by Eurostat, processed and uploaded into Eurostat's Demographic Database and in Eurostat's free dissemination online database. The data are also disseminated in several thematic and horizontal Eurostat's publications. Data are presented by single country and for aggregates of countries. For EU and Euro Area, only the current and the previous version of the aggregates are published. The currently disseminated aggregates are: EU-27, EU-25, EA-16, and EA-15. Moreover, data is disseminated for the European Economic Area (EEA) and the European Free Trade Association (EFTA). International marriages and divorces Statistics on the number of international marriages and divorces (2000-2007) were collected by Eurostat from national statistical institutes in September 2008. The data were further used by the European Commission for preparing  a proposal for a Council Regulation on the law applicable in divorce and legal separation. These data collected are available upon request.
    • July 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 15 July, 2018
      Select Dataset
      Data on marriages and divorces at national level are based on the annual demographic data collections in the field of demography carried out by Eurostat. The completeness of information depends on the availability of data reported by the National Statistical Institutes. The Joint demographic data collection is carried out in cooperation with United Nation Statistical Division (UNSD) in the summer of each year, having the deadline 15 September. During this data collection Eurostat collects from the national statistical institutes detailed data by sex, age and other characteristics for the demographic events (births, deaths, marriages and divorces) of the previous year and the population on 1 January of the current and previous years. More specifically, during year T the following data are collected and disseminated on fertility field: - total number of marriages and divorces - persons getting married during the reference year by previous legal marital status, year T-1 Data can be found under the section Marriages and divorces (demo_nup). The information is updated towards the end of each year based on information collected during the Joint data collection. Moreover, any update sent by the countries in-between data collections are validated, processed and uploaded into Eurostat's demographic database and in Eurostat's free dissemination online database as soon as possible. Aggregates are recalculated accordingly. The data transmitted by the National Statistical Institutes are validated by Eurostat, processed and uploaded into Eurostat's Demographic Database and in Eurostat's free dissemination online database. The data are also disseminated in several thematic and horizontal Eurostat's publications. Data are presented by single country and for aggregates of countries. For EU and Euro Area, only the current and the previous version of the aggregates are published. The currently disseminated aggregates are: EU-27, EU-25, EA-16, and EA-15. Moreover, data is disseminated for the European Economic Area (EEA) and the European Free Trade Association (EFTA). International marriages and divorces Statistics on the number of international marriages and divorces (2000-2007) were collected by Eurostat from national statistical institutes in September 2008. The data were further used by the European Commission for preparing  a proposal for a Council Regulation on the law applicable in divorce and legal separation.  These data collected are available upon request.
    • December 2008
      Source: Institute for Health Metrics and Evaluation
      Uploaded by: Peter Speyer
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      IHME research, published in the Lancet in 2008. The study, Tracking progress towards universal childhood immunizations and the impact of global initiatives, provides estimates with confidence intervals of the coverage of three-dose diphtheria, tetanus, and pertussis (DTP3) vaccination. The estimates take into account all publicly available data, including data from routine reporting systems and nationally representative surveys.
  • E
    • June 2018
      Source: National Statistics Office of Georgia
      Uploaded by: Knoema
      Accessed On: 10 July, 2018
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    • September 2018
      Source: Fraser Institute
      Uploaded by: Knoema
      Accessed On: 02 November, 2018
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      Data cited at: Fraser Institute   The economic freedom index measures the degree of economic freedom present in five major areas: [1] Size of Government; [2] Legal System and Security of Property Rights; [3] Sound Money; [4] Freedom to Trade Internationally; [5] Regulation. Within the five major areas, there are 24 components (area) in economic freedom index. Each component and sub-component is placed on a scale from 0 to 10.
    • December 2012
      Source: Liberia Institute of Statistics & Geo-Information Services
      Uploaded by: Knoema
      Accessed On: 21 May, 2013
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    • March 2018
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 21 November, 2018
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      .. - data not available Source: UNECE Statistical Database, compiled from national official sources. Definition: The economically inactive population includes all the persons who are not part of the labour force, i.e. are neither employed nor unemployed. General note: Data come from the Labour Force Survey (LFS), unless otherwise specified. Data are shown in thousands. Country: Armenia For the period of 1995-2006 data are based on integrated data received from various sources. Break in methodlogy (2007, 2014): from 2007 to 2013 data are based on the Integrated Survey of the Household Living Standards. Since 2014 data are based on the Labour Force Survey. Break in series (2008): 2007 data refer to population aged 16-75. Since 2008, application of ILO methodology, data cover population aged 15-75. Country: Austria Data below the threshold of 3 000 persons are not published, while caution should be taken in interpreting data below the threshold of 6 000 persons. Country: Austria Break in methodology (2004): Break in series due to change in data collection procedure. Country: Bulgaria Change in definition (1990): Data for & 39;Other reasons, including sickness& 39; include persons who are inactive for personal or family reasons. Country: Bulgaria Change in definition (1995 - 2002): Data for & 39;Other reasons, including sickness& 39; include persons who are inactive for personal or family reasons. Data refer to June and include persons on compolsory military service Country: Bulgaria Change in definition (2003 - 2012): Data for & 39;Other reasons, including sickness& 39; include persons who are inactive for personal or family reasons. Data are annual averages and exclude persons on compulsory military service. Country: Bulgaria Reference period (1990): Data refer to 1993 Country: Bulgaria Data below the threshold of 4 000 persons are not reliable due to small sample sizes and are not published. Country: Canada Data for Study, Retirement and Home-making include only persons who have left their jobs within the last 12 months. All other inactive persons are included in the category Other reasons, including sickness. Country: Canada Data do not cover the three northern territories (Yukon, Northwest and Nunavuk ). Country: Croatia Data given for 2013 onwards are calibrated according to the results of the Census 2011 and are not fully comparable with data given for previous years. Country: Cyprus Territorial change (2000 - 2012): Data cover government controlled area. Country: Czechia From 2010 a new variable covers retired persons. This creates differences in sum of reasons to total reasons. Country: Denmark Break in methodlogy (2009): Beark in series due to change in sources Country: Estonia Data for age group 15+ refers to 15-74; age group 65+ refers to 65-74. Country: Finland Change in definition (1990 - 2006): Data for age group 15+ refers to 15-74; age group 65+ refers to 65-74. Data for ?Home-making? include persons who take care of own children or other dependants. Data for ?Other reasons, including sickness? include disability and other reasons. Data for inactive persons aged 65+ were all classified as retired. Country: Finland Change in definition (2007 onward): Data for age group 15+ refers to 15-74; age group 65+ refers to 65-74. Data for ?Home-making? include persons who take care of own children or other dependants. Data for ?Other reasons, including sickness? include disability and other reasons. Country: France Data cover only Metropolitan France. Country: Georgia Change in definition (2008 onward): Inactive persons: homemaker - also includes a man who looks after infants or disabled persons Country: Georgia Territorial change (2000 onward): Data do not cover Abkhazia AR and Tskhinvali Region Country: Germany Break in methodlogy (2005): Until 2004, data refer to one reporting week. From 2005 data are annual average figures. Country: Greece Data refer to annual averages. Country: Hungary Change in definition (2000 - 2013): Data for age group 15+ refers to 15-74; age group 65+ refers to 65-74. Data on ?Home-making? category include persons on parental leave. Data on ?Other reasons, including sickness? include permanently disabled persons. Country: Iceland Break in methodology (2003): Break in series because of change to continuous survey every week of the year. Country: Iceland Change in definition (1990 onward): The survey sample covered population aged 16 to 74. Country: Iceland Reference period (1990): Data refer to 1991. Country: Ireland Inactive according to ILO criteria classified by PES Country: Israel Break in methodlogy (2000): In 1998: 1) Changes in the weighting method; 2) Transition to the 1995 Population Census estimates; See explanations: http://www.cbs.gov.il/www/publications/saka_change/tch_e.pdf Country: Israel Break in methodlogy (2001): Changes in the weighting method. See explanations: http://www.cbs.gov.il/www/saka_y/e_intro_f1_comparison-mimi.f Country: Israel Break in methodlogy (2009): 1) Update of the definition of the civilian labour force characteristics; 2) Transition to the 2008 Population Census estimates. See explanations: http://www.cbs.gov.il/publications11/1460/pdf/intro05_e.pdf Country: Israel Break in methodlogy (2012): 1) Transitiom from a quarterly to a monthly LFS; 2) Changes in the definitions of labour force characteristics (including compulsory and permanent military service into labour force). See explanations: http://www.cbs.gov.il/publications/labour_survey04/labour_f--orce_survey/answer_question_e_2012.pdf Country: Israel Change in definition (1995): From 1995, 1) Update of the definitions of labour force characteristics; 2) Changes in the Standard Industrial Classification of Economic Activities; See explanations: http://www.cbs.gov.il/www/publications/saka_change/tch_e.pdf Country: Israel Change in definition (2000): From 2000, changes in the questionnaire (Highest Diploma Received, Discouraged Workers, Employees hired through employment agencies or employment contractors); See explanations: http://www.cbs.gov.il/www/saka_y/e_intro_e_changes.pdf Country: Italy Break in methodlogy (2004): From 2004, there is a break in series due to change in survey and data collection procedure (continuous survey). Country: Kyrgyzstan 2003: break in series: change in methodology. Country: Latvia Change in definition (2002 - 2012): Age group 15+ refers to 15-74; age group 65+ refers to 65-74. Country: Latvia Reference period (1995): Data refer to 1996. Country: Luxembourg Reference period (1980): Data refers to year 1983 Country: Malta Some data not shown due to lack of reliability. Country: Moldova, Republic of Data exclude the territory of the Transnistria and municipality of Bender Country: Netherlands All inactive persons aged 65+ were categorized as retired through 2013, but are included in other categories from 2014. Country: Norway Data for age group 15-64 refers to 15-66; age group 25-49 refers to 25-54; age group 50-64 refers to 55-66; age group 65+ refers to 55-74 and age group 15+ refers to 15-74. Data for ?Retirement? include early retirement and disabled persons. Country: Poland Data are not fully comparable with the results of the surveys prior to 2010 as persons staying outside households for 12 months or longer are excluded from the survey (previously over 3 months). Country: Portugal Data from 2011 onwards are not directly comparable with data for the previous years due to new data collection methods used in the Portuguese Labour Force Survey series. Estimates below 4 500 individuals are not shown due to high coefficients of variation. Country: Romania Break in methodology (2002): Due to the revision of the definitions and the coverage, the data series of 2002-2012 are not perfectly comparable with data series of previous years. Break in series starting with year 2013. For years 2014 onward data were estimated using the resident population. For year 2013 data were estimated based on revised population figures (resident population) in accordance to the 2011 Census results. Country: Romania Reference period (1995): Data for 1995 refers to March 1995 Country: Russian Federation Change in definition (1990 - 2013): Data present the population aged 15-72 years Country: Russian Federation Reference period (1990): Data refer to 1992 Country: Russian Federation Territorial change (1990 - 2006): Data do not include the Chechen Republic Country: Serbia Data do not cover Kosovo and Metohija. Country: Slovenia Some data not shown due to low reliability. Country: Spain Data for age group 15+ refers to 16+; age group 15-24 refers to 16-24 and age group 15-64 refers to 16-64. Data are annual average of the four quarters of the year. Country: Switzerland Break in methodlogy (2010): Change to continuous survey. As of 2010: annual averages Country: Switzerland Reference period (1990): Data refer to 1991 Country: Switzerland Reference period (1990 - 2009): Data refer to 2nd quarter Country: Switzerland Some data were deleted as unreliable Country: Turkey Break in series (2014): Since 2014 series are not comparable with the previous years due to methodological changes in LFS. Country: Turkey Break in methodlogy (2004): Data are revised according to the 2008 population projections. Country: Ukraine Change in definition (2000 - 2012): Economicaly active population include persons aged 15-70, who can not be classified as "employed" and "unemployed". Country: Ukraine Territorial change (2000 - 2012): Data do not cover the area of radioactive contamination from the Chernobyl disaster. Country: United Kingdom Some data were deleted as unreliable
    • December 2015
      Source: United Nations Development Programme
      Uploaded by: Misha Gusev
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      Calculated using Mean Years of Schooling and Expected Years of Schooling.
    • December 2016
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 21 November, 2018
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      Source: UNECE Statistical Database, compiled from national official sources. Definition:Educational attainment is defined as the highest level successfully completed by the person, in the educational system of the country where the education was received. The levels of education are defined according to the International Standard Classification of Education (ISCED): - Primary: ISCED level 1 - Lower secondary: ISCED level 2 - Upper and post secondary non-tertiary: ISCED levels 3-4 - Tertiary: ISCED 1997 levels 5-6 or ISCED 2011 levels 5-8. In this table the upper secondary level includes post-secondary non-tertiary education. For most countries the transition from ISCED 1997 to ISCED 2011 is from the scool year 2013-2014. For more details see Country Footnotes. .. - data not available Country: Armenia Change in definition (1980 - 1990): Level of education ?not stated? includes population without education attainment. Country: Armenia Reference period (1980): Data refer to 1979 Country: Armenia Reference period (1990): Data refer to 1989 Country: Austria Break in methodology (2004): In 2014 a new weighting procedure for the LFS was introduced. Following this change in the weighting procedure, data was revised back to 2004. Country: Austria ISCED-11 (2014 onwards): Break in series due to the reclassification of a programme spanning levels: the qualification acquired upon successful completion of higher technical and vocational colleges is allocated in ISCED 2011 to ISCED level 5; under ISCED 1997 the same qualification was reported on ISCED level 4, but earmarked as equivalent to tertiary education Country: Austria Change in definition (1980 - 2000): Data before 2000 do not comply with ISCED97 as regards distinction between upper secondary and tertiary. ISCED97 5B mainly included in Upper Secondary. Country: Austria Change in definition (2004 - 2015): Data include ISCED Level 3c short in lower secondary level. Country: Azerbaijan Reference period (1980 - 2013): Data refer to end of year. Country: Belarus Additional information (1990 - 2013): Total includes population without education. Country: Belarus Break in methodlogy (1990): Data refer to 1989 census Country: Belarus Break in methodlogy (2000): Data refer to 1999 census Country: Belgium 2010: break in series: change in methodology. Measurement: Persons , Country: Bosnia and Herzegovina Population by educational attainment, educational level not stated refers to the population with no primary schooling and some primary. Country: Bulgaria Break in methodlogy (1980): Data are from 1985 census Country: Bulgaria Break in methodlogy (1990): Data are from 1992 census Country: Bulgaria Break in methodlogy (2001): Data are from 2001 census Country: Bulgaria Reference period (1995 - 2002): Data refer to June of respective year Country: Canada Additional information (1990 - onwards): Data cover non-institutionalized population in the 10 provinces, i.e. excluding the three Territories. Country: Croatia Change in definition (1980 - 1990): Data refer to population with permanent residence irrespective of actual residence and duration. "Education level-not stated" comprises persons with unknown education level as well as persons with no school at all. Country: Croatia Change in definition (2001 - 2013): "Education level-not stated" comprises persons with unknown education level as well as persons with no school at all. Country: Croatia Reference period (1980): Data refer to 1981 Country: Croatia Reference period (1990): Data refer to 1991 Country: Cyprus Change in definition (1990): Lower secondary level is included in upper secondary level Country: Cyprus Reference period (1990): Data refer to 1989 Country: Cyprus Reference period (1995): Data refer to 1992 Country: Cyprus Data cover only government controlled area Country: Cyprus From 2014, data compiled using ISCED 2011 classification. Country: Cyprus From 2000, persons who have not attended or finished primary education also included in primary education level. Country: Estonia Change in definition (1980 - 2000): Data are from censuses and refer to population aged 25+ Data for primary level attainment include persons who have not completed the primary level education. Country: Estonia Change in definition (2001 - 2013): Age group 25+ refers to 25-74, age group 50+ refers to 50-74. Data for primary level attainment include persons who have not completed the primary level education. Country: Estonia Change in definition (2012): Data is from census 2011. Data refer to 31.december 2011 Data for primary level attainment include persons who have not completed the primary level education. Country: Estonia Reference period (1980): Data refer to 1979 Country: Estonia Reference period (1990): Data refer to 1989 Country: Finland Data for lower secondary level include primary level. Country: Georgia Change in definition (1980 - 2013): Level of education ?not stated? includes population without education attainment Country: Georgia Reference period (1980): Data refer to 1979 Country: Georgia Reference period (1990): Data refer to 1989 Country: Germany Data from 1990 to 1998 are classified according to ISCED-76, data from 1999 to 2013 according to ISCED 97, data from 2014 on are classified according to ISCED 2011. Country: Greece Break in methodology (2000): From 2000, data refer to population residing in private households Country: Greece Change in definition (2001 - 2013): "Primary" includes also persons that did not completed ISCED 1 programs Country: Greece Data refer to annual averages. From 2014, estimates use ISCED-2011 classification. Country: Hungary Break in methodlogy (1995): Before 1995, data are from population censuses. From 2000, from Country: Hungary Change in definition (2000 - 2008): Data refer to population aged 25-74. Country: Iceland Break in methodology (2003): Change in data collection procedure. Data classified according to ISCED 2011. Country: Iceland Reference period (1990): 1990 refers to 1991 Country: Ireland From 2000, data refer to age group 25-64. From 2014, data are compiled according to ISCED-2011. As a result data breakdown by education level not fully comparable with previous years. Country: Ireland Reference period (1980): Data refer to1981 Country: Ireland Reference period (1990): Data refer to 1991 Country: Ireland Reference period (1995): Data refer to 1996 Country: Israel Break in methodlogy (2001): Changes in the weighting method. Country: Israel Break in methodlogy (2009): Transition to the 2008 Population Census estimates. Country: Israel Break in methodlogy (2012): Transitiom from a quarterly to a monthly LFS. Country: Israel From 2012, using ISCED-2011. Totals include population by educational attainment, pre-primary. Country: Italy Break in methodology (2004): Change in data collection procedure. From 2014, data classified by ISCED 2011. Country: Italy Change in definition (1980 - 1990): Data for primary level attainment include persons who have not completed the primary level education Country: Kyrgyzstan Break in methodlogy (2000): Data refer to 1999 Census Country: Kyrgyzstan Break in methodlogy (2009): Data refer to 2009 Census Country: Kyrgyzstan Reference period (1990): Data refer to 1989 Census Country: Latvia Change in definition (1995 - 2001): Population aged 15+. Data for primary level refers to level 0 and 1 of ISCED 1997 classification. Country: Latvia Change in definition (2002 onward): Population 15-74 age group. For 2002-2013, data for primary level refers to level 0 and 1 of ISCED 1997 classification. From 2014, data for primary level refers to level 0 and 1 of ISCED 2011 classification. Country: Latvia Reference period (1995): Data refer to 1996 Country: Luxembourg Additional information (1990 - onwards): Data for age group 25+ refer to 25-74. Country: Luxembourg Break in methodlogy (2003): Switch from a face-to-face to a telephone survey Country: Luxembourg Break in methodlogy (2009): Random Digit Dialing has replaced the register-based sampling Country: Luxembourg Change in definition (1990 - 2012): The categroy `Lower secodnary` also includes persons who have at most attained the primary level Country: Luxembourg Reference period (1990): Data refer to 1992 Country: Malta Some data not shown due to lack of reliability. Country: Moldova, Republic of Territorial change (2000 onward): Data exclude the territory of the Transnistria and municipality of Bender Country: Netherlands Since 2003, ''Primary'' includes also ISCED level 0 (persons who have not successfully completed ISCED 1 programs). Country: Norway Break in methodology (2007): As of 2007, the results of a survey on education completed abroad before immigration to Norway is included. As a result , the proportion of & 39;educational level not stated& 39; was reduced. All data compiled according ISCED 2011. Country: Poland Change in definition (1990 - 2002): Upper secondary level includes lower secondary level. Country: Poland Reference period (1990): Data refer to 1988 Country: Portugal Data from 2011 onwards are not directly comparable with data for the previous years due to new data collection methods used in the Portuguese Labour Force Survey series. Data from 2014 onward are compiled according to ISCED-2011. Data for ''educational level not stated'' refer to individuals who have not successfully completed ISCED level 1. Country: Romania Break in methodology (2002): Data series of 2002-2012 are not perfectly comparable with data series of previous years. For years 2014 onward data were estimated using the resident population. For year 2013 data were estimated based on revised population figures (resident population) in accordance to the 2011 Census results. Starting with year 2014 educational attainment collected according to ISCED 2011. Educational level not stated includes persons without any formal education graduated. Country: Serbia Data for education level not stated include population without education attainment. Country: Slovakia Change in definition (1995): data for total of education levels include only secondary and tertiary levels. Country: Slovakia Change in definition (2001 - 2011): data on primary education according to ISCED 97, level 1 is not available Country: Slovenia From 2014 data are compiled according to ISCED-2011 and persons with ISCED level 0 are excluded. Country: Spain Data are annual averages of the four quarters of the year. From 2014 data are compiled according to ISCED-2011 Country: Sweden Break in methodlogy (2002): Quality improvement and change in classification from ISCED 1976 to ISCED 1997. Country: Sweden Change in definition (1990 - 2013): Data refer to population aged 25-74 Country: Switzerland Break in methodlogy (2010): Major changes in data collection procedures (quaterly data instead of annual data). Country: Switzerland Change in definition (1990 - 2001): Lower sedondary education includes primary education Country: Switzerland Change in definition (2002): Change in definition of educational attainment levels Country: Switzerland Reference period (1990): Data refer to 1991 Country: Switzerland Since 2014, data are compiled according to ISCED-2011 Country: United States Change in definition (1980): Primary refers to grades 5-8, Lower Secondary refers to grade 9 in High School, no diploma, Upper Secondary refers to High School, college graduate, Tertiary refers to people who have completed Associate& 39;s degree through Doctorate degree, Not stated refers to people who didn& 39;t complete any schooling through 4th grade. Data based on completed schooling years. Country: United States Change in definition (1990 - 2015): Primary refers to grades 5-8, Lower Secondary refers to grade 9 in High School, no diploma, Upper Secondary refers to High School, college graduate, Tertiary refers to people who have completed Associate`s degree through Doctorate degree, Not stated refers to people who did not complete any schooling through 4th grade. Data based on degrees.
    • March 2018
      Source: National Statistics Office of Georgia
      Uploaded by: Knoema
      Accessed On: 04 July, 2018
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    • October 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 03 November, 2018
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      This consumption stands for final energy consumption. This means that the consumption in industry covers all industrial sectors with the exception of the energy sector, like power stations, oil refineries, coke ovens and all other installations transforming energy products into another form. Final energy consumption in transport covers mainly the consumption by railways and electrified urban transport systems. Final energy consumption in households/services covers quantities consumed by private households, small-scale industry, crafts, commerce, administrative bodies, services with the exception of transportation, agriculture and fishing.
    • October 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 24 October, 2018
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      NEW METHODOLOGY (from 2007 onwards) Average half-yearly electricity prices for households and industrial end-users. The end-users are characterised by predefined annual consumption bands. The prices are collected and published considering three levels of taxation prices excluding taxes and levies;prices excluding VAT and other recoverable taxes;prices including all taxes, levies and VAT.  For the disaggregated electricity prices, separate price components are available for households and industrial consumers for production costs of electricitynetwork coststaxes and leviesOLD METHODOLOGY (until 2007) Electricity prices for households and industrial standard consumers, valid on 1st January and on 1st July of each calendar year. Standard consumers are characterised by predefined annual consumption. The prices include electricity/basic price, transmission, system services, meter rental, distribution and other services. The prices are collected and published considering three levels of taxation (see above). For structural indicators tables, where only annual data is displayed both for new and old methodology in the same table, the prices refer to the price on 1st January of each year (until 2007) and to the first semester of each year (2008 and later). Data on electricity prices for industrial consumers are collected according to Directive 2008/92/EC of the European Parliament and of the Council of 22 October 2008 concerning a Community procedure to improve the transparency of gas and electricity prices charged to industrial end-users (recast) (Text with EEA relevance). Data for electricity prices for household end-users are collected on a voluntary basis The data collection covers the full spectrum of the 28 Member States of the European Union, Candidate Countries, Potential candidate countries and EFTA countries (except Switzerland).
    • October 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 24 October, 2018
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      NEW METHODOLOGY (from 2007 onwards) Average half-yearly electricity prices for households and industrial end-users. The end-users are characterised by predefined annual consumption bands. The prices are collected and published considering three levels of taxation prices excluding taxes and levies;prices excluding VAT and other recoverable taxes;prices including all taxes, levies and VAT.  For the disaggregated electricity prices, separate price components are available for households and industrial consumers for production costs of electricitynetwork coststaxes and leviesOLD METHODOLOGY (until 2007) Electricity prices for households and industrial standard consumers, valid on 1st January and on 1st July of each calendar year. Standard consumers are characterised by predefined annual consumption. The prices include electricity/basic price, transmission, system services, meter rental, distribution and other services. The prices are collected and published considering three levels of taxation (see above). For structural indicators tables, where only annual data is displayed both for new and old methodology in the same table, the prices refer to the price on 1st January of each year (until 2007) and to the first semester of each year (2008 and later). Data on electricity prices for industrial consumers are collected according to Directive 2008/92/EC of the European Parliament and of the Council of 22 October 2008 concerning a Community procedure to improve the transparency of gas and electricity prices charged to industrial end-users (recast) (Text with EEA relevance). Data for electricity prices for household end-users are collected on a voluntary basis The data collection covers the full spectrum of the 28 Member States of the European Union, Candidate Countries, Potential candidate countries and EFTA countries (except Switzerland).
    • December 2016
      Source: Food and Agriculture Organization
      Uploaded by: Knoema
      Accessed On: 13 January, 2017
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    • December 2017
      Source: Food and Agriculture Organization
      Uploaded by: Knoema
      Accessed On: 05 January, 2018
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      Greenhouse Gas (GHG) emissions from burning of savanna consist of methane (CH4) and nitrous oxide (N2O) gases produced from the burning of vegetation biomass in the following five land cover types: Savanna, Woody Savanna, Open Shrublands, Closed Shrublands, and Grasslands. The FAOSTAT emissions database is computed following Tier 1 IPCC 2006 Guidelines for National GHG Inventories (http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol4.html). GHG emissions are provided by country, regions and special groups, with global coverage, relative to the period 1990-present (with annual updates), expressed as Gg CH4, Gg N2O, Gg CO2eq and Gg CO2eq from both CH4 and N2O, by land cover class (savanna, woody savanna, closed shrubland, open shrubland, grassland) and by aggregates (all categories, savanna and woody savanna, closed and open shrubland). Implied emission factors for N2O and CH4 as well activity data (burned area and biomass burned) are also provided.
    • February 2018
      Source: Food and Agriculture Organization
      Uploaded by: Knoema
      Accessed On: 22 February, 2018
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      Agriculture Total contains all the emissions produced in the different agricultural emissions sub-domains (enteric fermentation, manure management, rice cultivation, synthetic fertilizers, manure applied to soils, manure left on pastures, crop residues, cultivation of organic soils, burning of crop residues, burning of savanna, energy use), providing a picture of the contribution to the total amount of GHG emissions from agriculture. GHG emissions from agriculture consist of non-CO2 gases, namely methane (CH4) and nitrous oxide (N2O), produced by crop and livestock production and management activities. The FAOSTAT emissions database is computed following Tier 1 IPCC 2006 Guidelines for National GHG Inventories (http://www.ipcc-nggip.iges.or.jp/public/2006gl/index.html). GHG emissions are provided by country, regions and special groups, with global coverage, relative to the period 1961-present (with annual updates) and with projections for 2030 and 2050, expressed as Gg CO2 and CO2eq (from CH4 and N2O), by underlying agricultural emission sub-domain and by aggregate (agriculture total, agriculture total plus energy, agricultural soils).
    • February 2018
      Source: Food and Agriculture Organization
      Uploaded by: Knoema
      Accessed On: 22 February, 2018
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      Greenhouse Gas (GHG) emissions from burning crop residues consist of methane (CH4) and nitrous oxide (N2O) gases produced by the combustion of a percentage of crop residues burnt on-site. The mass of fuel available for burning should be estimated taking into account the fractions removed before burning due to animal consumption, decay in the field, and use in other sectors (e.g., biofuel, domestic livestock feed, building materials, etc.). FAOSTAT emission estimates are computed at Tier 1 following the IPCC 2006 Guidelines for National GHG Inventories (http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol4.html). GHG emissions are provided by country, reguions and special groups, with global coverage, relative to the period 1961-present (with annual updates) and with projections for 2030 and 2050, expressed both as Gg CH4, Gg N2O, Gg CO2eq and CO2eq from CH4 and N2O, by crop (maize, rice, sugarcane and wheat) and by aggregates. Implied emission factors for N2O and CH4 as well activity data (biomass burned) are also provided.
    • February 2018
      Source: Food and Agriculture Organization
      Uploaded by: Knoema
      Accessed On: 22 February, 2018
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      Greenhouse gas (GHG) emissions from crop residues consist of direct and indirect nitrous oxide (N2O) emissions from nitrogen (N) in crop residues and forage/pasture renewal left on agricultural fields by farmers. Specifically, N2O is produced by microbial processes of nitrification and de-nitrification taking place on the deposition site (direct emissions), and after volatilization/re-deposition and leaching processes (indirect emissions). The FAOSTAT emissions database is computed following Tier 1 IPCC 2006 Guidelines for National GHG Inventories, Vol. 4, Ch. 2 and 11(http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol4.html). GHG emissions are provided as direct, indirect and total by country, regions and special groups, with global coverage, relative to the period 1961-present (with annual updates) and with projections for 2030 and 2050, expressed as Gg N2O and Gg CO2eq, by crop and N content in residues.
    • February 2018
      Source: Food and Agriculture Organization
      Uploaded by: Knoema
      Accessed On: 22 February, 2018
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      Greenhouse gas (GHG) emissions from enteric fermentation consist of methane gas produced in digestive systems of ruminants and to a lesser extent of non-ruminants. The FAOSTAT emissions database is computed following Tier 1 IPCC 2006 Guidelines for National GHG Inventories vol. 4, ch. 10 and 11 (http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol4.html). GHG emissions are provided by country, regions and special groups, with global coverage, relative to the period 1961-present (with annual updates) and with projections for 2030 and 2050, expressed both as Gg CH4 and Gg CO2eq, by livestock species (asses, buffaloes, camels, cattle (dairy and non-dairy), goats, horses, llamas, mules, sheep, swine (breeding and market)) and by species aggregates (all animals, camels and llamas, cattle, mules and asses, sheep and goats, swine). Implied emission factor for CH4 and activity data are also provided
    • February 2018
      Source: Food and Agriculture Organization
      Uploaded by: Knoema
      Accessed On: 22 February, 2018
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      GHG emissions from manure applied to soils consist of direct and indirect nitrous oxide (N2O) emissions from manure nitrogen (N) added to agricultural soils by farmers. Specifically, N2O is produced by microbial processes of nitrification and de-nitrification taking place on the application site (direct emissions), and after volatilization/re-deposition and leaching processes (indirect emissions). The FAOSTAT emissions database is computed following Tier 1 IPCC 2006 Guidelines for National GHG Inventories vol. 4, ch. 10 and 11 (http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol4.html). GHG emissions are provided as direct, indirect and total by country, regions and special groups, with global coverage, relative to the period 1961-present (with annual updates) and with projections for 2030 and 2050, expressed as Gg N2O and Gg CO2eq, by livestock species (asses, buffaloes, camels, cattle (dairy and non-dairy), chickens (broilers and layers), ducks, goats, horses, llamas, mules, sheep, swine (breeding and market) and turkeys) and by species aggregates (all animals, camels and llamas, cattle, chickens, mules and asses, poultry birds, sheep and goats, swine). Implied emission factor for N2O and activity data (N content in manure) are also provided.
    • February 2018
      Source: Food and Agriculture Organization
      Uploaded by: Knoema
      Accessed On: 22 February, 2018
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      GHG emissions from manure left on pastures consist of direct and indirect nitrous oxide (N2O) emissions from manure nitrogen (N) left on pastures by grazing livestock. Specifically, N2O is produced by microbial processes of nitrification and de-nitrification taking place on the deposition site (direct emissions), and after volatilization/re-deposition and leaching processes (indirect emissions). The FAOSTAT emissions database is computed following Tier 1 IPCC 2006 Guidelines for National GHG Inventories vol. 4, ch. 10 and 11 (http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol4.html). GHG emissions are provided by country, regions and special groups, with global coverage, relative to the period 1961-present (with annual updates) and with projections for 2030 and 2050, expressed as direct, indirect and total Gg N2O and Gg CO2eq, by livestock species (asses, buffaloes, camels, cattle (dairy and non-dairy), chickens (broilers and layers), ducks, goats, horses, llamas, mules, sheep, swine (breeding, market), turkeys) and by species aggregates (all animals, camels and llamas, cattle, chickens, mules and asses, poultry birds, sheep and goats, swine). Implied emission factor for N2O and N content in manure are also provided.
    • February 2018
      Source: Food and Agriculture Organization
      Uploaded by: Knoema
      Accessed On: 22 February, 2018
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    • February 2018
      Source: Food and Agriculture Organization
      Uploaded by: Knoema
      Accessed On: 22 February, 2018
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      Greenhouse gas (GHG) emissions from synthetic fertilizers consist of nitrous oxide gas from synthetic nitrogen additions to managed soils. Specifically, N2O is produced by microbial processes of nitrification and de-nitrification taking place on the addition site (direct emissions), and after volatilization/re-deposition and leaching processes (indirect emissions). The FAOSTAT emissions database is computed following Tier 1 IPCC 2006 Guidelines for National GHG Inventories vol. 4, ch. 11 (http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol4.html). GHG emissions are provided as direct, indirect and total by country, regions and special groups, with global coverage, relative to the period 1961-present (with annual updates) and with projections for 2030 and 2050, expressed as Gg N2O and Gg CO2eq. Implied emission factor for N2O and activity data (consumption) are also provided.
    • December 2017
      Source: Food and Agriculture Organization
      Uploaded by: Knoema
      Accessed On: 05 January, 2018
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      Greenhouse Gas (GHG) emissions from burning of biomass consist of methane and nitrous oxide gases from biomass combustion of forest land cover classes ‘Humid and Tropical Forest’ and ‘Other Forests’, and of methane, nitrous oxide, and carbon dioxide gases from combustion of organic soils. The FAOSTAT emissions database is computed following Tier 1 IPCC 2006 Guidelines for National GHG Inventories (http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol4.html). GHG emissions are provided by country, with global coverage, relative to the period 1990-present (with annual updates), expressed as Gg CH4, Gg N2O, Gg CO2, Gg CO2eq and Gg CO2eq from both CH4 and N2O, by land cover class (humid tropical forest, other forest, organic soils) and by aggregate (burning - all categories). Implied emission factors for N2O, CH4 and CO2 as well activity data (burned area and biomass burned) are also provided.
    • December 2017
      Source: Food and Agriculture Organization
      Uploaded by: Knoema
      Accessed On: 05 January, 2018
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      Greenhouse gas (GHG) emissions data from cropland are currently limited to emissions from cropland organic soils. They are those associated with carbon losses from drained histosols under cropland. The FAOSTAT emissions database is computed following Tier 1 IPCC 2006 Guidelines for National GHG Inventories (http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol5.html). GHG emissions are provided by country, region and special groups, with global coverage, relative to the period 1990-present (with annual updates), expressed as net emissions/removal Gg CO2 and Gg CO2eq. Implied emission factor for C, net stock change Gg C and activity data (area) are also provided.
    • February 2016
      Source: Food and Agriculture Organization
      Uploaded by: Knoema
      Accessed On: 09 February, 2017
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      Annual net CO2 emission/removal from Forest Land consist of net carbon stock gain/loss in the living biomass pool (aboveground and belowground biomass) associated with Forest and Net Forest Conversion. The FAOSTAT emissions database is computed following Tier 1 IPCC 2006 Guidelines for National GHG Inventories (http://www.ipcc-nggip.iges.or.jp/public/2006gl/index.html) and using area and carbon stocks data compiled by countries in the FAO Global Forest Resource Assessments (http://www.fao.org/forestry/fra/en/). GHG emissions are provided by country, regions and special groups, with global coverage, relative to the period 1990-present (with annual updates), expressed as net stock change Gg C, net emissions/removals Gg CO2 and CO2eq, by forest or net forest conversion and by aggregate (forest land). Implied emission factor for CO2 as well as activity data (area, net area difference, total forest area and carbon stock in living biomass) are also given.
    • December 2017
      Source: Food and Agriculture Organization
      Uploaded by: Knoema
      Accessed On: 05 January, 2018
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      Greenhouse gas (GHG) emissions data from grassland are currently limited to emissions from grassland organic soils. They are those associated with carbon losses from drained histosols under grassland. The FAOSTAT emissions database is computed following Tier 1 IPCC 2006 Guidelines for National GHG Inventories (http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol6.html). GHG emissions are provided by country, region and special groups, with global coverage, relative to the period 1990-present (with annual updates), expressed as net emissions/removal Gg CO2 and Gg CO2eq. Implied emission factor for C, net stock change Gg C and activity data (area) are also provided.
    • December 2017
      Source: Food and Agriculture Organization
      Uploaded by: Knoema
      Accessed On: 05 January, 2018
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      Land Use Total contains all GHG emissions and removals produced in the different Land Use sub-domains, representing the three IPCC Land Use categories: cropland, forest land, and grassland, collectively called emissions/removals from the Forestry and Other Land Use (FOLU) sector. FOLU emissions consist of CO2 (carbon dioxide), CH4 (methane) and N2O (nitrous oxide) associated with land management activities. CO2 emissions/removals are derived from estimated net carbon stock changes in above and below-ground biomass pools of forest land, including forest land converted to other land uses. CH4 and N2O, and additional CO2 emissions are estimated for fires and drainage of organic soils. The FAOSTAT emissions database is computed following Tier 1 IPCC 2006 Guidelines for National GHG Inventories (http://www.ipcc-nggip.iges.or.jp/public/2006gl/index.html). GHG emissions are provided as by country, regions and special groups, with global coverage, relative to the period 1990-present (with annual updates), expressed as Gg CO2eq from CH4 and N2O, net emissions/removals as GG CO2 and Gg CO2eq, by underlying land use emission sub-domain and by aggregate (land use total).
    • February 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 31 August, 2018
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      Employed migrants refer to the number of persons who changed their country of usual residence and were also employed during a specified brief period. Data are disaggregated by country of origin. A person's country of origin is that from which the person originates, i.e. the country of his or her citizenship (or, in the case of stateless persons, the country of usual residence).
    • December 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 03 December, 2018
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      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.
    • October 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 09 October, 2018
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      Employees are all those workers who hold paid employment jobs, which are those where the incumbents hold employment contracts which give them a basic remuneration not directly dependent upon the revenue of the unit for which they work. Data are disaggregated by economic activity according to the latest version of the International Standard Industrial Classification of All Economic Activities (ISIC) available for that year. Economic activity refers to the main activity of the establishment in which a person worked during the reference period and does not depend on the specific duties or functions of the person's job, but on the characteristics of the economic unit in which this person works.
    • November 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 27 November, 2018
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      Employees are all those workers who hold paid employment jobs, which are those where the incumbents hold employment contracts which give them a basic remuneration not directly dependent upon the revenue of the unit for which they work. Data are disaggregated by weekly hours actually worked, on the basis of the mean number of hours of work per week, and with reference to hours worked in all jobs of employed persons and in all types of working time arrangements (e.g. full-time and part-time). Hours actually worked include (a) direct hours or the time spent carrying out the tasks and duties of a job, (b) related hours, or the time spent maintaining, facilitating or enhancing productive activities (c) down time, or time when a person in a job cannot work due to machinery or process breakdown, accident, lack of supplies or power or Internet access and (d) resting time, or time spent in short periods of rest, relief or refreshment, including tea, coffee or prayer breaks, generally practised by custom or contract according to established norms and/or national circumstances. Hours actually worked excludes time not worked during activities such as: (a) Annual leave, public holidays, sick leave, parental leave or maternity/paternity leave, other leave for personal or family reasons or civic duty, (b) Commuting time between work and home when no productive activity for the job is performed; for paid employment, even when paid by the employer; (c) Time spent in certain educational activities; for paid employment, even when authorized, paid or provided by the employer; (d) Longer breaks distinguished from short resting time when no productive activity is performed (such as meal breaks or natural repose during long trips); for paid employment, even when paid by the employer.
    • December 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 03 December, 2018
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      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.
    • November 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 23 November, 2018
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      National accounts are a coherent set of macroeconomic indicators, which provide an overall picture of the economic situation and are widely used for economic analysis and forecasting, policy design and policy making. The data presented in this collection are the results of a pilot exercise on the sharing selected main GDP aggregates, population and employment data collected by different international organisations. It wasconducted by the Task Force in International Data Collection (TFIDC) which was established by the  Inter-Agency Group on Economic and Financial Statistics (IAG).  The goal of this pilot is to develop a set of commonly shared principles and working arrangements for data cooperation that could be implemented by the international agencies. The data sets are an experimental exercise to present national accounts data form various countries across the globe in one coherent folder, but users should be aware that these data are collected and validated by different organisations and not fully harmonised from a methodological point of view.  The domain consists of the following collections:
    • October 2018
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 21 November, 2018
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      .. - data not available Source: UNECE Statistical Database, compiled from national and international (EUROSTAT, OECD, CIS) official sources. Definition:Employment, as referred to the System of National Accounts 1993, covers all persons - both employees and self-employed - engaged in a productive activity that falls within the production boundary of the system. It includes both the residents and the non-residents who work for resident producer units. In case of deviation, the actual definition is provided in the country footnote. Employment data provided in this table generally differ from employment data provided in Gender Statistics, which cover only residents. General note: The UNECE secretariat presents time series ready for immediate analysis. When appropriate, source segments with methodological differences have been linked or rescaled to build long consistent time series. As a result, absolute figures presented in this table may differ from those published by National Statistical Offices and should be taken with caution. However, the derived growth rates correspond to the originally reported series. Regional aggregates are computed by UNECE secretariat. For more details see the composition of regions note. Country: Albania Employment: end of period. Country: Armenia Employment: LFS - based. Country: Azerbaijan Geographical coverage: excludes Nagorno-Karabakh. Population: Number of population for the latest year refers to the beginning of the year, not to an annual average as usually. Employment: LFS - based. Country: Bosnia and Herzegovina Employment:LFS - based. Country: Croatia Employment: LFS-based. Country: France Geographical Coverage: Data for France include the overseas departments (DOM). Country: Georgia Geographical Coverage: from 1993 excludes Abkhazia and South Ossetia (Tshinvali). Population: Number of population for the latest year refers to the beginning of the year, not to an annual average as usually. Employment: Register-based. Country: Israel Employment: LFS-based. Designation and data provided by Israel. The position of the United Nations on the question of Jerusalem is contained in General Assembly resolution 181 (II) and subsequent resolutions of the General Assembly and the Security Council concerning this question. Data include East Jerusalem. Country: Kazakhstan Employment: LFS-based. Country: Lithuania Employment: LFS-based. Country: Moldova, Republic of Geographical Coverage: from 1993 excludes Transnistria. Population: Number of population for the latest year refers to the beginning of the year, not to an annual average as usually. Employment: LFS-based. Country: Romania Employment: LFS-based. For the years 1990-2001 UNECE estimates. Country: Russian Federation Population: Number of population for the latest year refers to the beginning of the year, not to an annual average as usually. Employment: LFS-based. Data for Russian Federation was updated only until the end of 2013. Country: Serbia Geographical Coverage: from 1999, excludes Kosovo and Metohija. Employment: LFS - based. Country: The former Yugoslav Republic of Macedonia Employment: LFS-based. Country: Turkey Employment: Annual breakdowns by activity and quarterly data are LFS-based. Country: Ukraine Employment: LFS-based. Geographical coverage: from 2014, does not includes all territory of Ukraine.
    • December 2018
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 03 December, 2018
      Select Dataset
      .. - data not available Source: UNECE Statistical Database, compiled from national and international (EUROSTAT, OECD, CIS) official sources. Definition:Employment, as referred to the System of National Accounts 1993, covers all persons - both employees and self-employed - engaged in a productive activity that falls within the production boundary of the system. It includes both the residents and the non-residents who work for resident producer units. In case of deviation, the actual definition is provided in the country footnote. Employment data provided in this table generally differ from employment data provided in Gender Statistics, which cover only residents. General note: The UNECE secretariat presents time series ready for immediate analysis. When appropriate, source segments with methodological differences have been linked or rescaled to build long consistent time series. As a result, absolute figures presented in this table may differ from those published by National Statistical Offices and should be taken with caution. However, the derived growth rates correspond to the originally reported series. Regional aggregates are computed by UNECE secretariat. For more details see the composition of regions note. Country: Albania Employment: end of period. Country: Armenia Employment: LFS - based. Country: Azerbaijan Geographical coverage: excludes Nagorno-Karabakh. Population: Number of population for the latest year refers to the beginning of the year, not to an annual average as usually. Employment: LFS - based. Country: Bosnia and Herzegovina Employment:LFS - based. Country: Croatia Employment: LFS-based. Country: France Geographical Coverage: Data for France include the overseas departments (DOM). Country: Georgia Geographical Coverage: from 1993 excludes Abkhazia and South Ossetia (Tshinvali). Population: Number of population for the latest year refers to the beginning of the year, not to an annual average as usually. Employment: Register-based. Country: Iceland Employment: LFS - based. Country: Israel Employment: LFS-based. Designation and data provided by Israel. The position of the United Nations on the question of Jerusalem is contained in General Assembly resolution 181 (II) and subsequent resolutions of the General Assembly and the Security Council concerning this question. Data include East Jerusalem. Country: Kazakhstan Employment: LFS-based. Country: Kyrgyzstan Employment: LFS - based. Country: Lithuania Employment: LFS-based. Country: Moldova, Republic of Geographical Coverage: from 1993 excludes Transnistria. Population: Number of population for the latest year refers to the beginning of the year, not to an annual average as usually. Employment: LFS-based. Country: Romania Employment: LFS-based. For the years 1990-2001 UNECE estimates. Country: Russian Federation Population: Number of population for the latest year refers to the beginning of the year, not to an annual average as usually. Employment: LFS-based. Data for Russian Federation was updated only until the end of 2013. Country: Serbia Geographical Coverage: from 1999, excludes Kosovo and Metohija. Employment: LFS - based. Country: The former Yugoslav Republic of Macedonia Employment: LFS-based. Country: Turkey Employment: Annual breakdowns by activity and quarterly data are LFS-based. Country: Ukraine Employment: LFS-based. Geographical coverage: from 2014, does not includes all territory of Ukraine.
    • December 2018
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 03 December, 2018
      Select Dataset
      .. - data not available Source: UNECE Statistical Database, compiled from national and international (EUROSTAT, OECD, CIS) official sources. Definition:Employment, as referred to the System of National Accounts 1993, covers all persons - both employees and self-employed - engaged in a productive activity that falls within the production boundary of the system. It includes both the residents and the non-residents who work for resident producer units. In case of deviation, the actual definition is provided in the country footnote. Employment data provided in this table generally differ from employment data provided in Gender Statistics, which cover only residents. General note: The UNECE secretariat presents time series ready for immediate analysis. When appropriate, source segments with methodological differences have been linked or rescaled to build long consistent time series. As a result, absolute figures presented in this table may differ from those published by National Statistical Offices and should be taken with caution. However, the derived growth rates correspond to the originally reported series. Regional aggregates are computed by UNECE secretariat. For more details see the composition of regions note. Country: Albania Employment: end of period. Country: Armenia Employment: LFS - based. Country: Azerbaijan Geographical coverage: excludes Nagorno-Karabakh. Population: Number of population for the latest year refers to the beginning of the year, not to an annual average as usually. Employment: LFS - based. Country: Bosnia and Herzegovina Employment:LFS - based. Country: Croatia Employment: LFS-based. Country: France Geographical Coverage: Data for France include the overseas departments (DOM). Country: Georgia Geographical Coverage: from 1993 excludes Abkhazia and South Ossetia (Tshinvali). Population: Number of population for the latest year refers to the beginning of the year, not to an annual average as usually. Employment: Register-based. Country: Iceland Employment: LFS - based. Country: Israel Employment: LFS-based. Designation and data provided by Israel. The position of the United Nations on the question of Jerusalem is contained in General Assembly resolution 181 (II) and subsequent resolutions of the General Assembly and the Security Council concerning this question. Data include East Jerusalem. Country: Kazakhstan Employment: LFS-based. Country: Moldova, Republic of Geographical Coverage: from 1993 excludes Transnistria. Population: Number of population for the latest year refers to the beginning of the year, not to an annual average as usually. Employment: LFS-based. Country: Romania Employment: LFS-based. For the years 1990-2001 UNECE estimates. Country: Russian Federation Population: Number of population for the latest year refers to the beginning of the year, not to an annual average as usually. Employment: LFS-based. Data for Russian Federation was updated only until the end of 2013. Country: Serbia Geographical Coverage: from 1999, excludes Kosovo and Metohija. Employment: LFS - based. Country: The former Yugoslav Republic of Macedonia Employment: LFS-based. Country: Turkey Employment: Annual breakdowns by activity and quarterly data are LFS-based. Country: Ukraine Employment: LFS-based. Geographical coverage: from 2014, does not includes all territory of Ukraine.
    • May 2018
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 21 November, 2018
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      Source: UNECE Statistical Database, compiled from national and international (Eurostat) official sources. Definition: The employed are all the residents above a specified age who, during a specified brief period, either one week or one day, were in the following categories: (a) paid employment: (a1) at work: persons who, during the reference period, performed some work for wage or salary, in cash or in kind; (a2) with a job but not at work: persons who, having already worked in their present job, were temporarily not at work during the reference period and had a formal attachment to their job; (b) self-employment: (b1) at work: persons who, during the reference period, performed some work for profit or family gain, in cash or in kind; (b2) with an enterprise but not at work: persons with an enterprise, which may be a business enterprise, a farm or a service undertaking, who were temporarily not at work during the reference period for any specific reason. For additional information, see the International Conference of Labour Statisticians (ICLS). Part-time/full-time: A part-time worker is an employed person whose normal hours of work are less than those of comparable full-time workers. In most countries, the distinction between part-time and full-time work is based on self-declaration. In a few countries, work is defined as part-time when the hours usually worked are below a fixed threshold. Data for EU-27, Croatia, Iceland, Norway, the Former Yugoslav Republic of Macedonia and Turkey from the year 2008 corresponds to the NACE rev 2, before 2008 data is according to the NACE rev1.1. General note: Data come from the Labour Force Survey (LFS) unless otherwise specified. Data from the LFS and from population censuses normally comply with the definition above. .. - data not available Country: Albania 2007-2012: Part-time worker refers to an employed person whose usual hours of work are less than 35 hours/week. Country: Albania 2013-2015: Distinction between part-time and full-time workers is based on worker self-identification. Country: Armenia Break in methodlogy (2008): 2007 data refer to population aged 16-75. Since 2008 data refer to population aged 15-75. Break in methodlogy (2014): From 2007 to 2013 data are based on the Integrated Survey of the Household Living Standards. Since 2014 data are based on the Labour Force Survey. Country: Belarus 2014: changes in methodology Country: France Since 2014 data include also the French overseas departments (Guadeloupe, Martinique, Guyane, La Reunion) with the exception of Mayotte. Country: Georgia Territorial change (2002 onward): Data do not cover Abkhazia AR and Tskhinvali Region Country: Israel Break in methodlogy (2000): In 1998: 1) Changes in the weighting method; 2) Transition to the 1995 Population Census estimates; See explanations: http://www.cbs.gov.il/www/publications/saka_change/tch_e.pdf Country: Israel Break in methodlogy (2001): Changes in the weighting method. See explanations: http://www.cbs.gov.il/www/saka_y/e_intro_f1_comparison-mimi.f Country: Israel Break in methodlogy (2009): 1) Update of the definition of the civilian labour force characteristics; 2) Transition to the 2008 Population Census estimates. See explanations: http://www.cbs.gov.il/publications11/1460/pdf/intro05_e.pdf Country: Israel Break in methodlogy (2012): 1) Transitiom from a quarterly to a monthly LFS; 2) Changes in the definitions of labour force characteristics (including compulsory and permanent military service into labour force). See explanations: http://www.cbs.gov.il/publications/labour_survey04/labour_f--orce_survey/answer_question_e_2012.pdf Country: Israel Change in definition (1980): Data refers to population 14+. Country: Israel Change in definition (2005): 1) Update of the definitions of labour force characteristics; 2) Changes in the Standard Industrial Classification of Economic Activities; See explanations: http://www.cbs.gov.il/www/publications/saka_change/tch_e.pdf Country: Moldova, Republic of Data exclude the territory of the Transnistria and municipality of Bender Country: Russian Federation Change in definition (1990 - 2013): Data present the population aged 15-72 years. Underemployment - the person who work less than 30 hours in the surveyed week Country: Russian Federation Reference period (1990): Data refer to 1992 Country: Russian Federation Territorial change (1990 - 2006): Data do not include the Chechen Republic Country: Serbia Data do not cover Kosovo and Metohija. Country: Ukraine From 2014 data cover the territories under the government control. Country: Ukraine Data do not cover the persons who are still living in the area of Chernobyl contaminated with radioactive material. Data do not cover the persons who are living in institutions and those who are working in the army. Data refer to the population aged 15-70.
    • August 2018
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 21 November, 2018
      Select Dataset
      Source: UNECE Statistical Database, compiled from national and international (Eurostat and ILO) official sources. Definition: The employed are all the residents above a specified age who, during a specified brief period, either one week or one day, were in the following categories: (a) paid employment: (a1) at work: persons who, during the reference period, performed some work for wage or salary, in cash or in kind; (a2) with a job but not at work: persons who, having already worked in their present job, were temporarily not at work during the reference period and had a formal attachment to their job; (b) self-employment: (b1) at work: persons who, during the reference period, performed some work for profit or family gain, in cash or in kind; (b2) with an enterprise but not at work: persons with an enterprise, which may be a business enterprise, a farm or a service undertaking, who were temporarily not at work during the reference period for any specific reason. For additional information, see the International Conference of Labour Statisticians (ICLS). The occupation groups correspond to first-level categories in the 2008 version of the International Standard Classification of Occupations (ISCO-08). For the EU and EFTA member-states the year of transition from ISCO-88 to ISCO-08 is 2011. For other countries please see Country footnotes. The transition to ISCO-08 could entail a break in time series. General note: Data come from the Labour Force Survey (LFS) unless otherwise specified. Data from the LFS and from population censuses normally comply with the definition above. .. - data not available Country: Albania From 2010 occupational groups according to ISCO-08. Country: Armenia Break in methodlogy (2014): since 2014 data refer to the population aged 15-75 and are based on the Labour Force Survey.2001: data come from Population Census. Country: Azerbaijan Data compiled according to ISCO-08. Country: Azerbaijan Data are based on administrative registers. Country: Belarus Data compiled according to ISCO-88 Country: Belarus 2000 : data refer to 1999 and come from Population Census. Country: Belgium 1980 : data refer to 1983. Country: Bosnia and Herzegovina From year 2006 to 2010 data compiling using ISCO 88, from 2011 using ISCO 08. Country: Bulgaria 1995 : data refer to 1997. Country: Canada Change in definition (1990 onwards): Data are annual averages. Cells with 0 are estimates with less than 1,500 employed. Country: Canada Data do not cover the three northern territories (Yukon, Northwest and Nunavuk ) Country: Cyprus Data cover only the area controlled by the Republic of Cyprus. 1990 : data refer to 1992. Country: Estonia 1990 and 1995 : data refer to the population aged 15-69. From 2000 : data refer to the population aged 15-74. Country: Finland Data refer to the population aged 15-74. Country: France Since 2014, data include also the French overseas departments (Guadeloupe, Martinique, Guyane, La Reunion), with the exception of Mayotte. Country: Georgia Data do not cover Abkhazia and South Ossetia (Tshinvali). Country: Germany 1980 : data refer to 1983. Country: Iceland Data refer to the population aged 16-74. 1990 : data refer to 1991. Country: Israel Break in methodlogy (2000): In 1998: 1) Changes in the weighting method; 2) Transition to the 1995 Population Census estimates; See explanations: http://www.cbs.gov.il/www/publications/saka_change/tch_e.pdf Country: Israel Break in methodlogy (2001): Changes in the weighting method. See explanations: http://www.cbs.gov.il/www/saka_y/e_intro_f1_comparison-mimi.f Country: Israel Break in methodlogy (2009): 1) Update of the definition of the civilian labour force characteristics; 2) Transition to the 2008 Population Census estimates. See explanations: http://www.cbs.gov.il/publications11/1460/pdf/intro05_e.pdf Country: Israel Break in methodlogy (2012): 1) Transitiom from a quarterly to a monthly LFS; 2) Changes in the definitions of labour force characteristics (including compulsory and permanent military service into labour force). See explanations: http://www.cbs.gov.il/publications/labour_survey04/labour_f--orce_survey/answer_question_e_2012.pdf Country: Israel Change in definition (2000 - 2012): Changes in the questionnaire (Highest Diploma Received, Discouraged Workers, Employees hired through employment agencies or employment contractors); See explanations: http://www.cbs.gov.il/www/saka_y/e_intro_e_changes.pdf Country: Israel Change in definition (2013): Changes in the Standard Classification of Occupations based on ISCO-08; See explanations: http://www.cbs.gov.il/publications12/occupations_class11/pd--f/draft_h.pdf (draft, Hebrew only) Country: Kyrgyzstan Up to 2015 ISCO-88 has been used Country: Latvia 1995 : data refer to 1996. Country: Lithuania 1995 : data refer to 1997. Country: Moldova, Republic of Data exclude the territory of the Transnistria and municipality of Bender Country: Portugal 1990 : data refer to 1992. Country: Russian Federation Change in definition (2000 - 2013): Data present the population aged 15-72 years Country: Russian Federation Territorial change (1995 - 2006): Data do not include the Chechen Republic Country: Serbia Data do not cover Kosovo and Metohija. Starting in 2013 data compiled according ISCO-08. Country: Slovakia 1995 : the persons working in the armed forces are counted in the other groups. Country: Sweden Data refer to the population aged 16-64. Country: Switzerland 1990 : data refer to 1991. Country: Ukraine Change in definition (2000 - 2012): Distribution by institutional sectors of the economy based on the assessment carried out in accordance with the National Classification of Occupations developed on the basis of ISCO 88. Country: Ukraine Territorial change (2000 - 2012): Data do not cover the area of radioactive contamination from the Chernobyl disaster. Country: United Kingdom Data refer to the population aged 16+. Country: United States Data refer to the population aged 16+. Data do not cover the armed forces. Occupation groups : 'Professionals' includes 'Technicians and associate professionals'; 'Craft and related workers' includes 'Plant machine operators and assemblers'.
    • April 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 31 August, 2018
      Select Dataset
      The employed comprise all persons of working age who, during a specified brief period, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Employed migrants refer to individuals who changed their country of usual residence and were also employed during a specified brief period. Data are disaggregated by occupation according to the latest version of the International Standard Classification of Occupations (ISCO-08). Information on occupation provides a description of the set of tasks and duties which are carried out by, or can be assigned to, one person.
    • August 2018
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 21 November, 2018
      Select Dataset
      Source: UNECE Statistical Database, compiled from national official sources. Definition: The employed are all the persons above a specified age who, during a specified brief period, either one week or one day, were in the following categories: (a) paid employment: (a1) at work: persons who, during the reference period, performed some work for wage or salary, in cash or in kind; (a2) with a job but not at work: persons who, having already worked in their present job, were temporarily not at work during the reference period and had a formal attachment to their job; (b) self-employment: (b1) at work: persons who, during the reference period, performed some work for profit or family gain, in cash or in kind; (b2) with an enterprise but not at work: persons with an enterprise, which may be a business enterprise, a farm or a service undertaking, who were temporarily not at work during the reference period for any specific reason. For additional information, see the International Conference of Labour Statisticians (ICLS). The private sector covers private corporations (including those in foreign control), households and Non-Profit Institutions Serving Households (NPISHs). The public sector covers all sub-sectors of general government (mainly central, state and local government units, together with social security funds imposed and controlled by those units) and public corporations, i.e. corporations which are subject to control by government units (usually defined by the government owning the majority of shares). General note: Data come from the Labour Force Survey (LFS) unless otherwise specified. Data from the LFS and from population censuses normally comply with the definition above. .. - data not available Country: Armenia 2007 data refer to population aged 16-75. Break in methodlogy (2008): since 2008 data refer to population aged 15-75. Break in methodlogy(2001, 2002): For the periods of 1980-2000 and 2002-2006 data on employment are based on integrated data received from various sources. For 2001 data are from Population Census. Break in methodlogy (2007): From 2007 to 2013 data are based on the Integrated Survey of the Household Living Standards. Break in methodlogy (2014): Since 2014 data are based on the Labour Force Survey. Country: Austria Break in methodlogy (2004): Break in series due to change in data collection procedure. Country: Azerbaijan Data are based on Population Census, establishment survey and registers Country: Belarus Data are based on administrative registers. Data for private sector include corporations with mixed ownership. 2010: changes in methodology Country: Bosnia and Herzegovina Additional information (1990 - 2008): Data are based on administrative records and related sources Country: Bulgaria Change in definition (2003 - 2012): Annual average data Country: Bulgaria Reference period (1990): Data refer to 1993 (September). Country: Bulgaria Reference period (1995 - 2002): Data refer to June of the corresponding year Country: Canada Data for not stated refers to self-employed. Country: Croatia Data given for 2009 onwards are calibrated according to the results of the Census 2011 and are not fully comparable with data given for previous years. Country: Cyprus Change in definition (1980 - 2008): Data refer to full-time equivalent (FTE) employment. Data are based on official estimates Country: Cyprus Reference period (1980): Data refer to 1981 Country: Cyprus Territorial change (1980 - 2008): Data cover the area controlled by the Republic of Cyprus Country: Czechia Break in methodlogy (1990 - 2008): Data are based on Labour Force Survey, enterprise survey and registers Country: Denmark Data are based on administrative records and related sources Country: France Reference area: Metropolitan France Country: France Data are based on Labour Force Survey, enterprise survey and registers Country: Georgia Data do not cover Abkhazia and South Ossetia (Tshinvali). Country: Germany Additional information (1995 - 2007): Data are based on Labour Force Survey, enterprise survey and registers Country: Greece Data refer to annual averages. Country: Hungary Data are based on Labour Force Survey, enterprise survey and registers. Private sector : data include corporations with mixed ownership. Country: Ireland Data are based on administrative registers. 2008 : break in series due to change in methodology. The series previously published up to 2008 was derived from the Quarterly Public Sector inquiry (QPI). The data from 2008,2009 and 2010 is now generated from the Earnings,Hours and Employment Cost Survey (EHECS)There are different methodologies used in both.They are as follows: The QPI was data generated from one reference period in the quarter.The EHECS survey is an average over the full quarter. The QPI had some whole time equivalents in the data ,EHECS uses a head count. The data from EHECS will therefore be higher Country: Israel Change in definition (2000 - 2008): Data on public sector refer to General Government only. Country: Italy Additional information (1990 - 2008): Data are based on Labour Force Survey, enterprise survey and registers Country: Kyrgyzstan Additional information (1995 - onwards): Data for private sector are obtained by subtracting the number of employed in public sector from the total number of employed. Country: Latvia Change in definition (1995 - 2001): Data refer to the population aged 15+. Country: Latvia Change in definition (2002 - 2012): Data refer to the population aged 15-74. Country: Latvia Reference period (1995): Data refer to 1996. Country: Luxembourg Change in definition (1990 - 2008): There is no sector variable in the LFS. The public sector is defined as the sum of the NACE rev1 sections L and M Country: Luxembourg Change in definition (2009 - 2012): There is no sector variable in the LFS. The public sector is defined as the sum of the NACE rev2 sections O and P Country: Luxembourg Reference period (1980): Data refer to 1983 Country: Poland Data are not fully comparable with the results of the surveys prior to 2010 as persons staying outside households for 12 months or longer are excluded from the survey (previously over 3 months). Country: Romania Mixed sector - included in ''private sector'' for years 2007 onward; for year 1995-2006 mixed sector is included in the ''sector not stated'' row. Break in series starting with year 2009. For years 2014 onward data were estimated using the resident population. For years 2009-2013 data were estimated based on revised population figures (resident population) in accordance to the 2011 Census results. Country: Serbia Data do not cover Kosovo and Metohija. Country: Slovakia Data are based on Labour Force Survey, enterprise survey and registers. Country: Slovenia Data come from the Slovenian Statistical Register of Employment and cover persons who hold paid employment, self-empoyed persons who have compulsory social insurance and trainees. Data do not cover persons working abroad. Country: Sweden Break in methodlogy (2004 - 2005): For "Employment Public/private sector not stated" persons working abroad are included in 2005 and forward but seen as outside the labor force in 2004 and before. Country: Switzerland Break in methodlogy (2010): Change to continuous survey. As of 2010: annual averages Country: Switzerland Change in definition (1980 - 1990): Sector not stated : data include trainees. Country: Switzerland Reference period (2000 - 2009): Data refer to 2nd quarter Country: Tajikistan Change in definition (2004): Data include working migrants Country: Turkey Break in methodlogy (2004): Data are revised according to the 2008 population projections. Country: Ukraine From 2014 data cover the territories under the government control. Country: Ukraine For 2000-2011 data compiled according ISIC 3 Rev.1, since 2012 ISIC 4 is in use Country: Ukraine Data do not cover the area of radioactive contamination from the Chernobyl disaster.
    • October 2018
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 21 November, 2018
      Select Dataset
      Source: UNECE Statistical Database, compiled from national and international (Eurostat and ILO) official sources. Definition: The employed are all the residents above a specified age who, during a specified brief period, either one week or one day, were in the following categories: (a) paid employment: (a1) at work: persons who, during the reference period, performed some work for wage or salary, in cash or in kind; (a2) with a job but not at work: persons who, having already worked in their present job, were temporarily not at work during the reference period and had a formal attachment to their job; (b) self-employment: (b1) at work: persons who, during the reference period, performed some work for profit or family gain, in cash or in kind; (b2) with an enterprise but not at work: persons with an enterprise, which may be a business enterprise, a farm or a service undertaking, who were temporarily not at work during the reference period for any specific reason. For additional information, see the International Conference of Labour Statisticians (ICLS). The breakdown by kind of economic activity is grouped into 3 categories. Agriculture includes agriculture, hunting, forestry and fishing (ISIC Rev.3.1 Sections A-B or ISIC Rev.4 Section A). Industry includes mining and quarrying, manufacturing, electricity, gas and water supply, and construction (ISIC Rev.3.1 Sections C-F or ISIC Rev.4 Sections B-F ). Services comprise all other economic activities (ISIC Rev.3.1 Sections G-Q or ISIC Rev.4 Sections G-U). Total employment provided in this table generally differ from total employment provided in Economic Statistics, which cover both residents and non-residents (according to the System of National Accounts 1993). General note: Data come from the Labour Force Survey (LFS) unless otherwise specified in country footnotes. Data from the LFS and from population censuses normally comply with the definition above. .. - data not available Country: Albania Break in methodology (1980): from 1990 to 2006, data are based on administrative registers with sector breakdown according of NACE rev 1.1 Country: Albania Break in methodology (2007): As of 2007 data are based on the Labour Force Survey. Sectors broken down according to NACE rev 1.1 (2007-2014) and NACE rev since 2015. Country: Armenia Break in methodlogy (2007, 2014): For the period of 1980-2000 and 2002-2006 data on employment are based on integrated data received from various sources. From 2007 to 2013 data are based on the Integrated Survey of the Household Living Standards. Since 2014 data are based on the Labour Force Survey. Country: Armenia Break in methodlogy (2008): Data for 2007 refer to the age group 16-75. Since 2008 data refer to the age group 15-75. Country: Austria 1980-1990 : data refer to national definition (Life Subsistence Concept). From 1995 : data comply with ILO definition. Country: Azerbaijan Official estimates. 1980 : data refer to 1983. Country: Belarus Data refer to the national classification. Services include construction. Country: Belgium 1980 : data refer to 1983. Country: Bosnia and Herzegovina From year 2006 to 2011, data compiled using ISIC Rev 3.1, from 2012 using ISIC Rev 4. Country: Bulgaria 1995 : data refer to 1997. Country: Canada Data do not cover the three northern territories (Yukon, Northwest and Nunavuk ). Country: Croatia 1995 : data refer to 1996. Country: Cyprus Data cover only the area controlled by the Republic of Cyprus. 1990 : data refer to 1992. Country: Denmark 1980 : data refer to 1982. Country: Estonia 1990-1995 : data refer to the population aged 15-69. From 2000 : data refer to the population aged 15-74. Country: Finland Data refer to the population aged 15-74. Country: France Data do not cover overseas departments (DOM). Country: Georgia Break in methodology (1980 - 1995): Data are based on administrative registers Country: Georgia Territorial change (1995 onward): Data do not cover Abkhazia AR and Tskhinvali Region Country: Germany 1980 : data refer to 1983. From 1991 : data cover former German Democratic Republic (East Germany). Country: Hungary 1990 : data refer to 1992. Country: Iceland 1980 : data refer to 1981 and are based on administrative registers. 1990 : data refer to 1991. 1980 : data refer to the population aged 15-74. From 1990 : data refer to the population aged 16-74. Country: Ireland 1980 : data refer to 1983. Country: Israel Break in methodlogy (2000): In 1998: 1) Changes in the weighting method; 2) Transition to the 1995 Population Census estimates; See explanations: http://www.cbs.gov.il/www/publications/saka_change/tch_e.pdf Country: Israel Break in methodlogy (2001): Changes in the weighting method. See explanations: http://www.cbs.gov.il/www/saka_y/e_intro_f1_comparison-mimi.f Country: Israel Break in methodlogy (2009): 1) Update of the definition of the civilian labour force characteristics; 2) Transition to the 2008 Population Census estimates. See explanations: http://www.cbs.gov.il/publications11/1460/pdf/intro05_e.pdf Country: Israel Break in methodlogy (2012): 1) Transitiom from a quarterly to a monthly LFS; 2) Changes in the definitions of labour force characteristics (including compulsory and permanent military service into labour force). See explanations: http://www.cbs.gov.il/publications/labour_survey04/labour_f--orce_survey/answer_question_e_2012.pdf Country: Israel Break in methodlogy (2013): Changes in the Standard Industrial Classification of Economic Activities based on ISIC Rev.4; See explanations: http://www.cbs.gov.il/publications12/economic_activities11/--pdf/e_print.pdf Country: Israel Change in definition (1995): 1) Update of the definitions of labour force characteristics; 2) Changes in the Standard Industrial Classification of Economic Activities; See explanations: http://www.cbs.gov.il/www/publications/saka_change/tch_e.pdf Country: Israel Change in definition (2003): Changes in the Standard Industrial Classification of Economic Activities 2003, which mainly involved expanding the classification of high-tech industries; See explanations: http://www.cbs.gov.il/www/saka_y/e_int_g.pdf Country: Italy 1980 : data refer to 1983. 1980-1990 : data refer to the economically active population aged 14+, which includes the persons who have been seeking employment in the last 6 months. From 1995 : data refer to the economically active population aged 15+, which includes the persons who have been seeking employment in the last 30 days. Country: Kyrgyzstan Reference period (1995): Data refer to 1996 Country: Latvia 1995 : data refer to 1996. Country: Lithuania 1995 : data refer to 1997. Country: Luxembourg 1980 : data refer to 1983. Country: Moldova, Republic of Data exclude the territory of the Transnistria and municipality of Bender Country: Netherlands 1980 : data refer to 1983. Country: Poland 1990 : official estimates based on administrative registers. Country: Romania 1995 : data refer to the population aged 14+. Country: Russian Federation Change in definition (2000 - 2013): Data present the population aged 15-72 years Country: Russian Federation Territorial change (1990 - 2006): Data do not include the Chechen Republic Country: Serbia Territorial change (2000 onward): Data do not cover Kosovo and Metohija. Country: Sweden Data refer to the population aged 16-64. Country: Turkey Break in series (2014): Since 2014 series are not comparable with the previous years due to methodological changes in LFS. Country: Turkey Break in methodlogy (2004): Data are revised according to the 2008 population projections. Country: Turkey Up to 2008, economic activities in labour force survey (LFS) were coded by NACE Rev 1. From 2009 onwards, NACE Rev 2 has been used. Country: Ukraine For 2000-2011 data compiled according ISIC 3 Rev.1, since 2012 ISIC 4 is in use Country: Ukraine Territorial change (2000 - 2012): Data do not cover the area of radioactive contamination from the Chernobyl disaster. Country: United Kingdom Data refer to the population aged 16+. Country: United States Data refer to the population aged 16+. Agriculture excludes forestry and fishing. Country: Uzbekistan Services include construction
    • June 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 31 August, 2018
      Select Dataset
      The employed comprise all persons of working age who, during a specified brief period, were in one of the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). 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. Data for 1991-2016 are estimates while 2017-2021 data are projections. The dataset was updated as of November 2017. For more information, refer to the ILO estimates and projections methodological note.
    • November 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 27 November, 2018
      Select Dataset
      The employed comprise all persons of working age who, during a specified brief period, were in one of the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).
    • June 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 31 August, 2018
      Select Dataset
      The employed comprise all persons of working age who, during a specified brief period, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are disaggregated by economic activity, which refers to the main activity of the establishment in which a person worked during the reference period and does not depend on the specific duties or functions of the person's job, but on the characteristics of the economic unit in which this person works. 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. Data for 1991-2016 are estimates while 2017-2021 data are projections. The dataset was updated as of November 2017. For more information, refer to the indicator description and the ILO estimates and projections methodological note.
    • November 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 27 November, 2018
      Select Dataset
      The employed comprise all persons of working age who, during a specified brief period, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are disaggregated by economic activity according to the latest version of the International Standard Industrial Classification of All Economic Activities (ISIC) available for that year. Economic activity refers to the main activity of the establishment in which a person worked during the reference period and does not depend on the specific duties or functions of the person's job, but on the characteristics of the economic unit in which this person works.
    • November 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 27 November, 2018
      Select Dataset
      The employed comprise all persons of working age who, during a specified brief period, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are provided by institutional sector, which refers to disaggregations by public and private sector employment. Public sector employment covers employment in the government sector plus employment in publicly-owned resident enterprises and companies, operating at central, state (or regional) and local levels of government. It covers all persons employed directly by those institutions, regardless of the particular type of employment contract. Private sector employment comprises employment in all resident units operated by private enterprises, that is, it excludes enterprises controlled or operated by the government sector.
    • June 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 31 August, 2018
      Select Dataset
      The employed comprise all persons of working age who, during a specified brief period, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are disaggregated by occupation according to the latest version of the International Standard Classification of Occupations (ISCO). Information on occupation provides a description of the set of tasks and duties which are carried out by, or can be assigned to, one person. 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. Data for 1991-2016 are estimates while 2017-2021 data are projections. The dataset was updated as of November 2017. For more information, refer to the indicator description and the labour force estimates and projections methodological paper. 
    • November 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 27 November, 2018
      Select Dataset
      The employed comprise all persons of working age who, during a specified brief period, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are disaggregated by occupation according to the latest version of the International Standard Classification of Occupations (ISCO) available for that year. Information on occupation provides a description of the set of tasks and duties which are carried out by, or can be assigned to, one person.
    • June 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 31 August, 2018
      Select Dataset
      The employed comprise all persons of working age who, during a specified brief period, were in one of the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are disaggregated by status in employment according to the latest version of the International Standard Classification of Status in Employment (ICSE-93). Status in employment refers to the type of explicit or implicit contract of employment the person has with other persons or organizations. The basic criteria used to define the groups of the classification are the type of economic risk and the type of authority over establishments and other workers which the job incumbents have or will have. 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. Data for 1991-2016 are estimates while 2017-2021 data are projections. The dataset was updated as of November 2017. For more information, refer to the ILO estimates and projections methodological note.
    • November 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 27 November, 2018
      Select Dataset
      The employed comprise all persons of working age who, during a specified brief period, were in one of the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are disaggregated by status in employment according to the latest version of the International Standard Classification of Status in Employment (ICSE-93). Status in employment refers to the type of explicit or implicit contract of employment the person has with other persons or organizations. The basic criteria used to define the groups of the classification are the type of economic risk and the type of authority over establishments and other workers which the job incumbents have or will have.
    • November 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 27 November, 2018
      Select Dataset
      The employed comprise all persons of working age who, during a specified brief period, were in one of the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are disaggregated by weekly hours actually worked, on the basis of the mean number of hours of work per week, and with reference to hours worked in all jobs of employed persons and in all types of working time arrangements (e.g. full-time and part-time). Hours actually worked include (a) direct hours or the time spent carrying out the tasks and duties of a job, (b) related hours, or the time spent maintaining, facilitating or enhancing productive activities (c) down time, or time when a person in a job cannot work due to machinery or process breakdown, accident, lack of supplies or power or Internet access and (d) resting time, or time spent in short periods of rest, relief or refreshment, including tea, coffee or prayer breaks, generally practised by custom or contract according to established norms and/or national circumstances. Hours actually worked excludes time not worked during activities such as: (a) Annual leave, public holidays, sick leave, parental leave or maternity/paternity leave, other leave for personal or family reasons or civic duty, (b) Commuting time between work and home when no productive activity for the job is performed; for paid employment, even when paid by the employer; (c) Time spent in certain educational activities; for paid employment, even when authorized, paid or provided by the employer; (d) Longer breaks distinguished from short resting time when no productive activity is performed (such as meal breaks or natural repose during long trips); for paid employment, even when paid by the employer.
    • November 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 27 November, 2018
      Select Dataset
      The employed comprise all persons of working age who, during a specified brief period, were in one of the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are disaggregated by level of education, which refers to the highest levelof education completed, classified according to the International Standard Classification of Education (ISCE).
    • November 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 27 November, 2018
      Select Dataset
      The employed comprise all persons of working age who, during a specified brief period, were in one of the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are provided by age and geographical coverage, which entails a disaggregation by rural and urban areas.
    • November 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 27 November, 2018
      Select Dataset
      The employed comprise all persons of working age who, during a specified brief period, were in one of the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).
    • August 2018
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 21 November, 2018
      Select Dataset
      Source: UNECE Statistical Database, compiled from national and international (Eurostat and ILO) official sources. Definition: The status of employment is defined with reference to the distinction between 'paid employment' and 'self-employment' jobs. Workers holding paid-employment jobs have explicit (written or oral) or implicit employment contracts which give them a basic remuneration which is not directly dependent upon the revenue of the unit for which they work. Self-employment jobs are jobs where the remuneration is directly dependent upon the profits derived from the goods and services produced. Employees are all the workers who hold paid employment jobs. Employers are workers who hold self-employment jobs and have engaged, on a continuous basis, one or more persons to work for them in their business as employees. Own-account workers are workers who hold self-employment jobs and have not engaged, on a continuous basis, any employees to work for them during the reference period. Members of producers cooperatives are workers who hold self-employment jobs in a cooperative producing goods and services, in which each member takes part on an equal footing with other members in determining the organisation of production, sales and/or other work of the establishment, the investments and the distribution of the proceeds of the establishment amongst their members. Family workers are workers who hold self-employment jobs in a market-oriented establishment operated by a related person living in the same household. For additional information, see the International Classification of Status in Employment (ICSE-93). General note: Data come from the Labour Force Survey (LFS) unless otherwise specified. Data from the LFS and from population censuses normally comply with the definition above. .. - data not available Country: Austria 1980-1990 : data refer to national definition (Life Subsistence Concept). 1980 : data on employers include own-account workers and family workers. 1990 : data on employers include own-account workers. Country: Azerbaijan Data are based on Population Census and administrative registers. Country: Belarus Break in methodlogy (2000): Data refer to 1999 Population Census. Country: Belarus 2009: data are from the Population Census. Parts do not equal the totals due to employed persons not indicated their status in employment. Country: Belgium 1980 : data refer to 1983. Country: Bosnia and Herzegovina Estimates for family workers are less reliable in 2014-2015. Country: Bulgaria 1990 : data refer to 1993. Data on own-account workers include members of producers cooperatives. Country: Croatia 1995 : data refer to 1996. Country: Cyprus Data cover only the area controlled by the Republic of Cyprus. 1990 : data refer to 1992. Country: Czechia From 2002 : data on own-account workers include members of producers cooperatives. Country: Denmark 1980 : data refer to 1983; data on employers include own-account workers. Country: Estonia Data on employers and own-account workers include members of producers cooperatives. 1990-1995 : data refer to the population aged 15-69. From 2000 : data refer to the population aged 15-74. Country: Finland 1980-1995 : data on employers include own-account workers. Country: France Data do not cover overseas departments (DOM). 1980 : data refer to 1983. Country: Germany 1980 : data refer to 1983. Country: Greece 1980 : data refer to 1983. Country: Iceland 1990 : data refer to 1991. Country: Ireland 1980 : data refer to 1983. Country: Israel 1990: data refer to 1992. 1998, 2001: methodology revised, data not strictly comparable. Country: Latvia 1995 : data refer to 1996. Country: Lithuania 1995 : data refer to 1997. Data on employers include own-account workers. Country: Netherlands 1980 : data refer to 1983. 1980-2001 : data on employers include own-account workers and members of producers cooperatives. Country: Norway 1980-2001 : data on employers include own-account workers and members of producers cooperatives. Country: Poland 1990 : data refer to 1992. Country: Romania 1995: data refer to population aged 14+. Country: Russian Federation Data refer to population aged 15-72. Country: Serbia Data do not cover Kosovo and Metohija. Country: Spain Data refer to population aged 16+. 2005: methodology revised, data not strictly comparable. Country: Switzerland 1990 : data refer to 1991. Country: Turkey 2000: data revision based on Population Census 2000 Country: Ukraine Data do not cover the persons who are still living in the area of Chernobyl contaminated with radioactive material. Data do not cover the persons who are living in institutions and those who are working in the army. Data refer to the population aged 15-70. Country: United Kingdom 1980 : data refer to 1983. Country: United States Data on employers include own-account workers. Data refer to population aged 16+. 1994: methodology revised, data not strictly comparable
    • June 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 31 August, 2018
      Select Dataset
      The employed comprise all persons of working age who, during a specified brief period, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are disaggregated by economic activity, which refers to the main activity of the establishment in which a person worked during the reference period and does not depend on the specific duties or functions of the person's job, but on the characteristics of the economic unit in which this person works. 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. Data for 1991-2016 are estimates while 2017-2021 data are projections. The dataset was updated as of November 2017. For more information, refer to the indicator description and the ILO estimates and projections methodological note.
    • November 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 27 November, 2018
      Select Dataset
      The employed comprise all persons of working age who, during a specified brief period, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are disaggregated by economic activity, which refers to the main activity of the establishment in which a person worked during the reference period and does not depend on the specific duties or functions of the person's job, but on the characteristics of the economic unit in which this person works.
    • November 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 27 November, 2018
      Select Dataset
      The employed comprise all persons of working age who, during a specified brief period, were in one of the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are disaggregated by level of education, which refers to the highest levelof education completed, classified according to the International Standard Classification of Education (ISCE).
    • November 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 27 November, 2018
      Select Dataset
      The employed comprise all persons of working age who, during a specified brief period, were in one of the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are disaggregated by weekly hours actually worked, on the basis of the mean number of hours of work per week, and with reference to hours worked in all jobs of employed persons and in all types of working time arrangements (e.g. full-time and part-time). Hours actually worked include (a) direct hours or the time spent carrying out the tasks and duties of a job, (b) related hours, or the time spent maintaining, facilitating or enhancing productive activities (c) down time, or time when a person in a job cannot work due to machinery or process breakdown, accident, lack of supplies or power or Internet access and (d) resting time, or time spent in short periods of rest, relief or refreshment, including tea, coffee or prayer breaks, generally practised by custom or contract according to established norms and/or national circumstances. Hours actually worked excludes time not worked during activities such as: (a) Annual leave, public holidays, sick leave, parental leave or maternity/paternity leave, other leave for personal or family reasons or civic duty, (b) Commuting time between work and home when no productive activity for the job is performed; for paid employment, even when paid by the employer; (c) Time spent in certain educational activities; for paid employment, even when authorized, paid or provided by the employer; (d) Longer breaks distinguished from short resting time when no productive activity is performed (such as meal breaks or natural repose during long trips); for paid employment, even when paid by the employer.
    • June 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 31 August, 2018
      Select Dataset
      The employed comprise all persons of working age who, during a specified brief period, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are disaggregated by occupation according to the latest version of the International Standard Classification of Occupations (ISCO). Information on occupation provides a description of the set of tasks and duties which are carried out by, or can be assigned to, one person. 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. Data for 1991-2016 are estimates while 2017-2021 data are projections. The dataset was updated as of November 2017. For more information, refer to the indicator description and the labour force estimates and projections methodological paper. 
    • November 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 27 November, 2018
      Select Dataset
      The employed comprise all persons of working age who, during a specified brief period, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are disaggregated by occupation according to the latest version of the International Standard Classification of Occupations (ISCO). Information on occupation provides a description of the set of tasks and duties which are carried out by, or can be assigned to, one person.
    • June 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 31 August, 2018
      Select Dataset
      The employed comprise all persons of working age who, during a specified brief period, were in one of the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are disaggregated by status in employment according to the latest version of the International Standard Classification of Status in Employment (ICSE-93). Status in employment refers to the type of explicit or implicit contract of employment the person has with other persons or organizations. The basic criteria used to define the groups of the classification are the type of economic risk and the type of authority over establishments and other workers which the job incumbents have or will have. 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. Data for 1991-2016 are estimates while 2017-2021 data are projections. The dataset was updated as of November 2017. For more information, refer to the ILO estimates and projections methodological note.
    • November 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 27 November, 2018
      Select Dataset
      The employed comprise all persons of working age who, during a specified brief period, were in one of the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are disaggregated by status in employment according to the latest version of the International Standard Classification of Status in Employment (ICSE-93). Status in employment refers to the type of explicit or implicit contract of employment the person has with other persons or organizations. The basic criteria used to define the groups of the classification are the type of economic risk and the type of authority over establishments and other workers which the job incumbents have or will have.
    • June 2018
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 21 November, 2018
      Select Dataset
      .. - data not available Source: UNECE Transport Division Database. Please note that country footnotes are not always in alphabetical order.
    • March 2018
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 21 November, 2018
      Select Dataset
      Source: UNECE Statististical Database, compiled from national and international (Eurostat) official sources. Definition: The employment rate is the share of employed persons in the population of the corresponding sex and age group. Marital status is defined as the legal conjugal status of each individual in relation to the marriage laws or customs of the country. The following classification is used: - Never married (single), - Married, - Widowed (and not remarried), - Divorced (and not remarried). In some countries the legal status of separated also exists and persons of this group are included here in the group of married. General note: Data come from the Labour Force Survey (LFS) unless otherwise specified. .. - data not available Country: Armenia 2007 data refer to population aged 16-75. Break in methodlogy: since 2008 data refer to population aged 15-75.From 2007 to 2013 data are based on the Integrated Survey of the Household Living Standards.Break in methodlogy: since 2014 data are based on the Labour Force Survey. Country: Austria Break in methodology (2004): Break in series due to change in data collection procedure. Country: Bosnia and Herzegovina Estimates for the age group 65+ are less reliable for 2015. Country: Canada Data do not cover the three northern territories (Yukon, Northwest and Nunavuk ) Country: Georgia Change in definition (2008 onward): Unknown marital status refers to non-registered marriage Country: Georgia Territorial change (2000 onward): Data do not cover Abkhazia AR and Tskhinvali Region Country: Israel Break in methodlogy (2000): In 1998: 1) Changes in the weighting method; 2) Transition to the 1995 Population Census estimates; See explanations: http://www.cbs.gov.il/www/publications/saka_change/tch_e.pdf Country: Israel Break in methodlogy (2001): Changes in the weighting method. See explanations: http://www.cbs.gov.il/www/saka_y/e_intro_f1_comparison-mimi.f Country: Israel Break in methodlogy (2009): 1) Update of the definition of the civilian labour force characteristics; 2) Transition to the 2008 Population Census estimates. See explanations: http://www.cbs.gov.il/publications11/1460/pdf/intro05_e.pdf Country: Israel Break in methodlogy (2012): 1) Transitiom from a quarterly to a monthly LFS; 2) Changes in the definitions of labour force characteristics (including compulsory and permanent military service into labour force). See explanations: http://www.cbs.gov.il/publications/labour_survey04/labour_f--orce_survey/answer_question_e_2012.pdf Country: Israel Married persons include Married but living apart; From 2005, 1) Update of the definitions of labour force characteristics; 2) Changes in the Standard Industrial Classification of Economic Activities; See explanations: http://www.cbs.gov.il/www/publications/saka_change/tch_e.pdf Country: Moldova, Republic of Significance (2000 - 2012): Category "married" includes the persons who are not officially registered their marriage, but live together Country: Moldova, Republic of Data exclude the territory of the Transnistria and municipality of Bender Country: Russian Federation Change in definition (1990 - 2013): Data present the population aged 15-72 years Country: Russian Federation Reference period (1990): Data refer to 1992 Country: Russian Federation Territorial change (1990 - 2006): Data do not include the Chechen Republic Country: Serbia Data do not cover Kosovo and Metohija. Country: Turkey Break in methodlogy (2004): Data are revised according to the 2008 population projections. Country: Turkey Break in series (2014): Since 2014 series are not comparable with the previous years due to methodological changes in LFS. Country: Ukraine From 2014 data cover the territories under the government control. Country: Ukraine Change in definition (2000 - 2012): Determining the level of employment corresponds to the definition given above. Country: Ukraine Territorial change (2000 - 2012): Data do not cover the area of radioactive contamination from the Chernobyl disaster. Country: United States Age group 15+ refers to 16+; age group 15-24 refers to 16-24; age group 25-49 refers to 25-54 and age group 50-64 refers to 55-64.
    • December 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 04 December, 2018
      Select Dataset
      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.
    • November 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 27 November, 2018
      Select Dataset
      The employment-to-population ratio expresses the number of persons who are employed as a percent of the total working age population.
    • June 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 31 August, 2018
      Select Dataset
      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 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. Data for 1991-2016 are estimates while 2017-2021 data are projections. The dataset was updated as of November 2017. For more information, refer to the indicator description and the ILO estimates and projections methodological note.
    • November 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 27 November, 2018
      Select Dataset
      The employment-to-population ratio is the number of persons who are employed as a percent of the total of working age population.
    • December 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 04 December, 2018
      Select Dataset
      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.
    • November 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 27 November, 2018
      Select Dataset
      The employment-to-population ratio expresses the number of persons who are employed as a percent of the total working age population. Data provided only refers to males.
    • December 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 04 December, 2018
      Select Dataset
      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.
    • November 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 27 November, 2018
      Select Dataset
      The employment-to-population ratio expresses the number of persons who are employed as a percent of the total working age population. Data provided only refers to females.
    • December 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 04 December, 2018
      Select Dataset
      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.
    • September 2018
      Source: Statistics Finland
      Uploaded by: Knoema
      Accessed On: 29 November, 2018
      Select Dataset
      Revisions in these statisticsDescription Consepts and definitions *Year preliminary
    • September 2018
      Source: Statistics Finland
      Uploaded by: Knoema
      Accessed On: 30 November, 2018
      Select Dataset
      Revisions in these statisticsDescription kuvaus Consepts and definitions *Year preliminary
    • September 2018
      Source: Statistics Finland
      Uploaded by: Knoema
      Accessed On: 30 November, 2018
      Select Dataset
      Revisions in these statisticsDescription kuvaus Consepts and definitions *Year preliminary
    • May 2018
      Source: Federal Institute for Geosciences and Natural Resources
      Uploaded by: Knoema
      Accessed On: 16 May, 2018
      Select Dataset
    • June 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 23 August, 2018
      Select Dataset
      Eurostat Dataset Id:enpr_agmain The domain focuses on the Eastern European Neighbourhood Policy countries (ENP): Armenia (AM), Azerbaijan (AZ), Belarus (BY), Georgia (GE), Moldova (MD) and the Ukraine (UA). Data are provided for 200 to 300 indicators.
    • June 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 23 August, 2018
      Select Dataset
      The focus of this domain is on the European Neighbourhood Policy (ENP) countries in Eastern Europe, namely Armenia (AM), Azerbaijan (AZ), Belarus (BY), Georgia (GE), Moldova (MD) and Ukraine (UA). An extensive range of indicators is presented in this domain, including indicators from almost every theme covered by European statistics. Only annual data are published in this domain.
    • August 2016
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 22 September, 2016
      Select Dataset
      Eurostat Dataset Id:enpr_ecnacoi The domain focuses on the Eastern European Neighbourhood Policy countries (ENP): Armenia (AM), Azerbaijan (AZ), Belarus (BY), Georgia (GE), Moldova (MD) and the Ukraine (UA). Data are provided for 200 to 300 indicators.
    • June 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 23 August, 2018
      Select Dataset
      Eurostat Dataset Id:enpr_ecbop The domain focuses on the Eastern European Neighbourhood Policy countries (ENP): Armenia (AM), Azerbaijan (AZ), Belarus (BY), Georgia (GE), Moldova (MD) and the Ukraine (UA). Data are provided for 200 to 300 indicators.
    • June 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 23 August, 2018
      Select Dataset
      The focus of this domain is on the European Neighbourhood Policy (ENP) countries in Eastern Europe, namely Armenia (AM), Azerbaijan (AZ), Belarus (BY), Georgia (GE), Moldova (MD) and Ukraine (UA). An extensive range of indicators is presented in this domain, including indicators from almost every theme covered by European statistics. Only annual data are published in this domain.
    • June 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 03 September, 2018
      Select Dataset
      Eurostat Dataset Id:enpr_energy The domain focuses on the Eastern European Neighbourhood Policy countries (ENP): Armenia (AM), Azerbaijan (AZ), Belarus (BY), Georgia (GE), Moldova (MD) and the Ukraine (UA). Data are provided for 200 to 300 indicators.
    • June 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 23 August, 2018
      Select Dataset
      Eurostat Dataset Id:enpr_ecexint The domain focuses on the Eastern European Neighbourhood Policy countries (ENP): Armenia (AM), Azerbaijan (AZ), Belarus (BY), Georgia (GE), Moldova (MD) and the Ukraine (UA). Data are provided for 200 to 300 indicators.
    • August 2016
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 06 September, 2016
      Select Dataset
      Eurostat Dataset Id:enpr_agfor The domain focuses on the Eastern European Neighbourhood Policy countries (ENP): Armenia (AM), Azerbaijan (AZ), Belarus (BY), Georgia (GE), Moldova (MD) and the Ukraine (UA). Data are provided for 200 to 300 indicators.
    • June 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 23 August, 2018
      Select Dataset
      Eurostat Dataset Id:enpr_ecnagdp The domain focuses on the Eastern European Neighbourhood Policy countries (ENP): Armenia (AM), Azerbaijan (AZ), Belarus (BY), Georgia (GE), Moldova (MD) and the Ukraine (UA). Data are provided for 200 to 300 indicators.
    • June 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 23 August, 2018
      Select Dataset
      Eurostat Dataset Id:enpr_ecgov The domain focuses on the Eastern European Neighbourhood Policy countries (ENP): Armenia (AM), Azerbaijan (AZ), Belarus (BY), Georgia (GE), Moldova (MD) and the Ukraine (UA). Data are provided for 200 to 300 indicators.
    • June 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 03 September, 2018
      Select Dataset
      Eurostat Dataset Id:enpr_inisoc The domain focuses on the Eastern European Neighbourhood Policy countries (ENP): Armenia (AM), Azerbaijan (AZ), Belarus (BY), Georgia (GE), Moldova (MD) and the Ukraine (UA). Data are provided for 200 to 300 indicators.
    • June 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 23 August, 2018
      Select Dataset
      Eurostat Dataset Id:enpr_etmain The domain focuses on the Eastern European Neighbourhood Policy countries (ENP): Armenia (AM), Azerbaijan (AZ), Belarus (BY), Georgia (GE), Moldova (MD) and the Ukraine (UA). Data are provided for 200 to 300 indicators.
    • June 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 23 August, 2018
      Select Dataset
      The focus of this domain is on the European Neighbourhood Policy (ENP) countries in Eastern Europe, namely Armenia (AM), Azerbaijan (AZ), Belarus (BY), Georgia (GE), Moldova (MD) and Ukraine (UA). An extensive range of indicators is presented in this domain, including indicators from almost every theme covered by European statistics. Only annual data are published in this domain.
    • June 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 23 August, 2018
      Select Dataset
      Eurostat Dataset Id:enpr_psilc The domain focuses on the Eastern European Neighbourhood Policy countries (ENP): Armenia (AM), Azerbaijan (AZ), Belarus (BY), Georgia (GE), Moldova (MD) and the Ukraine (UA). Data are provided for 200 to 300 indicators.
    • June 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 03 September, 2018
      Select Dataset
      Eurostat Dataset Id:enpr_ecmain The domain focuses on the Eastern European Neighbourhood Policy countries (ENP): Armenia (AM), Azerbaijan (AZ), Belarus (BY), Georgia (GE), Moldova (MD) and the Ukraine (UA). Data are provided for 200 to 300 indicators.
    • June 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 23 August, 2018
      Select Dataset
      Eurostat Dataset Id:enpr_ecmny The domain focuses on the Eastern European Neighbourhood Policy countries (ENP): Armenia (AM), Azerbaijan (AZ), Belarus (BY), Georgia (GE), Moldova (MD) and the Ukraine (UA). Data are provided for 200 to 300 indicators.
    • August 2016
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 02 September, 2016
      Select Dataset
      Eurostat Dataset Id:enpr_psdemo The domain focuses on the Eastern European Neighbourhood Policy countries (ENP): Armenia (AM), Azerbaijan (AZ), Belarus (BY), Georgia (GE), Moldova (MD) and the Ukraine (UA). Data are provided for 200 to 300 indicators.
    • June 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 23 August, 2018
      Select Dataset
      Eurostat Dataset Id:enpr_ecprice The domain focuses on the Eastern European Neighbourhood Policy countries (ENP): Armenia (AM), Azerbaijan (AZ), Belarus (BY), Georgia (GE), Moldova (MD) and the Ukraine (UA). Data are provided for 200 to 300 indicators.
    • June 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 23 August, 2018
      Select Dataset
      Eurostat Dataset Id:enpr_scienc The domain focuses on the Eastern European Neighbourhood Policy countries (ENP): Armenia (AM), Azerbaijan (AZ), Belarus (BY), Georgia (GE), Moldova (MD) and the Ukraine (UA). Data are provided for 200 to 300 indicators.
    • June 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 23 August, 2018
      Select Dataset
      The focus of this domain is on the European Neighbourhood Policy (ENP) countries in Eastern Europe, namely Armenia (AM), Azerbaijan (AZ), Belarus (BY), Georgia (GE), Moldova (MD) and Ukraine (UA). An extensive range of indicators is presented in this domain, including indicators from almost every theme covered by European statistics. Only annual data are published in this domain.
    • August 2016
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 02 September, 2016
      Select Dataset
      Eurostat Dataset Id:enpr_siecr The domain focuses on the Eastern European Neighbourhood Policy countries (ENP): Armenia (AM), Azerbaijan (AZ), Belarus (BY), Georgia (GE), Moldova (MD) and the Ukraine (UA). Data are provided for 200 to 300 indicators.
    • August 2016
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 22 September, 2016
      Select Dataset
      Eurostat Dataset Id:enpr_siemp The domain focuses on the Eastern European Neighbourhood Policy countries (ENP): Armenia (AM), Azerbaijan (AZ), Belarus (BY), Georgia (GE), Moldova (MD) and the Ukraine (UA). Data are provided for 200 to 300 indicators.
    • August 2016
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 22 September, 2016
      Select Dataset
      Eurostat Dataset Id:enpr_sienv The domain focuses on the Eastern European Neighbourhood Policy countries (ENP): Armenia (AM), Azerbaijan (AZ), Belarus (BY), Georgia (GE), Moldova (MD) and the Ukraine (UA). Data are provided for 200 to 300 indicators.
    • September 2016
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 02 October, 2016
      Select Dataset
      Eurostat Dataset Id:enpr_sigeb The domain focuses on the Eastern European Neighbourhood Policy countries (ENP): Armenia (AM), Azerbaijan (AZ), Belarus (BY), Georgia (GE), Moldova (MD) and the Ukraine (UA). Data are provided for 200 to 300 indicators.
    • August 2016
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 02 September, 2016
      Select Dataset
      Eurostat Dataset Id:enpr_siinr The domain focuses on the Eastern European Neighbourhood Policy countries (ENP): Armenia (AM), Azerbaijan (AZ), Belarus (BY), Georgia (GE), Moldova (MD) and the Ukraine (UA). Data are provided for 200 to 300 indicators.
    • August 2016
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 02 September, 2016
      Select Dataset
      Eurostat Dataset Id:enpr_sisoc The domain focuses on the Eastern European Neighbourhood Policy countries (ENP): Armenia (AM), Azerbaijan (AZ), Belarus (BY), Georgia (GE), Moldova (MD) and the Ukraine (UA). Data are provided for 200 to 300 indicators.
    • June 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 23 August, 2018
      Select Dataset
      Eurostat Dataset Id:enpr_intour The domain focuses on the Eastern European Neighbourhood Policy countries (ENP): Armenia (AM), Azerbaijan (AZ), Belarus (BY), Georgia (GE), Moldova (MD) and the Ukraine (UA). Data are provided for 200 to 300 indicators.
    • June 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 23 August, 2018
      Select Dataset
      Eurostat Dataset Id:enpr_etsitc The domain focuses on the Eastern European Neighbourhood Policy countries (ENP): Armenia (AM), Azerbaijan (AZ), Belarus (BY), Georgia (GE), Moldova (MD) and the Ukraine (UA). Data are provided for 200 to 300 indicators.
    • June 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 23 August, 2018
      Select Dataset
      The focus of this domain is on the European Neighbourhood Policy (ENP) countries in Eastern Europe, namely Armenia (AM), Azerbaijan (AZ), Belarus (BY), Georgia (GE), Moldova (MD) and Ukraine (UA). An extensive range of indicators is presented in this domain, including indicators from almost every theme covered by European statistics. Only annual data are published in this domain.
    • June 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 03 September, 2018
      Select Dataset
      The focus of this domain is on the European Neighbourhood Policy (ENP) countries in Eastern Europe, namely Armenia (AM), Azerbaijan (AZ), Belarus (BY), Georgia (GE), Moldova (MD) and Ukraine (UA). An extensive range of indicators is presented in this domain, including indicators from almost every theme covered by European statistics. Only annual data are published in this domain.
    • June 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 23 August, 2018
      Select Dataset
      Eurostat Dataset Id:enpr_transp The domain focuses on the Eastern European Neighbourhood Policy countries (ENP): Armenia (AM), Azerbaijan (AZ), Belarus (BY), Georgia (GE), Moldova (MD) and the Ukraine (UA). Data are provided for 200 to 300 indicators.
    • December 2018
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 03 December, 2018
      Select Dataset
      This dataset presents the number of students enrolled in different education programmes by country of origin and sex.
    • August 2018
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 21 November, 2018
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      Source: UNECE Statistical Database, compiled from national and international (UNESCO Institute for Statistics) official sources. Definition: The net enrolment ratio is the number of students of the official school-age group (defined by each country) enrolled in secondary-level education per 100 persons of the same age group. The gross enrolment ratio is the number of students enrolled in secondary level education (regardless of their age) per 100 persons of the official school-age group corresponding to secondary-level education. The secondary level consists of lower and upper secondary levels of ISCED 2011. .. - data not available Measurement: Gross enrolment ratio , Country: Armenia Since the school year 2013-2014, the data have been compiled according ISCED 2011. Country: Austria Change in definition (1995 - 2012): NER: data include ISCED level 4 programmes and refer to official school age group assumed to be 10-17 years. Country: Austria Break in series (2013): From school year 2013-2014 onwards use of ISCED 2011. Country: Bulgaria NER data refer to students aged 11-20 and include a small number of ISCED level 4 students aged 19 to 20. Country: Croatia NER data refer to students aged 11-18. Country: Cyprus Data cover only government controlled area. Data refer to level 3 of ISCED 1997 only. 1980/1981, 1990/1991, 1995/1996: data refer to ISCED 1976 classification. 2000/2001: data refer to 1999/2000. Country: Czechia Change in definition (1995 - 2012): Data refer to full-time study only and exclude part-time study Country: Estonia NER data refer to students aged 13-17. Country: Finland 1990/1991: data refer to ISCED 1976 classification. Country: Georgia Data refer to beginning of the school year. Country: Germany Data cover the territory of Germany after reunification. 1980/1981, 1990/1991, 1995/1996: data refer to ISCED 1976 classification. For school years 2000/2001 - 2013/2014: data refer to ISCED 1997 classification. Data on students refer to beginning of the school year and data on population refer to beginning of the calendar year. Country: Hungary 2000/2001: data refer to 1999/2000. NER data refer to students aged 14-17. Data refer to levels 3 and 4 of ISCED classification. Country: Iceland 1980/1981-1995/1996: data refer to ISCED 1976 classification. Country: Ireland 1995/1996: data refer to ISCED 1976 classification. From 2000/2001: data refer to levels 2,3 and 4 of ISCED 1997 classification. Data refer to students aged 11-19. Country: Israel Data refer to level 3 of ISCED classification. 2000/2001: data exclude students registered in Ministry of Religious Affairs. Country: Italy Data refer to level 3 of ISCED classification and refer to the school year. Country: Latvia Break in methodlogy (2006): Changes in national education classification. Started from school year 2006/2007 level 2 includes grades 1-6, level 3 includes grades 7-12. Country: Lithuania Data refer to 1 January of the school year. NER data refer to students aged 11-18. Country: Moldova, Republic of Additional information (2006 - 2012): Stable population used during the enrollment rates calculation, because the actual population does not reflect the real situation of migration. Country: Moldova, Republic of Change in definition (1990 - 2005): Data refer to age group 11-17 years. Country: Moldova, Republic of Change in definition (2006 - 2012): Data refer to age group 11-18 years. Country: Montenegro Data refer to level 3 of ISCED classification. Country: Netherlands 1990/1991: data do not include special secondary education. Country: Poland Data refer to level 3 of ISCED 1997. Country: Romania Data refer to 1 July of the school year. Country: Serbia Territorial change (2003 - 2012): The Statistical Office of the Republic of Serbia has no available data on the AP Kosovo and Metohija. Country: Slovenia Data refer to 15 September of the school year. Country: Spain 2000/2001: data refer to 1999/2000. 1990/1991: NER data refer to students aged 11-18. From 1995: NER data refer to students aged 12-18. Data refer to October - September of the school year. Country: The former Yugoslav Republic of Macedonia Break in methodlogy (2010): From 2010/2011 implementation of the Law on Primary and Lower Secondary education Country: Turkey Change in definition (2000 onwards): From 1997/1998: compulsory education was expanded to 8 years by law.
    • July 2018
      Source: World Bank
      Uploaded by: Knoema
      Accessed On: 11 July, 2018
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      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.
    • January 2018
      Source: Environmental Performance Index
      Uploaded by: Knoema
      Accessed On: 02 February, 2018
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      The Environmental Performance Index (EPI) is constructed through the calculation and aggregation of 20 indicators reflecting national-level environmental data. These indicators are combined into nine issue categories, each of which fit under one of two overarching objectives. The two objectives that provide the overarching structure of the EPI are Environmental Health and Ecosystem Vitality. Environmental Health measures the protection of human health from environmental harm. Ecosystem Vitality measures ecosystem protection and resource management. These two objectives are further divided into nine issue categories that span high-priority environmental policy issues, including air quality, forests, fisheries, and climate and energy, among others. The issue categories are extensive but not comprehensive. Underlying the nine issue categories are 20 indicators calculated from country-level data and statistics. After more than 15 years of work on environmental performance measurement and six iterations of the EPI, global data are still lacking on a number of key environmental issues. These include: freshwater quality, toxic chemical exposures, municipal solid waste management, nuclear safety, wetlands loss, agricultural soil quality and degradation, recycling rates, adaptation, vulnerability, and resiliency to climate change, desertification.
    • January 2015
      Source: Economic Policy Institute
      Uploaded by: Knoema
      Accessed On: 28 April, 2016
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    • September 2015
      Source: Multiple Sources
      Uploaded by: Knoema
      Accessed On: 10 September, 2015
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    • December 2017
      Source: The General Aviation Manufacturers Association (GAMA)
      Uploaded by: Knoema
      Accessed On: 18 June, 2018
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      European Fleet Data
    • December 2018
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 12 December, 2018
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    • October 2015
      Source: International Monetary Fund
      Uploaded by: Knoema
      Accessed On: 22 October, 2015
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      Recent exchange rate movements have been unusually large, triggering a debate regarding their likely effects on trade. Historical experience in advanced and emerging market and developing economies suggests that exchange rate movements typically have sizable effects on export and import volumes. A 10 percent real effective depreciation in an economy’s currency is associated with a rise in real net exports of, on average, 1.5 percent of GDP, with substantial cross-country variation around this average. Although these effects fully materialize over a number of years, much of the adjustment occurs in the first year. The boost to exports associated with currency depreciation is found to be largest in countries with initial economic slack and with domestic financial systems that are operating normally. Some evidence suggests that the rise of global value chains has weakened the relationship between exchange rates and trade in intermediate products used as inputs into other economies’ exports. However, the bulk of global trade still consists of conventional trade, and there is little evidence of a general trend toward disconnect between exchange rates and total exports and imports.
    • July 2012
      Source: Knoema
      Uploaded by: Knoema
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      Source : United States Department of Agriculture; International Monetary Fund; UN Department of Economic and Social Affairs; Food and Agriculture Organization, The World Bank
    • October 2017
      Source: U.S. Department of Agriculture
      Uploaded by: Knoema
      Accessed On: 30 October, 2017
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      Percent of household final consumption expenditures spent on food, alcoholic beverages, and tobacco that were consumed at home, 2009-2016. The data are computed by Birgit Meade (202-694-5159), ERS/USDA, EUROMONITOR data, June 2015.
    • May 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 18 June, 2018
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    • June 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 16 June, 2018
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    • June 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 16 June, 2018
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    • June 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 16 June, 2018
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    • May 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 18 June, 2018
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    • May 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 18 June, 2018
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    • January 2018
      Source: United Nations Conference on Trade and Development
      Uploaded by: Knoema
      Accessed On: 30 May, 2018
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      This table presents annual statistics on international trade in services of individual economies by trading partner and by 78 selected service categories. In addition, the table contains data for services trade of various groups of economies with "world" and for selected principal service categories. The data presented are the result of the common work of UNCTAD, World Trade Organization (WTO) and International Trade Center (ITC)
    • November 2018
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 26 November, 2018
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      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.
    • September 2013
      Source: United Nations Conference on Trade and Development
      Uploaded by: Knoema
      Accessed On: 10 October, 2013
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      This table presents information on the external long-term indebtedness of developing economies (as debtors), expressed in millions of dollars, expressed as percentage of total long-term debt, as percentage of debt source and as percentage of region. The table also provides breakdown of public and publicly guaranteed debt by source of lending (as creditors).
  • F
    • May 2018
      Source: Food and Agriculture Organization
      Uploaded by: Knoema
      Accessed On: 31 May, 2018
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      The dataset includes data on gross and net production indices for various food and agriculture aggregates expressed in both totals and per capita.
    • September 2018
      Source: Food and Agriculture Organization
      Uploaded by: Knoema
      Accessed On: 10 September, 2018
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      The FAOSTAT monthly CPI & Food CPI database was based on the ILO CPI data until December 2014. In 2014, IMF-ILO-FAO agreed to transfer global CPI data compilation from ILO to IMF. Upon agreement, CPIs for all items and its sub components originates from the International Monetary Fund (IMF), and the UN Statistics Division(UNSD) for countries not covered by the IMF. However, due to a limited time coverage from IMF and UNSD for a number of countries, the Organisation for Economic Co-operation and Development (OECD), the Latin America and the Caribbean statistics (CEPALSTAT), Central Bank of Western African States (BCEAO), Eastern Caribbean Central Bank (ECCB) and national statistical office website data are used for missing historical data from IMF and UNSD food CPI.  The FAO CPI dataset for all items(or general CPI) and the Food CPI, consists of a complete and consistent set of time series from January 2000 onwards. These indices measure the price change between the current and reference periods of the average basket of goods and services purchased by households. The CPI,all items is typically used to measure and monitor inflation, set monetary policy targets, index social benefits such as pensions and unemployment benefits, and to escalate thresholds and credits in the income tax systems and wages in public and private wage contracts.   Note: For some countries quarterly data is mentioned as monthly data because of quarter (Time period of quarter) differs across countries. Please go to the link: "http://fenixservices.fao.org/faostat/static/documents/CP/CPI_e.pdf" for detail about countries' National index reference period, definition, data details.    
    • September 2018
      Source: Food and Agriculture Organization
      Uploaded by: Knoema
      Accessed On: 31 October, 2018
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      1.Following the recommendation of experts gathered in the Committee on World Food Security (CFS) Round Table on hunger measurement, hosted at FAO headquarters in September 2011, an initial set of indicators aiming to capture various aspects of food insecurity is presented here. 2.The choice of the indicators has been informed by expert judgment and the availability of data with sufficient coverage to enable comparisons across regions and over time. Many of these indicators are produced and published elsewhere by FAO and other international organizations. They are reported here in a single database with the aim of building a wide food security information system. More indicators will be added to this set as more data will become available. Note: Data represent values for time periods (1999-2001,2000-02,2005-07) and is shown as data for the last year of time period 2001, 2002,2007
    • June 2012
      Source: Food and Agriculture Organization
      Uploaded by: Knoema
      Accessed On: 18 July, 2012
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      This dataset represents Food Consumption, Food Production and Trade by various Food items. Note: data represent values for time periods (1990-1992, 1995-97, 2000-02, 2005-07) and is shown as data for the last year of time period (1992, 1997, 2002, 2007).
    • October 2018
      Source: Food and Agriculture Organization
      Uploaded by: Knoema
      Accessed On: 20 November, 2018
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      The Price domain of FAOSTAT contains data on prices received by farmers (called Producer prices) for primary crops, live animals, livestock primary products as collected at the point of initial sale (prices paid at the farm-gate). Data are provided for over 160 countries and for some 200 commodities. The Price domain provides price data in three units: i) Local Currency Units (LCU) ii) Standard Local Currency (SLC) iii) US Dollars.
    • November 2018
      Source: Food and Agriculture Organization
      Uploaded by: Knoema
      Accessed On: 07 December, 2018
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      Value of gross production has been compiled by multiplying gross production in physical terms by output prices at farm gate. Thus, value of production measures production in monetary terms at the farm gate level. Since intermediate uses within the agricultural sector (seed and feed) have not been subtracted from production data, this value of production aggregate refers to the notion of "gross production". Value of gross production is provided in both current and constant terms and is expressed in US dollars and Standard Local Currency (SLC). The current value of production measures value in the prices relating to the period being measured. Thus, it represents the market value of food and agricultural products at the time they were produced. Knowing this figure is helpful in understanding exactly what was happening within a given economy at that point in time. Often, this information can help explain economic trends that emerged in later periods and why they took place. Value of production in constant terms is derived using the average prices of a selected year or years, known as the base period. Constant price series can be used to show how the quantity or volume of products has changed, and are often referred to as volume measures. The ratio of the current and constant price series gives a measure of price movements. US dollar figures for value of gross production are converted from local currencies using official exchange rates as prevailing in the respective years. The SLC of a country is the local currency prevailing in the latest year. Expressing data series in one uniform currency is useful because it avoids the influence of revaluation in local currency, if any, on value of production
    • April 2018
      Source: Food and Agriculture Organization
      Uploaded by: Knoema
      Accessed On: 10 July, 2018
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      Producer Price Indices - AnnualIndices of agricultural producer prices measure the average annual change over time in the selling prices received by farmers (prices at the farm-gate or at the first point of sale). Annual data are provided for over 80 countries. The three categories of producer price indices available in FAOSTAT comprise: Single-item price indices, Commodity group indices and the Agriculture producer price index.
    • August 2018
      Source: Food and Agriculture Organization
      Uploaded by: Knoema
      Accessed On: 07 December, 2018
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      The Fertilizers by Product dataset contains information on product amounts for the Production, Trade, Agriculture Use and Other Uses of chemical and mineral fertilizers products, over the time series 2002-present. The fertilizer statistics data are validated separately for a set of over thirty individual products. Both straight and compound fertilizers are included.
    • October 2017
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 21 November, 2018
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      Source: UNECE Transport Division Database. Definitions Killed: Any person who was killed outright or who died within 30 days as a result of the accident. Injured: Any person, who was not killed, but sustained one or more serious or slight injuries as a result of the accident. Driver: Any person who drives a motor vehicle or other vehicle (including a cycle), or who guides cattle, singly or in herds, or flocks, or draught, pack or saddle animals on a road. Passenger: Any person, other than the driver, who is in or on a vehicle. Pedestrian: Any person other than a driver or a passenger according to the above definitions. Persons pushing or pulling a child?s carriage, a bath chair or invalid chair, or any other small vehicle without an engine, or pushing a cycle or moped, and handicapped persons travelling in invalid chairs propelled by such persons or moving at walking pace shall be treated as pedestrians. Road vehicle: A vehicle running on wheels and intended for use on roads. Motor vehicle: Any power-driven vehicle which is normally used for carrying persons or goods by road or for drawing, on the road, vehicles used for the carriage of persons or goods. This term embraces trolleybuses, that is to say, vehicles connected to an electric conductor and not rail-borne. It does not cover vehicles, such as agricultural tractors, which are only incidentally used for carrying persons or goods by road or for drawing, on the road, vehicles used for the carriage of persons or goods. Power driven vehicle: Any self propelled road vehicle, other than a moped and a rail-borne vehicle. Cycle: Any road vehicle which has at least two wheels and is propelled solely by the muscular energy of the person(s) on that vehicle, in particular by means of a pedal system, lever or handle (e.g. bicycles, tricycles, quadricycles and invalid carriages). Moped: Any two-wheeled or three-wheeled road vehicle which is fitted with an internal combustion engine having a cylinder capacity not exceeding 50 cc. (3.05 cu. in.) and a maximum design speed not exceeding 50 km (30 miles) per hour. Motor cycle: Two-wheeled road motor vehicle with or without side-car, including motor scooter, or three-wheeled road motor vehicle not exceeding 400 kg (900 lb.) unleaded weight. This term does not include mopeds. Passenger car: Road motor vehicle, other than a motor cycle, intended for the transport of passengers and seating not more than nine persons (including the driver). The term passenger car therefore covers taxis and hired vehicles, provided that they have fewer than ten seats. Motor coach or bus: Passenger road motor vehicle, seating more than nine persons (including the driver). Trolleybus: A passenger road vehicle, seating more than nine persons (including the driver), which is connected to electric conductors and which is not rail-borne. Tramcar: A passenger road vehicle, seating more than nine persons (including the driver), which is connected to electric conductors and which is rail borne. Please note that country footnotes are not always in alphabetical order. .. - data not available For European Union member states, Iceland, Norway, and Switzerland the source of data from year 2005 is CARE database. Country: Albania Included with motorcycles, if not available. Country: Ireland Included with motorcycles, if not available. Country: Poland Included with motorcycles, if not available. Country: Georgia '' Country: Latvia Persons are recorded as killed who die at the scene of the accident or within 7 days; persons who die later are recorded as injured. Country: Moldova, Republic of From 2008, breakdown by category of user does not sum to total as unknown category of user is not reported. Country: Portugal Data refer to continent only. Country: Portugal Persons are recorded as killed who die at the scene of the accident or during or immediately after transport from the scene of the accident; persons who die later are recorded as injured. Country: Spain Persons are recorded as killed who die within 24 hours as a result of the accident; persons who die later are recorded as injured. Country: Turkey Data by age group cover accidents only at Police responsibility area for years between 2000-2011 whereas for years between 2012-2015 data cover both Police and Gendermarie responsibility area. Until year 2015 figures on persons killed include the deaths only at the accident scene; however since year 2015 figures on persons killed also include the deaths within 30 days after the traffic accidents due to related accident and its impacts for people injured and sent to health facilities. 6 to 9 years refers to less than 10 years old. Country: United Kingdom Data refer to Great Britain. Country: United States Sum by category of user is not equal to total as unknown category of user is not shown. Country: Uzbekistan Less than 6 years refers to less than 7 years. 10 to 14 years refers to 8 to 15 years.
    • December 2017
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 21 November, 2018
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      For European Union member states, Iceland, Norway, and Switzerland the source of data from year 2005 is CARE database. Age Group: For European Union members, less than 6 years refers to less than 5 years (data provided through CARE database). Age Group: 18 - 20 years For European Union members, 18 to 20 years refers to 18 to 19 years (data provided through CARE database). Age Group: 21 - 24 years For European Union members, 21 to 24 years refers to 20 to 24 years (data provided through CARE database). Age Group: 6 - 9 years For European Union members, 6 to 9 years refers to 5 to 9 years (data provided through CARE database). Country: Georgia '' Country: Latvia Persons are recorded as killed who die at the scene of the accident or within 7 days; persons who die later are recorded as injured. Country: Portugal Persons are recorded as killed who die at the scene of the accident or during or immediately after transport from the scene of the accident; persons who die later are recorded as injured. Country: Spain Persons are recorded as killed who die within 24 hours as a result of the accident; persons who die later are recorded as injured. Country: Turkey Data by age group cover accidents only at Police responsibility area for years between 2000-2011 whereas for years between 2012-2015 data cover both Police and Gendermarie responsibility area. Until year 2015 figures on persons killed include the deaths only at the accident scene; however since year 2015 figures on persons killed also include the deaths within 30 days after the traffic accidents due to related accident and its impacts for people injured and sent to health facilities. 6 to 9 years refers to less than 10 years old. Country: United Kingdom Data refer to Great Britain. Country: Uzbekistan Less than 6 years refers to less than 7 years. 10 to 14 years refers to 8 to 15 years. Sex: Total Sum of males and females may not be equal to total in some countries where victim gender is unknown.
    • February 2018
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 26 June, 2018
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      FDI data are based on statistics provided by 35 OECD member countries and by Lithuania. BMD4: OECD Benchmark Definition of Foreign Direct Investment - 4th Edition
    • June 2018
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 06 July, 2018
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    • June 2018
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 02 July, 2018
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    • December 2009
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 27 May, 2014
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      Eurostat Dataset Id:educ_bo_ac_ent3 The Bologna declaration was signed in 1999 by 29 European ministers responsible for higher education. Today, 46 signatory countries are engaged in the process towards a European Higher Education Area (EHEA). The Bologna Process is an intergovernmental initiative which also involves the European Commission, the Council of Europe and UNESCO-CEPES, as well as representatives of higher education institutions, students, staff, employers and quality assurance agencies. It aims to create a European Higher Education Area by 2010, and to promote the European system of higher education worldwide. More information on the Bologna process is available on http://ec.europa.eu/education/higher-education/doc1290_en.htm. Many indicators on social dimension and mobility in the Bologna process come from the UOE data collection in the education statistics domain. The aim of the education statistics domain is to provide comparable statistics and indicators on key aspects of the education systems across Europe. The data cover participation and completion of education programmes, personnel in education and the cost and type of resources dedicated to education. The main source of data is the joint UIS (UNESCO Institute of Statistics)/OECD/Eurostat (UOE) questionnaires on education statistics, which constitute the core database on education. Data on regional enrolments and foreign language learning are collected additionally by Eurostat. Countries provide data, coming from administrative records, on the basis of commonly agreed definitions. From the UOE data collection, the following datasets on the Bologna Process are available: A. Widening access educ_bo_ac_ent2: Net entry rate (ISCED 5A) by age and sexeduc_bo_ac_ent3: Female entrants by field of education (ISCED 5A)educ_bo_ac_gent: Entrants at ISCED 5A and qualifying graduates of secondary schooling (ISCED 3A - 4A)educ_bo_ac_el1t: Students (ISCED 5A) studying part-time, by age B. Study framework educ_bo_fi_fgdp: Public expenditure on tertiary education (ISCED 5-6), as % of GDP or total public expenditureeduc_bo_fi_ftot: Annual total expenditure on educational institutions (ISCED 5-6) per full-time equivalent student with and without expenditure on research and ancillary serviceseduc_bo_fi_ffun: Tertiary education institutions' income from private sources (households and other private entities) as % of all public and private sourceseduc_bo_fi_fiaid: Public financial aid to tertiary students (ISCED 5-6), by type of aid, as % of public expenditure on tertiary education C. Student and staff mobility educ_bo_mo_el8o: Students (ISCED 5A and 6) who are nationals of a given country, studying in another country (EU-27, EFTA and CC) as % of the total enrolment in that countryeduc_bo_mo_el8i: Number of foreign students (world and Bologna Area) studying in a given country, as % of the total enrolment in that country, ISCED 5A and 6educ_bo_mo_gr4: Graduates (ISCED 5A and 6) from abroad (non-citizens, permanent residence and prior education outside the country) D. Effective outcomes and employability educ_bo_ou_gren: Gross graduation rate and net entry rate, ISCED 5A   The data for some countries which do not participate in the UOE data collection were provided to Eurostat specifically for the monitoring of the Bologna Process. Not being fully integrated in the UOE, the data sometimes might not be as comparable as the data for the remaining countries, due to differences in the underlying data sources and definitions. These data were provided by the following entities: Andorra (AD): data provided by the University of Andorra (indicators educ_bo_ac_ent3, educ_bo_fi_ffun, educ_bo_mo_el8i, educ_bo_mo_gr4)Armenia (AM): data provided by the Ministry of Education and Science (educ_bo_ac_gent, educ_bo_ac_el1t, educ_bo_mo_gr4, educ_bo_ou_gren)Georgia (GE): data provided by the NSI, Statistics Georgia (educ_bo_ac_ent3, educ_bo_ac_el1t, educ_bo_fi_fgdp, educ_bo_mo_gr4)Serbia (RS): data provided by the NSI, Statistical Office of the Republic of Serbia (educ_bo_mo_el8i)Ukraine (UA): data provided by the NSI, State Statistics Committee for Ukraine (educ_bo_ou_gren, educ_bo_ac_el1t, educ_bo_mo_el8i, educ_bo_mo_gr4, educ_bo_ou_gren)
    • December 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 03 December, 2018
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      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. Data for 1990-2015 are estimates while 2016-2030 data are projections. The dataset was updated as of July 2017. For more information, refer to the ILO estimates and projections methodological note.
    • November 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 27 November, 2018
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      Data refers to the number of women employed in the agricultural sector as a percent of total employment in agriculture
    • November 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 27 November, 2018
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      Data refers to the number of women employed in the industry sector as a percent of total employment in industry.
    • December 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 03 December, 2018
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      The female share of employment in managerial positions conveys the number of women in management as a percentage of employment in management. Employment in management is defined based on the International Standard Classification of Occupations. Two different measures are presented: one referring to total management (category 1 of ISCO-08 or ISCO-88), and another one referring to senior and middle management only, thus excluding junior management (category 1 in both ISCO-08 and ISCO-88 minus category 14 in ISCO-08 and minus category 13 in ISCO-88). This indicator is calculated based on data on employment by sex and occupation. For further information, see the SDG Indicators Metadata Repository or ILOSTAT’s indicator description.
    • November 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 27 November, 2018
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      Data provided refers to the number of women employed in the services sector as a percent of total employment in services.
    • September 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 01 October, 2018
      Select Dataset
      Not applicable
    • March 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 09 March, 2018
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      Not applicable
    • December 2017
      Source: Food and Agriculture Organization
      Uploaded by: Knoema
      Accessed On: 01 August, 2018
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      The dataset contains data on Import and Export Value (expressed in 1000 USD) for a selected list of fertilizers, from 1961 on wards. Country and country aggregate data are available. The fertilizers covered are: Nitrogenous fertilizers; Phosphate fertilizers; Potash fertilizers; Fertilizers Manufactured, nes; Fertilizers, Organic; Natural Phosphates; Natural Potassic Salts; Natural Sodium Nitrate.
    • October 2018
      Source: International Federation of Association Football
      Uploaded by: Knoema
      Accessed On: 17 November, 2018
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      Monthly updates of FIFA World Football Men's Ranking 
    • October 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 03 November, 2018
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      Final energy consumption includes all energy delivered to the final consumer's door (in the industry, transport, households and other sectors) for all energy uses. It excludes deliveries for transformation and/or own use of the energy producing industries, as well as network losses. This table presents the total final energy consumption (product 0000 – All products) and the energy consumption of a selected number of products or product groups.
    • August 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 31 August, 2018
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      This indicator is a proxy for the quality of health care. It represents the percentage of the population without access to health care due to financial resource deficit. The threshold for having sufficient financial resources is US$239 per person per year. A higher figure indicates worse levels of coverage. To estimate the quality of health care, this indicator uses as a proxy the relative difference between per capita health expenditure in a given country and its median value in countries with a low level of vulnerability.To establish whether a country is spending 'enough' or has 'enough' key health workers, it is necessary first to define what constitutes 'enough', i.e. set a threshold against which a country's performance can be compared. Opinions differ on what constitutes 'enough' in these contexts, not least because it is likely to be a moving target, influenced by prevailing health issues, demography etc. The ILO's approach for measuring financial deficit is to: (i) calculate the median expenditure on health (excluding OOP) in low-vulnerability countries, then (ii) for each country, compare spending against this median. In 2014, the median in low-vulnerability countries was US$239. For example, a country spending 50% less than the median in low-vulnerability countries has a financial deficit of 50%. This is one of five indicators measuring key dimensions of deficits in health care access and coverage. For analytical purposes the full set of indicators should be considered together.
    • December 2018
      Source: International Monetary Fund
      Uploaded by: Knoema
      Accessed On: 10 December, 2018
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      The Financial Soundness Indicators (FSIs) were developed by the IMF, together with the international community, with aim of supporting analysis and assessing strengths and vulnerabilities of financial systems. The Statistics Department of the IMF, disseminates data and metadata on selected FSIs provided by participating countries. For a description of the various FSIs, as well as the consolidation basis, consolidation adjustments, and accounting rules followed, please refer to the concepts and definitions document in the document tab. Reporting countries compile FSI data using different methodologies, which may also vary for different points in time for the same country. Users are advised to consult the accompanying metadata to conduct more meaning cross-country comparisons or to assess the evolution of a given FSI for any of the countries.
    • December 2018
      Source: International Monetary Fund
      Uploaded by: Knoema
      Accessed On: 10 December, 2018
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      The Reporting entities dataset provides information on the structure, size, and coverage of the financial institutions that are used for compiling financial soundness indicators. It provides a better understanding of the structure of the reporting entities in terms of the type of institution, number of entities, size of assets, and type of control. Reporting entities are domestically incorporated entities but are divided into two: domestically controlled and foreign controlled. The concepts of residency criterion and control are determined based on FSI Guide methodology which is in line with international best practices such as Systems of National Accounts. Data on reporting entities cover the branches, subsidiaries and the value of asset for both domestically and foreign controlled entities resident in the reporting country together their resident and non-resident subsidiaries.
    • September 2018
      Source: Statistics Finland
      Uploaded by: Knoema
      Accessed On: 10 December, 2018
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      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 definitionsRegionRegion and statesYearYear.DataImportThe value of imports, 1 000 000 euros.ExportThe value of exports, 1 000 000 euros.
    • February 2018
      Source: Statistics Finland
      Uploaded by: Knoema
      Accessed On: 29 November, 2018
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      The figures in the tables are final.Description of statisticsConcepts and definitions Classifications.. = Data not available or too uncertain for presentation, or subject to secrecy.From 2005, the employment pension insurance includes those aged 18 to 68, while previously the obligation to take out pension insurance for employees already started from the age of 14. This is visible in the employment statistics from 2005 onwards as a fall in employment by young people and a rise in the number of students. Statistics cannot be compiled reliably on employment by under-age people on the basis of register data.Citizenships are specified in the table if the number of people in the citizenship group exceeds 99.© Tilastokeskus - Statistics Finland
    • March 2018
      Source: Statistics Finland
      Uploaded by: Knoema
      Accessed On: 29 November, 2018
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      Concepts and definitionsDescription 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 in Doria (in Finnish) Publications on Vital statistics in Doria (in Finnish) Publications on Population censuses in Doria (in Finnish)NationalityIf 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.NationalityCzech RepublicCzech Republic + Former CzechoslovakiaSudanSudan + Former Sudan
    • March 2018
      Source: Statistics Finland
      Uploaded by: Knoema
      Accessed On: 29 November, 2018
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      Concepts and definitionsDescription Quality description 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) 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
    • September 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 03 September, 2018
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      Data on marriages and divorces at national level are based on the annual demographic data collections in the field of demography carried out by Eurostat. The completeness of information depends on the availability of data reported by the National Statistical Institutes. The Joint demographic data collection is carried out in cooperation with United Nation Statistical Division (UNSD) in the summer of each year, having the deadline 15 September. During this data collection Eurostat collects from the national statistical institutes detailed data by sex, age and other characteristics for the demographic events (births, deaths, marriages and divorces) of the previous year and the population on 1 January of the current and previous years. More specifically, during year T the following data are collected and disseminated on fertility field: - total number of marriages and divorces - persons getting married during the reference year by previous legal marital status, year T-1 Data can be found under the section Marriages and divorces (demo_nup). The information is updated towards the end of each year based on information collected during the Joint data collection. Moreover, any update sent by the countries in-between data collections are validated, processed and uploaded into Eurostat's demographic database and in Eurostat's free dissemination online database as soon as possible. Aggregates are recalculated accordingly. The data transmitted by the National Statistical Institutes are validated by Eurostat, processed and uploaded into Eurostat's Demographic Database and in Eurostat's free dissemination online database. The data are also disseminated in several thematic and horizontal Eurostat's publications. Data are presented by single country and for aggregates of countries. For EU and Euro Area, only the current and the previous version of the aggregates are published. The currently disseminated aggregates are: EU-27, EU-25, EA-16, and EA-15. Moreover, data is disseminated for the European Economic Area (EEA) and the European Free Trade Association (EFTA). International marriages and divorces Statistics on the number of international marriages and divorces (2000-2007) were collected by Eurostat from national statistical institutes in September 2008. The data were further used by the European Commission for preparing  a proposal for a Council Regulation on the law applicable in divorce and legal separation.  These data collected are available upon request.
    • March 2018
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 21 November, 2018
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      Source: UNECE Statistical Database, compiled from national and international (Eurostat) official sources. Definition: Data on first marriages are numbers of men and women who were married for the first time during the year, by age at last birthday. General note: Data come from registers, unless otherwise specified. .. - data not available Country: Albania Reference period (2007-2015): The data are for the total number of marriages not for the first Country: Albania Reference period (2007-2015): The data for the 15-19 age group refer to under 19 Country: Belgium Change in definition (2000-2015): both spouses are single before the marriage. In the preceding table, each spouse was selected separetely. Country: Belgium Since 2003, marriages between persons of the same sex are included. Country: Cyprus Data cover only government controlled area. Country: Georgia Territorial change (1995 onward): Data do not cover Abkhazia AR and Tskhinvali Region Country: Germany From 3 October 1990: data refer to the Federal Republic within its frontiers. Country: Kazakhstan Change in definition (1995 - 2008): Age group 0-14 refers to age less than 18; age group 15-19 refers to 18-19. Country: Malta From 2001: data include foreign residents. Country: Moldova, Republic of Age group 15-19 includes married at the age under 16 and 16-19. Country: Moldova, Republic of Territorial change (2000 onward): Data exclude the territory of the Transnistria and municipality of Bender Country: Russian Federation Additional information (2011 - 2012): Age group 15-19 includes married at age less than 15 Country: Serbia From 1998: data do not cover Kosovo and Metohija. Country: Tajikistan Data refer to registered marriages. Country: Turkey Change in definition (2002 - 2012): Age group 15 - 19 refers to 16-19. Measurement: Percent of corresponding total for all ages , Country: Ukraine Change in definition (1980 - 1995): Age group 0-14 refers to age less than 18; age group 15-19 refers to 18-19. Measurement: Percent of corresponding total for all ages , Country: Ukraine Change in definition (2000 - 2006): Age group 0-14 refers to age less than 16; age group 15-19 refers to 16-19.
    • November 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 23 November, 2018
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      Data on marriages and divorces at national level are based on the annual demographic data collections in the field of demography carried out by Eurostat. The completeness of information depends on the availability of data reported by the National Statistical Institutes. The Joint demographic data collection is carried out in cooperation with United Nation Statistical Division (UNSD) in the summer of each year, having the deadline 15 September. During this data collection Eurostat collects from the national statistical institutes detailed data by sex, age and other characteristics for the demographic events (births, deaths, marriages and divorces) of the previous year and the population on 1 January of the current and previous years. More specifically, during year T the following data are collected and disseminated on fertility field: - total number of marriages and divorces - persons getting married during the reference year by previous legal marital status, year T-1 Data can be found under the section Marriages and divorces (demo_nup). The information is updated towards the end of each year based on information collected during the Joint data collection. Moreover, any update sent by the countries in-between data collections are validated, processed and uploaded into Eurostat's demographic database and in Eurostat's free dissemination online database as soon as possible. Aggregates are recalculated accordingly. The data transmitted by the National Statistical Institutes are validated by Eurostat, processed and uploaded into Eurostat's Demographic Database and in Eurostat's free dissemination online database. The data are also disseminated in several thematic and horizontal Eurostat's publications. Data are presented by single country and for aggregates of countries. For EU and Euro Area, only the current and the previous version of the aggregates are published. The currently disseminated aggregates are: EU-27, EU-25, EA-16, and EA-15. Moreover, data is disseminated for the European Economic Area (EEA) and the European Free Trade Association (EFTA). International marriages and divorces Statistics on the number of international marriages and divorces (2000-2007) were collected by Eurostat from national statistical institutes in September 2008. The data were further used by the European Commission for preparing  a proposal for a Council Regulation on the law applicable in divorce and legal separation.  These data collected are available upon request.
    • December 2018
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 03 December, 2018
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      The OECD FISH Unit, in collaboration with the Environment Directorate and the Directorate for Science, Technology and Innovation, has developed patent-based innovation indicators that are suitable for tracking developments in fisheries-related technologies. The search strategy for fisheries and aquaculture related technologies adopts a mixed solution with a definition of the technical field of interest in fisheries and aquaculture innovation complemented by keywords, e.g. by looking for keywords in the International Patent Classification (IPC) codes and checking manually the relevance of the results in the text of patents (in the title, the abstract, etc). Technology domains are detailed in the ANNEX attached below. The indicators allow the assessment of countries' and firms' innovative performance as well as the design of governments' fisheries, aquaculture and innovation policies.
    • September 2017
      Source: United Nations World Food Programme
      Uploaded by: Knoema
      Accessed On: 21 December, 2017
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      IRMA is computed on one representative ton of the food aid basket the user has selected. The "representativity" of the ton comes from the fact that the shares of the commodities are the same as those in the total selected food basket. Therefore it can be used for comparisons among food aid baskets of different size and in understanding how much of their difference in nutritional content is due to the absolute value in metric tons of the donations and how much is due to the nutritional qualities of food delivered.   IRMA, IRMAs and IRMAt provide only information on their 'nutritional potential' of meeting average requirements.
    • September 2017
      Source: United Nations World Food Programme
      Uploaded by: Knoema
      Accessed On: 21 December, 2017
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      The energy intake of a human being is the only one among the nutrients that cannot in the short run be renounced without putting at immediate risk the possibility of survival itself. A lack of other nutrients increases susceptibility to infections and slows cognitive development and growth, contributing to poorer school performance and reduced work productivity. These effects are largely irreversible and long term, particularly when they occur at a young age. For these reasons, the IRMAs computation takes the content of Energy as a benchmark to compare with the other nutrients' content. For the calculation of IRMAs, we start with the IRMA values for each nutrient. IRMA of a nutrient counts the number of average individuals that could potentially be satisfied by the nutrient contained in a ton of food aid.   IRMA, IRMAs and IRMAt provide only information on their 'nutritional potential' of meeting average requirements.
    • September 2017
      Source: United Nations World Food Programme
      Uploaded by: Knoema
      Accessed On: 21 December, 2017
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      IRMAt (Individual Requirements Met on Average, Total) can be considered an alternative measure for food aid deliveries. By knowing how many tons of which commodity are contained in the food aid basket, it is easy to compute how many micrograms of nutrients there are in the overall basket. But, a measure like that would not be easy to interpret. Furthermore, each nutrient is measured in a different unit (for example, vitamin C is measured in micrograms and fat is measured in grams). IRMAt 'standardizes' the nutritional content of food aid by taking it as a percentage of human nutritional requirements. IRMAt of a nutrient is nothing but the number of individual requirements that could potentially be met on an annual basis by the total food aid deliveries selected. IRMAt values are descriptive of a food aid basket and are dependent on the absolute value in tonnage. They give information that reflects both nutritional content and the size of the food aid deliveries. From this point of view IRMAt can be considered a unit of measurement for food aid flows: it measures food aid basket by the number of average individuals that its nutritional content could potentially satisfy.   IRMA, IRMAs and IRMAt provide only information on their 'nutritional potential' of meeting average requirements.
    • December 2017
      Source: Food and Agriculture Organization
      Uploaded by: Knoema
      Accessed On: 07 December, 2018
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      Food Balance Sheet presents a comprehensive picture of the pattern of a country's food supply during a specified reference period. The food balance sheet shows for each food item - i.e. each primary commodity and a number of processed commodities potentially available for human consumption - the sources of supply and its utilization. The total quantity of foodstuffs produced in a country added to the total quantity imported and adjusted to any change in stocks that may have occurred since the beginning of the reference period gives the supply available during that period. On the utilization side a distinction is made between the quantities exported, fed to livestock, used for seed, put to manufacture for food use and non-food uses, losses during storage and transportation, and food supplies available for human consumption. The per caput supply of each such food item available for human consumption is then obtained by dividing the respective quantity by the related data on the population actually partaking of it. Data on per caput food supplies are expressed in terms of quantity and - by applying appropriate food composition factors for all primary and processed products - also in terms of caloric value and protein and fat content.
    • January 2018
      Source: Food and Agriculture Organization
      Uploaded by: Knoema
      Accessed On: 07 December, 2018
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      Commodity balances show balances of food and agricultural commodities in a standardized form. The scope of standardization is to present these data in a less detailed form for a selected number of commodities without causing any significant loss of the basic variables monitoring the agricultural sector. The selected commodities include the equivalents of their derived products falling in the same commodity group, but exclude the equivalents of by-products and derived commodities, which through processing, change their nature and become part of different commodity groups. A number of commodity/item aggregates have been included to offer synthetic information. Some of these are included with the aim of simplifying the extraction of all component commodities. Data shown in the item aggregates represent the sum of the component commodities as presented in this domain (standardized form). Commodity coverage: The commodity list in this domain has been generally confined to primary commodities - except for sugar, oils and fats and beverages. Whenever possible trade in processed commodities is expressed in the originating primary commodity equivalent. Rice is expressed in milled equivalent.
    • February 2018
      Source: Food and Agriculture Organization
      Uploaded by: Knoema
      Accessed On: 07 December, 2018
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      Food supply data is some of the most important data in FAOSTAT. In fact, this data is for the basis for estimation of global and national undernourishment assessment, when it is combined with parameters and other data sets. This data has been the foundation of food balance sheets ever since they were first constructed. The data is accessed by both business and governments for economic analysis and policy setting, as well as being used by the academic community.
    • January 2018
      Source: Food and Agriculture Organization
      Uploaded by: Knoema
      Accessed On: 07 December, 2018
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      Food supply data is some of the most important data in FAOSTAT. In fact, this data is for the basis for estimation of global and national undernourishment assessment, when it is combined with parameters and other data sets. This data has been the foundation of food balance sheets ever since they were first constructed. The data is accessed by both business and governments for economic analysis and policy setting, as well as being used by the academic community.
    • September 2014
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Pallavi S
      Accessed On: 04 October, 2014
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      The number of students enrolled refers to the count of students studying in the reference period. Each student enrolled in the education programmes covered by the corresponding category is counted once and only once. National data collection systems permitting, the statistics reflect the number of students enrolled at the beginning of the school / academic year. Preferably, the end (or near-end) of the first month of the school / academic year is chosen (special arrangements are made for part-year students who may not start studies at the beginning of the school year). Students are classified as foreign students (non-citizens) if they are not citizens of the country in which the data are collected. While pragmatic and operational, this classification is inappropriate for capturing student mobility because of differing national policies regarding the naturalisation of immigrants. Countries that have lower propensity to grant permanent residence to its immigrant populations are likely to report second generation immigrants as foreign students. Therefore, for student mobility and bilateral comparisons, interpretations of data based on the concept of foreign students should be made with caution. Students are classified as international students if they left their country of origin and moved to another country for the purpose of study. Depending on country-specific immigration legislation, mobility arrangements, and data availability, international students may be defined as students who are not permanent or usual residents of their country of study or alternatively as students who obtained their prior education in a different country, including another EU country.
    • August 2018
      Source: United Nations Conference on Trade and Development
      Uploaded by: Knoema
      Accessed On: 31 October, 2018
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    • December 2017
      Source: U.S. Department of Commerce, Bureau of Economic Analysis
      Uploaded by: Knoema
      Accessed On: 09 January, 2018
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      The activities of multinational enterprises statistics available here provide a picture of the overall activities of U.S. 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 U.S. affiliates of foreign parents conducted by BEA.
    • August 2018
      Source: United Nations Conference on Trade and Development
      Uploaded by: Knoema
      Accessed On: 13 August, 2018
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      This dataset contains information on foreign direct investment (FDI) inward and outward flows and stock, expressed in millions of dollars. These figures correspond to the Statistical Annexes of the UNCTAD World Investment Report. The World Investment Report, which is released in June each year (t), contains annual data up to the year before (t-1). However, at the time of publication, the data for the most recent year are still preliminary and are subject to revision by the national authorities. When they revise data, UNCTAD updates its database accordingly. The dataset also presents the following indicators: the percentage share of each economy/group in the world, and percentage ratios of FDI to GDP. Foreign direct investment (FDI) is an investment made by a resident enterprise in one economy (direct investor or parent enterprise) with the objective of establishing a lasting interest in an enterprise that is resident in an another economy (direct investment enterprise or foreign affiliate). The lasting interest implies the existence of a long-term relationship between the direct investor and the direct investment enterprise and a significant degree of influence on the management of the enterprise. The ownership of 10% or more of the voting power of a direct investment enterprise by a direct investor is evidence of such a relationship. FDI flows comprise mainly three components:acquisition or disposal of equity capital. FDI includes the initial equity transaction that meets the 10% threshold and all subsequent financial transactions and positions between the direct investor and the direct investment enterprise;reinvestment of earnings which are not distributed as dividends;inter-company debt. FDI flows are transactions recorded during the reference period (typically year or quarter). FDI stocks are the accumulated value held at the end of the reference period (typically year or quarter). In 2014, many countries implemented the new guidelines for the compilation of FDI data based on the Sixth edition of the Balance of Payments and International Investment Position Manual (BPM6) and the Fourth edition of OECD Benchmark Definition of Foreign Direct Investment (BD4). One of the major changes introduced in BPM6 and BD4 is the presentation of FDI statistics on an asset/liability basis instead of the directional principle (as recommended by the previous editions of these guidelines). On an asset/liability basis, direct investment statistics are organized according to whether the investment relates to an asset or a liability for the reporting country. Under the directional principle, the direct investment statistics are organized according to the direction of the investment for the reporting country - either inward or outward. The two presentations differ in their treatment of reverse investment (reverse investment is when an affiliate provides loans to its parent). Under the directional presentation, reverse investment is subtracted to derive the total outward or inward investment of the reporting economy. Therefore, FDI statistics on an asset/liability basis tends to be higher than those under the directional principle, but such is not always the case. While the presentation on an asset/liability basis is appropriate for macroeconomic analysis (i.e. the impact on the balance of payments), the presentation on directional principle is more appropriate to assist policymakers and government officials to formulate investment policies. This is because the presentation of the FDI data on directional basis reflects the direction of influence by the foreign direct investor underlying the direct investment: inward or outward direct investment. FDI data in this table are on directional principle, unless otherwise indicated
    • December 2009
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 27 May, 2014
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      Eurostat Dataset Id:educ_bo_mo_el8i The Bologna declaration was signed in 1999 by 29 European ministers responsible for higher education. Today, 46 signatory countries are engaged in the process towards a European Higher Education Area (EHEA). The Bologna Process is an intergovernmental initiative which also involves the European Commission, the Council of Europe and UNESCO-CEPES, as well as representatives of higher education institutions, students, staff, employers and quality assurance agencies. It aims to create a European Higher Education Area by 2010, and to promote the European system of higher education worldwide. More information on the Bologna process is available on http://ec.europa.eu/education/higher-education/doc1290_en.htm. Many indicators on social dimension and mobility in the Bologna process come from the UOE data collection in the education statistics domain. The aim of the education statistics domain is to provide comparable statistics and indicators on key aspects of the education systems across Europe. The data cover participation and completion of education programmes, personnel in education and the cost and type of resources dedicated to education. The main source of data is the joint UIS (UNESCO Institute of Statistics)/OECD/Eurostat (UOE) questionnaires on education statistics, which constitute the core database on education. Data on regional enrolments and foreign language learning are collected additionally by Eurostat. Countries provide data, coming from administrative records, on the basis of commonly agreed definitions. From the UOE data collection, the following datasets on the Bologna Process are available: A. Widening access educ_bo_ac_ent2: Net entry rate (ISCED 5A) by age and sexeduc_bo_ac_ent3: Female entrants by field of education (ISCED 5A)educ_bo_ac_gent: Entrants at ISCED 5A and qualifying graduates of secondary schooling (ISCED 3A - 4A)educ_bo_ac_el1t: Students (ISCED 5A) studying part-time, by age B. Study framework educ_bo_fi_fgdp: Public expenditure on tertiary education (ISCED 5-6), as % of GDP or total public expenditureeduc_bo_fi_ftot: Annual total expenditure on educational institutions (ISCED 5-6) per full-time equivalent student with and without expenditure on research and ancillary serviceseduc_bo_fi_ffun: Tertiary education institutions' income from private sources (households and other private entities) as % of all public and private sourceseduc_bo_fi_fiaid: Public financial aid to tertiary students (ISCED 5-6), by type of aid, as % of public expenditure on tertiary education C. Student and staff mobility educ_bo_mo_el8o: Students (ISCED 5A and 6) who are nationals of a given country, studying in another country (EU-27, EFTA and CC) as % of the total enrolment in that countryeduc_bo_mo_el8i: Number of foreign students (world and Bologna Area) studying in a given country, as % of the total enrolment in that country, ISCED 5A and 6educ_bo_mo_gr4: Graduates (ISCED 5A and 6) from abroad (non-citizens, permanent residence and prior education outside the country) D. Effective outcomes and employability educ_bo_ou_gren: Gross graduation rate and net entry rate, ISCED 5A   The data for some countries which do not participate in the UOE data collection were provided to Eurostat specifically for the monitoring of the Bologna Process. Not being fully integrated in the UOE, the data sometimes might not be as comparable as the data for the remaining countries, due to differences in the underlying data sources and definitions. These data were provided by the following entities: Andorra (AD): data provided by the University of Andorra (indicators educ_bo_ac_ent3, educ_bo_fi_ffun, educ_bo_mo_el8i, educ_bo_mo_gr4)Armenia (AM): data provided by the Ministry of Education and Science (educ_bo_ac_gent, educ_bo_ac_el1t, educ_bo_mo_gr4, educ_bo_ou_gren)Georgia (GE): data provided by the NSI, Statistics Georgia (educ_bo_ac_ent3, educ_bo_ac_el1t, educ_bo_fi_fgdp, educ_bo_mo_gr4)Serbia (RS): data provided by the NSI, Statistical Office of the Republic of Serbia (educ_bo_mo_el8i)Ukraine (UA): data provided by the NSI, State Statistics Committee for Ukraine (educ_bo_ou_gren, educ_bo_ac_el1t, educ_bo_mo_el8i, educ_bo_mo_gr4, educ_bo_ou_gren)
    • June 2014
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 11 December, 2015
      Select Dataset
      Eurostat Dataset Id:educ_enrl8 The aim of the education statistics domain is to provide comparable statistics and indicators on key aspects of the education systems across Europe. The data cover participation and completion of education programmes by pupils and students, personnel in education and the cost and type of resources dedicated to education. The standards on international statistics on education and training systems are set by the three international organisations jointly administering the UOE data collection:the United Nations Educational, Scientific, and Cultural Organisation Institute for Statistics (UNESCO-UIS),the Organisation for Economic Co-operation and Development (OECD) and,the Statistical Office of the European Union (EUROSTAT). The following topics are covered:Context - School-aged population, overall participation rates in educationDistribution of pupils/students by levelParticipation/enrolment in education (ISCED 0-4)Tertiary education participationTertiary education graduatesTeaching staff (ISCED 1-3)Pupil/students-teacher ratio and average class size (ISCED 1-3)Language learning (ISCED 1-3)Regional enrolmentsExpenditure on education in current pricesExpenditure on education in constant pricesExpenditure on education as % of GDP or public expenditureExpenditure on public and private educational institutionsFinancial aid to studentsFunding of education Other tables, used to measure progress towards the Lisbon objectives in education and training, are gathered in the Thematic indicators tables. They contain the following indicators: - Teachers and trainers - Mathematics, science and technology enrolments and graduates - Investments in education and training - Participation rates in education by age and sex - Foreign language learning - Student mobility
    • October 2018
      Source: U.S. Census Bureau
      Uploaded by: Knoema
      Accessed On: 08 October, 2018
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    • January 2016
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 21 November, 2018
      Select Dataset
      To view the original national data please open the questionnaires. Source: Joint Forest Europe / UNECE / FAO Questionnaire on Pan-European Indicators for Sustainable Forest Management. Country: Russian Federation The source of the data of Russian Federation is the National Report for the Joint Forest Europe / UNECE / FAO reporting on quantitative pan-European indicators 2011.
    • February 2016
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 21 November, 2018
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      To view the original national data please open the questionnaires. Source: Joint Forest Europe / UNECE / FAO Questionnaire on Pan-European Indicators for Sustainable Forest Management. Country: Russian Federation The source of the data of Russian Federation is the National Report for the Joint Forest Europe / UNECE / FAO reporting on quantitative pan-European indicators 2011.
    • March 2016
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 21 November, 2018
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      To view the original national data please open the questionnaires. Source: Joint Forest Europe / UNECE / FAO Questionnaire on Pan-European Indicators for Sustainable Forest Management. Country: Russian Federation The source of the data of Russian Federation is the National Report for the Joint Forest Europe / UNECE / FAO reporting on quantitative pan-European indicators 2011.
    • December 2017
      Source: Food and Agriculture Organization
      Uploaded by: Knoema
      Accessed On: 07 December, 2018
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      The database contains data on the production and trade in roundwood and primary wood and paper products for all countries and territories in the world. The main types of primary forest products included in are: roundwood, sawnwood, wood-based panels, pulp, and paper and paperboard. These products are detailed further. The definitions are available. The database contains details of the following topics: - Roundwood removals (production) by type of wood and assortment - Production and trade in roundwood, woodfuel and other basic products - Industrial roundwood by assortment and species - Sawnwood, panels and other primary products - Pulp and paper & paperboard. More detailed information on wood products, including definitions, can be found at http://www.fao.org/forestry/statistics/80572/en/
    • March 2016
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 21 November, 2018
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    • May 2018
      Source: Food and Agriculture Organization
      Uploaded by: Knoema
      Accessed On: 25 May, 2018
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    • February 2016
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 21 November, 2018
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      To view the original national data please open the questionnaires. Source: Joint Forest Europe / UNECE / FAO Questionnaire on Pan-European Indicators for Sustainable Forest Management. Country: Russian Federation The source of the data of Russian Federation is the National Report for the Joint Forest Europe / UNECE / FAO reporting on quantitative pan-European indicators 2011.
    • December 2016
      Source: Carbon Dioxide Information Analysis Center
      Uploaded by: Knoema
      Accessed On: 17 May, 2017
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      World and National CO2 Emissions from Fossil-Fuel Burning, Cement Manufacture, and Gas Flaring. Source: Tom Boden, Gregg Marland and Bob Andres (Oak Ridge National Laboratory)
    • May 2018
      Source: Fund for Peace
      Uploaded by: Knoema
      Accessed On: 15 May, 2018
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      The FSI focuses on the indicators of risk and is based on thousands of articles and reports that are processed by our CAST Software from electronically available sources. Measures of fragility, like Demographic Pressures,Refugees and IDPs and etc., have been scaled on 0 to 10 where 10 is highest fragility and 0 no fragility.
    • January 2018
      Source: Freedom House
      Uploaded by: Knoema
      Accessed On: 30 January, 2018
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      Freedom in the World is Freedom House’s flagship annual report, assessing the condition of political rights and civil liberties around the world. It is composed of numerical ratings and supporting descriptive texts for many countries. Freedom in the World has been published since 1973, allowing Freedom House to track global trends in freedom over more than 40 years. It has become the most widely read and cited report of its kind, used on a regular basis by policymakers, journalists, academics, activists, and many others.
    • April 2012
      Source: Agi Data
      Uploaded by: Knoema
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      Experts commonly support the notion that access to information is integral to the promotion of participation, transparency and accountability in any given society. A freedom of information framework aims at improving the efficiency of the government and increasing the transparency of its functioning by: 1. Regularly and reliably providing government documents to the public; 2. Educating the public on the significance of transparent government;3. Facilitating appropriate and relevant use of information in the lives of individuals
    • April 2017
      Source: Freedom House
      Uploaded by: Knoema
      Accessed On: 09 October, 2018
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      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.
    • April 2017
      Source: Freedom House
      Uploaded by: Knoema
      Accessed On: 09 October, 2018
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      Freedom on the Net measures the subtle and not-so-subtle ways that governments and non-state actors around the world restrict our intrinsic rights online. Freedom on the Net scores are based on a scale of 0 to 100 with 0 representing the best level of freedom on the net progress and 100 the worst. Note: 1)The 2017 ratings reflect the period of June 1, 2016 through May 31, 2017 2)The 2016 ratings reflect the period of June 1, 2015 through May 31, 2016. 3)The 2015 ratings reflect the period January 1 through December 31, 2014.
    • November 2018
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 15 November, 2018
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      Key statistical concept Although there are clear definitions for all the terms used in this survey, countries might have different methodologies to calculate tonne-kilometer and passenger-kilometers. Methods could be based on traffic or mobility surveys, use very different sampling methods and estimating techniques which could affect the comparability of their statistics. Also, if the definition on road fatalities is very clear and well applied by most countries, this is not the case for road injuries. Indeed, not only countries might have different definitions but the important underreporting of road injuries in most countries can distort analysis based on these data.
  • G
    • October 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 24 October, 2018
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      NEW METHODOLOGY (from 2007 onwards) Average half-yearly natural gas prices for households and industrial end-users. The end-users are characterised by predefined annual consumption bands.  The prices are collected and published considering three levels of taxation: prices excluding taxes and levies;prices excluding VAT and other recoverable taxes;prices including all taxes, levies and VAT   OLD METHODOLOGY (until 2007) Natural gas prices for households and industrial standard consumers, valid on 1st January and on 1st   July of each calendar year. Standard consumers are characterised by predefined annual consumption. The prices include gas basic price, transmission, system services, meter rental, distribution and other services. The prices are collected and published considering three levels of taxation (see above under new methodology): For structural indicators tables, where only annual data is displayed both for new and old methodology in the same table, the prices refer to the price on 1st January of each year (until 2007) and to the first semester of each year (2008 and later).For further details see : Data on energy prices are collected according to Directive 2008/92/EC of the European Parliament and of the Council of 22 October 2008 concerning a Community procedure to improve the transparency of gas and electricity prices charged to industrial end-users (recast) (Text with EEA relevance). Data for gas prices for household end-users are collected on a voluntary basis Gas prices are presented at national level for the Members of the European Union (except Cyprus and Malta for industrial end users, expect Cyprus (is not providing data for households), Malta and Finland, EFTA-country Liechtenstein, Candidate countries (except The former Yugoslav Republic of Macedonia, Albania for households and except Albania for industrial end-users), Potential candidate country Kosovo (under UNSCR 1244/1999).Â
    • October 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 24 October, 2018
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      NEW METHODOLOGY (from 2007 onwards) Average half-yearly natural gas prices for households and industrial end-users. The end-users are characterised by predefined annual consumption bands.  The prices are collected and published considering three levels of taxation: prices excluding taxes and levies;prices excluding VAT and other recoverable taxes;prices including all taxes, levies and VAT   OLD METHODOLOGY (until 2007) Natural gas prices for households and industrial standard consumers, valid on 1st January and on 1st   July of each calendar year. Standard consumers are characterised by predefined annual consumption. The prices include gas basic price, transmission, system services, meter rental, distribution and other services. The prices are collected and published considering three levels of taxation (see above under new methodology): For structural indicators tables, where only annual data is displayed both for new and old methodology in the same table, the prices refer to the price on 1st January of each year (until 2007) and to the first semester of each year (2008 and later).For further details see : Data on energy prices are collected according to Directive 2008/92/EC of the European Parliament and of the Council of 22 October 2008 concerning a Community procedure to improve the transparency of gas and electricity prices charged to industrial end-users (recast) (Text with EEA relevance). Data for gas prices for household end-users are collected on a voluntary basis Gas prices are presented at national level for the Members of the European Union (except Cyprus and Malta for industrial end users, expect Cyprus (is not providing data for households), Malta and Finland, EFTA-country Liechtenstein, Candidate countries (except The former Yugoslav Republic of Macedonia, Albania for households and except Albania for industrial end-users), Potential candidate country Kosovo (under UNSCR 1244/1999).Â
    • September 2017
      Source: Institute for Health Metrics and Evaluation
      Uploaded by: Knoema
      Accessed On: 07 November, 2017
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      Global Burden of Disease Study 2015 (GBD 2015) estimates were used in an analysis of national levels of personal healthcare access and quality based on 32 causes of disease and injury considered amenable to healthcare over time. This dataset includes the following global, regional, and national or territory-level estimates for 1990-2015: age-standardized risk-standardized death rates for 32 causes considered amenable to healthcare; the Healthcare Quality and Access (HAQ) Index and individual indices for each of the 32 causes on a scale of 0 to 100; and a frontier based on the relationship between the HAQ Index and the Socio-demographic Index (SDI). Results were published in The Lancet in May 2017 in "Healthcare Access and Quality Index based on mortality from causes amenable to personal healthcare in 195 countries and territories, 1990–2015: a novel analysis from the Global Burden of Disease Study 2015."
    • September 2017
      Source: Institute for Health Metrics and Evaluation
      Uploaded by: Knoema
      Accessed On: 14 November, 2017
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      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."
    • September 2017
      Source: Institute for Health Metrics and Evaluation
      Uploaded by: Knoema
      Accessed On: 08 November, 2017
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      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 daily smoking prevalence and smoking-attributable mortality and disease burden, as measured by disability-adjusted life years (DALYs), were produced by sex, age group, and year for 195 countries and territories. Estimates for deaths and DALYs (1990-2015) are available from the GBD Results Tool. Files available in this record include daily smoking prevalence (1980-2015) and annualized rate of change estimates. Study results were published in The Lancet in April 2017 in "Smoking prevalence and attributable disease burden in 195 countries and territories, 1990–2015: a systematic analysis from the Global Burden of Disease Study 2015." Date ranges have been considered as follows: 1990-2015 as 1990 1990-2005 as 2005 2005-2015 as 2015
    • September 2017
      Source: Institute for Health Metrics and Evaluation
      Uploaded by: Knoema
      Accessed On: 31 October, 2017
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      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. Estimates for deaths, disability-adjusted life years (DALYs), years lived with disability, years of life lost (YLLs), prevalence, and incidence for 32 cancer groups by age and sex for 21 regions, 195 countries and territories, and select subnational units for 1990-2015 (quinquennial) are available from the GBD Results Tool. Files available in this record are the web tables published in JAMA Oncology in December 2016 in "Global, Regional, and National Cancer Incidence, Mortality, Years of Life Lost, Years Lived with Disability, and Disability-Adjusted Life Years for 32 Cancer Groups, 1990-2015: A Systematic Analysis for the Global Burden of Disease Study."
    • September 2017
      Source: Institute for Health Metrics and Evaluation
      Uploaded by: Knoema
      Accessed On: 27 October, 2017
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      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.
    • November 2018
      Source: Institute for Health Metrics and Evaluation
      Uploaded by: Knoema
      Accessed On: 23 November, 2018
      Select Dataset
      The Global Burden of Disease Study 2017 (GBD 2017), 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. Developed by GBD researchers and used to help produce these estimates, the Socio-demographic Index (SDI) is a composite indicator of development status strongly correlated with health outcomes. It is the geometric mean of 0 to 1 indices of total fertility rate under the age of 25 (TFU25), mean education for those ages 15 and older (EDU15+), and lag distributed income (LDI) per capita. As a composite, a location with an SDI of 0 would have a theoretical minimum level of development relevant to health, while a location with an SDI of 1 would have a theoretical maximum level. This dataset provides tables with SDI values for all estimated GBD 2017 locations for 1950–2017 and groupings by location based on their 2017 values.
    • November 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 23 November, 2018
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      National accounts are a coherent set of macroeconomic indicators, which provide an overall picture of the economic situation and are widely used for economic analysis and forecasting, policy design and policy making. The data presented in this collection are the results of a pilot exercise on the sharing selected main GDP aggregates, population and employment data collected by different international organisations. It wasconducted by the Task Force in International Data Collection (TFIDC) which was established by the  Inter-Agency Group on Economic and Financial Statistics (IAG).  The goal of this pilot is to develop a set of commonly shared principles and working arrangements for data cooperation that could be implemented by the international agencies. The data sets are an experimental exercise to present national accounts data form various countries across the globe in one coherent folder, but users should be aware that these data are collected and validated by different organisations and not fully harmonised from a methodological point of view.  The domain consists of the following collections:
    • November 2018
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 30 November, 2018
      Select Dataset
      National accounts are a coherent set of macroeconomic indicators, which provide an overall picture of the economic situation and are widely used for economic analysis and forecasting, policy design and policy making. The data presented in this collection are the results of a pilot exercise on the sharing selected main GDP aggregates, population and employment data collected by different international organisations. It wasconducted by the Task Force in International Data Collection (TFIDC) which was established by the  Inter-Agency Group on Economic and Financial Statistics (IAG).  The goal of this pilot is to develop a set of commonly shared principles and working arrangements for data cooperation that could be implemented by the international agencies. The data sets are an experimental exercise to present national accounts data form various countries across the globe in one coherent folder, but users should be aware that these data are collected and validated by different organisations and not fully harmonised from a methodological point of view.  The domain consists of the following collections:
    • December 2018
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 11 December, 2018
      Select Dataset
      Source: UNECE Statistical Database, compiled from national and international (CIS, EUROSTAT, IMF, OECD, World Bank) official sources. General note: The UNECE secretariat presents time series ready for immediate analysis. When appropriate, source segments with methodological differences have been linked and rescaled to build long consistent time series. The national accounts estimates are compiled according to 2008 SNA (System of National Accounts 2008) or 1993 SNA (System of National Accounts 1993). Constant price estimates are based on data compiled by the National Statistical Offices (NSOs), which reflect various national practices (different base years, fixed base, chain, etc.). To facilitate international comparisons, the data reported by the NSOs have been scaled to the current price value of of the common reference year. The resulting chain constant price data are not additive. Common currency (US$) estimates are computed by the secretariat using purchasing power parities (PPPs), which are the rates of currency conversion that equalise the purchasing power of different currencies. PPPs, and not exchange rates, should be used in international comparisons of GDP and its components. Regional aggregates are computed by the secretariat. For national accounts all current price aggregates are sums of national series converted into US$ at current PPPs of GDP; all constant price aggregates are calculated by summing up national series scaled to the price level of the common reference year and then converted into US$ using PPPs of GDP of the common reference year. Due to conversion and rounding the resulting aggregates and components could be non-additive. For more details see the composition of regions note. Growth rates (per cent) are over the preceding period, unless otherwise specified. Contributions to per cent growth in GDP (in percentage points) are over the preceding period, unless otherwise specified.Country/Region: IsraelDesignation and data provided by Israel. The position of the United Nations on the question of Jerusalem is contained in General Assembly resolution 181 (II) and subsequent resolutions of the General Assembly and the Security Council concerning this question. Data include East Jerusalem.
    • December 2018
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 11 December, 2018
      Select Dataset
      Source: UNECE Statistical Database, compiled from national and international (CIS, EUROSTAT, IMF, OECD, World Bank) official sources. General note: The UNECE secretariat presents time series ready for immediate analysis. When appropriate, source segments with methodological differences have been linked and rescaled to build long consistent time series. The national accounts estimates are compiled according to 2008 SNA (System of National Accounts 2008) or 1993 SNA (System of National Accounts 1993). Constant price estimates are based on data compiled by the National Statistical Offices (NSOs), which reflect various national practices (different base years, fixed base, chain, etc.). To facilitate international comparisons, the data reported by the NSOs have been scaled to the current price value of of the common reference year. The resulting chain constant price data are not additive. Common currency (US$) estimates are computed by the secretariat using purchasing power parities (PPPs), which are the rates of currency conversion that equalise the purchasing power of different currencies. PPPs, and not exchange rates, should be used in international comparisons of GDP and its components. Regional aggregates are computed by the secretariat. For national accounts all current price aggregates are sums of national series converted into US$ at current PPPs of GDP; all constant price aggregates are calculated by summing up national series scaled to the price level of the common reference year and then converted into US$ using PPPs of GDP of the common reference year. Due to conversion and rounding the resulting aggregates and components could be non-additive. For more details see the composition of regions note. Growth rates (per cent) are over the preceding period, unless otherwise specified. Contributions to per cent growth in GDP (in percentage points) are over the preceding period, unless otherwise specified. .. - data not available Country: Albania Currency: Albanian lek (ALL). Country: Armenia Currency: Armenian dram (AMD), replaced the Soviet rouble at 1:200 in 1993. All data are expressed in the latest currency units. Country: Austria Currency: Euro (€); prior to 1999 - Austrian Schilling (ATS); historical data converted at 1999 fixed conversion rate of 13.7603 ATS/€. Country: Azerbaijan Currency: New Azerbaijanian manat (AZN), in 2006 replaced old manat (AZM) at 1:5000. All data are expressed in the latest currency units. Country: Belarus Currency: Belarusian rouble (BYR) redenominated at 1:10 in 1994, at 1:1000 in 2000, and again 1:10000 in July 2016. All data are expressed in the latest currency units. Country: Belgium Currency: Euro (€); prior to 1999 - Belgian Franc (BEF); historical data converted at 1999 fixed conversion rate of 40.3399 BEF/€. Country: Bosnia and Herzegovina Currency: Bosnia and Herzegovina, convertible marka (BAM). Geographical coverage: GDP and population cover the Federation of Bosnia and Herzegovina and Republika Srpska. Country: Bulgaria Currency : Bulgarian leva (BGN), redenominated at 1:1000 in 1999. All data are expressed in the latest currency units. Country: Canada Currency: Canadian dollar (CAD). Country: Croatia Currency: Croatian kuna (HRK), replaced the Croat dinar at 1:1000 in 1994. All data are expressed in the latest currency units. Country: Cyprus Currency : Euro (€); prior to 2008 - Cypriot pound (CYP); historical data converted into €. Country: Czechia Currency : Czech koruna (CZK). Country: Denmark Currency : Danish krone (DKK). Country: Estonia Currency : Euro (€); prior to 2011 - Estonian kroon (EEK), replaced the Soviet rouble in 1992 with a peg to the deutsche mark (8:1). Data are converted to the latest currency. Country: Finland Currency : Euro (€); prior to 1999 - Finnish markka (FIM); historical data converted at 1999 fixed conversion rate of 5.94573 FIM/€. Country: France Currency : Euro (€); prior to 1999 - French franc (FRF); historical data converted at 1999 fixed conversion rate of 6.55957 FRF/€. Country: Georgia Currency: Georgian lari (GEL), replaced the lari-kupon at 1: 1000000 in 1995. All data are expressed in the latest currency units. Geographical coverage: from 1993, excludes Abkhazia and South Ossetia (Tshinvali). Country: Germany Currency : Euro (€); prior to 1999 - Deutsche Mark (DEM); historical data converted at 1999 fixed conversion rate of 1.95583 DEM/€. Geographical coverage: The statistics for Germany refer to Germany after unification. Official data for Germany after unification are available only from 1991 onwards. Country: Greece Currency: Euro (€); prior to 2001 - Greek Drachma (GRD); historical data converted at 1999 fixed conversion rate of 340.75 GRD/€. Country: Hungary Currency : Hungarian forint (HUF). Country: Iceland Currency: Iceland krona (ISK). Country: Ireland Currency : Euro (€); prior to 1999 - Irish Punt (IEP); historical data converted at 1999 fixed conversion rate of 0.787564 IEP/€. Country: Israel Currency: New shekel (ILS). Geographical coverage: Designation and data provided by Israel.The position of the United Nations on the question of Jerusalem is contained in General Assembly resolution 181 (II) and subsequent resolutions of the General Assembly and the Security Council concerning this question. Data include East Jerusalem. Country: Italy Currency: Euro (€); prior to 1999 - Italian Lira (ITL); historical data converted at 1999 fixed conversion rate of 1936.27 ITL/€. Country: Kazakhstan Currency: Kazakh tenge (KZT), replaced the Soviet rouble at 1:500 in 1992. All data are expressed in the latest currency units. Country: Kyrgyzstan Currency: Kyrgyz som (KGS). Country: Latvia Currency: Euro (€); prior to 2014 - Latvian lat (LVL), replaced Latvian rouble at 1:200 in 1993. All data are expressed in the latest currency unit. Country: Lithuania Currency: Euro (€); prior to 2015 - Lithuanian litas (LTL). All data are expressed in the latest currency unit. Country: Luxembourg Currency: Euro (€); prior to 1999 - Luxembourg Franc (LUF); historical data converted at 1999 fixed conversion rate of 40.3399 LUF/€. Country: Malta Currency : Euro (€); prior to 2008 - Maltese lira (MTL); historical data converted into euro. Country: Moldova, Republic of Currency: Moldovan leu (MDL). Geographical coverage: from 1993, excludes Transnistria. Country: Montenegro Currency: Euro (€); prior to 2001 - Deutsche Mark (DEM); historical data converted at 1999 fixed conversion rate of 1.95583 DEM/€. Country: Netherlands Currency: Euro (€); prior to 1999 - Dutch Guilder (NLG); historical data converted at 1999 fixed conversion rate of 2.20371 NLG/€. Country: Norway Currency: Norvegian krone (NOK). Country: Poland Currency : Polish zloty (PLZ), redenominated at 1:10000 in 1995. All data are expressed in the latest currency units. Country: Portugal Currency : Euro (€); prior to 1999 - Portuguese Escudo (PTE); historical data converted at 1999 fixed conversion rate of 200.482 PTE/€. Country: Romania Currency: New Romanian leu (RON). Country: Russian Federation Currency: Russian rouble (RUB), redenominated at 1:1000 in 1998. All data are expressed in the latest currency units. Data for Russian Federation was updated only until the end of 2013. Country: Serbia Currency : Serbian Dinar (RSD). Geographical coverage: from 1999, excludes Kosovo and Metohija. Country: Slovakia Currency : Euro (€); prior to 2008 - Slovak koruna (SKK). Data are converted to the latest currency. Country: Slovenia Currency : Euro (€); prior to 2007 - Slovenian tolar (SIT); historical data converted at fixed conversion rate of 239,640 SIT/€. Country: Spain Currency : Euro (€); prior to 1999 - Spanish Peseta (ESP); historical data converted at 1999 fixed conversion rate of 166.386 ESP/€. Country: Sweden Currency : Swedish krona (SEK). Country: Switzerland Currency: Swiss franc (CHF). Country: Tajikistan Currency : Tajik somoni (TJS), replaced the Tajik rouble at 1:1000 in 2000. The Tajik rouble replaced the Soviet rouble at 1:100 in 1994. All data are expressed in the latest currency units. Country: The former Yugoslav Republic of Macedonia Currency : Macedonian denar (MKD), replaced the Yugoslav dinar at 1:1 in 1992, redenominated at 1:100 in 1993. All data are expressed in the latest currency units. Country: Turkey Currency : Turkish lira (TRL). Country: Turkmenistan Currency : Turkmen manat (TMM), replaced the Soviet rouble at 1:500 in 1993. All data are expressed in the latest currency units. Country: Ukraine Currency : Ukrainian hryvnia (UAH), replaced the former karbovanets at 1:100000 in 1996. All data are expressed in the latest currency units. Geographical coverage: from 2014, does not includes all territory of Ukraine. Country: United Kingdom Currency: British pound (GBP). Country: United States Currency: United States dollar (USD). Country: Uzbekistan Currency: Uzbekistani sum (UZS), replaced the Soviet rouble at 1:1000 in 1993. All data are expressed in the latest currency units.
    • December 2018
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 11 December, 2018
      Select Dataset
      Source: UNECE Statistical Database, compiled from national and international (CIS, EUROSTAT, IMF, OECD, World Bank) official sources. General note: The UNECE secretariat presents time series ready for immediate analysis. When appropriate, source segments with methodological differences have been linked and rescaled to build long consistent time series. The national accounts estimates are compiled according to 2008 SNA (System of National Accounts 2008) or 1993 SNA (System of National Accounts 1993). Constant price estimates are based on data compiled by the National Statistical Offices (NSOs), which reflect various national practices (different base years, fixed base, chain, etc.). To facilitate international comparisons, the data reported by the NSOs have been scaled to the current price value of of the common reference year. The resulting chain constant price data are not additive. Common currency (US$) estimates are computed by the secretariat using purchasing power parities (PPPs), which are the rates of currency conversion that equalise the purchasing power of different currencies. PPPs, and not exchange rates, should be used in international comparisons of GDP and its components. Regional aggregates are computed by the secretariat. For national accounts all current price aggregates are sums of national series converted into US$ at current PPPs of GDP; all constant price aggregates are calculated by summing up national series scaled to the price level of the common reference year and then converted into US$ using PPPs of GDP of the common reference year. Due to conversion and rounding the resulting aggregates and components could be non-additive. For more details see the composition of regions note. Growth rates (per cent) are over the preceding period, unless otherwise specified. Contributions to per cent growth in GDP (in percentage points) are over the preceding period, unless otherwise specified. .. - data not available Country: Albania Currency: Albanian lek (ALL). Country: Armenia Currency: Armenian dram (AMD), replaced the Soviet rouble at 1:200 in 1993. All data are expressed in the latest currency units. Country: Austria Currency: Euro (€); prior to 1999 - Austrian Schilling (ATS); historical data converted at 1999 fixed conversion rate of 13.7603 ATS/€. Country: Azerbaijan Currency: New Azerbaijanian manat (AZN), in 2006 replaced old manat (AZM) at 1:5000. All data are expressed in the latest currency units. Country: Belarus Currency: Belarusian rouble (BYR) redenominated at 1:10 in 1994, at 1:1000 in 2000, and again 1:10000 in July 2016. All data are expressed in the latest currency units. Country: Belgium Currency: Euro (€); prior to 1999 - Belgian Franc (BEF); historical data converted at 1999 fixed conversion rate of 40.3399 BEF/€. Country: Bosnia and Herzegovina Currency: Bosnia and Herzegovina, convertible marka (BAM). Geographical coverage: GDP and population cover the Federation of Bosnia and Herzegovina and Republika Srpska. Country: Bulgaria Currency : Bulgarian leva (BGN), redenominated at 1:1000 in 1999. All data are expressed in the latest currency units. Country: Canada Currency: Canadian dollar (CAD). Country: Croatia Currency: Croatian kuna (HRK), replaced the Croat dinar at 1:1000 in 1994. All data are expressed in the latest currency units. Country: Cyprus Currency : Euro (€); prior to 2008 - Cypriot pound (CYP); historical data converted into €. Country: Czechia Currency : Czech koruna (CZK). Country: Denmark Currency : Danish krone (DKK). Country: Estonia Currency : Euro (€); prior to 2011 - Estonian kroon (EEK), replaced the Soviet rouble in 1992 with a peg to the deutsche mark (8:1). Data are converted to the latest currency. Country: Finland Currency : Euro (€); prior to 1999 - Finnish markka (FIM); historical data converted at 1999 fixed conversion rate of 5.94573 FIM/€. Country: France Currency : Euro (€); prior to 1999 - French franc (FRF); historical data converted at 1999 fixed conversion rate of 6.55957 FRF/€. Country: Georgia Currency: Georgian lari (GEL), replaced the lari-kupon at 1: 1000000 in 1995. All data are expressed in the latest currency units. Geographical coverage: from 1993, excludes Abkhazia and South Ossetia (Tshinvali). Country: Germany Currency : Euro (€); prior to 1999 - Deutsche Mark (DEM); historical data converted at 1999 fixed conversion rate of 1.95583 DEM/€. Geographical coverage: The statistics for Germany refer to Germany after unification. Official data for Germany after unification are available only from 1991 onwards. Country: Greece Currency: Euro (€); prior to 2001 - Greek Drachma (GRD); historical data converted at 1999 fixed conversion rate of 340.75 GRD/€. Country: Hungary Currency : Hungarian forint (HUF). Country: Iceland Currency: Iceland krona (ISK). Country: Ireland Currency : Euro (€); prior to 1999 - Irish Punt (IEP); historical data converted at 1999 fixed conversion rate of 0.787564 IEP/€. Country: Israel Designation and data provided by Israel. The position of the United Nations on the question of Jerusalem is contained in General Assembly resolution 181 (II) and subsequent resolutions of the General Assembly and the Security Council concerning this question. Data include East Jerusalem. Country: Israel Currency: New shekel (ILS). Geographical coverage: Designation and data provided by Israel.The position of the United Nations on the question of Jerusalem is contained in General Assembly resolution 181 (II) and subsequent resolutions of the General Assembly and the Security Council concerning this question. Data include East Jerusalem. Country: Italy Currency: Euro (€); prior to 1999 - Italian Lira (ITL); historical data converted at 1999 fixed conversion rate of 1936.27 ITL/€. Country: Kazakhstan Currency: Kazakh tenge (KZT), replaced the Soviet rouble at 1:500 in 1992. All data are expressed in the latest currency units. Country: Kyrgyzstan Currency: Kyrgyz som (KGS). Country: Latvia Currency: Euro (€); prior to 2014 - Latvian lat (LVL), replaced Latvian rouble at 1:200 in 1993. All data are expressed in the latest currency unit. Country: Lithuania Currency: Euro (€); prior to 2015 - Lithuanian litas (LTL). All data are expressed in the latest currency unit. Country: Luxembourg Currency: Euro (€); prior to 1999 - Luxembourg Franc (LUF); historical data converted at 1999 fixed conversion rate of 40.3399 LUF/€. Country: Malta Currency : Euro (€); prior to 2008 - Maltese lira (MTL); historical data converted into euro. Country: Moldova, Republic of Currency: Moldovan leu (MDL). Geographical coverage: from 1993, excludes Transnistria. Country: Montenegro Currency: Euro (€); prior to 2001 - Deutsche Mark (DEM); historical data converted at 1999 fixed conversion rate of 1.95583 DEM/€. Country: Netherlands Currency: Euro (€); prior to 1999 - Dutch Guilder (NLG); historical data converted at 1999 fixed conversion rate of 2.20371 NLG/€. Country: Norway Currency: Norvegian krone (NOK). Country: Poland Currency : Polish zloty (PLZ), redenominated at 1:10000 in 1995. All data are expressed in the latest currency units. Country: Portugal Currency : Euro (€); prior to 1999 - Portuguese Escudo (PTE); historical data converted at 1999 fixed conversion rate of 200.482 PTE/€. Country: Romania Currency: New Romanian leu (RON). Country: Russian Federation Currency: Russian rouble (RUB), redenominated at 1:1000 in 1998. All data are expressed in the latest currency units. Data for Russian Federation was updated only until the end of 2013. Country: Serbia Currency : Serbian Dinar (RSD). Geographical coverage: from 1999, excludes Kosovo and Metohija. Country: Slovakia Currency : Euro (€); prior to 2008 - Slovak koruna (SKK). Data are converted to the latest currency. Country: Slovenia Currency : Euro (€); prior to 2007 - Slovenian tolar (SIT); historical data converted at fixed conversion rate of 239,640 SIT/€. Country: Spain Currency : Euro (€); prior to 1999 - Spanish Peseta (ESP); historical data converted at 1999 fixed conversion rate of 166.386 ESP/€. Country: Sweden Currency : Swedish krona (SEK). Country: Switzerland Currency: Swiss franc (CHF). Country: Tajikistan Currency : Tajik somoni (TJS), replaced the Tajik rouble at 1:1000 in 2000. The Tajik rouble replaced the Soviet rouble at 1:100 in 1994. All data are expressed in the latest currency units. Country: The former Yugoslav Republic of Macedonia Currency : Macedonian denar (MKD), replaced the Yugoslav dinar at 1:1 in 1992, redenominated at 1:100 in 1993. All data are expressed in the latest currency units. Country: Turkey Currency : Turkish lira (TRL). Country: Turkmenistan Currency : Turkmen manat (TMM), replaced the Soviet rouble at 1:500 in 1993. All data are expressed in the latest currency units. Country: Ukraine Currency : Ukrainian hryvnia (UAH), replaced the former karbovanets at 1:100000 in 1996. All data are expressed in the latest currency units. Geographical coverage: from 2014, does not includes all territory of Ukraine. Country: United Kingdom Currency: British pound (GBP). Country: United States Currency: United States dollar (USD). Country: Uzbekistan Currency: Uzbekistani sum (UZS), replaced the Soviet rouble at 1:1000 in 1993. All data are expressed in the latest currency units.
    • March 2018
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 06 April, 2018
      Select Dataset
      GDP: Expenditure Approach, in National Currency, by Country and Expenditure
    • December 2018
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 11 December, 2018
      Select Dataset
      Source: UNECE Statistical Database, compiled from national and international (CIS, EUROSTAT, IMF, OECD, World Bank) official sources. General note: The UNECE secretariat presents time series ready for immediate analysis. When appropriate, source segments with methodological differences have been linked and rescaled to build long consistent time series. The national accounts estimates are compiled according to 2008 SNA (System of National Accounts 2008) or 1993 SNA (System of National Accounts 1993). Constant price estimates are based on data compiled by the National Statistical Offices (NSOs), which reflect various national practices (different base years, fixed base, chain, etc.). To facilitate international comparisons, the data reported by the NSOs have been scaled to the current price value of of the common reference year. The resulting chain constant price data are not additive. Common currency (US$) estimates are computed by the secretariat using purchasing power parities (PPPs), which are the rates of currency conversion that equalise the purchasing power of different currencies. PPPs, and not exchange rates, should be used in international comparisons of GDP and its components. Regional aggregates are computed by the secretariat. For national accounts all current price aggregates are sums of national series converted into US$ at current PPPs of GDP; all constant price aggregates are calculated by summing up national series scaled to the price level of the common reference year and then converted into US$ using PPPs of GDP of the common reference year. Due to conversion and rounding the resulting aggregates and components could be non-additive. For more details see the composition of regions note. Growth rates (per cent) are over the preceding period, unless otherwise specified. Contributions to per cent growth in GDP (in percentage points) are over the preceding period, unless otherwise specified. .. - data not available Country: Albania Currency: Albanian lek (ALL). Country: Armenia Currency: Armenian dram (AMD), replaced the Soviet rouble at 1:200 in 1993. All data are expressed in the latest currency units. Country: Austria Currency: Euro (€); prior to 1999 - Austrian Schilling (ATS); historical data converted at 1999 fixed conversion rate of 13.7603 ATS/€. Country: Azerbaijan Currency: New Azerbaijanian manat (AZN), in 2006 replaced old manat (AZM) at 1:5000. All data are expressed in the latest currency units. Country: Belarus Currency: Belarusian rouble (BYR) redenominated at 1:10 in 1994, at 1:1000 in 2000, and again 1:10000 in July 2016. All data are expressed in the latest currency units. Country: Belgium Currency: Euro (€); prior to 1999 - Belgian Franc (BEF); historical data converted at 1999 fixed conversion rate of 40.3399 BEF/€. Country: Bosnia and Herzegovina Currency: Bosnia and Herzegovina, convertible marka (BAM). Geographical coverage: GDP and population cover the Federation of Bosnia and Herzegovina and Republika Srpska. Country: Bulgaria Currency : Bulgarian leva (BGN), redenominated at 1:1000 in 1999. All data are expressed in the latest currency units. Country: Canada Currency: Canadian dollar (CAD). Country: Croatia Currency: Croatian kuna (HRK), replaced the Croat dinar at 1:1000 in 1994. All data are expressed in the latest currency units. Country: Cyprus Currency : Euro (€); prior to 2008 - Cypriot pound (CYP); historical data converted into €. Country: Czechia Currency : Czech koruna (CZK). Country: Denmark Currency : Danish krone (DKK). Country: Estonia Currency : Euro (€); prior to 2011 - Estonian kroon (EEK), replaced the Soviet rouble in 1992 with a peg to the deutsche mark (8:1). Data are converted to the latest currency. Country: Finland Currency : Euro (€); prior to 1999 - Finnish markka (FIM); historical data converted at 1999 fixed conversion rate of 5.94573 FIM/€. Country: France Currency : Euro (€); prior to 1999 - French franc (FRF); historical data converted at 1999 fixed conversion rate of 6.55957 FRF/€. Country: Georgia Currency: Georgian lari (GEL), replaced the lari-kupon at 1: 1000000 in 1995. All data are expressed in the latest currency units. Geographical coverage: from 1993, excludes Abkhazia and South Ossetia (Tshinvali). Country: Germany Currency : Euro (€); prior to 1999 - Deutsche Mark (DEM); historical data converted at 1999 fixed conversion rate of 1.95583 DEM/€. Geographical coverage: The statistics for Germany refer to Germany after unification. Official data for Germany after unification are available only from 1991 onwards. Country: Greece Currency: Euro (€); prior to 2001 - Greek Drachma (GRD); historical data converted at 1999 fixed conversion rate of 340.75 GRD/€. Country: Hungary Currency : Hungarian forint (HUF). Country: Iceland Currency: Iceland krona (ISK). Country: Ireland Currency : Euro (€); prior to 1999 - Irish Punt (IEP); historical data converted at 1999 fixed conversion rate of 0.787564 IEP/€. Country: Israel Designation and data provided by Israel. The position of the United Nations on the question of Jerusalem is contained in General Assembly resolution 181 (II) and subsequent resolutions of the General Assembly and the Security Council concerning this question. Data include East Jerusalem. Country: Israel Currency: New shekel (ILS). Geographical coverage: Designation and data provided by Israel.The position of the United Nations on the question of Jerusalem is contained in General Assembly resolution 181 (II) and subsequent resolutions of the General Assembly and the Security Council concerning this question. Data include East Jerusalem. Country: Italy Currency: Euro (€); prior to 1999 - Italian Lira (ITL); historical data converted at 1999 fixed conversion rate of 1936.27 ITL/€. Country: Kazakhstan Currency: Kazakh tenge (KZT), replaced the Soviet rouble at 1:500 in 1992. All data are expressed in the latest currency units. Country: Kyrgyzstan Currency: Kyrgyz som (KGS). Country: Latvia Currency: Euro (€); prior to 2014 - Latvian lat (LVL), replaced Latvian rouble at 1:200 in 1993. All data are expressed in the latest currency unit. Country: Lithuania Currency: Euro (€); prior to 2015 - Lithuanian litas (LTL). All data are expressed in the latest currency unit. Country: Luxembourg Currency: Euro (€); prior to 1999 - Luxembourg Franc (LUF); historical data converted at 1999 fixed conversion rate of 40.3399 LUF/€. Country: Malta Currency : Euro (€); prior to 2008 - Maltese lira (MTL); historical data converted into euro. Country: Moldova, Republic of Currency: Moldovan leu (MDL). Geographical coverage: from 1993, excludes Transnistria. Country: Montenegro Currency: Euro (€); prior to 2001 - Deutsche Mark (DEM); historical data converted at 1999 fixed conversion rate of 1.95583 DEM/€. Country: Netherlands Currency: Euro (€); prior to 1999 - Dutch Guilder (NLG); historical data converted at 1999 fixed conversion rate of 2.20371 NLG/€. Country: Norway Currency: Norvegian krone (NOK). Country: Poland Currency : Polish zloty (PLZ), redenominated at 1:10000 in 1995. All data are expressed in the latest currency units. Country: Portugal Currency : Euro (€); prior to 1999 - Portuguese Escudo (PTE); historical data converted at 1999 fixed conversion rate of 200.482 PTE/€. Country: Romania Currency: New Romanian leu (RON). Country: Russian Federation Currency: Russian rouble (RUB), redenominated at 1:1000 in 1998. All data are expressed in the latest currency units. Data for Russian Federation was updated only until the end of 2013. Country: Serbia Currency : Serbian Dinar (RSD). Geographical coverage: from 1999, excludes Kosovo and Metohija. Country: Slovakia Currency : Euro (€); prior to 2008 - Slovak koruna (SKK). Data are converted to the latest currency. Country: Slovenia Currency : Euro (€); prior to 2007 - Slovenian tolar (SIT); historical data converted at fixed conversion rate of 239,640 SIT/€. Country: Spain Currency : Euro (€); prior to 1999 - Spanish Peseta (ESP); historical data converted at 1999 fixed conversion rate of 166.386 ESP/€. Country: Sweden Currency : Swedish krona (SEK). Country: Switzerland Currency: Swiss franc (CHF). Country: Tajikistan Currency : Tajik somoni (TJS), replaced the Tajik rouble at 1:1000 in 2000. The Tajik rouble replaced the Soviet rouble at 1:100 in 1994. All data are expressed in the latest currency units. Country: The former Yugoslav Republic of Macedonia Currency : Macedonian denar (MKD), replaced the Yugoslav dinar at 1:1 in 1992, redenominated at 1:100 in 1993. All data are expressed in the latest currency units. Country: Turkey Currency : Turkish lira (TRL). Country: Turkmenistan Currency : Turkmen manat (TMM), replaced the Soviet rouble at 1:500 in 1993. All data are expressed in the latest currency units. Country: Ukraine Currency : Ukrainian hryvnia (UAH), replaced the former karbovanets at 1:100000 in 1996. All data are expressed in the latest currency units. Geographical coverage: from 2014, does not includes all territory of Ukraine. Country: United Kingdom Currency: British pound (GBP). Country: United States Currency: United States dollar (USD). Country: Uzbekistan Currency: Uzbekistani sum (UZS), replaced the Soviet rouble at 1:1000 in 1993. All data are expressed in the latest currency units.
    • December 2018
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 11 December, 2018
      Select Dataset
      Source: UNECE Statistical Database, compiled from national and international (CIS, EUROSTAT, IMF, OECD, World Bank) official sources. General note: The UNECE secretariat presents time series ready for immediate analysis. When appropriate, source segments with methodological differences have been linked and rescaled to build long consistent time series. The national accounts estimates are compiled according to 2008 SNA (System of National Accounts 2008) or 1993 SNA (System of National Accounts 1993). Constant price estimates are based on data compiled by the National Statistical Offices (NSOs), which reflect various national practices (different base years, fixed base, chain, etc.). To facilitate international comparisons, the data reported by the NSOs have been scaled to the current price value of of the common reference year. The resulting chain constant price data are not additive. Common currency (US$) estimates are computed by the secretariat using purchasing power parities (PPPs), which are the rates of currency conversion that equalise the purchasing power of different currencies. PPPs, and not exchange rates, should be used in international comparisons of GDP and its components. Regional aggregates are computed by the secretariat. For national accounts all current price aggregates are sums of national series converted into US$ at current PPPs of GDP; all constant price aggregates are calculated by summing up national series scaled to the price level of the common reference year and then converted into US$ using PPPs of GDP of the common reference year. Due to conversion and rounding the resulting aggregates and components could be non-additive. For more details see the composition of regions note. Growth rates (per cent) are over the preceding period, unless otherwise specified. Contributions to per cent growth in GDP (in percentage points) are over the preceding period, unless otherwise specified. .. - data not available Country: Armenia Currency : Armenian dram (AMD). Country: Austria Currency : Euro (€). Country: Azerbaijan Currency: New Azerbaijanian manat (AZN), in 2006 replaced old manat (AZM) at 1:5000. All data are expressed in the latest currency units. Country: Belarus Currency : Belarusian rouble (BYR), redenominated at 1:1000 in 2000 and redenominated at 1:10 000 in July 2016. All data are expressed in the latest currency units. Country: Belgium Currency: Euro (€); prior to 1999 - Belgian Franc (BEF); historical data converted at 1999 fixed conversion rate of 40.3399 BEF/€. Country: Bulgaria Currency: Bulgarian leva (BGN), redenominated at 1:1000 in 1999. All data are expressed in the latest currency units. Country: Canada Currency: Canadian dollar (CAD). Country: Croatia Currency: Croatian kuna (HRK). Country: Cyprus Currency: Euro (€); prior to 2008 - Cypriot pound (CYP); historical data converted into €. Country: Czechia Currency: Czech koruna (CZK). Country: Denmark Currency: Danish krone (DKK). Country: Estonia Currency: Euro (€). Country: Finland Currency: Euro (€). Country: France Currency: Euro (€). Country: Georgia Currency: Georgian lari (GEL). Geographical coverage: from 1993 excludes Abkhazia and South Ossetia (Tshinvali). Country: Germany Currency: Euro (€). Geographical coverage: The statistics for Germany refer to Germany after unification. Country: Greece Currency: Euro (€); prior to 2001 - Greek Drachma (GRD); historical data converted at 1999 fixed conversion rate of 340.75 GRD/€. Country: Hungary Currency: Hungarian forint (HUF). Country: Iceland Currency: Iceland krona (ISK). Country: Ireland Currency: Euro (€). Country: Israel Designation and data provided by Israel. The position of the United Nations on the question of Jerusalem is contained in General Assembly resolution 181 (II) and subsequent resolutions of the General Assembly and the Security Council concerning this question. Data include East Jerusalem. Country: Israel Currency: New shekel (ILS). Country: Italy Currency: Euro (€). Country: Kazakhstan Currency: Kazakh tenge (KZT). Country: Kyrgyzstan Currency: Kyrgyz som (KGS). Country: Latvia Currency: Euro (€). Country: Lithuania Currency: Euro (€). Country: Luxembourg Currency: Euro (€). Country: Malta Currency: Euro (€); prior to 2008 - Maltese lira (MTL); historical data converted into €. Country: Moldova, Republic of Currency: Moldovan leu (MDL). Geographical coverage: from 1993 excludes Transnistria. Country: Netherlands Currency: Euro (€). Country: Norway Currency: Norvegian krone (NOK). Country: Poland Currency: Polish zloty (PLZ). Country: Portugal Currency: Euro (€). Country: Russian Federation Currency: Russian rouble (RUB). Data for Russian Federation was updated only until the end of 2013. Country: Serbia Currency : Serbian Dinar (RSD). Geographical coverage:from 1999 excludes Kosovo and Metohija. Country: Slovakia Currency: Euro (€); prior to 2008 - Slovak koruna (SKK). Data are converted to the latest currency. Country: Slovenia Currency: Euro (€); prior to 2007 - Slovenian tolar (SIT); historical data converted at fixed conversion rate of 239,640 SIT/€. Country: Spain Currency: Euro (€). Country: Sweden Currency: Swedish krona (SEK). Country: Switzerland Currency: Swiss franc (CHF). Country: The former Yugoslav Republic of Macedonia Currency: Macedonian denar (MKD). Country: Turkey Currency: Turkish lira (TRY). Country: Ukraine Currency: Ukrainian hryvnia (UAH). Geographical coverage: from 2014, does not includes all territory of Ukraine. Country: United Kingdom Currency: British pound (GBP). Country: United States Currency: United States dollar (USD).
    • December 2018
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 11 December, 2018
      Select Dataset
      Source: UNECE Statistical Database, compiled from national and international (CIS, EUROSTAT, IMF, OECD, World Bank) official sources. General note: The UNECE secretariat presents time series ready for immediate analysis. When appropriate, source segments with methodological differences have been linked and rescaled to build long consistent time series. The national accounts estimates are compiled according to 2008 SNA (System of National Accounts 2008) or 1993 SNA (System of National Accounts 1993). Constant price estimates are based on data compiled by the National Statistical Offices (NSOs), which reflect various national practices (different base years, fixed base, chain, etc.). To facilitate international comparisons, the data reported by the NSOs have been scaled to the current price value of of the common reference year. The resulting chain constant price data are not additive. Common currency (US$) estimates are computed by the secretariat using purchasing power parities (PPPs), which are the rates of currency conversion that equalise the purchasing power of different currencies. PPPs, and not exchange rates, should be used in international comparisons of GDP and its components. Regional aggregates are computed by the secretariat. For national accounts all current price aggregates are sums of national series converted into US$ at current PPPs of GDP; all constant price aggregates are calculated by summing up national series scaled to the price level of the common reference year and then converted into US$ using PPPs of GDP of the common reference year. Due to conversion and rounding the resulting aggregates and components could be non-additive. For more details see the composition of regions note. Growth rates (per cent) are over the preceding period, unless otherwise specified. Contributions to per cent growth in GDP (in percentage points) are over the preceding period, unless otherwise specified. .. - data not available Country: Israel Designation and data provided by Israel. The position of the United Nations on the question of Jerusalem is contained in General Assembly resolution 181 (II) and subsequent resolutions of the General Assembly and the Security Council concerning this question. Data include East Jerusalem.
    • December 2018
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 11 December, 2018
      Select Dataset
      Source: UNECE Statistical Database, compiled from national and international (CIS, EUROSTAT, IMF, OECD, World Bank) official sources. General note: The UNECE secretariat presents time series ready for immediate analysis. When appropriate, source segments with methodological differences have been linked and rescaled to build long consistent time series. The national accounts estimates are compiled according to 2008 SNA (System of National Accounts 2008) or 1993 SNA (System of National Accounts 1993). Constant price estimates are based on data compiled by the National Statistical Offices (NSOs), which reflect various national practices (different base years, fixed base, chain, etc.). To facilitate international comparisons, the data reported by the NSOs have been scaled to the current price value of of the common reference year. The resulting chain constant price data are not additive. Common currency (US$) estimates are computed by the secretariat using purchasing power parities (PPPs), which are the rates of currency conversion that equalise the purchasing power of different currencies. PPPs, and not exchange rates, should be used in international comparisons of GDP and its components. Regional aggregates are computed by the secretariat. For national accounts all current price aggregates are sums of national series converted into US$ at current PPPs of GDP; all constant price aggregates are calculated by summing up national series scaled to the price level of the common reference year and then converted into US$ using PPPs of GDP of the common reference year. Due to conversion and rounding the resulting aggregates and components could be non-additive. For more details see the composition of regions note. Growth rates (per cent) are over the preceding period, unless otherwise specified. Contributions to per cent growth in GDP (in percentage points) are over the preceding period, unless otherwise specified. .. - data not available Country: Albania Currency: Albanian lek (ALL). Country: Armenia Currency: Armenian dram (AMD), replaced the Soviet rouble at 1:200 in 1993. All data are expressed in the latest currency units. Country: Austria Currency: Euro (€); prior to 1999 - Austrian Schilling (ATS); historical data converted at 1999 fixed conversion rate of 13.7603 ATS/€. Country: Azerbaijan Currency: New Azerbaijanian manat (AZN), in 2006 replaced old manat (AZM) at 1:5000. All data are expressed in the latest currency units. Country: Belarus Currency: Belarusian rouble (BYR) redenominated at 1:10 in 1994, at 1:1000 in 2000, and again 1:10000 in July 2016. All data are expressed in the latest currency units. Country: Belgium Currency: Euro (€); prior to 1999 - Belgian Franc (BEF); historical data converted at 1999 fixed conversion rate of 40.3399 BEF/€. Country: Bosnia and Herzegovina Currency: Bosnia and Herzegovina, convertible marka (BAM). Geographical coverage: GDP and population cover the Federation of Bosnia and Herzegovina and Republika Srpska. Country: Bulgaria Currency : Bulgarian leva (BGN), redenominated at 1:1000 in 1999. All data are expressed in the latest currency units. Country: Canada Currency: Canadian dollar (CAD). Country: Croatia Currency: Croatian kuna (HRK), replaced the Croat dinar at 1:1000 in 1994. All data are expressed in the latest currency units. Country: Cyprus Currency : Euro (€); prior to 2008 - Cypriot pound (CYP); historical data converted into €. Country: Czechia Currency : Czech koruna (CZK). Country: Denmark Currency : Danish krone (DKK). Country: Estonia Currency : Euro (€); prior to 2011 - Estonian kroon (EEK), replaced the Soviet rouble in 1992 with a peg to the deutsche mark (8:1). Data are converted to the latest currency. Country: Finland Currency : Euro (€); prior to 1999 - Finnish markka (FIM); historical data converted at 1999 fixed conversion rate of 5.94573 FIM/€. Country: France Currency : Euro (€); prior to 1999 - French franc (FRF); historical data converted at 1999 fixed conversion rate of 6.55957 FRF/€. Country: Georgia Currency: Georgian lari (GEL), replaced the lari-kupon at 1: 1000000 in 1995. All data are expressed in the latest currency units. Geographical coverage: from 1993, excludes Abkhazia and South Ossetia (Tshinvali). Country: Germany Currency : Euro (€); prior to 1999 - Deutsche Mark (DEM); historical data converted at 1999 fixed conversion rate of 1.95583 DEM/€. Geographical coverage: The statistics for Germany refer to Germany after unification. Official data for Germany after unification are available only from 1991 onwards. Country: Greece Currency: Euro (€); prior to 2001 - Greek Drachma (GRD); historical data converted at 1999 fixed conversion rate of 340.75 GRD/€. Country: Hungary Currency : Hungarian forint (HUF). Country: Iceland Currency: Iceland krona (ISK). Country: Ireland Currency : Euro (€); prior to 1999 - Irish Punt (IEP); historical data converted at 1999 fixed conversion rate of 0.787564 IEP/€. Country: Israel Currency: New shekel (ILS). Geographical coverage: Designation and data provided by Israel.The position of the United Nations on the question of Jerusalem is contained in General Assembly resolution 181 (II) and subsequent resolutions of the General Assembly and the Security Council concerning this question. Data include East Jerusalem. Country: Italy Currency: Euro (€); prior to 1999 - Italian Lira (ITL); historical data converted at 1999 fixed conversion rate of 1936.27 ITL/€. Country: Kazakhstan Currency: Kazakh tenge (KZT), replaced the Soviet rouble at 1:500 in 1992. All data are expressed in the latest currency units. Country: Kyrgyzstan Currency: Kyrgyz som (KGS). Country: Latvia Currency: Euro (€); prior to 2014 - Latvian lat (LVL), replaced Latvian rouble at 1:200 in 1993. All data are expressed in the latest currency unit. Country: Lithuania Currency: Euro (€); prior to 2015 - Lithuanian litas (LTL). All data are expressed in the latest currency unit. Country: Luxembourg Currency: Euro (€); prior to 1999 - Luxembourg Franc (LUF); historical data converted at 1999 fixed conversion rate of 40.3399 LUF/€. Country: Malta Currency : Euro (€); prior to 2008 - Maltese lira (MTL); historical data converted into euro. Country: Moldova, Republic of Currency: Moldovan leu (MDL). Geographical coverage: from 1993, excludes Transnistria. Country: Montenegro Currency: Euro (€); prior to 2001 - Deutsche Mark (DEM); historical data converted at 1999 fixed conversion rate of 1.95583 DEM/€. Country: Netherlands Currency: Euro (€); prior to 1999 - Dutch Guilder (NLG); historical data converted at 1999 fixed conversion rate of 2.20371 NLG/€. Country: Norway Currency: Norvegian krone (NOK). Country: Poland Currency : Polish zloty (PLZ), redenominated at 1:10000 in 1995. All data are expressed in the latest currency units. Country: Portugal Currency : Euro (€); prior to 1999 - Portuguese Escudo (PTE); historical data converted at 1999 fixed conversion rate of 200.482 PTE/€. Country: Romania Currency: New Romanian leu (RON). Country: Russian Federation Currency: Russian rouble (RUB), redenominated at 1:1000 in 1998. All data are expressed in the latest currency units. Data for Russian Federation was updated only until the end of 2013. Country: Serbia Currency : Serbian Dinar (RSD). Geographical coverage: from 1999, excludes Kosovo and Metohija. Country: Slovakia Currency : Euro (€); prior to 2008 - Slovak koruna (SKK). Data are converted to the latest currency. Country: Slovenia Currency : Euro (€); prior to 2007 - Slovenian tolar (SIT); historical data converted at fixed conversion rate of 239,640 SIT/€. Country: Spain Currency : Euro (€); prior to 1999 - Spanish Peseta (ESP); historical data converted at 1999 fixed conversion rate of 166.386 ESP/€. Country: Sweden Currency : Swedish krona (SEK). Country: Switzerland Currency: Swiss franc (CHF). Country: Tajikistan Currency : Tajik somoni (TJS), replaced the Tajik rouble at 1:1000 in 2000. The Tajik rouble replaced the Soviet rouble at 1:100 in 1994. All data are expressed in the latest currency units. Country: The former Yugoslav Republic of Macedonia Currency : Macedonian denar (MKD), replaced the Yugoslav dinar at 1:1 in 1992, redenominated at 1:100 in 1993. All data are expressed in the latest currency units. Country: Turkey Currency : Turkish lira (TRL). Country: Turkmenistan Currency : Turkmen manat (TMM), replaced the Soviet rouble at 1:500 in 1993. All data are expressed in the latest currency units. Country: Ukraine Currency : Ukrainian hryvnia (UAH), replaced the former karbovanets at 1:100000 in 1996. All data are expressed in the latest currency units. Geographical coverage: from 2014, does not includes all territory of Ukraine. Country: United Kingdom Currency: British pound (GBP). Country: United States Currency: United States dollar (USD). Country: Uzbekistan Currency: Uzbekistani sum (UZS), replaced the Soviet rouble at 1:1000 in 1993. All data are expressed in the latest currency units.
    • December 2018
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 11 December, 2018
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      Source: UNECE Statistical Database, compiled from national and international (CIS, EUROSTAT, IMF, OECD, World Bank) official sources. General note: The UNECE secretariat presents time series ready for immediate analysis. When appropriate, source segments with methodological differences have been linked and rescaled to build long consistent time series. The national accounts estimates are compiled according to 2008 SNA (System of National Accounts 2008) or 1993 SNA (System of National Accounts 1993). Constant price estimates are based on data compiled by the National Statistical Offices (NSOs), which reflect various national practices (different base years, fixed base, chain, etc.). To facilitate international comparisons, the data reported by the NSOs have been scaled to the current price value of of the common reference year. The resulting chain constant price data are not additive. Common currency (US$) estimates are computed by the secretariat using purchasing power parities (PPPs), which are the rates of currency conversion that equalise the purchasing power of different currencies. PPPs, and not exchange rates, should be used in international comparisons of GDP and its components. Regional aggregates are computed by the secretariat. For national accounts all current price aggregates are sums of national series converted into US$ at current PPPs of GDP; all constant price aggregates are calculated by summing up national series scaled to the price level of the common reference year and then converted into US$ using PPPs of GDP of the common reference year. Due to conversion and rounding the resulting aggregates and components could be non-additive. For more details see the composition of regions note. Growth rates (per cent) are over the preceding period, unless otherwise specified. Contributions to per cent growth in GDP (in percentage points) are over the preceding period, unless otherwise specified. .. - data not available Country: Israel Designation and data provided by Israel. The position of the United Nations on the question of Jerusalem is contained in General Assembly resolution 181 (II) and subsequent resolutions of the General Assembly and the Security Council concerning this question. Data include East Jerusalem.
    • November 2018
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 21 November, 2018
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      Source: UNECE Statistical Database, compiled from national and international official sources. For footnotes on Total population, in persons: click here For footnotes on Population aged 65+ as percentage of total population: click here For footnotes on Total fertility rate: click here For footnotes on Life expectancy at birth: click here For footnotes on Life expectancy at age 65: click here For footnotes on Mean age at first marriage: click here For footnotes on Economic activity rate: click here For footnotes on Proportion of workers in a managerial position: click here For footnotes on Gender pay gap as difference in monthly earnings: click here For footnotes on Long term unemployment rate:click here For footnotes on Proportion among population aged 25-49 with tertiary educational attainment:click here For footnotes on Tertiary students, percent of both sexes:click here For footnotes on Members of national parliament, percent of both sexes:click here For footnotes on Senior civil servants, percent of both sexes:click here For footnotes on Time spent by employed persons on free time activities:click here For footnotes on Employment rate of persons aged 25-49 with children under 3:click here For footnotes on Researchers, percent of both sexes:click here For footnotes on Victims of serious assaults, percent of both sexes:click here .. - data not available
    • January 2017
      Source: International Monetary Fund
      Uploaded by: Knoema
      Accessed On: 09 February, 2017
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      This dataset includes gender inequality and development indices.
    • August 2018
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 21 November, 2018
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      Source: UNECE Statistical Database, compiled from national and international official sources. Definitions: Gender pay gap is the difference between men’s and women’s average earnings from employment, shown as a percentage of men’s average earnings.The UNECE gender statistics database presents two indicators on gender pay gap, which represent two different concerns of gender equality. Gender Pay Gap in hourly wage rates refers to the gender gap in average hourly earnings. This indicator aims to capture the difference between men’s and women’s overall position in the labor market. It measures the difference between men’s and women’s wage rates independent of the number of hours worked, the type of activity or the type of occupation. Gender Pay Gap in monthly earnings refers to the gender gap in average monthly earnings. This indicator aims to capture the variance between men’s and women’s earnings over a specific period of time. It reflects differences in time worked and type of work performed, which translates into gender differences in economic autonomy. Wage rates are earnings elements meant to be measured, as stipulated by the ILO Resolution concerning an integrated system of wages statistics (ILO, 1973), in relation to an appropriate time period such as the hour, day, week, month or other customary period used for purposes of determining the wage rates concerned. In the case of these statistics, the reference time period is the hour. Wage rates should include basic wages, cost-of-living allowances and other guaranteed and regularly paid allowances, but exclude overtime payments, bonuses and gratuities, family allowances and other social security payments made by employers. Ex gratia payments in kind, supplementary to normal wage rates, are also excluded. Earnings relate to remuneration in cash and in kind paid to employees, as a rule at regular intervals, for time worked or work done together with remuneration for time not worked, such as for annual vacation, other paid leave or holidays. Earnings include direct wages and salaries for the time worked, or work done, remuneration for time not worked, bonuses and gratuities and housing and family allowances paid by the employer directly to his employee. Earnings exclude employers’ contributions in respect of their employees paid to social security and pension schemes and also the benefits received by employees under these schemes. Earnings also exclude severance and termination pay. Gross earnings refer to total earnings before any deductions are made by the employer in respect of taxes, contributions of employees to social security and pension schemes, life insurance premiums, union dues and other obligations of employees. Net earnings refer to pay allocated to the worker after deductions are made by the employer in respect of taxes, contributions of employees to social security and pension schemes, life insurance premiums, union dues and other obligations of employees. For the EU and EFTA member states, data on Gender Pay Gap in hourly wage rates cover the economic activities as follows: industry, construction and services, except public administration, defense, compulsory social security, activities of households as employers and extra-territorial organisations and bodies (NACE Rev.2, sections from B to S excluding O). .. - data not available Country: Albania 2000: data refer to October 1998. Country: Armenia For gender pay gap in monthly earnings, data cover paid employees. Country: Austria Gross monthly earnings refer to the monthly amount in the main job. It includes usual paid overtime, tips and commission but excludes income from investments, assets, savings, stocks and shares. Profit share and bonuses are taken into account. Supplement payments (13th, 14th month, holiday pay...) are not included as they are not surveyed in this question, but they could be modeld (average gross monthly earning per group x14/12) under the simplified assumption that people are employed for the whole year and all receive these benefits. Country: Belarus Data refer to December of each year. Country: Belarus Collection method: enterprise-based data. Enterprises with less than 100 employees are excluded. Country: Bulgaria Data cover employees only and are compiled from enterprise survey (four-yearly Structure of Earnings Survey). Overtime payments are included in average earnings. Country: Canada For GPG in hourly earnings, data covers employees only, self-employed are excluded. Country: Croatia For gender pay gap in hourly earnings, basic earnings exclude housing and family allowances. Refers to NACE Rev 2 activities B-S Country: Cyprus Data are based on the results of the Structure of Earnings Survey (SES) for years 2006 and 2010. Data for 2006 and 2010 have been revised to better reflect the definitions provided by UNECE.Hourly Wage Rate includes normal salary and regular bonuses paid to the employee (including payments for shift work). It excludes overtime payments, irregular bonuses and payments in kind.Monthly earnings include normal salary, regular bonuses paid to the employee (including payments for shift work) and payments for overtime. They exclude irregular bonuses and payments in kind.Coverage: Enterprises in all economic activities, excluding Agriculture, Fishing, Activities of Private Households and Extra-territorial Organisations. All enterprises covered had one or more employees. Self-employed are not covered.Geographical coverage: data refer to Government controlled areas only. Country: Czechia Since 2011 all employees included in the sample surveys,including employees of enterprises with less than ten employees, employees of non-profit organizations, and also own-account workers that had not been measuredbefore. Country: Estonia For gender pay gap in monthly earnings, data exclude self-employed persons. From 2014, breakdown by education is according to ISCED-2011. Country: Finland The method of defining part/full-timers changed in 2001. Country: Finland Data do not include irregular bonuses, housing and family allowances. Average monthly earnings data cover only full-time employees. Country: France For gender pay gap in hourly earnings, data from 2006 are compiled from European Structure of Earnings Surveys. Earlier data are compiled from national sources. For gender pay gap in monthly earnings, the underlying average earnings data for 2006 are compiled from EU Structure of Earnings Survey and cover employees in enterprises of 10 or more employees only. People working in public sector are not covered in data up to 2009. From 2014 data include overseas departments. Country: Georgia Territorial change (2000 onward): Data do not cover Abkhazia AR and Tskhinvali Region Country: Germany For gender pay gap in hourly earnings, data from 2006 are compiled from European Structure of Earnings Surveys. Earlier data are compiled from national sources. For gender pay gap in monthly earnings, the underlying average earnings data for 2006 are compiled from EU Structure of Earnings Survey and cover employees in enterprises of 10 or more employees only. People working in public sector are not covered. From 2014 breakdown by education compiled using ISCED-2011. Country: Greece For gender pay gap in hourly earnings, data from 2002 are compiled from European Structure of Earnings Surveys. Earlier data are compiled from national sources. For gender pay gap in monthly earnings, the underlying average earnings data from 2006 on are compiled from EU Structure of Earnings Survey and cover employees in enterprises of 10 or more employees only. People working in public sector are not covered. Country: Hungary Data include only full-time employees. B-S (-O), 10 employees or more Country: Iceland Change in definition (2000 - 2004): Only private sector - econmic activities ISIC-rev.3 D,F,G,I Country: Iceland Change in definition (2005 - 2008): Only private sector - econmic activities ISIC-rev.3 D,F,G,I,J Country: Iceland Change in definition (2009 onward): Private and public sector - economic activities ISIC-rev.4 C,D,E,F,G,H,J,K,O,P,Q. For all years data refer to average income from employment. Country: Israel Change in definition (2006 - 2012): Data cover both - paid employees and self-employed Country: Italy Monthly earnings data are compiled from households surveys (EU-SILC) from 2006 to 2009 and from European Structure of Earnings Survey (SES) from 2010 onwards. The main difference with the SES definition is that the SES definition refers to the month of october and excludes bonuses and other items not payable each month. There is a break in the series between 2009 and 2010. Country: Kazakhstan Average monthly nominal wages per employee is determined by dividing the amount of accrued payroll to the actual number of employees and the number of months in the reporting period. Country: Kyrgyzstan Figures for hourly earnings are obtained by dividing the average monthly earnings by the average number of monthly working hours. Country: Latvia Additional information (2002 onward): Data by education level are calculated for enterprises with number of employees 10 and more for NACE Rev.1.1 sections C-K (excluding L) on 2002 and 2006 and for NACE Rev.2 sections B-S (excluding O) on 2010 according to the methodology of structural indicator of European Comission Gender Pay Gap (GPG). Country: Latvia Data cover paid employees only. Part-timers earnings have been equivalised to fill-time units. All data exclude remuneration of kind. Country: Lithuania The gross earnings data on which GPG in monthly earnings are based exclude housing and family allowances. Country: Luxembourg For gender pay gap in hourly earnings, data from 2006 are compiled from European Structure of Earnings Surveys. For gender pay gap in monthly earnings, data are compiled from European Structure of Earnings Surveys. Average monthly earnings are based on full-time equivalent employees, reference month is october. NACE B to S exclunding O Country: Malta For gender pay gap in hourly earnings, data from 2006 are compiled from European Structure of Earnings Surveys. For gender pay gap in monthly earnings, the underlying average earnings data for 2006 are compiled from EU Structure of Earnings Survey and cover employees in enterprises of 10 or more employees only. People working in public sector are not covered.