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Kazakhstan

  • President:Nursultan Abishuly Nazarbayev
  • Prime Minister:Bakhytzhan Sagintayev
  • Capital city:Astana
  • Languages:Kazakh (official, Qazaq) 74% (understand spoken language), Russian (official, used in everyday business, designated the "language of interethnic communication") 94.4% (understand spoken language) (2009 est.)
  • Government
  • National statistics office
  • Population, persons:18,037,646 (2017)
  • Area, sq km:2,699,700 (2017)
  • GDP per capita, US$:8,837 (2017)
  • GDP, billion current US$:159.4 (2017)
  • GINI index:26.9 (2015)
  • Ease of Doing Business rank:36 (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 В И К П Р С Ч Э
  • 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
    • 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
    • January 2019
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 22 January, 2019
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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.
    • January 2019
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 22 January, 2019
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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.
    • February 2019
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 12 February, 2019
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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 2018
      Source: Ministry of Tourism, Government of India
      Uploaded by: Knoema
      Accessed On: 10 April, 2018
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      This dataset provides data for foreign tourist arrivals distributed by age  group.
    • July 2015
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Denis Chernyshev
      Accessed On: 03 December, 2015
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    • February 2019
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 15 February, 2019
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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.
    • April 2018
      Source: Agricultural Market Information System
      Uploaded by: Knoema
      Accessed On: 29 May, 2018
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      Data Source - CBS Notes: Financial Year 2016/17, 2017/18, 2018/19 is taken as 2017, 2018, 2019
    • April 2018
      Source: Agricultural Market Information System
      Uploaded by: Knoema
      Accessed On: 29 May, 2018
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      Data Source - IGS Notes: Financial Year 2016/17, 2017/18, 2018/19 is taken as 2017, 2018, 2019
    • April 2018
      Source: Agricultural Market Information System
      Uploaded by: Knoema
      Accessed On: 29 May, 2018
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      Data Source - PSD Notes: Financial Year 2016/17, 2017/18, 2018/19 is taken as 2017, 2018, 2019
    • 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.
    • December 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 pesticides per area of cropland (arable land and permanent crops) at national level in a time series from 1990 to 2014. 
    • 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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    • November 2018
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 26 November, 2018
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      The gross nutrient balances (N and P) are calculated as the difference between the total quantity of nutrient inputs entering an agricultural system (mainly fertilizers, livestock manure), and the quantity of nutrient outputs leaving the system (mainly uptake of nutrients by crops and grassland). Gross nutrient balances are expressed in tonnes of nutrient surplus (when positive) or deficit (when negative). This calculation can be used as a proxy to reveal the status of environmental pressures, such as declining soil fertility in the case of a nutrient deficit, or for a nutrient surplus the risk of polluting soil, water and air. The nutrient balance indicator is also expressed in terms of kilogrammes of nutrient surplus per hectare of agricultural land to facilitate the comparison of the relative intensity of nutrients in agricultural systems between countries.
    • July 2018
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 27 September, 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.
    • June 2013
      Source: World Bank
      Uploaded by: Knoema
      Accessed On: 21 November, 2014
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      Data cited at: The World Bank https://datacatalog.worldbank.org/ Topic: All The Ginis Dataset Publication: https://datacatalog.worldbank.org/dataset/all-ginis-dataset License: http://creativecommons.org/licenses/by/4.0/   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 2019
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 11 February, 2019
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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.
    • December 2017
      Source: Islamic Development Bank
      Uploaded by: Knoema
      Accessed On: 29 March, 2018
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    • 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.
    • March 2018
      Source: Stockholm International Peace Research Institute
      Uploaded by: Knoema
      Accessed On: 14 November, 2018
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      'Information from the Stockholm International Peace Research Institute (SIPRI), https://www.sipri.org/databases/armstransfers'   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/
    • February 2019
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 12 February, 2019
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    • 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
    • February 2019
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 12 February, 2019
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    • January 2019
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 22 January, 2019
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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.
    • February 2019
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 12 February, 2019
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    • January 2019
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 22 January, 2019
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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.
    • February 2019
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 12 February, 2019
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    • January 2019
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 22 January, 2019
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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
    • January 2019
      Source: Bahrain Open Data Portal
      Uploaded by: Knoema
      Accessed On: 04 February, 2019
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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.
    • June 2015
      Source: Barro-Lee
      Uploaded by: Knoema
      Accessed On: 12 October, 2015
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      Data cited at: Barro-Lee  
    • 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.
    • 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
    • January 2019
      Source: United Nations COMTRADE
      Uploaded by: Knoema
      Accessed On: 22 January, 2019
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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).
    • 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 2014
      Source: Knoema
      Uploaded by: Knoema
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    • June 2018
      Source: BP
      Uploaded by: Knoema
      Accessed On: 18 June, 2018
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      The BP Statistical Review of World Energy has provided high-quality, objective and globally consistent data on world energy markets. The Review is one of the most widely respected and authoritative publications in the field of energy economics, used for reference by the media, academia, world governments and energy companies. A new edition is published every June. Historical data from 1965 for many sections.
    • January 2016
      Source: Multiple Sources
      Uploaded by: Denis Chernyshev
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      Sources: IMF (Regional Economic Reports, January 2015 and November 2015), http://www.imf.org/external/pubs/ft/reo/reorepts.aspx?ddlYear=-1&ddlRegions=9 The Wall Street Journal, http://graphics.wsj.com/lists/opec-meeting  
    • February 2019
      Source: World Bank
      Uploaded by: Knoema
      Accessed On: 15 February, 2019
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      Data cited at: The World Bank https://datacatalog.worldbank.org/ Topic: Jobs Publication: https://datacatalog.worldbank.org/dataset/jobs License: http://creativecommons.org/licenses/by/4.0/   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.
    • February 2019
      Source: Government of Canada
      Uploaded by: Knoema
      Accessed On: 15 February, 2019
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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
    • February 2019
      Source: Statistics Canada
      Uploaded by: Knoema
      Accessed On: 15 February, 2019
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      For the location "Puerto Rico" data is available from 1990.
    • December 2018
      Source: Institute for Health Metrics and Evaluation
      Uploaded by: Knoema
      Accessed On: 02 January, 2019
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      Data cited: Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2016 (GBD 2016) Cancer Incidence, Mortality, Years of Life Lost, Years Lived with Disability, and Disability-Adjusted Life Years 1990-2016. Seattle, United States: Institute for Health Metrics and Evaluation (IHME), 2018.   The Global Burden of Disease Study 2016 (GBD 2016), coordinated by the Institute for Health Metrics and Evaluation (IHME), estimated the burden of diseases, injuries, and risk factors for 195 countries and territories and at the subnational level for a subset of countries. Estimates for deaths, disability-adjusted life years (DALYs), years lived with disability (YLDs), years of life lost (YLLs), prevalence, and incidence for 29 cancer groups by age and sex for 1990-2016 are available from the GBD Results Tool. Files available in this record are the web tables published in JAMA Oncology in June 2018 in "Global, Regional, and National Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life-years for 29 Cancer Groups, 1990 to 2016."
    • 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
    • January 2019
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 29 January, 2019
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      Source: UNECE Transport Division Database. Definitions: Inland waterways transport (IWT) : Any movement of goods and/or passengers using an IWT vessel on a given inland waterways network. When an IWT vessel is being carried on another vehicle, only the movement of the carrying vehicle (active mode) is taken into account. National inland waterways transport : Inland waterways 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 IWT vessel is registered. It may involve transit through a second country. International inland waterways transport : Inland waterways transport between two places (a place of loading/embarkment and a place of unloading/disembarkment) located in two different countries. It may involve transit through one or more additional countries. Goods carried by inland waterways : Any goods moved by IWT freight vessel. This includes all packaging and equipment such as containers, swap-bodies or pallets. Tonne-kilometre by inland waterways : Unit of measure of goods transport which represents the transport of one tonne by inland waterways over one kilometre. Goods loaded : Goods placed on an IWT vessel and dispatched by inland waterways. Transshipment from one IWT vessel to another is regarded as loading after unloading. The same applies to changes of pusher tugs or tugs. Goods unloaded : Goods taken of an IWT vessel after transport by inland waterways. Transshipment from one IWT vessel to another is regarded as unloading before re-loading. The same applies to changes of pusher tugs or tugs. International - loaded Goods having left the country by inland waterways (other than goods in transit by inland waterways throughout) : Goods which, having been loaded on an IWT vessel in the country, left the country by inland waterways and were unloaded in another country. International - unloaded Goods having entered the country by inland waterways (other than goods in transit by inland waterways throughout) : Goods which, having been loaded on an IWT vessel in another country, entered the country by inland waterways and were unloaded there. Goods in transit by inland waterways throughout : Goods which entered the country by inland waterways and left the country by inland waterways at a point different from the point of entry, after having been carried across the country solely by inland waterways in the same IWT freight vessel. Transshipments from one IWT vessel to another and changes of pusher tugs or tugs are regarded as loading/ unloading. Please note that country footnotes are not always in alphabetical order. .. - data not available Country: Bulgaria Push/tow and self-propelled vessels refer to vessel type 1 to 4. Country: Croatia Self-propelled vessels includes transport by seagoing vessels Country: Czechia Push/tow vessels refers to non-self propelled vessels and other vessels. Country: United States 2012 tonne kilometer data does not include imports
    • January 2019
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 29 January, 2019
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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.
    • February 2019
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 12 February, 2019
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      Source: UNECE Transport Division Database. Definitions: Oil pipeline transport : Any movement of crude or refined liquid petroleum products in a given oil pipeline network. National oil pipeline transport : Oil pipeline transport between two places (a pumping-in place and a pumping-out place) located in the same country or in that part of the seabed allocated to it. It may involve transit through a second country. International oil pipeline transport : Oil pipeline transport between two places (a pumping-in place and a pumping-out place) located in two different countries or on those parts of the seabed allocated to them. It may involve transit through one or more additional countries. Goods transported by oil pipeline : Any crude or refined liquid petroleum products moved by oil pipelines. Tonne-kilometre by oil pipeline : Unit of measure of transport which represents transport of one tonne of goods by oil pipeline over one kilometre. International - loaded Goods having left the country by oil pipeline ( other than goods in transit by oil pipeline throughout ) : Goods which, having been pumped into an oil pipeline in the country or that part of the seabed allocated to it, left the country by oil pipeline and were pumped out in another country. International - unloaded Goods having entered the country by oil pipeline (other than goods in transit by oil pipeline throughout) : Goods which, having been pumped into an oil pipeline in another country or that part of the seabed allocated to it, entered the country by oil pipeline and were pumped out there. Goods in transit by oil pipeline throughout : Goods which entered the country by oil pipeline and left the country by oil pipeline at a point different from the point of entry, after having been transported across the country solely by oil pipeline. Goods which entered and/or left the country in question by vessels after pumping into/pumping out of an oil pipeline at the frontier are included. Please note that country footnotes are not always in alphabetical order. .. - data not available Country: Serbia Territorial change (2000 onward): Data do not cover Kosovo and Metohija. Country: Canada Data reported in cubic meters. Country: Turkey Data includes only crude petroleum transport of Petroleum Pipeline Corporation and Turkish Petroleum Corporation (TPAO)
    • January 2019
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 22 January, 2019
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      A case of occupational injury is the case of a worker incurring an occupational injury as a result of an occupational accident. An occupational injury that is fatal is the result of an occupational accident where death occurred within one year from the day of the accident. 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.
    • December 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 08 January, 2019
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      A case of occupational injury is the case of a worker incurring an occupational injury as a result of an occupational accident. An occupational injury that is fatal is the result of an occupational accident where death occurred within one year from the day of the accident.
    • January 2019
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 22 January, 2019
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      A case of non-fatal occupational injury is the case of a worker incurring an occupational injury as a result of an occupational accident not leading to death. The non-fatal occupational injury entails a loss of working time. 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.
    • January 2019
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 08 January, 2019
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      A case of non-fatal occupational injury is the case of a worker incurring an occupational injury as a result of an occupational accident not leading to death. The non-fatal occupational injury entails a loss of working time.
    • October 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 06 November, 2018
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      A case of non-fatal occupational injury is the case of a worker incurring a non-fatal occupational injury as a result of an occupational accident, which entailed a loss of working time. Incapacity for work is the inability of the victim of an occupational accident, due to an occupational injury, to perform the normal duties of work in the job or post occupied at the time of the occupational accident. The incapacity for work can be permanent, when the persons injured were never able to perform again the normal duties of work in the job or post occupied at the time of the occupational accident causing the injury, or temporary, when the workers injured were unable to work from the day after the day of the accident, but were later able to perform again the normal duties of work in the job or post occupied at the time of the occupational accident causing the injury within a period of one year from the day of the accident.
    • January 2019
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 22 January, 2019
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      A case of non-fatal occupational injury is the case of a worker incurring a non-fatal occupational injury as a result of an occupational accident, which entailed a loss of working time. Incapacity for work is the inability of the victim of an occupational accident, due to an occupational injury, to perform the normal duties of work in the job or post occupied at the time of the occupational accident. The incapacity for work can be permanent, when the persons injured were never able to perform again the normal duties of work in the job or post occupied at the time of the occupational accident causing the injury, or temporary, when the workers injured were unable to work from the day after the day of the accident, but were later able to perform again the normal duties of work in the job or post occupied at the time of the occupational accident causing the injury within a period of one year from the day of the accident. 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: 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
    • 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;
    • January 2016
      Source: World Bank
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      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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    • December 2017
      Source: World Bank
      Uploaded by: Knoema
      Accessed On: 27 November, 2018
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      Data cited at: The World Bank https://datacatalog.worldbank.org/ Topic: 2017 Climate Investment Funds – Clean Technology Fund (CTF) Results Data Publication: https://datacatalog.worldbank.org/dataset/2016-climate-investment-funds-%E2%80%93-clean-technology-fund-ctf-results-data License: http://creativecommons.org/licenses/by/4.0/   The results data is based on the portfolio of CTF projects and has been compiled on behalf of the following multilateral development banks: ADB, AFDB, EBRD, IDB, IFC and IBRD. It follows the principles outlined under the Revised CTF Results Framework and includes five core indicators that help determine whether and to what extent the CTF interventions achieve the proposed project/ program outcome objectives involving: (a) Avoided greenhouse gas (GHG) emissions; (b) Increased finance for low carbon development mobilized; (c) Increased supply of renewable energy (RE); (d) Increased access to public transport; (e) Increased energy efficiency. Please note that this is based on 53 out of 55 projects reporting results and does not include (2) CONFIDENTIAL projects. Reporting Year: 2017
    • July 2018
      Source: End Coal
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      Accessed On: 16 July, 2018
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      Data cited at: End Coal https://endcoal.org/ Topic: Coal Plants by country Publication URL: https://endcoal.org/global-coal-plant-tracker/summary-statistics/ License: https://creativecommons.org/licenses/by-nc/4.0/   Coal Power Plants Statistics
    • November 2018
      Source: International Labour Organization
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      Accessed On: 21 November, 2018
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      The collective bargaining coverage rate conveys the number of employees whose pay and/or conditions of employment are determined by one or more collective agreement(s) as a percentage of the total number of employees. Collective bargaining coverage includes, to the extent possible, workers covered by collective agreements in virtue of their extension. Collective bargaining coverage rates are adjusted for the possibility that some workers do not have the right to bargain collectively over wages (e.g. workers in the public services who have their wages determined by state regulation or other methods involving consultation), unless otherwise stated in the notes. The statistics presented in this table result from an ILO data compilation effort (including an annual questionnaire and numerous special enquiries), with contributions from J. Visser.
    • January 2019
      Source: International Labour Organization
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      Accessed On: 22 January, 2019
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    • January 2018
      Source: Food and Agriculture Organization
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      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.
    • November 2018
      Source: World Bank
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      Accessed On: 19 November, 2018
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      Data cited at: The World Bank https://datacatalog.worldbank.org/ Topic: Commodity Markets Outlook Publication: http://www.worldbank.org/en/research/commodity-markets License: http://creativecommons.org/licenses/by/4.0/   Report on Commodity Markets Outlook, 2018 October Financial Years-1970/71,1980/1981,2017/2018,2018/2019 have been considered as 1971,1981,2018,2019 respectively.
    • March 2016
      Source: UNESCO Institute for Statistics
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      Accessed On: 22 March, 2016
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    • February 2019
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 12 February, 2019
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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.
    • January 2019
      Source: Bank for International Settlements
      Uploaded by: Knoema
      Accessed On: 28 January, 2019
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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".  
    • January 2019
      Source: Bank for International Settlements
      Uploaded by: Knoema
      Accessed On: 21 January, 2019
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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
    • 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.
    • January 2019
      Source: International Monetary Fund
      Uploaded by: Knoema
      Accessed On: 24 January, 2019
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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.
    • November 2018
      Source: Food and Agriculture Organization
      Uploaded by: Knoema
      Accessed On: 17 December, 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.    
    • February 2019
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 15 February, 2019
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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.
    • February 2019
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 12 February, 2019
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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.
    • February 2019
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 12 February, 2019
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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).
    • February 2019
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 12 February, 2019
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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.
    • January 2019
      Source: Transparency International
      Uploaded by: Pallavi S
      Accessed On: 01 February, 2019
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      Data cited at CORRUPTION PERCEPTIONS INDEX 2018 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/cpi2018 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.
    • April 2018
      Source: The United States President's Emergency Plan for AIDS Relief
      Uploaded by: Knoema
      Accessed On: 08 August, 2018
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      Operating unit-level results for 2016 and prior years represent aggregated totals. For 2015 and 2016, results are available at the subnational level. For 2014 results and prior, the data can only be viewed and explored in aggregate country or regional form. General patterns can be explored for all results, allowing the investigation of trends within and among different operating units. Some variation exists between indicator versions from PEPFAR during 2004-2010, 2011-2014, and 2015-2016. More detail regarding these differences can be found in the indicator reference documents and in reference materials attached to this dashboard.
    • January 2019
      Source: NYU Stern
      Uploaded by: Knoema
      Accessed On: 13 February, 2019
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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. Notes:  Data published on 2018-Jan considered as 2018, similarly 2019-Jan as 2019 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  
    • February 2019
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 15 February, 2019
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      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.
    • April 2018
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 21 May, 2018
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      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
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      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
    • June 2018
      Source: Reputation Institute
      Uploaded by: Knoema
      Accessed On: 03 July, 2018
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      Country RepTrak | Top Countries by ReputationThe Global RepTrak® 100 is a study that Reputation Institute conducts annually to measure the reputation of the world’s 100 most highly-regarded and familiar global companies in 15 countries. Included firms must meet the following qualifications: 1) Have a significant economic presence in the 15 largest economies 2) Have an above average reputation in its home country 3) Have global familiarity over 40% It is the largest Global reputation study, with ~170,000 ratings   Normative Scale for Reputation track pulse score Excellent/Top Tier: 80+ Strong/Robust: 70-79 Avg./Moderate: 60-69 Weak/Vulnerable: 40-59 Poor/Lowest: <40
    • December 2015
      Source: International Monetary Fund
      Uploaded by: Knoema
      Accessed On: 18 April, 2016
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      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
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      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).
    • January 2019
      Source: Numbeo
      Uploaded by: Knoema
      Accessed On: 15 February, 2019
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      Data cited at: Numbeo 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.  
    • February 2013
      Source: RAND Corporation
      Uploaded by: Knoema
      Accessed On: 18 November, 2015
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      This report describes the results of a study of the sources and reliability of the supply of imported materials on which United States manufacturers are dependent. It should be of interest to a broad spectrum of individuals and organizations in the materials and manufacturing sectors as well as government, private sector, and non-profit organizations involved with or concerned about those sectors. This research was sponsored by the National Intelligence Council and conducted within the Intelligence Policy Center of the RAND National Defense Research Institute, a federally funded research and development center sponsored by the Office of the Secretary of Defense, the Joint Staff, the Unified Combatant Commands, the Navy, the Marine Corps, the defense agencies, and the defense Intelligence Community
    • April 2018
      Source: European Commission
      Uploaded by: Knoema
      Accessed On: 10 May, 2018
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      Monthly and Cumulative Crude Oil Imports in Intra EU, December 2017 Note: (1) Source: Council Regulation (EC) n°2964/95 of 20 December 1995. (2) The cif price includes the fob price (the price actually invoiced at the port of loading), the cost of transport, insurance and certain charges linked to crude oil transfer operations. (3) Due to confidentiality Czech Republic is excluded from EU(28). (4) For Romania November-2016 and December-2016 are estimations derived from Eurostat data
    • December 2017
      Source: Food and Agriculture Organization
      Uploaded by: Knoema
      Accessed On: 05 January, 2018
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      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.
    • June 2018
      Source: United Nations Office on Drugs and Crime
      Uploaded by: Shakthi Krishnan
      Accessed On: 18 September, 2018
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    • December 2017
      Source: European Commission
      Uploaded by: Shakthi Krishnan
      Accessed On: 01 October, 2018
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      The European Commission provides statistics on EU crude oil imports and crude oil supply costs.
    • May 2018
      Source: United Nations Conference on Trade and Development
      Uploaded by: Knoema
      Accessed On: 28 June, 2018
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      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
    • June 2017
      Source: Bank of Canada
      Uploaded by: Knoema
      Accessed On: 05 December, 2018
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      The Bank of Canada’s Credit Rating Assessment Group (CRAG) comprehensive database of sovereign defaults draws on previously published data sets compiled by various official and private sector sources. It combines elements of these, together with new information, to develop estimates of stocks of government obligations in default, including bonds and other marketable securities, bank loans, and official loans in default, valued in U.S. dollars, for the years 1960 to 2016 on both a country-by-country and a global basis. This update of CRAG’s database, and subsequent updates, will be useful to researchers analyzing the economic and financial effects of individual sovereign defaults and, importantly, the impact on global financial stability of episodes involving multiple sovereign defaults.
    • January 2019
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 22 January, 2019
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      Days lost due to temporary incapacity refers to the total number of calendar days during which those persons temporarily incapacitated were unable to work, excluding the day of the accident, up to a maximum of one year. Temporary absences from work of less than one day for medical treatment are not included. 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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      Days lost due to temporary incapacity refers to the total number of calendar days during which those persons temporarily incapacitated were unable to work, excluding the day of the accident, up to a maximum of one year. Temporary absences from work of less than one day for medical treatment are not included.
    • January 2019
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 22 January, 2019
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      Time lost per occupational injury is defined as the average number of calendar days lost per new cases of non-fatal occupational injury resulting in temporary incapacity.
    • 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.
    • January 2019
      Source: AIRBUS
      Uploaded by: Knoema
      Accessed On: 24 January, 2019
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      Defense Aircraft Summary
    • April 2017
      Source: Islamic Development Bank
      Uploaded by: Knoema
      Accessed On: 07 September, 2017
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    • May 2018
      Source: United Nations Statistics Division
      Uploaded by: Knoema
      Accessed On: 04 December, 2018
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      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.
    • April 2018
      Source: Institute for Health Metrics and Evaluation
      Uploaded by: Knoema
      Accessed On: 03 May, 2018
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      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.
    • May 2007
      Source: International Telecommunication Union
      Uploaded by: Knoema
      Accessed On: 28 May, 2015
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      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
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      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
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      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.
    • January 2019
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 22 January, 2019
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      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.
    • September 2012
      Source: Americans for Divorce Reform
      Uploaded by: Knoema
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      Divorce Indicators across countries
    • 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
    • September 2018
      Source: Fraser Institute
      Uploaded by: Knoema
      Accessed On: 02 November, 2018
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      Data cited at: "Economic Freedom of the World: 2018 Annual Report"@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.
    • February 2019
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 12 February, 2019
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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
      Select Dataset
      Calculated using Mean Years of Schooling and Expected Years of Schooling.
    • February 2019
      Source: World Bank
      Uploaded by: Knoema
      Accessed On: 14 February, 2019
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      Data cited at: The World Bank https://datacatalog.worldbank.org/ Topic:Education Statistics Publication: https://datacatalog.worldbank.org/dataset/education-statistics License: http://creativecommons.org/licenses/by/4.0/   The World Bank EdStats All Indicator Query holds over 4,000 internationally comparable indicators that describe education access, progression, completion, literacy, teachers, population, and expenditures. The indicators cover the education cycle from pre-primary to vocational and tertiary education.
    • 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.
    • 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).
    • February 2019
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 15 February, 2019
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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.
    • February 2019
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 15 February, 2019
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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.
    • August 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 03 September, 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, and presented for a selection of categories at the 2-digit level of the classification. 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.
    • February 2019
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 15 February, 2019
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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 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.
    • February 2019
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 15 February, 2019
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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.
    • February 2019
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 15 February, 2019
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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.
    • February 2019
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 18 February, 2019
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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:
    • February 2019
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 15 February, 2019
      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: 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.
    • February 2019
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 15 February, 2019
      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.
    • February 2019
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 15 February, 2019
      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.
    • April 2018
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 31 August, 2018
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      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 economic activity according to the latest version of the International Standard Industrial Classification of All Economic Activities (ISIC Rev.4). Economic activity refers to the main activity of the establishment in which the person worked during the reference period (it 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).
    • February 2019
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 12 February, 2019
      Select Dataset
      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.
    • February 2019
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 12 February, 2019
      Select Dataset
      Source: UNECE Statistical Database, compiled from national and international (Eurostat and ILO) 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 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 to ISCO-08 is 2011, for other countries please see Country footnotes. The level of education is the highest level successfully completed in the educational system of the country where the education is received. The levels are defined with reference to the International Standard Classifications of Education ISCED 1997 and ISCED 2011. For the EU and EFTA member-states the levels of education are classified according to ISCED 2011 from 2014. For other countries please see Country footnotes. The transition from ISCO-88 to ISCO-08 and from ISCED 1997 to ISCED 2011 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: Armenia Data for 2001 are from Population Census. Since 2014 data are based on the Labour Force Survey. Country: Azerbaijan Data compiled according to ISCO-08. Country: Belarus Break in methodlogy (2000): Data refer to 1999 Population Census. Measurement: Employment (thousands) , Country: Belarus Data compiled according to ISCO-88 Measurement: Percent of corresponding total of both sexes , Country: Belarus Data compiled according to ISCO-88 Measurement: Employment (thousands) , Country: Belarus Parts by education level may not add up due to the persons who did not indicate their levels of education Measurement: Percent of corresponding total of both sexes , Country: Belarus Parts by education level may not add up due to the persons who did not indicate their levels of education Country: Bosnia and Herzegovina From 2006 to 2014 data compiled using ISCED 97, from 2015 using ISCED 11. 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: 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: Moldova, Republic of Data exclude the territory of the Transnistria and municipality of Bender Country: Russian Federation Change in definition (2000 - 2013): Data present the population aged 15-72 years Country: Russian Federation Territorial change (2000 - 2006): Data do not include the Chechen Republic Country: Serbia Data do not cover Kosovo and Metohija. From 2013 data compiled according to ISCO-08. 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 Until 2012, all occupations were coded according to ISCO-88. Since 2013, all occupations have been coded according to ISCO-08. 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 States Data for occupation refer to population 15+ and who have worked in the past 5 years. Data do not cover the armed forces. Occupation is classified according to the Standard Occupational Classification (SOC) 2000 manual (www.bls.gov/soc). For individuals with two or more jobs, data refer to the job having the greatest number of hours. For unemployed persons and persons who are not currently employed but report having a job within the last five years, data refer to their last job.
    • February 2019
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 12 February, 2019
      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'.
    • 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.
    • February 2019
      Source: United Nations Economic Commission for Europe
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      Accessed On: 12 February, 2019
      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
    • February 2019
      Source: International Labour Organization
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      Accessed On: 15 February, 2019
      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.
    • February 2019
      Source: International Labour Organization
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      Accessed On: 12 February, 2019
      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).
    • November 2018
      Source: International Labour Organization
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      Accessed On: 13 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).
    • February 2019
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 15 February, 2019
      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.
    • February 2019
      Source: International Labour Organization
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      Accessed On: 12 February, 2019
      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
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      Accessed On: 13 November, 2018
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      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).
    • February 2019
      Source: International Labour Organization
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      Accessed On: 12 February, 2019
      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.
    • February 2019
      Source: International Labour Organization
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      Accessed On: 15 February, 2019
      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. 
    • February 2019
      Source: International Labour Organization
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      Accessed On: 12 February, 2019
      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.
    • February 2019
      Source: International Labour Organization
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      Accessed On: 15 February, 2019
      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.
    • February 2019
      Source: International Labour Organization
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      Accessed On: 12 February, 2019
      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.
    • February 2019
      Source: International Labour Organization
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      Accessed On: 12 February, 2019
      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.
    • February 2019
      Source: International Labour Organization
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      Accessed On: 12 February, 2019
      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).
    • February 2019
      Source: International Labour Organization
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      Accessed On: 12 February, 2019
      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.
    • February 2019
      Source: United Nations Economic Commission for Europe
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      Accessed On: 12 February, 2019
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      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
    • February 2019
      Source: International Labour Organization
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      Accessed On: 15 February, 2019
      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.
    • February 2019
      Source: International Labour Organization
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      Accessed On: 15 February, 2019
      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.
    • February 2019
      Source: International Labour Organization
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      Accessed On: 15 February, 2019
      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).
    • February 2019
      Source: International Labour Organization
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      Accessed On: 15 February, 2019
      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.
    • February 2019
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 15 February, 2019
      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. 
    • February 2019
      Source: International Labour Organization
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      Accessed On: 15 February, 2019
      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.
    • February 2019
      Source: International Labour Organization
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      Accessed On: 15 February, 2019
      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.
    • February 2019
      Source: International Labour Organization
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      Accessed On: 15 February, 2019
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      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.
    • January 2019
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 22 January, 2019
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      .. - data not available Source: UNECE Transport Division Database. Please note that country footnotes are not always in alphabetical order.
    • February 2019
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 12 February, 2019
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      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.
    • February 2019
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 15 February, 2019
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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.
    • February 2019
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 15 February, 2019
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      The employment-to-population ratio expresses the number of persons who are employed as a percent of the total working age population.
    • February 2019
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 15 February, 2019
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      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.
    • February 2019
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 15 February, 2019
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      The employment-to-population ratio is the number of persons who are employed as a percent of the total of working age population.
    • February 2019
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 15 February, 2019
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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.
    • February 2019
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 15 February, 2019
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      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.
    • February 2019
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 15 February, 2019
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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.
    • February 2019
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 15 February, 2019
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      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.
    • February 2019
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 15 February, 2019
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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.
    • September 2018
      Source: Statistics Finland
      Uploaded by: Knoema
      Accessed On: 30 November, 2018
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      Data cited at: Statistics Finland http://www.stat.fi/index_en.html Publication: 005 -- Energy import and export by country, % http://pxnet2.stat.fi/PXWeb/pxweb/en/StatFin/StatFin__ene__ehk/statfin_ehk_pxt_005_en.px License: http://creativecommons.org/licenses/by/4.0/ Revisions in these statistics Description kuvaus Consepts and definitions *Year preliminary
    • September 2018
      Source: Statistics Finland
      Uploaded by: Knoema
      Accessed On: 30 November, 2018
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      Data cited at: Statistics Finland http://www.stat.fi/index_en.html Publication: 006 -- Energy import and export by country, proportion % http://pxnet2.stat.fi/PXWeb/pxweb/en/StatFin/StatFin__ene__ehk/statfin_ehk_pxt_006_en.px License: http://creativecommons.org/licenses/by/4.0/ Revisions in these statistics Description kuvaus Consepts and definitions *Year preliminary
    • September 2018
      Source: Statistics Finland
      Uploaded by: Knoema
      Accessed On: 29 November, 2018
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      Data cited at: Statistics Finland http://www.stat.fi/index_en.html Publication: 004 -- Energy import and export by country http://pxnet2.stat.fi/PXWeb/pxweb/en/StatFin/StatFin__ene__ehk/statfin_ehk_pxt_004_en.px License: http://creativecommons.org/licenses/by/4.0/ Revisions in these statistics Description Consepts and definitions *Year preliminary
    • October 2018
      Source: Knoema
      Uploaded by: Knoema
      Accessed On: 02 October, 2018
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    • May 2018
      Source: Federal Institute for Geosciences and Natural Resources
      Uploaded by: Knoema
      Accessed On: 16 May, 2018
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    • December 2018
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 03 December, 2018
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      This dataset presents the number of students enrolled in different education programmes by country of origin and sex.
    • February 2019
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 12 February, 2019
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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
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      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.
    • September 2015
      Source: Multiple Sources
      Uploaded by: Knoema
      Accessed On: 10 September, 2015
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    • December 2018
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 23 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 2018
      Source: InterNations
      Uploaded by: Knoema
      Accessed On: 02 November, 2018
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      In 2018, Expat Insider, one of the world’s largest and most comprehensive surveys on life abroad, achieved a major milestone for its fifth anniversary: the number of respondents reached a new record high. In total, 18,135 expats from across the globe took part in the survey. They represent 178 nationalities and are living in 187 countries or territories, from over 1,600 participants in Germany to one each in Greenland and Equatorial Guinea, providing a unique insight into life abroad in 2018.
    • 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.
    • March 2017
      Source: Statistics Bureau of Japan
      Uploaded by: Knoema
      Accessed On: 08 January, 2019
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      Statistics Name: Current Survey of Supply and Demand for Petroleum Products Annual Report Petroleum Products Import and Export of Petroleum Products/Monthly Export by Area and Country F.Y.
    • March 2017
      Source: Statistics Bureau of Japan
      Uploaded by: Knoema
      Accessed On: 08 January, 2019
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      Statistics Name: Current Survey of Supply and Demand for Petroleum Products Annual Report Petroleum Products Import and Export of Petroleum Products/Monthly Export by Area and Country Month
    • February 2015
      Source: World Integrated Trade Solution
      Uploaded by: Knoema
      Accessed On: 04 January, 2019
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      The Export of Value Added (EVA) dataset illustrates the strength of economy- wide linkages. It provides data on how value added structures and services linkages to trade have evolved over time. Thanks to repeated updating of the GTAP dataset, we have data for both cross border linkages in recent years, and how these have changed since the early 1990s. This serves as the basis for the database, which builds on Christen, Francois, and Hoekman (2012) and Francois, Manchin, and Tomberger (2012). We work with a panel of global input-output data (a set of global social accounting matrices spanning intermittent years from 1992 to 2011) that covers not only key OECD economies, but also a range of developing countries as well. Sector_GMatrix:  This matrix contains the total domestic value added based on linkages. Depending whether rows or columns are considered its sum corresponds to forward (row) or backward (colunn) linkages. Thus reading a row for a given sector (sector presented on the y-axis) provides information about how much this sector went into each sector (on the x-axis) as inputs DomVAshare: This vector denotes the domestic share of value added of gross value of output per sector. GXshare: Denotes the share of each sector in total exports per country based on the gross value of exports. DXshare: Denotes the share of each sector’s exports of total exports per country based on direct value added, ignoring linkages. VXsharefwd: Denotes the total value added in exports based on forward linkages per sector and country. VXsharebwd: Denotes the total value added in exports based on backward linkages. It is obtained by taking the column-sums of matrix H.
    • July 2018
      Source: United Nations Conference on Trade and Development
      Uploaded by: Knoema
      Accessed On: 18 January, 2019
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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
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      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).
    • May 2017
      Source: Islamic Development Bank
      Uploaded by: Knoema
      Accessed On: 15 June, 2017
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  • 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: 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 2011
      Source: Food and Agricultural Policy Research Institute
      Uploaded by: Knoema
      Accessed On: 25 December, 2012
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      FAPRI U.S. and World Outlook presents multi-year projections for the United States and world agricultural sectors. These projections serve as a baseline for evaluating and comparing alternative macroeconomic, policy, weather, and technological scenarios. These reports have been produced annually and used by congressional and agricultural leaders since 1985.
    • January 2019
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 22 January, 2019
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      An occupational injury is defined as any personal injury, disease or death resulting from an occupational accident; The case is fatal where death occurred within one year of the day of the accident. Data provided refers to new fatal occupational injuries per 100'000 in reference group coverage.
    • January 2019
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 22 January, 2019
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      The incidence rate is the average number of new cases of fatal occupational injury during the calendar year per 100,000 workers in the reference group. Data are presented disaggregated by sex and economic activity, according to the latest version available of the International Standard Industrial Classification of all Economic Activities (ISIC).
    • February 2019
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 15 February, 2019
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      This indicator conveys the rate of fatal occupational injuries per 100'000 workers in the reference group. An occupational injury is defined as any personal injury, disease or death resulting from an occupational accident; an occupational injury is therefore distinct from an occupational disease, which is a disease contracted as a result of an exposure over a period of time to risk factors arising from work activity. An occupational accident is an unexpected and unplanned occurrence, including acts of violence, arising out of or in connection with work which results in one or more workers incurring a personal injury, disease or death. A case of occupational injury is the case of one worker incurring an occupational injury as a result of one occupational accident. An occupational injury could be fatal (as a result of occupational accidents and where death occurred within one year of the day of the accident) or non-fatal with lost work time. The workers in the particular group under consideration and covered by the source of the statistics of occupational injuries are known as the workers in the reference group. In the case of a notification system, it is the number of workers in, for example, the establishments or selected economic activities covered by the system as set out in the relevant legislation or regulations. For further information, see the SDG Indicators Metadata Repository or ILOSTAT's indicator description.
    • January 2019
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 22 January, 2019
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      The incidence rate is the average number of new cases of fatal occupational injury during the calendar year per 100,000 workers in the reference group.
    • 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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    • February 2019
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 15 February, 2019
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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.
    • February 2019
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 15 February, 2019
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      Data refers to the number of women employed in the agricultural sector as a percent of total employment in agriculture
    • February 2019
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 15 February, 2019
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      Data refers to the number of women employed in the industry sector as a percent of total employment in industry.
    • February 2019
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 11 February, 2019
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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.
    • February 2019
      Source: International Labour Organization
      Uploaded by: Knoema
      Accessed On: 15 February, 2019
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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.
    • 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 
    • 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.
    • February 2019
      Source: International Monetary Fund
      Uploaded by: Knoema
      Accessed On: 18 February, 2019
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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.
    • February 2019
      Source: International Monetary Fund
      Uploaded by: Knoema
      Accessed On: 12 February, 2019
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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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      Data cited at: Statistics Finland http://www.stat.fi/index_en.html Publication: 004 -- International trade in services by region, 1 000 000 euros http://pxnet2.stat.fi/PXWeb/pxweb/en/StatFin/StatFin__kan__tpulk/statfin_tpulk_pxt_004.px License: http://creativecommons.org/licenses/by/4.0/ The statistics on international trade in goods and services cover international trade in balance of payments terms on the quarterly level. The statistics form a link for goods trade in customs and balance of payments terms, describe the breakdown of quarterly trade in services, and indicate the total exports of goods and services by area. . = Category not applicable. .. = Data not available or too uncertain for presentation, or subject to secrecy. Description of statistics Concepts and definitionsRegion Region and statesYear Year.Data Import The value of imports, 1 000 000 euros.Export The value of exports, 1 000 000 euros.
    • February 2019
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 12 February, 2019
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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.
    • February 2018
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 12 March, 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.
    • 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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    • 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
    • June 2014
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 11 December, 2015
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      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
    • January 2019
      Source: Ministry of Finance, R.O.C. (Taiwan)
      Uploaded by: Knoema
      Accessed On: 23 January, 2019
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      The statistics cover only goods exported to and imported from the economic territory of the Republic of China (Taiwan, Penghu, Kinmen and Matsu). Fish caught and sold overseas by national fishing vessels are also included in exports.Total Exports = Exports + Re-exports, Total Imports = Imports + Re-imports.Exports/re-exports is based on F.O.B. value. Imports/re-imports is based on C.I.F value.The same currency exchange rate from NT dollar to US dollar is applied to either imports/re-imports or exports/re-exports, which is the midpoint between selling and buying rates announced by Customs every 10 days in a month for filling Customs declaration purpose.Notes:  1. Prior to 2015, the value of exports includes bunker oil for the use of national vessels, aircrafts and other means of conveyance engaged in international trade. 2. Prior to 1998, the value of exports and imports by Continent/Country excludes re-exports and re-imports.  
    • February 2019
      Source: U.S. Census Bureau
      Uploaded by: Knoema
      Accessed On: 08 February, 2019
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    • January 2019
      Source: Kuwait Central Statistical Bureau
      Uploaded by: Knoema
      Accessed On: 15 January, 2019
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    • November 2018
      Source: Federal Competitiveness and Statistics Authority, United Arab Emirates
      Uploaded by: Knoema
      Accessed On: 13 November, 2018
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      Data cited at: https://uaenumbers.fcsa.gov.ae/UAEITSS2018U3
    • 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/
    • May 2018
      Source: Food and Agriculture Organization
      Uploaded by: Knoema
      Accessed On: 25 May, 2018
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    • 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.
    • October 2017
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 04 December, 2017
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      Data include pension funds per the OECD classification by type of pension plans and by type of pension funds. All types of plans are included (occupational and personal, mandatory and voluntary). The OECD classification considers both funded and book reserved pension plans that are workplace-based (occupational pension plans) or accessed directly in retail markets (personal pension plans). Both mandatory and voluntary arrangements are included. The data include plans where benefits are paid by a private sector entity (classified as private pension plans by the OECD) as well as those paid by a funded public sector entity. A full description of the OECD classification can be found at:http://www.oecd.org/dataoecd/0/49/38356329.pdf. Pension funds include also some personal pension arrangements like the Individual Retirement Accounts (IRAs) in the United States as well as funds for government workers. The coverage of the statistics follows the regulatory and supervisory framework. All authorised pension funds are therefore normally covered by the Global Pension Statistics exercise. Assets pertaining to reserve funds in social security systems are excluded.
    • March 2018
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 21 May, 2018
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      Data include pension funds per the OECD classification by type of pension plans and by type of pension funds. All types of plans are included (occupational and personal, mandatory and voluntary). The OECD classification considers both funded and book reserved pension plans that are workplace-based (occupational pension plans) or accessed directly in retail markets (personal pension plans). Both mandatory and voluntary arrangements are included. The data include plans where benefits are paid by a private sector entity (classified as private pension plans by the OECD) as well as those paid by a funded public sector entity. A full description of the OECD classification can be found at: http://www.oecd.org/dataoecd/0/49/38356329.pdf.  Pension funds include also some personal pension arrangements like the Individual Retirement Accounts (IRAs) in the United States as well as funds for government workers. The coverage of the statistics follows the regulatory and supervisory framework. All authorised pension funds are therefore normally covered by the Global Pension Statistics exercise. Assets pertaining to reserve funds in social security systems are excluded.
  • G
    • 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: 27 October, 2017
      Select Dataset
      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
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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. 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.
    • February 2019
      Source: Global Database of Events, Language, and Tone
      Uploaded by: Knoema
      Accessed On: 09 February, 2019
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      Data cited at: Global Database of Events, Language, and Tone   The GDELT Event Database records over 300 categories of physical activities around the world, from riots and protests to peace appeals and diplomatic exchanges, georeferenced to the city or mountain top, across the entire planet dating back to January 1, 1979 and updated every 15 minutes. Essentially it takes a sentence like "The United States criticized Russia yesterday for deploying its troops in Crimea, in which a recent clash with its soldiers left 10 civilians injured" and transforms this blurb of unstructured text into three structured database entries, recording US CRITICIZES RUSSIA, RUSSIA TROOP-DEPLOY UKRAINE (CRIMEA), and RUSSIA MATERIAL-CONFLICT CIVILIANS (CRIMEA). Nearly 60 attributes are captured for each event, including the approximate location of the action and those involved. This translates the textual descriptions of world events captured in the news media into codified entries in a grand "global spreadsheet."
    • February 2019
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 18 February, 2019
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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:
    • February 2019
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 18 February, 2019
      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:
    • February 2019
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 15 February, 2019
      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.
    • February 2019
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 15 February, 2019
      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.
    • February 2019
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 15 February, 2019
      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
    • February 2019
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 15 February, 2019
      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.
    • February 2019
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 15 February, 2019
      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).
    • February 2019
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 15 February, 2019
      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.
    • February 2019
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 15 February, 2019
      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.
    • February 2019
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 15 February, 2019
      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.
    • February 2019
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 12 February, 2019
      Select Dataset
      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
      Select Dataset
      This dataset includes gender inequality and development indices.
    • February 2019
      Source: United Nations Economic Commission for Europe
      Uploaded by: Knoema
      Accessed On: 12 February, 2019
      Select Dataset
      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. Country: Moldova, Republic of From 2012 information is presented without the data on districts from the left side of the river Nistru and municipality Bender. Through 2011 data are for September for units with 20 and more employees. Starting with 2012 data are for units with one and more employees. Country: Netherlands The underlying average earnings refer to employees only and do not include bonuses, gratuities, housing and family allowances. Country: Norway Data refer to full-time equivalent of paid employees only. Reference period: III quarter of each year. Data includes various additional allowances, bonuses, commissions and do not include payment for overtime work. Country: Poland Change in definition (2001 - 2004): Data refer to full-time employees only. Family allowances are not inclueded. Country: Poland Change in definition (2006 onward): Data cover employees only. Family allowances are not included. Country: Romania Additional information (1990 - 2001): Data cover the entire country and are related to enterprises with 1+ employees. The average monthly gross earnings refers to the entire year. Country: Russian Federation Change in definition (2005 - 2013): Underlying Earnings data do not include end of year, seniority, bonus payments and other nonrecurrent payments . Data include employees worked whole October; data exclude non-regular, temporary, contractual, absent due to different reasons (maternity, sabbatical, annual leave), part-time workers and others. Country: Slovakia Data on monthly earnings cover all economic activities (all NACE Rev.2 sections) Country: Slovenia In 2007 EURO was introduced instead of the national currency SIT. Country: Slovenia Change in definition (2003 onward): Data refer to full-time employees only. Country: Slovenia Provisional value (2014) Country: Spain Additional information (2000): The results have been obtained as annual average of quarterly data form a wage survey. The coverage are local units with 5 or more employees. Country: Spain From 2002-2003, the coverage is local units with 10 or more employees. Since 2004, coverage has been extended to all size units. ISCED-97 is used 2002-2010 and ISCED-11 in 2014. Country: Sweden Change in definition (2000 - 2013): The Data cover only employees and exlude irregular bonuses and gratuities. Country: Switzerland For monthly earnings, up to 2010 the data cover employees in private and public federal sectors. Since 2012, the data concern only the private sector. Country: Switzerland The underlying average earnings data exclude overtime pay and family allowances and refer to full-time equivalents. GPG figures computed from median earnings instead of averages. Country: Ukraine From 2014 data cover the territories under the government control. Country: Ukraine Up to 2009, the data do not cover small businesses, since 2010 the data include enterprises, institutions and organizations with 10 and more employees. Country: United Kingdom Monthly earnings are from the UK Annual Survey of Hours and Earnings (ASHE) and defined as average gross weekly earnings for the reference period (Gpay), multiplied by 4.348. Earnings are of those over 15 only.
    • January 2019
      Source: World Bank
      Uploaded by: Knoema
      Accessed On: 01 February, 2019
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      Data cited at: The World Bank https://datacatalog.worldbank.org/ Topic: Gender Statistics Publication: https://datacatalog.worldbank.org/dataset/gender-statistics License: http://creativecommons.org/licenses/by/4.0/
    • February 2015
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 17 February, 2015
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      The GID-DB is a database providing researchers and policymakers with key data on gender-based discrimination in social institutions. This data helps analyse women’s economic empowerment and understand gender gaps in other key areas of development. Covering 160 countries, the GID-DB contains comprehensive information on legal, cultural and traditional practices that discriminate against women and girls.
    • November 2018
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 23 November, 2018
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    • December 2018
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 03 December, 2018
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      This table provides information on the main relevant indicators. The data have mainly been supplied by the World Bank, and cover, where available: -Current Gross National Income (GNI) in US $ millions; -GNI per capita (US $); -Population; -Energy use as kilogram of oil per capita; -Average Life Expectancy of Adults; and -Adult Literacy Rate as a percentage of the country population. Data for Sudan include South Sudan, with the exception of total population, which is reported separately.
    • December 2017
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 01 February, 2018
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      Bilateral ODA commitments by purpose. Data cover the years 2005 to 2009. Amounts are expressed in USD million. The sectoral distribution of bilateral ODA commitments refers to the economic sector of destination (i.e. the specific area of the recipient's economic or social structure whose development is, or is intended to be fostered by the aid), rather than to the type of goods or services provided. These are aggregates of individual projects notified under the Creditor Reporting System, supplemented by reporting on the sectoral distribution of technical co-operation, and on actual disbursements of food and emergency aid.
    • February 2018
      Source: German Chemicals Industry Association
      Uploaded by: Knoema
      Accessed On: 26 April, 2018
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      Facts and figures for chemistry (2017), Foreign Trade
    • October 2015
      Source: HelpAge International
      Uploaded by: Knoema
      Accessed On: 16 October, 2015
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      The aim of the Index is both to capture the multidimensional nature of the quality of life and wellbeing of older people, and to provide a means by which to measure performance and promote improvements. We have chosen 13 different indicators for the four key domains of Income security, Health status, Capability, and Enabling environment. Domain 1: Income security The income security domain assesses people's access to a sufficient amount of income, and the capacity to use it independently, in order to meet basic needs in older age. Domain 2: Health status The three indicators used for the health domain provide information about physical and psychological wellbeing. Domain 3: Capability The employment and education indicators in this domain look at different aspects of the empowerment of older people. Domain 4: Enabling environment This domain uses data from Gallup World View to assess older people's perception of social connectedness, safety, civic freedom and access to public transport - issues older people have singled out as particularly important.
    • March 2018
      Source: Food and Agriculture Organization
      Uploaded by: Knoema
      Accessed On: 28 March, 2018
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      This database contains statistics on production volume and value by species, country or area, fishing area and culture environment
    • July 2011
      Source: World Bank
      Uploaded by: Knoema
      Accessed On: 21 September, 2017
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      Data cited at: The World Bank https://datacatalog.worldbank.org/ Topic: Global Bilateral Migration Database Publication: https://datacatalog.worldbank.org/dataset/global-bilateral-migration-database License: http://creativecommons.org/licenses/by/4.0/   Global Bilateral Migration Database: Global matrices of bilateral migrant stocks spanning the period 1960-2000, disaggregated by gender and based primarily on the foreign-born concept are presented. Over one thousand census and population register records are combined to construct decennial matrices corresponding to the last five completed census rounds. For the first time, a comprehensive picture of bilateral global migration over the last half of the twentieth century emerges. The data reveal that the global migrant stock increased from 92 to 165 million between 1960 and 2000. South-North migration is the fastest growing component of international migration in both absolute and relative terms. The United States remains the most important migrant destination in the world, home to one fifth of the world’s migrants and the top destination for migrants from no less than sixty sending countries. Migration to Western Europe remains largely from elsewhere in Europe. The oil-rich Persian Gulf countries emerge as important destinations for migrants from the Middle East, North Africa and South and South-East Asia. Finally, although the global migrant stock is still predominantly male, the proportion of women increased noticeably between 1960 and 2000.
    • December 2018
      Source: TRACE International
      Uploaded by: Knoema
      Accessed On: 30 January, 2019
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      'The TRACE Matrix measures business bribery risk in all countries. Developed in collaboration with RAND Corporation, the TRACE Matrix provides the business community with a powerful new tool for anti-bribery risk assessment. It assesses countries across four domains – Business Interactions with Government, Anti-bribery Laws and Enforcement, Government and Civil Service Transparency, and the Capacity for Civil Society Oversight, including the role of the media – as well as nine sub-domains. Business interactions with government includes the sub-domains of “contact with government,” “expectation of paying bribes” and “regulatory burden.” These indicators capture aspects of the “touches with government” that TRACE identified as very important for business bribery through regulatory and business interviews they conducted. Anti-corruption laws enacted by a country and information about enforcement of those laws. Government and civil service transparency, which includes indicators concerning whether government budgets are publicly available and whether there are regulations addressing conflicts of interest for civil servants. Information concerning the extent of press freedom and social development, both of which serve as indicators of a robust civil society that can provide government oversight. The overall country risk score is a combined and weighted score of four domains. For each of these four "domains" (and related sub-domains), the TRACE Matrix aggregates relevant data obtained from leading public interest and international organizations, including the United Nations, the World Bank and the World Economic Forum. Based on statistical analysis of this information, each country is assigned not only an overall score between 1 and 100—with 100 representing the greatest risk—but also scores for each of the four domains and nine sub-domains.'
    • March 2018
      Source: Food and Agriculture Organization
      Uploaded by: Knoema
      Accessed On: 11 April, 2018
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      Contains the volume of fish catches landed by country or territory of capture, by species or a higher taxonomic level, by FAO major fishing areas, and year for all commercial, industrial, recreational and subsistence purpose
    • November 2017
      Source: Food and Agriculture Organization
      Uploaded by: Knoema
      Accessed On: 22 November, 2017
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      This database contains statistics on the annual production of fishery commodities and imports and exports of fishery commodities by country and commodities in terms of volume and value from 1976.
    • November 2016
      Source: DHL
      Uploaded by: Knoema
      Accessed On: 07 December, 2016
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      DHL released the third edition of its Global Connectedness Index (GCI), a detailed analysis of the state of globalization around the world. The latest report, authored by internationally acclaimed globalization expert Professor Pankaj Ghemawat together with Steven A. Altman, shows that global connectedness, measured by cross-border flows of trade, capital, information and people, has recovered most of its losses incurred during the financial crisis. Especially the depth of international interactions – the proportion of interactions that cross national borders – gained momentum in 2013 after its recovery had stalled in the previous year. Nonetheless, trade depth, as a distinct dimension of globalization, continues to stagnate and the overall level of global connectedness remains quite limited, implying that there could be gains of trillions of US dollars if boosted in future years.
    • December 2013
      Source: Transparency International
      Uploaded by: Knoema
      Accessed On: 20 February, 2014
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      Data cited at: Global Corruption Barometer (2013) 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: http://www.transparency.org/gcb2013/in_detail#sthash.hey9okGH.dpuf
    • July 2017
      Source: International Telecommunication Union
      Uploaded by: Shakthi Krishnan
      Accessed On: 13 September, 2017
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        The Global Cybersecurity Index (GCI) is a survey that measures the commitment of Member States to cybersecurity in order to raise awareness. The GCI revolves around the ITU Global Cybersecurity Agenda (GCA) and its five pillars (legal, technical, organizational, capacity building and cooperation). For each of these pillars, questions were developed to assess commitment. Through consultation with a group of experts, these questions were weighted in order to arrive at an overall GCI score. The survey was administered through an online platform through which supporting evidence was also collected. One-hundred and thirty-four Member States responded to the survey throughout 2016. Member States who did not respond were invited to validate responses determined from open-source research. As such, the GCI results reported herein cover all 193 ITU Member States. The 2017 publication of the GCI continues to show the commitment to cybersecurity of countries around the world. The overall picture shows improvement and strengthening of all five elements of the cybersecurity agenda in various countries in all regions. However, there is space for further improvement in cooperation at all levels, capacity building and organizational measures. As well, the gap in the level of cybersecurity engagement between different regions is still present and visible. The level of development of the different pillars varies from country to country in the regions, and while commitment in Europe remains very high in the legal and technical fields in particular, the challenging situation in the Africa and Americas regions shows the need for continued engagement and support. In addition to providing the GCI score, this report also provides a set of illustrative practices that give insight into the achievements of certain countries.
    • December 2018
      Source: International Monetary Fund
      Uploaded by: Knoema
      Accessed On: 13 February, 2019
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      The Global Debt Database (GDD) is the result of a multiyear investigative process that started with the October 2016 Fiscal Monitor. The dataset comprises total gross debt of the (private and public) non financial sector for an unbalanced panel of 190 advanced economies, emerging market economies and low-income countries, dating back to 1950. For more details on the methodology and definitions, please refer to Mbaye, Moreno Badia and Chae (2018). 
    • November 2018
      Source: Institute for Health Metrics and Evaluation
      Uploaded by: Knoema
      Accessed On: 30 November, 2018
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      Research by the Global Burden of Disease Health Financing Collaborator Network produced retrospective national health spending estimates for 1995-2016 for 184 countries. The estimates cover total health spending, and health spending disaggregated by source into government spending, out-of-pocket, prepaid private, and development assistance for health. National health spending by source, including development assistance for health, was estimated based on a diverse set of data, including program reports, budget data, national estimates, and 964 National Health Accounts. The resulting estimates were used to help produce forecasted health spending estimates for 2015-2040. Results of the study were published in The Lancet in April 2017 in "Evolution and patterns of global health financing 1995–2016: development assistance for health, and government, prepaid private, and out-of-pocket health spending in 184 countries."
    • March 2017
      Source: World Economic Forum
      Uploaded by: Knoema
      Accessed On: 19 April, 2017
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      Data cited at: The World Economic Forum https://www.weforum.org/ Topic: Global Energy Architecture Performance Index Report 2017 Publication URL: https://www.weforum.org/reports/global-energy-architecture-performance-index-report-2017 License: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode   The Energy Architecture Performance Index (EAPI) uses a set of indicators to highlight the performance of various countries across each facet of their energy architecture, determining to what extent nations have been able to create affordable, sustainable and secure energy systems   1)Economic growth and development: The extent to which energy architecture supports, rather than detracts from, economic growth and development 2) Environmental sustainability: The extent to which energy architecture has been constructed to minimize negative environmental externalities 3) Energy access and security: The extent to which energy architecture is at risk of an energy security impact, and whether adequate access to energy is provided to all parts of the population   Note: For detail methodology please visit:"http://www3.weforum.org/docs/WEF_GlobalEnergyArchitecturePerformance_Index_2017.pdf"
    • June 2015
      Source: International Monetary Fund
      Uploaded by: Knoema
      Accessed On: 19 July, 2018
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      The energy subsidy estimates reported here are based on the broad notion of post-tax subsidies, which arise when consumer prices are below supply costs plus a tax to reflect environmental damage and an additional tax applied to all consumption goods to raise government revenues. Pre-tax subsidies, which arise when consumer prices are below supply costs, are also reported as a component of post-tax subsidies. These subsidies will not necessarily coincide with definitions used by governments or with their reported subsidy numbers. The energy subsidy estimates are not available for the following countries in 2013: Bhutan, Central African Republic, Chad, Comoros, Eritrea, Fiji, Gambia, Guinea, Guinea-Bissau, Kiribati, Kosovo, Lao P.D.R., Liberia, Maldives, Marshall Islands, Mauritius, Micronesia, Niger, Palau, Samoa, San Marino, São Tomé and Príncipe, Seychelles, Sierra Leone, Solomon Islands, South Sudan, St. Lucia, St. Vincent and the Grenadines, Swaziland, Timor-Leste, Tonga, Tuvalu, and Vanuatu. In 2015, estimates are not available for two addtional countries: Burundi and Togo.
    • November 2017
      Source: Global Entrepreneurship and Development Institute
      Uploaded by: Knoema
      Accessed On: 16 March, 2018
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      Global Entrepreneurship Index provides information about global entrepreneurship sub Index ranks and scoring of all countries.It also provides information about certain indicators like attitudes,abilities and aspirations with their ranks and scores
    • January 2018
      Source: Global Entrepreneurship Monitor
      Uploaded by: Knoema
      Accessed On: 09 February, 2018
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      The GEM Adult Population Survey (APS) measures the level and nature of entrepreneurial activity around the world. It is administered to a representative national sample of at least 2000 respondents. The Global Entrepreneurship Monitor is the world's foremost study of entrepreneurship. Through a vast, centrally coordinated, internationally executed data collection effort, GEM is able to provide high quality information, comprehensive reports and interesting stories, to enhance the understanding of the entrepreneurial phenomenon.  
    • April 2018
      Source: Global Entrepreneurship Monitor
      Uploaded by: Knoema
      Accessed On: 03 April, 2018
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      The GEM National Expert Survey (NES) monitors the factors that are believed to have a significant impact on entrepreneurship, known as the Entrepreneurial Framework Conditions (EFCs). It is administered to a minimum of 36 carefully chosen 'experts' in each country. The Global Entrepreneurship Monitor is the world's foremost study of entrepreneurship. Through a vast, centrally coordinated, internationally executed data collection effort, GEM is able to provide high quality information, comprehensive reports and interesting stories, to enhance the understanding of the entrepreneurial phenomenon.
    • April 2018
      Source: United Nations Statistics Division
      Uploaded by: Knoema
      Accessed On: 21 November, 2018
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      Environmental Indicators disseminate global environment statistics on ten indicator themes compiled from a wide range of data sources. The themes and indicator tables were selected based on the current demands for international environmental statistics and the availability of internationally comparable data. Indicator tables, charts and maps with relatively good quality and coverage across countries, as well as links to other international sources, are provided under each theme. Statistics on Water and Waste are based on official statistics supplied by national statistical offices and/or ministries of environment (or equivalent institutions) in response to the biennial UNSD/UNEP Questionnaire on Environment Statistics, complemented with comparable statistics from OECD and Eurostat, and water resources data from FAO Aqua stat. Statistics on other themes were compiled by UNSD from other international sources. In a few cases, UNSD has made some calculations in order to derive the indicators. However, generally no adjustments have been made to the values received from the source. UNSD is not responsible for the quality, completeness/availability, and validity of the data. Environment statistics is still in an early stage of development in many countries, and data are often sparse. The indicators selected here are those of relatively good quality and geographic coverage. Information on data quality and comparability is given at the end of each table together with other important metadata.
    • November 2018
      Source: Institute for Health Metrics and Evaluation
      Uploaded by: Knoema
      Accessed On: 23 November, 2018
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      Research by the Global Burden of Disease Health Financing Collaborator Network produced projected health spending estimates for 2016-2040 for 188 countries. The estimates cover total health spending, and health spending disaggregated by source into government spending, out-of-pocket, prepaid private, and development assistance for health. GDP and all-sector government spending were extracted for 1980–2015 and used with retrospective health spending estimates for 1995-2015 to forecast GDP, all-sector government spending, and health spending through 2040. Results of the study were published in The Lancet in April 2018 in "Trends in future health financing and coverage: future health spending and universal health coverage in 188 countries, 2016–2040."
    • February 2019
      Source: World Bank
      Uploaded by: Knoema
      Accessed On: 15 February, 2019
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      Data cited at: The World Bank https://datacatalog.worldbank.org/ Topic: Global Financial Development Publication: https://datacatalog.worldbank.org/dataset/global-financial-development License: http://creativecommons.org/licenses/by/4.0/   The Global Financial Development Database is an extensive dataset of financial system characteristics for 206 economies. The database includes measures of (1) size of financial institutions and markets (financial depth), (2) degree to which individuals can and do use financial services (access), (3) efficiency of financial intermediaries and markets in intermediating resources and facilitating financial transactions (efficiency), and (4) stability of financial institutions and markets (stability).For a complete description of the dataset and a discussion of the underlying literature, see: Martin Cihak; Asli Demirguc-Kunt; Erik Feyen; and Ross Levine, 2012. "Benchmarking Financial Systems Around the World." World Bank Policy Research Working Paper 6175, World Bank, Washington, D.C.
    • February 2019
      Source: World Bank
      Uploaded by: Knoema
      Accessed On: 15 February, 2019
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      Data cited at: The World Bank https://datacatalog.worldbank.org/ Topic: Global Financial Inclusion (Global Findex) Database Publication: https://datacatalog.worldbank.org/dataset/global-financial-inclusion-global-findex-database License: http://creativecommons.org/licenses/by/4.0/   The Global Financial Inclusion Database provides 850+ country-level indicators of financial inclusion summarized for all adults and disaggregated by key demographic characteristics-gender, age, education, income, employment status and rural residence. Covering more than 140 economies, the indicators of financial inclusion measure how people save, borrow, make payments and manage risk. The reference citation for the data is: Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar, and Jake Hess. 2018. The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution. World Bank: Washington, DC.
    • October 2018
      Source: World Bank
      Uploaded by: Knoema
      Accessed On: 14 November, 2018
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      Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems.  The dataset help us to know about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.
    • March 2018
      Source: Food and Agriculture Organization
      Uploaded by: Knoema
      Accessed On: 11 April, 2018
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      Contains global production statistics (capture and aquaculture). This database contains the volume of aquatic species caught by country or area, by species items, by FAO major fishing areas, and year, for all commercial, industrial, recreational and subsistence purposes. The harvest from mariculture, aquaculture and other kinds of fish farming is also included
    • January 2014
      Source: Oxfam
      Uploaded by: Knoema
      Accessed On: 30 May, 2014
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      Around the world, one in eight people go to bed hungry every night, even though there is enough food for everyone. Our graph illustrates how overconsumption, misuse of resources and waste are common elements of a system that leaves hundreds of millions without enough to eat.
    • September 2015
      Source: Food and Agriculture Organization
      Uploaded by: Knoema
      Accessed On: 05 October, 2015
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      DescriptionThe Global Forest Resources Assessment 2015 (FRA 2015) is the most comprehensive assessment of forests and forestry to date - not only in terms of the number of countries and people involved - but also in terms of scope. It examines the current status and recent trends for about 90 variables covering the extent, condition, uses and values of forests and other wooded land, with the aim of assessing all benefits from forest resources. Information has been collated from 233 countries and territories for four points in time: 1990, 2000, 2005 and 2010. The results are presented according to the seven thematic elements of sustainable forest management. FAO worked closely with countries and specialists in the design and implementation of FRA 2010 - through regular contact, expert consultations, training for national correspondents and ten regional and subregional workshops. More than 900 contributors were involved, including 178 officially nominated national correspondents and their teams. The outcome is better data, a transparent reporting process and enhanced national capacity in developing countries for data analysis and reporting. The final report of FRA 2010 was published at the start of the latest biennial meeting of the FAO' Committee on Forestry and World Forest Week, in Rome.
    • December 2018
      Source: World Economic Forum
      Uploaded by: Shakthi Krishnan
      Accessed On: 03 January, 2019
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      Data cited at: The World Economic Forum https://www.weforum.org/ Topic:  The Global Gender Gap Report 2018 Publication URL: https://www.weforum.org/reports/the-global-gender-gap-report-2018 License: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode   Gender parity is fundamental to whether and how economies and societies thrive. Ensuring the full development and appropriate deployment of half of the world’s total talent pool has a vast bearing on the growth, competitiveness and future-readiness of economies and businesses worldwide. The Global Gender Gap Report benchmarks 149 countries on their progress towards gender parity across four thematic dimensions: Economic Participation and Opportunity, Educational Attainment, Health and Survival, and Political Empowerment. In addition, this year’s edition studies skills gender gaps related to Artificial Intelligence (AI)
    • December 2018
      Source: World Economic Forum
      Uploaded by: Knoema
      Accessed On: 28 January, 2019
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       Data cited at: The World Economic Forum https://www.weforum.org/ Topic: The Global Gender Gap Report 2018 Publication URL: https://www.weforum.org/reports/the-global-gender-gap-report-2018 License: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode   This dataset provides education and skills related indicators that present in Global Gender Gap Report
    • November 2018
      Source: Emission Database for Global Atmospheric Research
      Uploaded by: Knoema
      Accessed On: 14 February, 2019
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      Direct greenhouse gases: Carbon Dioxide (CO2), Methane (CH4), Nitrous Oxide (N2O), Hydrofluorocarbons (HFC-23, 32, 125, 134a, 143a, 152a, 227ea, 236fa, 245fa, 365mfc, 43-10-mee), Perfluorocarbons (PFCs: CF4, C2F6, C3F8, c-C4F8, C4F10, C5F12, C6F14, C7F16), Sulfur Hexafluoride (SF6), Nitrogen Trifluoride (NF3) and Sulfuryl Fluoride (SO2F2). Emissions are calculated by individual countries using country-specific information. The countries are organized in different world regions for illustration purposes. Emissions of some small countries are presented together with other countries depending on country definition and availability of activity statistics. Source: European Commission, Joint Research Centre (JRC)/PBL Netherlands Environmental Assessment Agency.
    • October 2017
      Source: Emission Database for Global Atmospheric Research
      Uploaded by: Knoema
      Accessed On: 10 January, 2018
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      Emissions are calculated for the following substances: 1) Direct greenhouse gases: Carbon Dioxide (CO2), Methane (CH4), Nitrous Oxide (N2O), Hydrofluorocarbons (HFC-23, 32, 125, 134a, 143a, 152a, 227ea, 236fa, 245fa, 365mfc, 43-10-mee), Perfluorocarbons (PFCs: CF4, C2F6, C3F8, c-C4F8, C4F10, C5F12, C6F14, C7F16), Sulfur Hexafluoride (SF6), Nitrogen Trifluoride (NF3) and Sulfuryl Fluoride (SO2F2); 2) Ozone precursor gases: Carbon Monoxide (CO), Nitrogen Oxides (NOx), Non-Methane Volatile Organic Compounds (NMVOC) and Methane (CH4). 3) Acidifying gases: Ammonia (NH3), Nitrogen oxides (NOx) and Sulfur Dioxide (SO2). 4) Primary particulates: Fine Particulate Matter (PM10) - Carbonaceous speciation (BC , OC) is under progress. 5) Stratospheric Ozone Depleting Substances: Chlorofluorocarbons (CFC-11, 12, 113, 114, 115), Halons (1211, 1301, 2402), Hydrochlorofluorocarbons (HCFC-22, 124, 141b, 142b), Carbon Tetrachloride (CCl4), Methyl Bromide (CH3Br) and Methyl Chloroform (CH3CCl2). Emissions (EM) for a country C are calculated for each compound x on an annual basis (y) and sector wise (for i sectors, multiplying on the one hand the country-specific activity data (AD), quantifying the human activity for each of the i sectors, with the mix of j technologies (TECH) for each sector i, and with their abatement percentage by one of the k end-of-pipe (EOP) measures for each technology j, and on the other hand the country-specific emission factor (EF) for each sector i and technology j with relative reduction (RED) of the uncontrolled emission by installed abatement measure k. Emissions in are calculated by individual countries using country-specific information. The countries are organized in different world regions for illustration purposes. Emissions of some small countries are presented together with other countries depending on country definition and availability of activity statistics.
    • September 2017
      Source: World Health Organization
      Uploaded by: Knoema
      Accessed On: 23 October, 2017
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      The GHO data provides access to indicators on priority health topics including mortality and burden of diseases, the Millennium Development Goals (child nutrition, child health, maternal and reproductive health, immunization, HIV/AIDS, tuberculosis, malaria, neglected diseases, water and sanitation), non communicable diseases and risk factors, epidemic-prone diseases, health systems, environmental health, violence and injuries, equity among others
    • October 2018
      Source: International Food Policy Research Institute
      Uploaded by: Knoema
      Accessed On: 29 October, 2018
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      Global Hunger Index, 2018   The Global Hunger Index (GHI) is a tool designed to comprehensively measure and track hunger globally, regionally, and by country. Each year, the International Food Policy Research Institute (IFPRI) calculates GHI scores in order to assess progress, or the lack thereof, in decreasing hunger. The GHI is designed to raise awareness and understanding of regional and country differences in the struggle against hunger. Since 2015, GHI scores have been calculated using a revised and improved formula. The revision replaces child underweight, previously the sole indicator of child under-nutrition, with two indicators of child under-nutrition—child wasting and child stunting—which are equally weighted in the GHI calculation. The revised formula also standardizes each of the component indicators to balance their contribution to the overall index and to changes in the GHI scores over time. GHI scores are calculated using a three-step process that draws on available data from various sources to capture the multidimensional nature of hunger: 1. Undernourishment: The share of the population that is undernourished (that is, whose caloric intake is insufficient). 2. Child wasting and stunting: The share of children under the age of five who are wasted (that is, who have low weight for their height, reflecting acute under-nutrition). 3.Child Stunting: The share of children under the age of five who are stunted (that is, who have low height for their age, reflecting chronic under-nutrition). 4. Child Mortality: The mortality rate of children under the age of five (in part, a reflection of the fatal mix of inadequate nutrition and unhealthy environments).   Note: Values for the years are taken as per below table.1Global Hunger Index Scores2Proportion of Undernourished in the Population (%)3Prevalence of Wasting in Children Under Five Years(%)4Prevalence of Stunting in Children Under Five Years (%)5Prevalence of underweight in children under five years (%)   Date for above indicators are taken as per below year ranges. 1   2   3   4   5   Date Range Date Range Date Range Date Range Date Range 2018 2013-2017 2018 2015-2017 2018 2013-2017 2018 2013-2017 2012 2009-2013 2017 2012-2016 2017 2014-2016 2017 2012-2016 2017 2012-2016 2011 2008-2012 2015 2010-2016 2015 2014-2016 2015 2012-2016 2015 2012-2016 2010 2005-2010 2014 2009-2013 2013 2014-2016 2013 2010-2014 2013 2010-2014 2009 2004-2009 2013 2008-2012 2012 2011-2013 2010 2008-2012 2010 2008-2012 2008 2003-2008 2012 2005-2010 2011 2010-2012 2008 2006-2010 2008 2006-2010 2007 2002-2007 2011 2004-2009 2010 2009-2011 2005 2003-2007 2005 2003-2007 2006 2001-2006 2010 2008-2012 2009 2005-2007 2000 1998-2002 2000 1998-2002 2005 2003-2007 2009 2002-2007 2008 2007-2009 1995 1993-1997 1995 1993-1997 2004 2000-2005 2008 2006-2010 2007 2003-2005 1992 1990-1994 1992 1990-1994 2003 1999-2003 2005 2003-2007 2006 2002-2004 1990 1988-1992 1990 1988-1992 2000 1998-2002 2001 1994-1998 2005 2004-2006         1997 1993-1998 2000 1998-2002 2004 2001-2003         1995 1993-1997 1996 1988-1992 2003 2000-2002         1990 1988-1992 1995 1993-1997 2000 1999-2001         1980 1977-1982 1992 1990-1994 1997 1995-1997             1990 1988-1992 1995 1994-1996                 1992 1991-1993                 1990 1990-1992                 1980 1979-1981               6. "Under-five Mortality  Rate(%)" year range has not been specified in source. GHI Severity Scale ≤ 9.9 low 10.0–19.9 moderate 20.0–34.9 serious 35.0–49.9 alarming 50.0 ≤ extremely alarming
    • November 2018
      Source: International Telecommunication Union
      Uploaded by: Knoema
      Accessed On: 17 January, 2019
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    • July 2016
      Source: World Economic Forum
      Uploaded by: Knoema
      Accessed On: 13 January, 2017
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      Data cited at: The World Economic Forum https://www.weforum.org/ Topic: The Global Information Technology Report 2016 Publication URL: https://www.weforum.org/reports/the-global-information-technology-report-2016 License: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode   This Dataset contains proprietary and non-proprietary data used in the computation of the World Economic's Forum Networked Readiness Index. By making this data available, the Forum aims to inform multi-stakeholder dialogue, foster evidence-based, data-driven decisions, allow measuring progress, and support research by academia, journalists and others.
    • July 2018
      Source: Global Innovation Index
      Uploaded by: Knoema
      Accessed On: 02 August, 2018
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      The Global Innovation Index (GII) provides detailed metrics about the innovation performance of 126 countries which represent 90.8% of the world’s population and 96.3% of global GDP. Its 80 indicators explore a broad vision of innovation, including political environment, education, infrastructure and business sophistication.   The GII 2018 marks the 11th edition of the GII, and the beginning of its second decade providing data and insights gathered from tracking innovation across the globe. This year’s edition, is dedicated to the theme of Energizing the World with Innovation. It analyses the energy innovation landscape of the next decade and identifies possible breakthroughs in fields such as energy production, storage, distribution, and consumption. It also looks at how breakthrough innovation occurs at the grassroots level and describes how small-scale renewable systems are on the rise.
    • August 2018
      Source: Internal Displacement Monitoring Centre
      Uploaded by: Knoema
      Accessed On: 29 August, 2018
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      Global Internal Displacement Database (GIDD) aims to provide comprehensive information on internal displacement worldwide. It covers all countries and territories for which IDMC has obtained data on situations of internal displacement, and provides data on situations of internal displacement associated with conflict and generalized violence (2014-2015), displacement associated with sudden-onset natural hazard-related disasters (2008-2015).
    • February 2016
      Source: Material Flows
      Uploaded by: Knoema
      Accessed On: 14 June, 2016
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    • September 2018
      Source: Oxford Poverty & Human Development Initiative
      Uploaded by: Knoema
      Accessed On: 17 January, 2019
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      The global Multidimensional Poverty Index (MPI) is an international measure of acute poverty covering over 100 developing countries. It complements traditional income-based poverty measures by capturing the severe deprivations that each person faces at the same time with respect to education, health and living standards. The MPI assesses poverty at the individual level. If someone is deprived in a third or more of ten (weighted) indicators (see left), the global index identifies them as ‘MPI poor’, and the extent – or intensity – of their poverty is measured by the number of deprivations they are experiencing. The MPI can be used to create a comprehensive picture of people living in poverty, and permits comparisons both across countries, regions and the world and within countries by ethnic group, urban/rural location, as well as other key household and community characteristics. This makes it invaluable as an analytical tool to identify the most vulnerable people – the poorest among the poor, revealing poverty patterns within countries and over time, enabling policy makers to target resources and design policies more effectively. The global MPI was developed by OPHI with the UN Development Programme (UNDP) for inclusion in UNDP’s flagship Human Development Report in 2010. It has been published in the HDR ever since.    
    • June 2018
      Source: Open Knowledge International
      Uploaded by: Knoema
      Accessed On: 13 June, 2018
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    • January 2019
      Source: Milken Institute
      Uploaded by: Knoema
      Accessed On: 11 February, 2019
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      he Global Opportunity Index answers a pressing need for information that's vital to a thriving global economy like what policies can governments pursue to attract foreign direct investment (FDI), expand their economies, and accelerate job creation, what do multinational companies, other investors, and development agencies need to know before making large-scale, long-term capital commitments.   Methodology:  The GOI considers economic and financial factors that influence investment activities as well as key business, legal and regulatory policies that governments can modify to support and often drive investments. Overall, the GOI tracks countries’ performance on 51 variables aggregated in five categories, each measuring an aspect of the country’s attractiveness for investors.   The assigned composite index value is the average score of the five categories (called component scores). Each variable is normalized from 0 to 10. Within each category, the normalized variables are given equal weight and aggregated, resulting in a normalized category score between 0, indicating the least favorable conditions for investment, and 10, signaling the most favorable. The index covers 133 countries. The index methodology is reviewed for each publication to reflect changes in data sources or other relevant adjustments.
    • December 2016
      Source: World Bank
      Uploaded by: Knoema
      Accessed On: 09 October, 2018
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      GPSS data (Accounts & Access, retail payment transactions and RTGS transactions – volumes and values). The World Bank’s Global Payment Systems Survey (GPSS) surveys national and regional central banks and monetary authorities on the status of payment systems. The GPSS is the only global survey that combines quantitative and qualitative measures of payment system development and covers all aspects of national payment systems – from infrastructure and the legal and regulatory environment to technological and business model innovations, international remittances, and oversight framework. The GPSS aims to take an accurate snapshot of payment systems worldwide to obtain information on payment system reforms and the factors which hinder and/or facilitate them in order to help guide policy-dialogue at the international and national levels, and World Bank Group technical assistance. In April 2007, the World Bank launched the first Global Payment Systems Survey among national central banks to collect information on the situation of national payment and securities settlement systems worldwide and provide a payment systems snapshot of both advanced and emerging economies in order to identify main issues that should guide the agenda of authorities, multilateral and market players in the field over the next few years.
    • June 2018
      Source: Institute for Economics and Peace
      Uploaded by: Knoema
      Accessed On: 10 July, 2018
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      Data cited at: Institute for Economics and Peace The Global Peace Index 2018 report finds that the global level of peace has deteriorated by 0.27% over the last year. This is the fourth successive year of deterioration, finding that 92 countries have deteriorated, while 71 countries have improved. The report reveals a world in which tensions, conflicts and crises that have emerged over the past decade remain unresolved, causing a gradual, sustained decline in global levels of peacefulness.
    • February 2019
      Source: GlobalPetrolPrices.com
      Uploaded by: Knoema
      Accessed On: 12 February, 2019
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      Data cited at: Global Petrol Prices web site - https://www.globalpetrolprices.com/ License: https://creativecommons.org/licenses/by-nc/4.0/ Data is getting collected Every Tuesday evening from the Global Petrol Prices website. Weekly Average data is available from 28-Dec-2015 onward. Monthly average price is available for the period of January, 2013 - July, 2013    
    • May 2014
      Source: Institute for Health Metrics and Evaluation
      Uploaded by: Kirill Kosenkov
      Accessed On: 27 August, 2015
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      Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013. Comparable estimates based on systematically identified surveys, reports, and published studies (n=1769) that included data for height and weight, both through physical measurements and self-reports, using mixed effects linear regression to correct for bias in self-reports. Data for prevalence of obesity and overweight by age, sex, country, and year (n=19 244) obtained with a spatiotemporal Gaussian process regression model to estimate prevalence with 95% uncertainty intervals (UIs). Research by the staff of the Institute for Health Metrics and Evalutaion with co-authors. Published online 28 May 2014, "The Lancet" Volume 384, No. 9945, p766–781. DOI: http://dx.doi.org/10.1016/S0140-6736(14)60460-8
    • July 2018
      Source: Jones Lang LaSalle
      Uploaded by: Knoema
      Accessed On: 05 September, 2018
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      The 2018 Global Real Estate Transparency Index covers 100 markets and is based on 186 indicators. These variables are divided into six areas –performance measurement, market fundamentals, governance of listed vehicles, regulatory & legal frameworks, transaction process and environmental sustainability.   Tier 1: Highly Transparent - Total Composite Score: 1.00–1.96 Tier 2: Transparent - Total Composite Score: 1.97–2.65 Tier 3: Semi-Transparent - Total Composite Score: 2.66–3.50 Tier 4: Low Transparency - Total Composite Score: 3.51–4.16 Tier 5: Opaque - Total Composite Score: 4.17–5.00
    • March 2018
      Source: A. T. Kearney
      Uploaded by: Knoema
      Accessed On: 06 April, 2018
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      The Global Retail Development Index™ is an annual study that ranks the top 30 developing countries for retail expansion worldwide. The Index analyzes 25 macroeconomic and retail-specific variables to help retailers devise successful global strategies and to identify developing market investment opportunities. The GRDI is unique because it identifies today's most successful markets and those that offer the most potential for the future.
    • December 2018
      Source: World Health Organization
      Uploaded by: Knoema
      Accessed On: 22 January, 2019
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      Note: The dataset has been collected from "Global status report on road safety 2018".  For this report, 2018 data were used for the review of vehicle standards; 2017 data were used for the review of legislation, road standards and post-crash care; fatality estimates were based on data from 2016. The Global status report on road safety 2018, launched by WHO in December 2018, highlights that the number of annual road traffic deaths has reached 1.35 million. Road traffic injuries are now the leading killer of people aged 5-29 years. The burden is disproportionately borne by pedestrians, cyclists and motorcyclists, in particular those living in developing countries. The report suggests that the price paid for mobility is too high, especially because proven measures exist. These include strategies to address speed and drinking and driving, among other behaviors; safer infrastructure like dedicated lanes for cyclists and motorcyclists; improved vehicle standards such as those that mandate electronic stability control; and enhanced post-crash care. Drastic action is needed to put these measures in place to meet any future global target that might be set and save lives.
    • December 2014
      Source: World Health Organization
      Uploaded by: Knoema
      Accessed On: 06 June, 2018
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      The Global status report on violence prevention 2014, which reflects data from 133 countries, is the first report of its kind to assess national efforts to address interpersonal violence, namely child maltreatment, youth violence, intimate partner and sexual violence, and elder abuse. Jointly published by WHO, the United Nations Development Programme, and the United Nations Office on Drugs and Crime, the report reviews the current status of violence prevention efforts in countries, and calls for a scaling up of violence prevention programmes; stronger legislation and enforcement of laws relevant for violence prevention; and enhanced services for victims of violence.
    • February 2019
      Source: countryeconomy.com
      Uploaded by: Knoema
      Accessed On: 13 February, 2019
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      Global Stock Market Indexes, Daily Update
    • January 2014
      Source: United Nations Office on Drugs and Crime
      Uploaded by: Knoema
      Accessed On: 05 April, 2018
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      The "Global Study on Homicide 2013" throw lights on the worst of crimes - the "unlawful death purposefully inflicted on a person by another person." In 2012, intentional homicide took the lives of almost half a million people. The study of intentional homicide is relevant not only because it is the study of the ultimate crime, whose ripple effect goes far beyond the initial loss of human life, but because lethal violence can create a climate of fear and uncertainty. Intentional homicide also victimizes the family and community of the victim, who can be considered secondary victims, and when justice is not served, impunity can lead to further victimization in the form of the denial of the basic human right to justice. Percentage of homicides by firearm, number of homicides by firearm and homicide by firearm rate per 100,000 population. Intentional homicide is defined as unlawful death purposefully inflicted on a person by another person.
    • April 2014
      Source: United Nations Office on Drugs and Crime
      Uploaded by: Knoema
      Accessed On: 20 May, 2016
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      Intentional homicide is defined as unlawful death purposefully inflicted on a person by another person
    • January 2018
      Source: INSEAD
      Uploaded by: Knoema
      Accessed On: 17 April, 2018
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      This data presents high-level way of mapping individual countries in terms of talent competitiveness consists of comparing their GTCI scores to their GDP per capita for the selected indicators.In its first year, the GTCI model covers 103 countries,representing 86.3% of the world’s population and 96.7% of the world’s GDP (in current US dollars).It is a simplified manner of acquiring a first assessment about the ways in which competitiveness relates to overall level of economic development of a nation.
    • June 2018
      Source: KPMG
      Uploaded by: Knoema
      Accessed On: 03 July, 2018
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      Covers data on corporate, indirect and individual income tax rates throughout 163 countries across the world during the period from 2006 to 2018. Provided by KPMG.
    • November 2017
      Source: Institute for Economics and Peace
      Uploaded by: Knoema
      Accessed On: 11 December, 2017
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      Data cited at: Institute for Economics and Peace   The Global Terrorism Index (GTI) is a comprehensive study which accounts for the direct and indirect impact of terrorism in 163 countries in terms of its effect on lives lost, injuries, property damage and the psychological aftereffects of terrorism. This study covers 99.6 per cent of the world’s population. It aggregates the most authoritative data source on terrorism today, the Global Terrorism Database (GTD) collated by the National Consortium for the Study of Terrorism and Responses to Terrorism (START) into a composite score in order to provide an ordinal ranking of nations on the negative impact of terrorism. The GTD is unique in that it consists of systematically and comprehensively coded data on domestic as well as international terrorist incidents and now includes more than 140,000 cases. Note: "Change in score values" have been calculated for 2015 by score in 2015 minus score in 2014 (Score_2015-Score_2014). For rest of the years according to source.
    • May 2018
      Source: World Health Organization
      Uploaded by: Knoema
      Accessed On: 12 December, 2018
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      Global Trends in Prevalence of Tobacco Smoking 2000-2025