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.
World Electricity access database
Hundreds of millions of people have gained access to modern energy over the last two decades, especially in China and India. Rapid economic development in several developing countries, increasing urbanisation and ongoing energy access programmes have been important factors in this achievement. The IEA Access to Energy database provides a snapshot of progress made toward meeting the ultimate goal of universal access.
Note: For indicator population without access, value 1 represent <1 except Botswana, Guatemala countries
The primary World Bank collection of development indicators, compiled from officially-recognized international sources. It presents the most current and accurate global development data available, and includes national, regional and global estimates
Data cited at: https://infrastructureafrica.opendataforafrica.org/pbuerhd
The Africa Infrastructure Development Index (AIDI) is produced by the African Development Bank. The AIDI serves a number of key objectives, principally: (i) to monitor and evaluate the status and progress of infrastructure development across the continent; (ii) to assist in resource allocation within the framework of ADF replenishments; and (iii) to contribute to policy dialogue within the Bank and between the Bank, RMCs and other development organizations.
The World Economic Outlook (WEO) database contains selected macroeconomic data series from the statistical appendix of the World Economic Outlook report, which presents the IMF staff's analysis and projections of economic developments at the global level, in major country groups and in many individual countries. The WEO is released in April and September/October each year.
Use this database to find data on national accounts, inflation, unemployment rates, balance of payments, fiscal indicators, trade for countries and country groups (aggregates), and commodity prices whose data are reported by the IMF.
Data are available from 1980 to the present, and projections are given for the next two years. Additionally, medium-term projections are available for selected indicators. For some countries, data are incomplete or unavailable for certain years.
Changes to the April 2019 Database:
FYR Macedonia is now called North Macedonia.
In February 2019, Zimbabwe adopted a new local currency unit, the RTGS dollar, which has become the official unit of account. Efforts are underway to revise and update all national accounts series to the new RTGS dollar. Current data are based on IMF staff estimates of price and exchange rate developments in US (and RTGS) dollars. Staff estimates of US dollar values may differ from authorities’ estimates.
Economic growth accelerated in more than half the world’s economies in both 2017 and 2018. Developed economies expanded at a steady pace of 2.2 per cent in both years, and growth rates in many countries have risen close to their potential, while unemployment rates in several developed economies have dropped to historical lows. Among the developing economies, the regions of East and South Asia remain on relatively strong growth trajectory, expanding by 5.8 per cent and 5.6 per cent, respectively in 2018. Many commodityexporting countries, notably fuel exporters, are continuing a gradual recovery, although they remain exposed to volatile prices. The impact of the sharp drop in commodity markets in 2014/15 also continues to weigh on fiscal and external balances and has left a legacy of higher levels of debt.
Global economic growth remained steady at 3.1 per cent in 2018, as a fiscally induced acceleration in the United States of America offset slower growth in some other large economies. Economic activity at the global level is expected to expand at a solid pace of 3 per cent in 2019, but there are increasing signs that growth may have peaked. The growth in global industrial production and merchandise trade volumes has been tapering since the beginning of 2018, especially in trade-intensive capital and intermediate goods sectors. Leading indicators point to some softening in economic momentum in many countries in 2019, amid escalating trade disputes, risks of financial stress and volatility, and an undercurrent of geopolitical tensions. At the same time, several developed economies are facing capacity constraints, which may weigh on growth in the short term.
Data cited at: Mo Ibrahim Foundation - http://mo.ibrahim.foundation/iiag/downloads/
Ibrahim Index of African Governance (IIAG) - is comprehensive statistical tool assessing African countries' performance in provision of public goods and services. Consisting of 133 variables derived from 32 independent sources IIAG measures governance performance across 4 pillars: Safety and Rule of Law, Participation and Human Rights, Sustainable Economic Opportunity and Human Development. All-embracing nature of the index makes it fairly the best instrument for setting long-term political, social and economical goals concerning the African region.
Data cited at: The World Bank https://datacatalog.worldbank.org/
Topic: Africa's Infrastructure: Airports
Publication: https://datacatalog.worldbank.org/dataset/africas-infrastructure-airports
License: http://creativecommons.org/licenses/by/4.0/
The Africa Infrastructure Country Diagnostic (AICD) has data collection and analysis on the status of the main network infrastructures. The AICD database provides cross-country data on network infrastructure for nine major sectors: air transport, information and communication technologies, irrigation, ports, power, railways, roads, water and sanitation.
The indicators are defined as to cover key areas for policy making: affordability, access, pricing as well as institutional, fiscal and financial aspects. The analysis encompasses public expenditure trends, future investment needs and sector performance reviews. It offers users the opportunity to view AICD results, download documents and materials, search databases and perform customized analysis.
Africa's Power Infrastructure: Investment, Integration, Efficiency by Anton Eberhard, Orvika Rosnes, Maria Shkaratan, Haakon Vennemo and Published by the World Bank.
Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings.
The series is part of the ILO estimates and is harmonized to account for differences in national data and scope of coverage, collection and tabulation methodologies as well as for other country-specific factors. For more information, refer to the ILO estimates and projections methodological note.
The Agri-environmental Indicators—Land domain provides information on the annual evolution of the distribution of agricultural and forest areas, and their sub-components, including irrigated areas, at national, regional and global levels.
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.
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.
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.
Data source(s) used: Crimes reported to the Judicial authorities by the State Police, Carabinieri and Guardia di Finanza: Are processed the data on felonies and people who were reported by police to the court
Other data characteristics: Data referring to social demographic characteristics of alleged offenders could not coincide with data on reports because of the different timing of extraction from police forces database.The sum of the crimes by province could not coincide with the total of the region, and that of the regions with the total Italy, because of the missed precise statement, for some crimes, of the place where they have been committed (or of the region of the committed crime but not of the province).
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.
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.
Suggested citation: Luo, T., R. Young, and P. Reig. 2015. "Aqueduct projected water stress rankings." Technical note. Washington, DC: World Resources Institute, August 215. Available online at http://www.wri.org/publication/aqueduct-projected-water-stress-country-rankings.
Supplemental Materials: Country Scores
WRI projected future country-level water stress for 2020, 2030, and 2040 under business-as-usual (BAU), optimistic, and pessimistic scenarios. Each tab lists country projected water stress scores for each scenario and year, weighted by overall water withdrawals. Scores weighted by individual sectors (agricultural, domestic, and industrial) are provided as well.
These global projections are best suited to making comparisons among countries for the same year and among scenarios and decades for the same region. More detailed and localized data or scenarios can better estimate potential outcomes for specific regions and expose large sub-national variations that are subsumed under countrywide water-stress values. The country indicators face persistent limitations in attempting to simplify complex information, such as spatial and temporal variations, into a single number. They also do not account for the governance and investment structure of the water sector in different countries.
It is important to note the inherent uncertainty in estimating any future conditions, particularly those associated with climate change, future population and economic trends, and water demand. Additionally, care should be taken when examining the change rates of a country’s projected stress levels between one year and another, because the risk-score thresholds are not linear. For more information on these limitations, see the technical note.
Projections are described in further detail in:
Luck, M., M. Landis, and F. Gassert, “Aqueduct Water Stress Projections: Decadal Projections of Water Supply and Demand Using CMIP5 GCMs,” Technical note (Washington, DC: World Resources Institute, April 2015), http://www.wri.org/publication/aqueduct-water-stress-projections.
Water Stress
withdrawals / available flow
Water stress measures total annual water withdrawals (municipal, industrial, and agricultural) expressed as a percentage of the total annual available blue water. Higher values indicate more competition among users.
Score Value
[0-1) Low (<10%)
[1-2) Low to medium (10-20%)
[2-3) Medium to high (20-40%)
[3-4) High (40-80%)
[4-5] Extremely high (>80%)
Data cited at: Arab Monetary Fund (AMF)
Note:For 2017 values are Estimated and 2018 & 2019 values are Forecast.
Source: Arab Monetary Fund, Arab Economic Report Database and national, regional and international sources.
Data cited at: The World Bank https://datacatalog.worldbank.org/
Topic: Arab World Education Performance Indicators
Publication: https://datacatalog.worldbank.org/dataset/arab-world-education-performance-indicators
License: http://creativecommons.org/licenses/by/4.0/
The Arab World Education Performance Indicators compiles data on education outcomes in 22 Arab States member countries in an aggregated and standardized manner. It allows users to compare the performance of each country along the following 6 important dimensions of education performance: access, equity, quality, efficiency, relevance, and Knowledge Economy readiness.
Data cited at: Stockholm International Peace Research Institute (SIPRI)
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/
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.
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.
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
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).
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.
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.
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
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."
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.
Global and regional deaths of children under 5 years of age by cause. Estimates generated by the WHO and Maternal and Child Epidemiology Estimation Group (MCEE) 2018.
Data cited at: Statistics Finland http://www.stat.fi/index_en.html
Publication: 030 -- Citizenship by sex, by region and municipality in 1990 to 2017
http://pxnet2.stat.fi/PXWeb/pxweb/en/StatFin/StatFin__vrm__vaerak/statfin_vaerak_pxt_030.px
License: http://creativecommons.org/licenses/by/4.0/
Concepts and definitions
Description
Quality description
These statistics apply the regional division of 1 January 2018 to the whole time series.
Population statistics from 1750 to 2000 have been digitised into PDF format in the National Library's Doria service.
Publications on Population structure and vital statistics in Doria (in Finnish)
Publications on Population censuses in Doria (in Finnish)
Area
For reasons of privacy protection, cells with less than 10 cases of citizenship, country of birth,
background country or language by municipality have been marked with two dots. Continent sums have not
been hidden in municipality data nor have regional data concerning individual languages or countries.
Citizenship
If a person has two nationalities and one of them is Finnish, he/she will be
included in statistics as a Finnish national.
The used classification of continents is the classification of
Eurostat, where Cyprus and Turkey belong to Europe.
Citizens of non-autonomous states are summed under the mother country.
Citizenship
Czech Republic
Czech Republic + Former Czechoslovakia
Sudan
Sudan + Former Sudan
The Population Register contains information on all persons who have resided in Greenland after 1 January 1977.
The purpose of the register is to be the basis for population statistics, and to supplement other personal information with basic information about each person, like address and family relations.The Population register is updated with information from CPR (Administrative Population Register) where the following information is retrieved:
name, gender, age, place of birth, citizenship, marital status, reference to mother, father and spouse, address of residence and more.
According to §13 of the Act on Greenland Statistics, no person-related information is disclosed from the register, except for personal numbers, randomly drawn for surveys
The consolidated banking statistics (CBS) measure international banking activity from a nationality perspective, focusing on the country where the banking group's parent is headquartered.
While residence-based data such as the locational banking statistics indicate where positions are booked, they do not always identify where underlying decisions are made. This is because banking offices in one country may operate within a business model decided by the group's controlling parent, which may be headquartered in another country. The CBS capture the worldwide claims of banking groups based in reporting countries and exclude intragroup positions, similar to the consolidation approach followed by banking supervisors. The CBS provide several different measures of banking groups' country risk exposures, on either an immediate counterparty or an ultimate risk basis. The most appropriate exposure measure depends on the issue being analysed. The benchmark measure in the CBS is foreign claims, which capture credit to borrowers outside a banking group's home country.
Measure for all Combinations - Amounts Outstanding / Stocks
Note: Under "Reporting country" they have removed "Euro Area".
Data cited at : https://www.bis.org/statistics/index.htm
Below Parameters are common for all combinations :
Frequency - Quarterly
Measure -Amounts Outstanding / Stocks
CBS Bank Type - Domestic Banks
CBS Reporting Basis - Immediate Counterparty Basis
Balance Sheet Position - Total Claims
Type of Instruments - All Instruments
Remaining Maturity - All Maturities
Currency Type of Booking Location - All Currencies
Counterparty Sector - All Sectors
Data cited at : https://www.bis.org/statistics/index.htm
Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings.
The series is part of the ILO estimates and is harmonized to account for differences in national data and scope of coverage, collection and tabulation methodologies as well as for other country-specific factors. For more information, refer to the ILO estimates and projections methodological note.
The CDIS database presents detailed data on "inward" direct investment positions (i.e., direct investment into the reporting economy) cross-classified by economy of immediate investor, and data on "outward" direct investment positions (i.e., direct investment abroad by the reporting economy) cross-classified by economy of immediate investment. The CDIS database contains breakdowns of direct investment position data, including, in most instances, separate data on net equity and net debt positions, as well as tables that present "mirror" data (i.e., tables in which data from the reporting economy are shown side-by-side with the data obtained from all other counterpart reporting economies).
Data cited at 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.
Chinese Taipei is WTO terminology for the territory referred to by the United Nations as Taiwan Province of China. Please also refer to the official announcement by WTO at www.wto.org/english/news_e/pres01_e/pr253_e.htm As of 2017, the data includes an additional six entities (CTBTO, ICC, UNCDF, UNFCCC, UNRISD, UNSSC)
Citation:
Damodaran, Aswath, Equity Risk Premiums (ERP): Determinants, Estimation and Implications – The 2016 Edition (March 5, 2016). Available at SSRN: https://ssrn.com/abstract=2742186 or http://dx.doi.org/10.2139/ssrn.2742186
This dataset summarizes the latest bond ratings and appropriate default spreads for different countries. While you can use these numbers as rough estimates of country risk premiums, you may want to modify the premia to reflect the additional risk of equity markets. To estimate the long term country equity risk premium, I start with a default spread, which I obtain in one of two ways:
(1) I use the local currency sovereign rating (from Moody's: www.moodys.com) and estimate the default spread for that rating (based upon traded country bonds) over a default free government bond rate. For countries without a Moody's rating but with an S&P rating, I use the Moody's equivalent of the S&P rating. To get the default spreads by sovereign rating, I use the CDS spreads and compute the average CDS spread by rating. Using that number as a basis, I extrapolate for those ratings for which I have no CDS spreads.
(2) I start with the CDS spread for the country, if one is available and subtract out the US CDS spread, since my mature market premium is derived from the US market. That difference becomes the country spread. For the few countries that have CDS spreads that are lower than the US, I will get a negative number.
You can add just this default spread to the mature market premium to arrive at the total equity risk premium. I add an additional step. In the short term especially, the equity country risk premium is likely to be greater than the country's default spread. You can estimate an adjusted country risk premium by multiplying the default spread by the relative equity market volatility for that market (Std dev in country equity market/Std dev in country bond). I have used the emerging market average of 1.12 (estimated by comparing a emerging market equity index to an emerging market government/public bond index) to estimate country risk premium.I have added this to my estimated risk premium of 5.08% for mature markets (obtained by looking at the implied premium for the S&P 500) to get the total risk premium.
Notes: The year of publication has been considered as per publication date. For example, data published on 2018-Jan considered as 2018, similarly 2019-Jan as 2019
Data cited at: Statistics Finland http://www.stat.fi/index_en.html
Publication: 012 -- Country of birth according to age and sex by region in 1990 to 2017
http://pxnet2.stat.fi/PXWeb/pxweb/en/StatFin/StatFin__vrm__vaerak/statfin_vaerak_pxt_012.px
License: http://creativecommons.org/licenses/by/4.0/
Concepts and definitions
Description
Quality description
These statistics apply the regional division of 1 January 2018 to the whole time series.
Population statistics from 1750 to 2000 have been digitised into PDF format in the National Library's Doria service.
Publications on Population structure in Doria (in Finnish)
Publications on Vital statistics in Doria (in Finnish)
Publications on Population censuses in Doria (in Finnish)
Country of birth
The used classification of continents is the classification of
Eurostat, where Cyprus and Turkey belong to Europe.
Non-autonomous states are summed under their mother country.
Country of birth
Sudan
Sudan + Former Sudan
The World Bank's Country Policy and Institutional Assessment is done annually for all its borrowing countries. It has evolved into a set of criteria, which are grouped in four clusters: (a) economic management; (b) structural policies; (c) policies for social inclusion and equity; and (d) public sector management and institutions. The number of criteria, currently sixteen, reflect a balance between ensuring that all key factors that foster pro-poor growth and poverty alleviation are captured, without overly burdening the evaluation process. Ratings for each of the criteria reflect a variety of indicators, observations, and judgments. They focus on the quality of each country's current policies and institutions - which are the main determinant of present aid effectiveness prospects. To fully underscore the importance of the CPIA in the IDA Performance Based Allocations, the overall country score is referred to as the IDA Resource Allocation Index (IRAI)
Note: CPA data for 2018 and 2019 are projections from the 2016 Survey on Forward Spending Plans.
Country Programmable Aid (CPA), outlined in our Development Brief and also known as “core” aid, is the portion of aid donors programme for individual countries, and over which partner countries could have a significant say. CPA is much closer than ODA to capturing the flows of aid that goes to the partner country, and has been proven in several studies to be a good proxy of aid recorded at country level. CPA was developed in 2007 in close collaboration with DAC members. It is derived on the basis of DAC statistics and was retroactively calculated from 2000 onwards
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.
Data cited at: Numbeo
Methodology: The Index has been calculated twice per year by considering latest 36 months.
A). Beginning of the Year and
B). Mid Year
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.
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.
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.
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.
This 10th edition of the Institute for Health Metrics and Evaluation’s annual Financing Global Health report provides the most up-to-date estimates of development assistance for health, domestic spending on health, health spending on two key infectious diseases – malaria and HIV/AIDS – and future scenarios of health spending. Several transitions in global health financing inform this report: the influence of economic development on the composition of health spending; the emergence of other sources of development assistance funds and initiatives; and the increased availability of disease-specific funding data for the global health community. For funders and policymakers with sights on achieving 2030 global health goals, these estimates are of critical importance. They can be used for identifying funding gaps, evaluating the allocation of scarce resources, and comparing funding across time and countries.
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.
Data cited at: https://egypt.opendataforafrica.org/wjsacpb
Distribution Number of employees and private sector investment in Egypt, according to nationality and type of hiring foreigners
Note: All data contained within is provisional. The annual number of cases of measles and rubella officially reported by a member state is only available by July of each following year (through the joint WHO UNICEF annual data collection exercise).
“provisional data based on monthly data reported to WHO (Geneva) as of April 2019”.
Measles cases are defined as laboratory confirmed, epidemiologically linked, and clinical cases as reported to the World Health Organization. Some countries report cases at irregular intervals, providing multiple months of data in a one month period. Future months are reported as 0 and will be updated as data is available. When data is used in public settings, please acknowledge the data source is the World Health Organization.
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.
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.
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.
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).
Greenhouse Gas (GHG) emissions from burning crop residues consist of methane (CH4) and nitrous oxide (N2O) gases produced by the combustion of a percentage of crop residues burnt on-site. The mass of fuel available for burning should be estimated taking into account the fractions removed before burning due to animal consumption, decay in the field, and use in other sectors (e.g., biofuel, domestic livestock feed, building materials, etc.). FAOSTAT emission estimates are computed at Tier 1 following the IPCC 2006 Guidelines for National GHG Inventories (http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol4.html). GHG emissions are provided by country, reguions and special groups, with global coverage, relative to the period 1961-present (with annual updates) and with projections for 2030 and 2050, expressed both as Gg CH4, Gg N2O, Gg CO2eq and CO2eq from CH4 and N2O, by crop (maize, rice, sugarcane and wheat) and by aggregates. Implied emission factors for N2O and CH4 as well activity data (biomass burned) are also provided.
Greenhouse gas (GHG) emissions from crop residues consist of direct and indirect nitrous oxide (N2O) emissions from nitrogen (N) in crop residues and forage/pasture renewal left on agricultural fields by farmers. Specifically, N2O is produced by microbial processes of nitrification and de-nitrification taking place on the deposition site (direct emissions), and after volatilization/re-deposition and leaching processes (indirect emissions). The FAOSTAT emissions database is computed following Tier 1 IPCC 2006 Guidelines for National GHG Inventories, Vol. 4, Ch. 2 and 11(http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol4.html). GHG emissions are provided as direct, indirect and total by country, regions and special groups, with global coverage, relative to the period 1961-present (with annual updates) and with projections for 2030 and 2050, expressed as Gg N2O and Gg CO2eq, by crop and N content in residues.
Greenhouse gas (GHG) emissions from enteric fermentation consist of methane gas produced in digestive systems of ruminants and to a lesser extent of non-ruminants. The FAOSTAT emissions database is computed following Tier 1 IPCC 2006 Guidelines for National GHG Inventories vol. 4, ch. 10 and 11 (http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol4.html). GHG emissions are provided by country, regions and special groups, with global coverage, relative to the period 1961-present (with annual updates) and with projections for 2030 and 2050, expressed both as Gg CH4 and Gg CO2eq, by livestock species (asses, buffaloes, camels, cattle (dairy and non-dairy), goats, horses, llamas, mules, sheep, swine (breeding and market)) and by species aggregates (all animals, camels and llamas, cattle, mules and asses, sheep and goats, swine). Implied emission factor for CH4 and activity data are also provided
GHG emissions from manure applied to soils consist of direct and indirect nitrous oxide (N2O) emissions from manure nitrogen (N) added to agricultural soils by farmers. Specifically, N2O is produced by microbial processes of nitrification and de-nitrification taking place on the application site (direct emissions), and after volatilization/re-deposition and leaching processes (indirect emissions). The FAOSTAT emissions database is computed following Tier 1 IPCC 2006 Guidelines for National GHG Inventories vol. 4, ch. 10 and 11 (http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol4.html). GHG emissions are provided as direct, indirect and total by country, regions and special groups, with global coverage, relative to the period 1961-present (with annual updates) and with projections for 2030 and 2050, expressed as Gg N2O and Gg CO2eq, by livestock species (asses, buffaloes, camels, cattle (dairy and non-dairy), chickens (broilers and layers), ducks, goats, horses, llamas, mules, sheep, swine (breeding and market) and turkeys) and by species aggregates (all animals, camels and llamas, cattle, chickens, mules and asses, poultry birds, sheep and goats, swine). Implied emission factor for N2O and activity data (N content in manure) are also provided.
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.
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.
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.
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.
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.
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.
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).
Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings.
The series is part of the ILO estimates and is harmonized to account for differences in national data and scope of coverage, collection and tabulation methodologies as well as for other country-specific factors. For more information, refer to the ILO estimates and projections methodological note.
Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings.
The series is part of the ILO estimates and is harmonized to account for differences in national data and scope of coverage, collection and tabulation methodologies as well as for other country-specific factors. For more information, refer to the ILO estimates and projections methodological note.
National accounts are a coherent set of macroeconomic indicators, which provide an overall picture of the economic situation and are widely used for economic analysis and forecasting, policy design and policy making. The data presented in this collection are the results of a pilot exercise on the sharing selected main GDP aggregates, population and employment data collected by different international organisations. It wasconducted by the Task Force in International Data Collection (TFIDC) which was established by the Inter-Agency Group on Economic and Financial Statistics (IAG). The goal of this pilot is to develop a set of commonly shared principles and working arrangements for data cooperation that could be implemented by the international agencies. The data sets are an experimental exercise to present national accounts data form various countries across the globe in one coherent folder, but users should be aware that these data are collected and validated by different organisations and not fully harmonised from a methodological point of view. The domain consists of the following collections:
The 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.
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.
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.
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.
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.
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.
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.
Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings.
The series is part of the ILO estimates and is harmonized to account for differences in national data and scope of coverage, collection and tabulation methodologies as well as for other country-specific factors. For more information, refer to the ILO estimates and projections methodological note.
The 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.
Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings.
The series is part of the ILO estimates and is harmonized to account for differences in national data and scope of coverage, collection and tabulation methodologies as well as for other country-specific factors. For more information, refer to the ILO estimates and projections methodological note.
Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings.
The series is part of the ILO estimates and is harmonized to account for differences in national data and scope of coverage, collection and tabulation methodologies as well as for other country-specific factors. For more information, refer to the ILO estimates and projections methodological note.
Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings.
The series is part of the ILO estimates and is harmonized to account for differences in national data and scope of coverage, collection and tabulation methodologies as well as for other country-specific factors. For more information, refer to the ILO estimates and projections methodological note.
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
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
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
Data cited at: Wendling, Z. A., Emerson, J. W., Esty, D. C., Levy, M. A., de Sherbinin, A., et al. (2018). 2018 Environmental Performance Index. New Haven, CT: Yale Center for Environmental Law & Policy. https://epi.yale.edu/
The Environmental Performance Index (EPI) is constructed through the calculation and aggregation of 20 indicators reflecting national-level environmental data. These indicators are combined into nine issue categories, each of which fit under one of two overarching objectives. The two objectives that provide the overarching structure of the EPI are Environmental Health and Ecosystem Vitality. Environmental Health measures the protection of human health from environmental harm. Ecosystem Vitality measures ecosystem protection and resource management. These two objectives are further divided into nine issue categories that span high-priority environmental policy issues, including air quality, forests, fisheries, and climate and energy, among others. The issue categories are extensive but not comprehensive. Underlying the nine issue categories are 20 indicators calculated from country-level data and statistics. After more than 15 years of work on environmental performance measurement and six iterations of the EPI, global data are still lacking on a number of key environmental issues. These include: freshwater quality, toxic chemical exposures, municipal solid waste management, nuclear safety, wetlands loss, agricultural soil quality and degradation, recycling rates, adaptation, vulnerability, and resiliency to climate change, desertification.
The estimated resident population (ERP) is the official measure of the Australian population. This dataset contains annual ERP by country of birth, age and sex at the Australia level. At the state/territory level it is available for Census years only.
Population_Estimates:_Concepts,_Sources_and_Methods_2009
The estimated resident population (ERP) is the official measure of the Australian population. This dataset contains annual ERP by country of birth, age and sex at the Australia level. At the state/territory level it is available for Census years only.
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).
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.
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).
If a search in the StatBank does not return any result, this does not necessarily mean that there is no trade or that the country code is not valid in the particular period. A zero (0) could also imply that the figures are confidential or that the value is less than half of the unit used.
From 2006 on the following countries had their belonging to continent changed - Cyprus from Asia to Europe, and Armenia, Georgia, Kyrgyzstan, Kazakhstan, Tajikistan, Turkmenistan and Uzbekistan from Europe to Asia. The total for Trade are/continent includes this change - while in the selections of countries in the pull-down menu for European/Asian countries it is the present classification that will be shown for the whole time period (Cypros belonging to Europe and the other countries to Asia).
Statistics Norway do not publish figures for the trade region previously Comecon after 2011.
For more information, see About the statistics
Monthly figures are released on the 15th of the month after the observation period (the previous month), or the first subsequent working day. These figures are preliminary. Corresponding yearly figures are published together with the monthly figures for December. With regard to the current year, all the monthly figures are updated in every publication. Final figures for the preceding year are released twice. For the first time in May the following year, while the corrected final figures are published in May one year later.
country
EU
Croatia is included in the trade with the EU from 2014 on.
Palestine (2013-)
Previously: West Bank/Gaza Stripe (2001-2012)
If a search in the StatBank does not return any result, this does not necessarily mean that there is no trade or that the country code is not valid in the particular period. A zero (0) could also imply that the figures are confidential or that the value is less than half of the unit used.
From 2006 on the following countries had their belonging to continent changed - Cyprus from Asia to Europe, and Armenia, Georgia, Kyrgyzstan, Kazakhstan, Tajikistan, Turkmenistan and Uzbekistan from Europe to Asia. The total for Trade are/continent includes this change - while in the selections of countries in the pull-down menu for European/Asian countries it is the present classification that will be shown for the whole time period (Cypros belonging to Europe and the other countries to Asia).
Statistics Norway do not publish figures for the trade region previously Comecon after 2011.
For more information, see About the statistics
Monthly figures are released on the 15th of the month after the observation period (the previous month), or the first subsequent working day. These figures are preliminary. Corresponding yearly figures are published together with the monthly figures for December. With regard to the current year, all the monthly figures are updated in every publication. Final figures for the preceding year are released twice. For the first time in May the following year, while the corrected final figures are published in May one year later.
country
EU
Croatia is included in the trade with the EU from 2014 on.
Palestine (2013-)
Previously: West Bank/Gaza Stripe (2001-2012)
Data cited at: Maximiliano Herrera's Human Rights Site
Fahrenheit values calculated using formula.
Fahrenheit T(°F) = T(°C) × 9/5 + 32
for Country data calculated average for each country locations.
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.
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.
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
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
Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings.
The labour force comprises all persons of working age who furnish the supply of labour for the production of goods and services during a specified time-reference period. It refers to the sum of all persons of working age who are employed and those who are unemployed. The working-age population is commonly defined as persons aged 15 years and older, but this varies from country to country. The series is part of the ILO estimates and is harmonized to account for differences in national data and scope of coverage, collection and tabulation methodologies as well as for other country-specific factors. For more information, refer to the ILO estimates and projections methodological note.
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.
Data cited at: Statistics Finland http://www.stat.fi/index_en.html
Publication: 008 -- Nationality according to age and sex by region in 1990 to 2017
http://pxnet2.stat.fi/PXWeb/pxweb/en/StatFin/StatFin__vrm__vaerak/statfin_vaerak_pxt_008.px
License: http://creativecommons.org/licenses/by/4.0/
Concepts and definitions
Description
Quality description
These statistics apply the regional division of 1 January 2018 to the whole time series.
Population statistics from 1750 to 2000 have been digitised into PDF format in the National Library's Doria service.
Publications on Population structure in Doria (in Finnish)
Publications on Vital statistics in Doria (in Finnish)
Publications on Population censuses in Doria (in Finnish)Nationality
If a person has two nationalities and one of them is Finnish, he/she will be
included in statistics as a Finnish national.
The used classification of continents is the classification of
Eurostat, where Cyprus and Turkey belong to Europe.
Citizens of non-autonomous states are summed under the mother country.Nationality
Czech Republic
Czech Republic + Former CzechoslovakiaSudan
Sudan + Former Sudan
Data cited at: Statistics Finland http://www.stat.fi/index_en.html
Publication: 009 -- Finnish citizens with dual nationality by age and second nationality in 2000 to 2017
http://pxnet2.stat.fi/PXWeb/pxweb/en/StatFin/StatFin__vrm__vaerak/statfin_vaerak_pxt_009.px
License: http://creativecommons.org/licenses/by/4.0/
Concepts and definitions
Description
Quality description
Population statistics from 1750 to 2000 have been digitised into PDF format in the National Library's Doria service.
Publications on Population structure in Doria (in Finnish)
Publications on Vital statistics in Doria (in Finnish)
Publications on Population censuses in Doria (in Finnish)
Second nationality
If a person has two nationalities and one of them is Finnish, he/she will be
included in statistics as a Finnish national.
Second nationality
Czech Republic
Czech Republic + Former Czechoslovakia
Sudan
Sudan + Former Sudan
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.
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.
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.
Collect food prices data in your country and earn up to $120 every month.We are looking for data collectors who will go to the specific markets weekly, collect data on food prices for about 25 items and submit them into our system.
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.
This dataset contains information on foreign direct investment (FDI) inward and outward flows and stock, expressed in millions of dollars.
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 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.
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.
2019 values are year to date
The dataset provided by the iGA via http://www.data.gov.bh and terms of use available at http://www.data.gov.bh/en/TermsOfUse . To the full extent permitted by law the iGA is not liable for any damage or loss of any kind caused directly or indirectly by the use of the datasets or any derived analyses or application
Qatar: Foreign Merchandise Trade
The foreign trade data reflects a clear image of the stages of economical growth in the State of Qatar, as it shows the commodities flow in the shape of national exports, re-exports, and imports to / from different countries of the world. The foreign trade tables contains detailed data for visible imports by country of origin and Exports and Re-Exports by country of destination. The commodity tables are classified by sections and items of the Harmonized System (H.S.) issued by World Customs Organization, which was adapted according to GCC’s needs to meet the actual movement of foreign trade in the region, in addition to other statistical tables. The customs declaration entry issued by Customs Department is the main source of statistical information according to the Special Trade System adopted by the State of Qatar.
country of birthKosovo: 2008-03-04, Sweden declared Kosovos independence.country of birthData for countries with fewer than 100 persons in total are reported in the country group other countries of birth.Population for all countries of birth is reported in this table: Population by country of birth, age and sex.years since last immigrationYears since last immigration to Sweden is based on registered year of immigration. 0 years means that immigration to Sweden took place during the reference year, 1 year means that immigration took place the year before the reference year, etc.
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/
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)
The AfDB Statistics Department and the Fragile States Unit have compiled this data set from various sources (the World Bank, WHO, IMF, and many others)
Data cited at: Fragile States Index - https://fragilestatesindex.org/
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.
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.
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.
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."
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
The Global Burden of Disease Study 2015 (GBD 2015), coordinated by the Institute for Health Metrics and Evaluation (IHME), estimated the burden of diseases, injuries, and risk factors at the global, regional, national, territorial, and, for a subset of countries, subnational level.
This dataset measures progress towards the Millennium Development Goal 5 (MDG 5) target of a 75% reduction in the maternal mortality ratio between 1990 and 2015. Maternal mortality ratio estimates for 21 regions, 195 countries and territories and 4 United Kingdom subnational units for 1990-2015 (quinquennial) are available by age and cause from the GBD Results Tool. Files available in this record include tables published in The Lancet in October 2016 in "Global, regional, and national levels of maternal mortality, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015.
Data cited at: Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2017 (GBD 2017) Health-related Sustainable Development Goals (SDG) Indicators 1990-2030. Seattle, United States: Institute for Health Metrics and Evaluation (IHME), 2018.
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 from 1990 to 2017. The United Nations established, in September 2015, the Sustainable Development Goals (SDGs), which specify 17 universal goals, 169 targets, and 232 indicators leading up to 2030. Drawing from GBD 2017, this dataset provides estimates on progress for 41 health-related SDG indicators for 195 countries and territories from 1990 to 2017, and projections, based on past trends, for 2018 to 2030. Estimates are also included for the health-related SDG index, a summary measure of overall performance across the health-related SDGs.
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.
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."
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).Â
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:
Data cited at: The World Bank https://datacatalog.worldbank.org/
Topic: Gender Statistics
Publication: https://datacatalog.worldbank.org/dataset/gender-statistics
License: http://creativecommons.org/licenses/by/4.0/
The GID-DB is a database providing researchers and policymakers with key data on gender-based discrimination in social institutions. This data helps analyse women’s empowerment and understand gender gaps in other key areas of development.Covering 180 countries and territories, the GID-DB contains comprehensive information on legal, cultural and traditional practices that discriminate against women and girls.
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.
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.
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.
Data cited at: The World Bank https://datacatalog.worldbank.org/
Topic: Global Bilateral Migration Database
Publication: https://datacatalog.worldbank.org/dataset/global-bilateral-migration-database
License: http://creativecommons.org/licenses/by/4.0/
Global Bilateral Migration Database:
Global matrices of bilateral migrant stocks spanning the period 1960-2000, disaggregated by gender and based primarily on the foreign-born concept are presented. Over one thousand census and population register records are combined to construct decennial matrices corresponding to the last five completed census rounds.
For the first time, a comprehensive picture of bilateral global migration over the last half of the twentieth century emerges. The data reveal that the global migrant stock increased from 92 to 165 million between 1960 and 2000. South-North migration is the fastest growing component of international migration in both absolute and relative terms. The United States remains the most important migrant destination in the world, home to one fifth of the world’s migrants and the top destination for migrants from no less than sixty sending countries. Migration to Western Europe remains largely from elsewhere in Europe. The oil-rich Persian Gulf countries emerge as important destinations for migrants from the Middle East, North Africa and South and South-East Asia. Finally, although the global migrant stock is still predominantly male, the proportion of women increased noticeably between 1960 and 2000.
'The 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.'
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
Data cited at: Global Carbon Atlas - http://www.globalcarbonatlas.org/en/CO2-emissions
The Global Carbon Project facilitates access to data to encourage its use and promote a good understanding of the carbon cycle. Respecting original data sources is key to help secure the support of data providers to enhance, maintain and update valuable data.
FOSSIL FUEL EMISSIONS
CDIAC: Boden, TA, Andres RJ, and Marland, G 2017. Global, Regional, and National Fossil-Fuel CO2 Emissions. Carbon Dioxide Information Analysis Center (CDIAC), Oak Ridge National Laboratory, US Department of Energy, Oak Ridge, Tenn., USA DOI:10.3334/CDIAC/00001_V2017. Available at: http://cdiac.ess-dive.lbl.gov/trends/emis/meth_reg.html
UNFCCC, 2018. National Inventory Submissions 2018. United Nations Framework Convention on Climate Change. Available at: http://unfccc.int/process/transparency-and-reporting/reporting-and-review-under-the-convention/greenhouse-gas-inventories-annex-i-parties/national-inventory-submissions-2018, accessed June 2018.
BP, 2018. Statistical Review of World Energy. Available at: http://www.bp.com/en/global/corporate/energy-economics.html
This database contains statistics on the annual production of fishery commodities and imports and exports of fishery commodities by country and commodities in terms of volume and value from 1976.
The 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.
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."
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.
Research by the Global Burden of Disease Health Financing Collaborator Network produced projected health spending estimates for 2017-2050 for 195 countries and territories. The estimates cover total health spending, and health spending disaggregated by source into three domestic financing source categories (government, out-of-pocket, and prepaid private) and development assistance for health (DAH). Retrospective health spending estimates for 1995-2016 and key covariates (including GDP per capita, total government spending, total fertility rate, and fraction of the population older than 65 years) were used to forecast GDP and health spending through 2050. Estimates are reported in constant 2018 US dollars, constant 2018 purchasing-power parity-adjusted (PPP) dollars, and as a percent of gross domestic product.
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.
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.
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
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.
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.
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.
Citation: Global Health Observatory (GHO) Data: https://www.who.int/gho/en/: World Health Organization; 2019. Licence: CC BY-NC-SA 3.0 IGO
The GHO data provides access to indicators on priority health topics including mortality and burden of diseases, the Millennium Development Goals (child nutrition, child health, maternal and reproductive health, immunization, HIV/AIDS, tuberculosis, malaria, neglected diseases, water and sanitation), non communicable diseases and risk factors, epidemic-prone diseases, health systems, environmental health, violence and injuries, equity among others.
Global 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
The GII is a source of insight into the multidimensional facets of innovation-driven growth. Providing 80 detailed metrics for 129 economies in 2019, the GII has become one of the leading references for measuring an economy’s innovation performance. Moving into its 12th edition this year, the GII has evolved into a valuable benchmarking tool that can facilitate public-private dialogue and where policy-makers, business leaders, and other stakeholders can evaluate innovation progress on an annual basis.
Each year the GII presents a thematic component that tracks global innovation. In this year’s edition, it analyzes the medical innovation landscape of the next decade, looking at how technological and non-technological medical innovation will transform the delivery of healthcare worldwide. It also explores the role and dynamics of medical innovation as it shapes the future of healthcare, and the potential influence this may have on economic growth.
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).
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.
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
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.
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.
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.
This dataset shows indicators of trade balances as the following: - Normalized trade balance, - Trade balance as percentage of imports and, - Trade balance as percentage of nominal gross domestic product (GDP).
Normalized trade balance (NTB) of goods and services is defined as the trade balance (total exports less total imports) divided by the total trade (exports plus imports).
NTB=(EX-IM)/EX+IM)
This table shows exports, imports and sum/average of exports and imports as percentage of nominal gross domestic product (GDP). The indicators are calculated for trade in goods, trade in services and total trade in goods and services.
This dataset provides a comprehensive view of the functions, or socioeconomic objectives, that government aims to achieve through various kinds of expenditure, comprising detailed classifications of general public service, defense, public order and safety, economic affairs, environment protection, housing and community services, health, recreation, culture and religion, education, and social protection services.
This dataset provides a comprehensive view of government expense, including detailed classifications of compensation of employees, use of goods and services, consumption of fixed capital, interest payable, subsidies payable, grants payable, social benefits, and other expense.
This dataset provides a comprehensive view of government revenue, including detailed classifications of taxes, social contributions, grants receivable, and other revenue.
This dataset provides an overview of total financial assets and liabilities classified by the sector to which the counterpart claim belongs. The counterpart sectors include non-financial corporations, the central bank, deposit taking corporations, other financial corporation sectors, government sectors, international organizations, external financial corporations, external general government, and other external sectors.
This dataset provides a comprehensive view of the integrated balance sheet. In other words, changes between the opening and closing stock positions in assets and liabilities are explained through transactions, holding gains/losses, and other changes in the volume of assets and liabilities. Data on net investment in non-financial assets – a component of total expenditure – on its components and related stock positions are provided.
This dataset contains selected indicators for monitoring progress towards green growth to support policy making and inform the public at large. The indicator bring together the OECD's statistics, indicators and measures of progress. The dataset covers OECD countries as well as BRIICS economies (Brazil, Russian Federation, India, Indonesia, China and South Africa), and selected countries when possible. The indicators are selected according to well specified criteria and embedded in a conceptual framework, which is structured around four groups to capture the main features of green growth: Environmental and resource productivity, to indicate whether economic growth is becoming greener with more efficient use of natural capital and to capture aspects of production which are rarely quantified in economic models and accounting frameworks; The natural asset base, to indicate the risks to growth from a declining natural asset base; Environmental quality of life, to indicate how environmental conditions affect the quality of life and wellbeing of people; Economic opportunities and policy responses, to indicate the effectiveness ofpolicies in delivering green growth and describe the societal responses needed to secure business and employment opportunities.
This dataset provides information on gross domestic product (GDP), total and per capita at current and constant (2010) prices also it contains annual average growth rates of gross domestic product (GDP), total and per capita, in per cent. The total GDP is expressed in millions of dollars, while GDP per capita is expressed in dollars.
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).Â
The goal of this pilot is to develop a set of commonly shared principles and working arrangements for data cooperation that could be implemented by the international agencies. The data sets are an experimental exercise to present national accounts data form various countries across the globe in one coherent folder, but users should be aware that these data are collected and validated by different organisations and not fully harmonised from a methodological point of view. The domain consists of the following collections:
The maritime transport domain contains quarterly and annual data. Maritime transport data refer to gross weight of goods (in tonnes), passenger movements (in number of passengers) as well as for vessel traffic (in number of vessels and in gross tonnage of vessels). Data for transport of goods transported on Ro-Ro units or in containers are also expressed in number of units or number of TEUs (20 foot equivalent units). Data at regional level (NUTS 2, 1 and 0) are also available.
The statistics on maritime transport are collected within Directive 2009/42/EC and Commission Decision 2008/861/EC, as amended by Commission Decision 2010/216/EU of the European Parliament and of the Council of 14 April 2010, by Regulation 1090/2010 of the European Parliament and of the Council of 24 November 2010 and by Commission Delegated Decision 2012/186/EU of 3 February 2012. Data are collected by the national competent authorities in the reporting countries using a variety of data sources, such as port administration systems, national maritime databases, customs databases or questionnaires to ports or shipping agents (see section 18.1). The maritime transport data have been calculated using data collected at port level. The data are displayed at port level, regional level, Maritime Coastal Area (MCA) level and country level. The data are presented in six collections, displaying main annual results, short sea shipping, passengers, goods vessel traffic and regional statistics.
The maritime transport domain contains quarterly and annual data.
Maritime transport data refer to gross weight of goods (in tonnes), passenger movements (in number of passengers) as well as for vessel traffic (in number of vessels and in gross tonnage of vessels). Data for transport of goods transported on Ro-Ro units or in containers are also expressed in number of units or number of TEUs (20 foot equivalent units). Data at regional level (NUTS 2, 1 and 0) are also available.
The statistics on maritime transport are collected within Directive 2009/42/EC and Commission Decision 2008/861/EC, as amended by Commission Decision 2010/216/EU of the European Parliament and of the Council of 14 April 2010, by Regulation 1090/2010 of the European Parliament and of the Council of 24 November 2010 and by Commission Delegated Decision 2012/186/EU of 3 February 2012. Data are collected by the national competent authorities in the reporting countries using a variety of data sources, such as port administration systems, national maritime databases, customs databases or questionnaires to ports or shipping agents (see section 18.1). The maritime transport data have been calculated using data collected at port level. The data are displayed at port level, regional level, Maritime Coastal Area (MCA) level and country level. The data are presented in six collections, displaying main annual results, short sea shipping, passengers, goods vessel traffic and regional statistics.
Gulf Dataset 2015 includes below Topics.
1. The Arab World Online 2014: Trends in Internet and Mobile Usage in the Arab Region
http://img.b8cdn.com/images/uploads/article_docs/en_gip-mbrsg_bayt_internet_final_20422_EN.pdf
2. Just Falafel Index 2015: Given the popularity and convenience of the Big Mac Index by The Economist, bqdoha.com devised a similar, but more local, solution/version for currency valuation.
http://www.bq-magazine.com/magazine-content/2015/01/just-falafel-index
3. CO2 emissions in GCC countries 2014
http://www.bq-magazine.com/gcc-illustrated/2014/08/co2-emissions-gcc-countries
4. Satisfaction with access to quality healthcare in the GCC
http://www.bq-magazine.com/gcc-illustrated/2014/07/healthcare-access-gcc
Health Nutrition and Population Statistics database provides key health, nutrition and population statistics gathered from a variety of international and national sources. Themes include global surgery, health financing, HIV/AIDS, immunization, infectious diseases, medical resources and usage, noncommunicable diseases, nutrition, population dynamics, reproductive health, universal health coverage, and water and sanitation.
This dataset presents HNP data by wealth quintile since 1990s to present. It covers more than 70 indicators, including childhood diseases and interventions, nutrition, sexual and reproductive health, mortality, and other determinants of health, for more than 90 low- and middle-income countries. The data sources are Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS).
Global Burden of Disease Study 2016 (GBD 2016) Healthcare Access and Quality Index Based on Amenable Mortality 1990–2016. Global Burden of Disease Study 2016 (GBD 2016) estimates were used in an analysis of personal healthcare access and quality for 195 countries and territories, as well as selected subnational locations, over time. This dataset includes the following global, regional, national, and selected subnational estimates for 1990-2016: age-standardized risk-standardized death rates from 24 non-cancer causes considered amenable to healthcare; age-standardized mortality-to-incidence ratios for 8 cancers considered amenable to healthcare; and the Healthcare Access and Quality (HAQ) Index and individual scores for each of the 32 causes on a scale of 0 to 100. Code used to produce the estimates is also included. Results were published in The Lancet in May 2018 in "Measuring performance on the Healthcare Access and Quality Index for 195 countries and territories and selected subnational locations: a systematic analysis from the Global Burden of Disease Study 2016
The Human Development Index (HDI) is a summary measure of achievements in three key dimensions of human development: a long and healthy life, access to knowledge and a decent standard of living. The HDI is the geometric mean of normalized indices for each of the the three dimensions.
Data cited at: ICTD/UNU-WIDER, ‘Government Revenue Dataset’, 2018, https://www.wider.unu.edu/project/government-revenue-dataset'
ICTD Government Revenue Dataset, 2018
A major obstacle to cross-country research on the role of revenue and taxation in development has been the weakness of available data. Government Revenue Dataset (GRD), developed through the International Centre for Tax and Development (ICTD), is aimed at overcoming this obstacle. It meticulously combines data from several major international databases, as well as drawing on data compiled from all available International Monetary Fund (IMF) Article IV reports. It achieves marked improvements in data coverage and accuracy, including a standardized approach to revenue from natural resources, and holds the promise of significant improvement in the credibility and robustness of research in this area. Dataset contains Central, General and merged government revenue data reported as % of GDP.
Data cited at: The World Bank https://datacatalog.worldbank.org/
Topic: IDA Results Measurement System
Publication: https://datacatalog.worldbank.org/dataset/ida-results-measurement-system
License: http://creativecommons.org/licenses/by/4.0/
The IDA Results Measurement System dataset measures progress on aggregate outcomes for IDA countries for selected indicators. It includes key country outcome indicators covering areas that are consistent with the Millennium Development Goals, are priorities in many national development plans and/or poverty reduction strategies, and reflect IDA's activities in IDA countries. The indicators capture both the economic growth and the human development priorities of ongoing IDA programs.
Member countries are allocated votes at the time of membership and subsequently for additional subscriptions to capital. Votes are allocated differently in each organization. Each member receives the votes it is allocated under IDA replenishments according to the rules established in each IDA replenishment resolution. Votes consist of subscription votes and membership votes.
IHME results from paper, Worldwide mortality in men and women aged 15–59 years from 1970 to 2010: a systematic analysis, published online in The Lancet on April 30 2010. This dataset provides global estimates of adult mortality risk, 45q15 (probability of death between the ages of 15 years and 60 years), between 1970 and 2010.
IHME results from paper, Neonatal, post neonatal, childhood, and under-5 mortality for 187 countries, 1970-2010: a systematic analysis of progress towards Millennium Development Goal 4, published online in The Lancet on May 24 2010. This dataset provides estimates of neonatal, post neonatal, childhood, and under-5 mortality for 187 countries between 1970 and 2010.
IHME research, published online in The Lancet in April 2010, with data from a global assessment of levels and trends in maternal mortality for the years 1980-2008. The study, Maternal mortality for 181 countries, 1980-2008: a systematic analysis of progress towards Millennium Development Goal 5, provides global, regional, and national level estimates of the maternal mortality ratio (MMR - the number of maternal deaths per 100,000 live births) as well as the number of maternal deaths.
IHME results data from global analysis of maternal mortality for years 1990-2011 published online in The Lancet in September 2011. The study, Progress towards Millennium Development Goals 4 and 5 on maternal and child mortality: an updated systematic analysis, provides global and country level estimates of the maternal mortality ratio (MMR - the number of maternal deaths per 100,000 live births) and the number of maternal deaths.
Covering 187 countries including most low-income countries, the toolkit provides indicators on export product diversification and export product quality from 1962-2010. The measures in this toolkit are based on an updated version of the UN–NBER dataset, which harmonizes COMTRADE bilateral trade flow data at the 4-digit SITC (Rev. 1) level. The export diversification and quality database was developed by IMF staff under an IMF-DFID research collaboration.
The Export Diversification Database has three main indicators: the Export Diversification Index, the Extensive Margin, and the Intensive Margin. Higher values for the all three indices indicate lower diversification. The Export Quality Database contains export quality measures across different aggregation levels of export products. Higher values for the quality indices indicate higher quality levels.
The World Economic Outlook (WEO) database contains selected macroeconomic data series from the statistical appendix of the World Economic Outlook report, which presents the IMF staff's analysis and projections of economic developments at the global level, in major country groups and in many individual countries. The WEO is released in April and September/October each year.
This database contains information on several demographic and labour market characteristics of the population of 28 OECD countries around the year 2000, by country of birth. The OECD countries included are Australia, Austria, Belgium, Canada, the Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Japan, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, the Slovak Republic, Spain, Sweden, Switzerland, Turkey, the United Kingdom and the United States. Most of the thematic files of the database include three core variables: the country of residence, the country of birth and educational attainment. Other variables available in the database include age, gender, citizenship, duration of stay, labour force status, occupation, sector of activity and field of study. In general, the database covers all individuals aged 15 and older.
The sources for this database are mainly census data, from the 2000 round of censuses. Census data were used for 22 countries. Countries not taking periodic censuses but keeping population registers have provided data extracted from these registers; this is the case for four countries: Denmark, Finland, Norway and Sweden. For some countries, not all themes covered in the database are present in the national census or register. Labour force surveys, provided by Eurostat and averaged over the period 1998-2002, have been used to fill the gaps where possible.
This database contains information on several demographic and labour market characteristics of the population of 28 OECD countries around the year 2000, by country of birth. The OECD countries included are Australia, Austria, Belgium, Canada, the Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Japan, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, the Slovak Republic, Spain, Sweden, Switzerland, Turkey, the United Kingdom and the United States. Most of the thematic files of the database include three core variables: the country of residence, the country of birth and educational attainment. Other variables available in the database include age, gender, citizenship, duration of stay, labour force status, occupation, sector of activity and field of study. In general, the database covers all individuals aged 15 and older.
This database contains information on several demographic and labour market characteristics of the population of 28 OECD countries around the year 2000, by country of birth. The OECD countries included are Australia, Austria, Belgium, Canada, the Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Japan, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, the Slovak Republic, Spain, Sweden, Switzerland, Turkey, the United Kingdom and the United States. Most of the thematic files of the database include three core variables: the country of residence, the country of birth and educational attainment. Other variables available in the database include age, gender, citizenship, duration of stay, labour force status, occupation, sector of activity and field of study. In general, the database covers all individuals aged 15 and older with a tertiary education.
This database contains information on several demographic and labour market characteristics of the population of 28 OECD countries around the year 2000, by country of birth. The OECD countries included are Australia, Austria, Belgium, Canada, the Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Japan, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, the Slovak Republic, Spain, Sweden, Switzerland, Turkey, the United Kingdom and the United States. Most of the thematic files of the database include three core variables: the country of residence, the country of birth and educational attainment. Other variables available in the database include age, gender, citizenship, duration of stay, labour force status, occupation, sector of activity and field of study. In general, the database covers all individuals aged 15 and older.
The sources for this database are mainly census data, from the 2000 round of censuses. Census data were used for 22 countries. Countries not taking periodic censuses but keeping population registers have provided data extracted from these registers; this is the case for four countries: Denmark, Finland, Norway and Sweden. For some countries, not all themes covered in the database are present in the national census or register. Labour force surveys, provided by Eurostat and averaged over the period 1998-2002, have been used to fill the gaps where possible.
The sources for this database are mainly census data, from the 2000 round of censuses. Census data were used for 22 countries. Countries not taking periodic censuses but keeping population registers have provided data extracted from these registers; this is the case for four countries: Denmark, Finland, Norway and Sweden. For some countries, not all themes covered in the database are present in the national census or register. Labour force surveys, provided by Eurostat and averaged over the period 1998-2002, have been used to fill the gaps where possible. The exact national source and reference period for each file is given in Table A.1 (see the methodological document).
This database contains information on several demographic and labour market characteristics of the population of 28 OECD countries around the year 2000, by country of birth. The OECD countries included are Australia, Austria, Belgium, Canada, the Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Japan, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, the Slovak Republic, Spain, Sweden, Switzerland, Turkey, the United Kingdom and the United States. Most of the thematic files of the database include three core variables: the country of residence, the country of birth and educational attainment. Other variables available in the database include age, gender, citizenship, duration of stay, labour force status, occupation, sector of activity and field of study. In general, the database covers all individuals aged 15 and older.
This Dataset shows the modelled parasite rate for Plasmodium falciparum for the years 2000-2015 for all African countries where it is endemic. The Dataset shows the percentage of 2-10 year olds infected by the parasite for each year.
The allocation of bilateral intermediate imports across using industries assumes that import coefficients are the same for all trade partners, i.e. SHAREipkt is identical across exporter countries. Hence, the bilateral pattern of imported intermediates from industry p is the same across all using industries k. However, it is different from the bilateral pattern of total imports from industry p because trade data (measured by VALUEijpt) allows distinguishing bilateral imports of intermediates from final good imports in industry p. While the BEC classification enables the identification of intermediate goods, no similar classification is available for trade in services, due to the high level of aggregation in services trade data. While goods trade data are based on customs declarations allowing the identification of goods at a highly disaggregated level, services trade data are based on a variety of information such as business accounts, administrative sources, surveys, and estimation techniques (Manual on Statistics of International Trade in Services, 2002). Hence, in the case of trade in services, VALUEijpt is the total value of imports of service p, i.e. both final and intermediate (and not only services that are used in the production of other goods and services, as in the case of goods data). By making an additional assumption and adjusting SHAREipkt, it is however possible to calculate trade in intermediate services. In the case of services imports, SHAREipkt is the share of imported service inputs p used by industry k in total imports of p of country i. In the case of services, besides the assumption that all trading partners have the same distribution of intermediate imports p across using industries k, it is furthermore required that the share of intermediate services in overall bilateral services imports of country i is the same across all partner countries j. Finally, it should be mentioned that trade data reported in the trade statistics do not fully match imports as reported in I-O tables. One main reason is that while trade data is recorded at consumer prices, I-O tables are evaluated at producer prices. There are also other differences such as the treatment of re-exports, scrap metal, waste products and second hand goods or unallocated trade data.
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 2015. For more information, refer to the ILO estimates and projections methodological note.
The Global Trade Alert (GTA) was launched in June 2009 when it was feared that the global financial crisis would lead governments to adopt widespread 1930s-style beggar-thy-neighbour policies. Although global in scope, the GTA has given particular attention to the policy choices of the G-20 governments ever since their leaders made a “no protectionism” pledge in Washington DC in November 2008. Although initially conceived as a trade policy monitoring initiative, as thousands of policy announcements have been documented, the GTA has become a widely-used input for analysis and decision-making by firms, industry associations, journalists, researchers, international organisations, and governments. This reflects the fact that, as the International Monetary Fund noted in 2016, the GTA “has the most comprehensive coverage of all types of trade-discriminatory and trade liberalizing measures.”
Data Cited at: https://www.globaltradealert.org
Data cited at: Heritage Foundation
Economic freedom is the fundamental right of every human to control his or her own labor and property. In an economically free society, individuals are free to work, produce, consume, and invest in any way they please, with that freedom both protected by the state and unconstrained by the state. In economically free societies, governments allow labor, capital and goods to move freely, and refrain from coercion or constraint of liberty beyond the extent necessary to protect and maintain liberty itself.
Economic Freedom Scores: Range and level of freedom
80–100:- Free
70–79.9:- Mostly Free
60–69.9:- Moderately Free
50–59.9:- Mostly Unfree
0–49.9:- Repressed
This dataset covers Value of Imports of Merchandise Into India By Principal Countries of Consignment and Value of Exports of Merchandise (Indian Produce And Manufactures) From India by Principal Countries of Destination. In addition, it has Summary Merchandise Trade By Revised Economic Regions. Note: FY2000-01 referred as 2001. Total Imports/exports includes other countries also.
The dataset provides the data on the direction of imports and exports by regions and Countries in Crore rupees and Million U.S, dollars
India's Economic Survey: Direction of Trade, 2018-19
Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings.
The series is part of the ILO estimates and is harmonized to account for differences in national data and scope of coverage, collection and tabulation methodologies as well as for other country-specific factors. For more information, refer to the ILO estimates and projections methodological note.
Data source(s) used: Vital statistics on causes of death: The Vital statistics system on causes of death is the main source for the evaluation of the health status of the population, and for the health programs and resources allocation. Data on causes of all deaths occurring in Italy during a calendar year are collected by the death certificates Istat/D.4 and D.4 bis. The physician must fill the health section of the certificate (part A) and the civil status officer of the appurtenant municipality must fill the demographic section of the certificate (part B).
Inflow of migrants refer to the number of immigrants who changed their country of usual residence during the reference period. A person's country of usual residence is the country in which the person has a place to live where he or she normally spends the daily period of rest. Temporary travel abroad for purposes of recreation, holiday, business, medical treatment or religious pilgrimage does not entail a change in the country of usual residence. Data are disaggregated by sex and 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).
The Fertilizer archive dataset contains information on the Production, Trade and Consumption of chemical and mineral fertilizers products, both in total nutrients and in amount of product, over the time series 1961 to 2002. The dataset also contains data on Prices paid by farmers expressed in local currencies (as a consequence no country aggregates are available) for single fertilizer products. This dataset is an archive and it is disseminated as it was in the previous FAOSTAT System. No dataset updates made or to be made in the future.
Improving agricultural productivity has been the world's primary means of assuring that the needs of a growing population don't outstrip the ability of humanity to supply food. Over the past 50 years, productivity growth in agriculture has allowed food to become more abundant and cheaper (see Growth in Global Agricultural Productivity: An Update, Amber Waves, November 2013, and New Evidence Points to Robust But Uneven Productivity Growth in Global Agriculture, Amber Waves, September 2012). A broad concept of agricultural productivity is total factor productivity (TFP). TFP takes into account all of the land, labor, capital, and material resources employed in farm production and compares them with the total amount of crop and livestock output. If total output is growing faster than total inputs, we call this an improvement in total factor productivity ("factor" = input). TFP differs from measures like crop yield per acre or agricultural value-added per worker because it takes into account a broader set of inputs used in production. TFP encompasses the average productivity of all of these inputs employed in the production of all crop and livestock commodities. "Growth accounting" provides a practicable way of measuring changes in agricultural TFP across a broad set of countries and regions, and for the world as a whole, given limited international data on production outputs, inputs, and their economic values. The approach (described in detail in Documentation and Methods) gives agricultural TFP growth rates, but not TFP levels, across the countries and regions of the world in a consistent, comparable way. Most of the data for the analysis comes from FAOSTAT. In some cases Food and Agriculture Organization (FAO) input and output data are supplemented with data from national statistical sources. Note: To facilitate international comparisons, certain simplifying assumptions must be made, and as such the estimates of TFP growth reported here may not be exactly the same as TFP growth estimates reported in other studies using different assumptions or methods. In particular, our TFP estimates for the United States differ slightly from those reported in ERS' Agricultural Productivity in the U.S. data product.
Data cited at: The World Bank https://datacatalog.worldbank.org/
Topic: International Debt Statistics
Publication: https://datacatalog.worldbank.org/dataset/international-debt-statistics
License: http://creativecommons.org/licenses/by/4.0/
Focuses on financial flows, trends in external debt, and other major financial indicators for low- and middle-income countries. Includes over 200 time series indicators from 1970 to 2016, for most reporting countries, and pipeline data for scheduled debt service payments on existing commitments to 2024.
Note: Total reserves in months of imports=(Total reserves/Total Imports)*12
This dataset is the basis for the International Food Security Assessment, 2017-27 released in July 2017. This annual ERS report projects food availability and access for 76 low- and middle-income countries over a 10-year period. The dataset includes annual country-level data on area, yield, production, nonfood use, trade, and consumption for grains and root and tuber crops (combined as R&T in the documentation tables), food aid, total value of imports and exports, gross domestic product, and population compiled from a variety of sources.
The estimates are based on official statistics on the foreign-born or the foreign population, classified by sex, and age. Most of the statistics utilised to estimate the international migrant stock were obtained from population censuses. Additionally, population registers and nationally representative surveys provided information on the number and composition of international migrants.
The designations employed and the material in this publication do not imply the expression of any opinion whatsoever on the part of the Secretariat of the United Nations concerning the legal status of any country, territory or area or its authorities, or concerning the delimitation of its frontiers or boundaries. The term “country” as used in this publication also refers, as appropriate, to territories or areas. The names and composition of geographical areas follow those presented in “Standard country or area codes for statistical use” (ST/ESA/STAT/SER.M/49/Rev.3), available at http://unstats.un.org/unsd/methods/m49/m49.htm.
Most of the data published in this database are taken from the individual contributions of national correspondents appointed by the OECD Secretariat with the approval of the authorities of Member countries. Consequently, these data have not necessarily been harmonised at international level. This network of correspondents, constituting the Continuous Reporting System on Migration (SOPEMI), covers most OECD Member countries as well as the Baltic States, Bulgaria and Romania. SOPEMI has no authority to impose changes in data collection procedures. It is an observatory which, by its very nature, has to use existing statistics. However, it does play an active role in suggesting what it considers to be essential improvements in data collection and makes every effort to present consistent and well-documented statistics.
International patent protection: 1960–2005 Walter G. Park ∗
Department of Economics, American University, 4400 Massachusetts Avenue NW, Washington, DC 20016, USA
Received 24 October 2007; received in revised form 14 December 2007; accepted 29 January 2008 Available online 10 March 2008
http://fs2.american.edu/wgp/www/res_policy08.pdf
Time series on international reserves (including gold), by individual country, expressed in millions of dollars. It further presents the number of months of merchandise imports that these reserves could finance at current imports level, as well as annual changes in total reserves.
Compared to the other 11 countries, United States has averaged more pregnancies, births, and abortions per 1,000 girls while having the lowest ratio of births to abortions.
Data cited at: CBS StatLine databank https://opendata.cbs.nl/statline/portal.html?_la=en&_catalog=CBS
Publication: International trade; Imports and exports of services by country, 2003-2013
https://opendata.cbs.nl/portal.html?_la=en&_catalog=CBS&tableId=80414ENG&_theme=1118
License: http://creativecommons.org/licenses/by/4.0/
This table contains information on Dutch imports and exports of services broken down by various service types and countries (groups). From 2006 onwards more detailed information is available than the years before. In addition, the annual figures show more detailed information than the quarterly figures.
Data available from 2003 to 2013.
Status of the figures:
The figures are definite.
Changes as of 8 October 2014:
None, this table has been discontinued.
When will new figures be published?
No longer applicable.
Data cited at: The Water Footprint Network https://waterfootprint.org/en/
Topic: International virtual water flow statistics
Publication: https://waterfootprint.org/en/resources/waterstat/international-virtual-water-flow-statistics/
Reference: Hoekstra, A.Y. & Mekonnen, M.M. (2012) The water footprint of humanity, Proceedings of the National Academy of Sciences, 109(9): 3232–3237
License: https://creativecommons.org/licenses/by-sa/3.0/
Data cited at: Internet World Stats
Internet World Stats is an International website that features up to date world Internet Usage, Population Statistics, Travel Stats and Internet Market Research Data, for over 233 individual countries and world regions.
Internet users are individuals who have used the Internet (from any location) in the last 3 months. The Internet can be used via a computer, mobile phone, personal digital assistant, games machine, digital TV etc.
The FAO Statistics Division has compiled an updated dataset series of capital stock in Agriculture from 1975-2007 using 2005 constant prices as the base year. The dataset on capital stock in agriculture are important for analyzing a number of policy issues related to sustainable growth of agriculture and achieving food security.
UN FAO Resource Statistics - Machinery. The Agricultural Resources domain covers: Investment, Land and irrigation, Labor, Machinery, Fertilizers, Pesticides, Population. The Resources domain considers factors of production for the agricultural sector. Broadly speaking, this section details how countries differ in endowments of the three classic inputs: labor, land and capital. Qualitative differences are important for each but are particularly difficult to summarize in a single indicator for land, the productivity of which depends heavily on water and soil conditions.
This table contains figures on affiliates under foreign control by investing country in the total manufacturing, total services and total business enterprise sectors.
The Joint External Debt Hub (JEDH)-jointly developed by the Bank for International Settlements (BIS), the International Monetary Fund (IMF), the Organization for Economic Cooperation and Development (OECD) and the World Bank (WB)-brings together external debt data and selected foreign assets from international creditor/market and national debtor sources. The JEDH replaces the Joint BIS-IMF-OECD-WB Statistics on External Debt, a website that was launched in 1999 to provide international data, mainly from creditor sources, on the external debt of developing and transition countries and territories.
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 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 general methodological note and the labour force estimates and projections methodological paper.
Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings.
The labour force comprises all persons of working age who furnish the supply of labour for the production of goods and services during a specified time-reference period. It refers to the sum of all persons of working age who are employed and those who are unemployed. The 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 general methodological note and the labour force estimates and projections methodological paper.
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 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 general methodological note and the labour force estimates and projections methodological paper.
Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings.
The labour force comprises all persons of working age who furnish the supply of labour for the production of goods and services during a specified time-reference period. It refers to the sum of all persons of working age who are employed and those who are unemployed. The 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 general methodological note and the labour force estimates and projections methodological paper.
Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings.
The labour force comprises all persons of working age who furnish the supply of labour for the production of goods and services during a specified time-reference period. It refers to the sum of all persons of working age who are employed and those who are unemployed. The 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 general methodological note and the labour force estimates and projections methodological paper.
This indicator is a proxy for rights to social security and health. It represents the percentage of the population without legal health coverage. Coverage includes affiliated members of health insurance or estimation of the population having free access to health care services provided by the State. A higher figure indicates higher percentage of the population without legal health coverage.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.
The dataset presents the liner shipping bilateral connectivity index (LSBCI), which indicates a country pair's integration level into global liner shipping networks. The LSBCI is an extension of UNCTAD’s country-level Liner Shipping Connectivity Index (LSCI) and based on a proper bilateralization transformation.
The dataset presents the liner shipping connectivity index (LSCI), which indicates a country's integration level into global liner shipping networks. The index base year is 2004, and the base value is on a country showing a maximum figure for 2004.
Data cited at : https://www.bis.org/statistics/index.htm
Locational Banking Statistics : Cross-Border Positions, by Residence and Sector of Counterparty
The Logistics Performance Index overall score reflects assessments of a country's logistics based on efficiency of the customs clearance process, quality of trade- and transport-related infrastructure, ease of arranging competitively priced shipments, quality of logistics services, ability to track and trace consignments, and frequency with which shipments reach the consignee within the scheduled time. The index ranges from 1 to 5, with a higher score representing better performance. Data are from Logistics Performance Index surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. 2011 round of surveys covered more than 6,000 country assessments by nearly 1,000 international freight forwarders. Respondents evaluated eight markets on six core dimensions using a scale from 1 (worst) to 5 (best). The markets are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. Scores for the six areas are averaged across all respondents and aggregated to a single score using principal components analysis. Details of the survey methodology and index construction methodology are in Connecting to Compete 2012: Trade Logistics in the Global Economy (2012).
The FAOSTAT Macro Indicators database provides a selection of country-level macroeconomic indicators taken from National Accounts series and relating to total economy (TE), Agriculture, Forestry and Fishing (AFF), Manufacturing (MAN), and Manufacturing of Food, beverage and tobacco products (FBT). All data relating to Total Economy, Agriculture, Forestry and Fishing, and Total Manufacturing originates from the United Nations Statistics Division (UNSD) which maintains and annually updates the "National Accounts Estimates of Main Aggregates" database. It consists of a complete and consistent set of time series of the main National Accounts (NA) aggregates of all UN Members States and other territories in the world for which National Accounts information is available. The UNSD database's content is based on the countries' official NA data reported to UNSD through the annual National Accounts Questionnaire, supplemented with data estimates for any years and countries with incomplete or inconsistent information. FAOSTAT Macro Indicators database reproduces a selection of time series from the UNSD National Accounts Estimates of Main Aggregates such as GDP, GFCF and sectoral VA. Additional analytical indicators such as annual per capita GDP (calculated using annual population series from the UNSD) and annual growth rates for GDP, GFCF and VA are included toghether with the investment ratio GFCF/GDP and the sectors'contribution to total economy GDP. Series on value added on Manufacture of Food, Beverages and Tobacco products originates - in order of priority - from OECD Annual National Accounts and UNIDO INDSTAT2 databases. In order to ensure that sub-industry series are consistent in levels with National Accounts based series, which is needed to support comparability across industries (agriculture vs. agro-industry and sub-industries), we proceed to a rescaling exercise of UNIDO originating series on UNSD National Accounts Estimates of Main Aggregates data series.
Center for Systemic Peace, Major Episodes of Political Violence, 1946-2017 (War List), Annual Set lists annual, cross-national, time-series data on interstate, societal, and communal warfare magnitude scores (independence, interstate, ethnic, and civil; violence and warfare) for all countries; Full Set (1946-2012) includes both country data and scores for neighboring countries and regional context for all independent countries (does not include independence wars)
Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings.
The labour force comprises all persons of working age who furnish the supply of labour for the production of goods and services during a specified time-reference period. It refers to the sum of all persons of working age who are employed and those who are unemployed. The working-age population is commonly defined as persons aged 15 years and older, but this varies from country to country. The series is part of the ILO estimates and is harmonized to account for differences in national data and scope of coverage, collection and tabulation methodologies as well as for other country-specific factors. For more information, refer to the ILO estimates and projections methodological note.
The Maritime Transport Costs (MTC)database contains data from 1991 to the most recent available year of bilateral maritime transport costs. Transport costs are available for 43 importing countries (including EU15 countries as a custom union) from 218 countries of origin at the detailed commodity (6 digit) level of the Harmonized System 1988.
This dataset should only be used in conjunction with the paper Clarifying Trade Costs in Maritime Transport which outlines methodology, data coverage and caveats to its use.
Key Statistical Concept
Import charges represent the aggregate cost of all freight, insurance and other charges (excluding import duties) incurred in bringing the merchandise from alongside the carrier at the port of export and placing it alongside the carrier at the first port of entry in the importing country. Insurance charges are therefore included in the transport cost variables and are estimated to be approximately 1.5% of the import value of the merchandise.
The data presented come from two international sources: (1) UN and International Resource Panel "Global Material Flows Database" for non-EU OECD and non-OECD countries, and (2) Eurostat "Material Flows and Resource Productivity" database for EU OECD countries.
It should be born in mind that the data should be interpreted with caution and that the time series presented here may change in future as work on methodologies for MF accounting progresses. Furthermore, data contain rough estimates for OECD and BRIICS aggregates.
These data refer to material resources, i.e. materials originating from natural resources that form the material basis of the economy: metals (ferrous, non-ferrous) non-metallic minerals (construction minerals, industrial minerals), biomass (wood, food) and fossil energy carriers.
The use of materials in production and consumption processes has many economic, social and environmental consequences. These consequences often extend beyond the borders of countries or regions, notably when materials are traded internationally, either in the form of raw materials or as products embodying them. They differ among the various materials and among the various stages of the resource life cycle (extraction, processing, use, transport, end-of-life management). From an environmental point of view these consequences depend on:the rate of extraction and depletion of renewable and non-renewable resource stocksthe extent of harvest and the reproductive capacity and natural productivity of renewable resourcesthe associated environmental burden (e.g. pollution, waste, habitat disruption), and its effects on environmental quality (e.g. air, water, soil, biodiversity, landscape) and on related environmental services
These data inform about physical flows of material resources at various levels of detail and at various stages of the flow chain. The information shows:
a) the material basis of economies and its composition by major material groups, considering:the extraction of raw materials;the trade balance in physical terms;the consumption of materials;the material inputs
b) the consumption of selected materials that are of environmental and economic significance.
c) in-use stocks of selected products that are of environmental and economic significance.
Domestic extraction used (DEU) refers to the flows of raw materials extracted or harvested from the environment and that physically enter the economic system for further processing or direct consumption (they are used by the economy as material factor inputs).
Imports (IMP) and exports (EXP) are major components of the direct material flow indicators DMI (domestic material input) and DMC (domestic material consumption). They cannot be taken as indication of domestic resource requirements.
Domestic material consumption (DMC) refers to the amount of materials directly used in an economy, which refers to the apparent consumption of materials. DMC is computed as DEU minus exports plus imports.
Direct material input (DMI) is computed as DEU plus imports.
The material groups are:
Food: food crops (e.g. cereals, roots, sugar and oil bearing crops, fruits, vegetables), fodder crops (including grazing), wild animals (essentially marine catches), small amounts of non-edible biomass (e.g. fibres, rubber), and related products including livestock.
Wood: harvested wood and traded products essentially made of wood (paper, furniture, etc.).
Construction minerals: non-metallic construction minerals whether primary or processed. They comprise marble, granite, sandstone, porphyry, basalt, other ornamental or building stone (excluding slate); chalk and dolomite; sand and gravel; clays and kaolin; limestone and gypsum.
Industrial minerals: non-metallic industrial minerals whether primary or processed (e.g. salts, arsenic, potash, phosphate rocks, sulphates, asbestos).
Metals: metal ores, metals and products mainly made of metals.
Fossil energy materials/carriers: coal, crude oil, natural gas and peat, as well as manufactured products predominantly made of fossil fuels (e.g. plastics, synthetic rubber).
This indicator is a proxy for health system outcomes. It represents the number of maternal deaths per 10 000 live births. A higher figure indicates worse outcomes. This is one of five indicators measuring key dimensions (drivers) of deficits in health care access and coverage. For analytical purposes the full set of indicators should be considered together.
This Dataset contains Indicators related to IC Development Index and Tables from "Measuring the Information Society Report 2018, Volume 1"
For Indicators for other ICT Development data please refer: https://knoema.com/ITUKIICT2019/global-ict-developments
The median age marks the point where half the group is older than that age and half is younger. 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 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 general methodological note and the labour force estimates and projections methodological paper.
This table presents merchandise trade complementarity index which assesses the suitability of preferential trade agreement between two economies given the structure of one potential partners’ exports match the imports of the other potential partner. Changes over time may indicate whether the trade profiles are becoming more or less compatible.
Merchandise: Trade value, volume, unit value, terms of trade indices and purchasing power index of exportsThis dataset contains export and import volume indices, rounding out trade value. Export and import unit value indices, derived terms of trade and purchasing power of exports indices are also provided in various base and reference years (2000, 2010 and 2015).The value index is the current value of exports (FOB) or imports (CIF) converted to United States dollars and expressed in percentage. The volume index is derived as the percentage ratio of the export or import value index to the corresponding unit value index (value index / unit value index *100) unless otherwise noted at country level.The weights used for the calculation of the indices correspond to base year 2000. For convenience, and to facilitate international comparisons, the series have been rescaled to new references 2010=100 and 2015=100.
Merchandise trade matrix - detailed products, exports and imports in thousands of United States dollars, Annual
This dataset presents merchandise trade by trading partner and product based on three digit level SITC Revision 3 commodity classification, expressed in thousands of dollars. In addition, data are also summarized by geographical region, economic and trade grouping, for both reporting country and its trading partner, and by product grouping.
This Dataset presents merchandise trade by trading partner and product based on the SITC commodity classification, Revision 3, at the one- and two-digit level, expressed in thousands of United States dollars. The data are also summarized by group of economies, for both reporting economy and trading partner, and by broader product groups.
This Dataset presents product concentration and diversification indices. The diversification index indicates whether the structure of exports or imports by product of a given country or country group differs from the world pattern. The product concentration index shows how exports and imports of individual countries or country groups are concentrated on a few products or otherwise distributed in a more homogeneous manner among a series of products.
This dataset shows the value of total merchandise exports and imports, expressed in millions of United States dollars and percentages of the world total
This dataset shows statistics on the international maritime transport. It contains data on the size of the world merchant fleet by flag of registration and by type of ship. Data are presented in thousands of dead-weight tons (DWT). The dataset presents also, for each region or country 1) its share in the world fleet, and 2) the share of a ship-type in its fleet. From 2011 onwards, the figures on numbers of ships are also available, as well as the data in gross tonnage (GT).
Economic developments in the Middle East, North Africa, Afghanistan, and Pakistan (MENAP) continue to reflect the diversity of conditions prevailing across the region. Most high-income oil exporters, primarily in the GCC, continue to record steady growth and solid economic and financial fundamentals, albeit with medium-term challenges that need to be addressed. In contrast, other countries --Iraq, Libya, Syria -- mired in conflicts with not just humanitarian but also economic consequences. And yet other countries, mostly oil importers, are making continued but uneven progress in advancing their economic agenda, often in tandem with political transitions and amidst difficult social conditions. In most of these countries, without extensive economic and structural reforms, economic prospects for the medium term remain insufficient to reduce high unemployment and improve living standards. Economic activity in the Caucasus and Central Asia (CCA) region is weakening, mainly because of the near-term slowdown and rising regional tensions affecting Russia, a key trading partner and sources of remittance and investment inflows, as well as weaker domestic demand in a number of CCA countries. Near-term risks are to the downside and tied to the fortunes of large trading partners. Policies need to focus on bolstering economic stability and, where needed, short-term support to ailing economic growth. In addition, a new model for high, sustained, diversified, and inclusive growth is needed to set the direction for economic policies for the next decade.
Migrants comprise individuals who changed their country of usual residence. A person's country of usual residence is the country in which the person has a place to live where he or she normally spends the daily period of rest. Temporary travel abroad for purposes of recreation, holiday, business, medical treatment or religious pilgrimage does not entail a change in the country of usual residence. 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).
Data source(s) used: Persons registered in and cancelled from the population register due to change of residence:The English description of the source is not available at this time, for methodological details go to the Siqual system
Immigration and emigration in the Netherlands and the administrative corrections by country of birth, sex, age and marital status. Data available from: 1995 Status of the figures: All data recorded in this publication are final data. Changes as from 18 June 2018: The final figures of 2017 have been added. Changes as from 26 April 2018: The underlying coding of classifications used in this table has been adjusted. It is now in line with the standard encoding defined by CBS. The structure and data of the table have been adjusted. The age classification has been simplified: the five-year groups have been removed. This makes the table better suited for the interface of the new StatLine. If you are missing these figures, please contact Infoservice (see section 5). When will new figures be published? The final figures of 2018 will be added in the second quarter of 2019 in this publication.
Data cited at: The World Bank https://datacatalog.worldbank.org/
Topic: Millennium Development Goals
Publication: https://datacatalog.worldbank.org/dataset/millennium-development-goals
License: http://creativecommons.org/licenses/by/4.0/
Relevant indicators drawn from the World Development Indicators, reorganized according to the goals and targets of the Millennium Development Goals (MDGs). The MDGs focus the efforts of the world community on achieving significant, measurable improvements in people's lives by the year 2015: they establish targets and yardsticks for measuring development results. Gender Parity Index (GPI)= Value of indicator for Girls/ Value of indicator for Boys. For e.g GPI=School enrolment for Girls/School enrolment for Boys. A value of less than one indicates differences in favor of boys, whereas a value near one (1) indicates that parity has been more or less achieved. The greater the deviation from 1 greater the disparity is.
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) and ground-level ozone (O3) have 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.
Exposure to ground-level ozone (O3) has serious consequences for human health, contributing to, or triggering, respiratory diseases. These include breathing problems, asthma and reduced lung function (WHO, 2016; Brauer et al., 2016). Ozone exposure is highest in emission-dense countries with warm and sunny summers. The most important determinants are background atmospheric chemistry, climate, anthropogenic and biogenic emissions of ozone precursors such as volatile organic compounds, and the ratios between different emitted chemicals.
The National Accounts Main Aggregates Database presents a series of analytical national accounts tables from 1970 onwards for more than 200 countries and areas of the world. It is the product of a global cooperation effort between the Economic Statistics Branch of the United Nations Statistics Division, international statistical agencies and the national statistical services of these countries and is developed in accordance with the recommendation of the Statistical Commission at its first session in 1947 that the Statistics Division should publish regularly the most recent available data on national accounts for as many countries and areas as possible. The database is updated in December of each year with newly available national accounts data for all countries and areas.
Different series numbers (column “Series”) are used to store different time-series versions of national accounts statistics. Series numbers with two digits (10,20) refer to data compiled following the SNA 1968 national accounts methodology, while series numbers with three digits (100, 200, etc) refer to data compiled using the SNA 1993 national accounts methodology whereas series number with four digits (1000, 1100) refer to data compiled using the SNA 2008 national accounts methodology. In addition to different methodologies, different series numbers are used when data are reported in different currencies, fiscal years, or by different sources. Furthermore, data are stored under a new series number whenever there are significant changes in compilation practices which make the time series no longer comparable.
Note: Ethiopia [upto 1993] and Ethiopia [from 1993] merged to get Ethiopia, Similarly Sudan (upto 2011) is combined with Sudan.
This indicator shows the percentage change of the CPI between a month and the same month of the previous year. In cases where the period of reference is a quarter, data refers to the percentage change from the same quarter of the previous year.
National Health Accounts (NHA) provides evidence to monitor trends in health spending for all sectors- public and private, different health care activities, providers, diseases, population groups and regions in a country. It helps in developing nationals
3The Africa Infrastructure Country Diagnostic (AICD) was an unprecedented knowledge program on Africa’s infrastructure that grew out of the pledge by the G8 Summit of 2005 at Gleneagles to substantially increase ODA assistance to Africa, particularly to the infrastructure sector, and the subsequent formation of the Infrastructure Consortium for Africa (ICA). The AICD study was founded on the recognition that sub-Saharan Africa (SSA) suffers from a very weak infrastructural base, and that this is a key factor in the SSA region failing to realize its full potential for economic growth, international trade, and poverty reduction.
The study broke new ground, with primary data collection efforts covering network service infrastructures (ICT, power, water & sanitation, road transport, rail transport, sea transport, and air transport) from 2001 to 2006 in 24 selected African countries. Between them, these countries account for 85 percent of the sub-Saharan Africa population, GDP, and infrastructure inflows. The countries included in the initial study were: Benin, Burkina Faso, Cameroon, Cape Verde, Chad, Côte d’Ivoire, Democratic Republic of Congo, Ethiopia, Ghana, Kenya, Lesotho, Madagascar, Malawi, Mozambique, Namibia, Niger, Nigeria, Rwanda, South Africa, Senegal, Sudan, Tanzania, Uganda, and Zambia.
The study also represents an unprecedented effort to collect detailed economic and technical data on African infrastructure in relation to the fiscal costs of each of the sectors, future sector investment needs, and sector performance indicators. As a result, it has been possible for the first time to portray the magnitude of the continent’s infrastructure challenges and to provide detailed and substantiated estimates on spending needs, funding gaps, and the potential efficiency dividends to be derived from policy reforms.
Data cited at: The Water Footprint Network https://waterfootprint.org/en/
Topic: National water footprint statistics
Publication: https://waterfootprint.org/en/resources/waterstat/national-water-footprint-statistics/
Water footprints of national consumption (1996-2005)
Reference: Hoekstra, A.Y. & Mekonnen, M.M. (2012) 'The water footprint of humanity’, Proceedings of the National Academy of Sciences, 109(9): 3232–3237.
Water footprints of national production (1996-2005)
Reference: Hoekstra, A.Y. & Mekonnen, M.M. (2012) 'The water footprint of humanity’, Proceedings of the National Academy of Sciences, 109(9): 3232–3237.
License: https://creativecommons.org/licenses/by-sa/3.0/
A tourist refers a person who travels to and stays in places outside his usual environment for not more than 183 days for purposes other than employment.
This table gives information on official financial flows by type and sources. It is further broken down by individual country, geographical region and economic grouping (as recipients); and expressed in millions of dollars, as percentage of total flows and as percentage of region.
Data cited at: https://data.gov.om/OMFRTRD2016
this Data set covers the statistical indicators illustrating the development of trade between Oman and other countries, and classification of merchandise exports, re-exports and merchandise imports by commodity group, nature of materials, their final utilization and port of entry. It includes also a table on of the balance of payments estimates.
The commodity classification used in the presentation of foreign trade data is the Hormonised System, which has been adopted in Oman since 1987, in addition to the SITC Revision (4) for international comparison. Commodity values are estimated in Rial Omani on the basis of the (C.I.F.) value for imports (i.e. the cost, insurance and freight of goods to the custom points in Oman) and (F.O.B.) for exports and re-exports.
Data cited at: Open Data Watch https://opendatawatch.com/
Topic: Open Data Inventory (ODIN) data
Publication: http://odin.opendatawatch.com/data/download
License: https://creativecommons.org/licenses/by/4.0/
Score Type Options: Three sets of scores are available: raw, weighted, or standardized. Raw scores have values between 0 and 1 as recorded in the original assessment; subscores are simple totals. Weighted scores use a predefined weighting matrix; subscores are simple totals. Standardized scores are scaled from 0 to 100; subscores are weighted averages.
Data cited at: Statistics Finland http://www.stat.fi/index_en.html
Publication: 032 -- Origin and background country by sex, by region and municipality in 1990 to 2017
http://pxnet2.stat.fi/PXWeb/pxweb/en/StatFin/StatFin__vrm__vaerak/statfin_vaerak_pxt_032.px
License: http://creativecommons.org/licenses/by/4.0/
Concepts and definitions
Description
Quality description
These statistics apply the regional division of 1 January 2018 to the whole time series.
Population statistics from 1750 to 2000 have been digitised into PDF format in the National Library's Doria service.
Publications on Population structure and vital statistics in Doria (in Finnish)
Publications on Population censuses in Doria (in Finnish)
Area
For reasons of privacy protection, cells with less than 10 cases of citizenship, country of birth,
background country or language by municipality have been marked with two dots. Continent sums have not
been hidden in municipality data nor have regional data concerning individual languages or countries.
Background country
Background country data are explained in the Concepts webpage, see link
Other official flows are official sector transactions which do not meet the ODA criteria, e.g.:
i.) Grants to developing countries for representational or essentially commercial purposes;
ii.) Official bilateral transactions intended to promote development but having a grant element of less than 25 per cent;
iii.) Official bilateral transactions, whatever their grant element, that are primarily export-facilitating in purpose. This category includes by definition export credits extended directly to an aid recipient by an official agency or institution ("official direct export credits");
iv.) The net acquisition by governments and central monetary institutions of securities issued by multilateral development banks at market terms;
v.) Subsidies (grants) to the private sector to soften its credits to developing countries [see Annex 3, paragraph A3.5.iv)b)];
vi.) Funds in support of private investment.
This table contains figures on the activity of affiliates located abroad by host country in the total manufacturing, total services and total business enterprise sectors. The units used to present data in AMNE are millions of national currency for monetary variables and units for the other variables. Monetary variables are in current prices. Euro-area countries: national currency data is expressed in euro beginning with the year of entry into the Economic and Monetary Union (EMU). For years prior to the year of entry into EMU, data have been converted from the former national currency using the appropriate irrevocable conversion rate. This presentation facilitates comparisons within a country over time and ensures that the historical evolution is preserved. Please note, however, that pre-EMU euro are a notional unit and should not be used to form area aggregates or to carry out cross-country comparisons.
Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings.
The series is part of the ILO estimates and is harmonized to account for differences in national data and scope of coverage, collection and tabulation methodologies as well as for other country-specific factors. For more information, refer to the ILO estimates and projections methodological note.
Passport Index is an interactive tool, which collects, displays and ranks the passports of the world.
You can discover the world’s passports on a map, by country name, by Passport Power Rank and even by the color of their cover.
Visa Free Score
Passports accumulate points for each visa free country that their holders can visit without a visa, or they can obtain a visa on arrival.
Passport Power Rank
Passports are ranked based on their Visa Free Score. The higher the Visa Free Score, the better the Passport Power Rank.
Methodology
The country list is based on the 193 UN member countries and 6 territories (Macao, Kosovo, etc.) for a total of 199. Territories annexed to other countries such as Norfolk Island, French Polynesia, etc. are excluded. Data is based on research from publicly available sources, as well as information shared by government agencies.
Data cited at: "WIPO statistics database" which is made available under a BY 3.0 IGO License
Total Patent Applications Total Count by Applicant's Origin
Zaire, officially the Republic of Zaire, was the name of Democratic Republic of the Congo between 1971 and 1997.
The OECD Environment Directorate, in collaboration with the Directorate for Science, Technology and Innovation, has developed patent-based innovation indicators that are suitable for tracking developments in environment-related technologies. The indicators allow the assessment of countries' and firms' innovative performance as well as the design of governments' environmental and innovation policies.
The patent statistics presented here are constructed using data extracted from the Worldwide Patent Statistical Database (PATSTAT) of the European Patent Office (EPO) using algorithms developed by the OECD. Consistent with other patent statistics provided in OECD.Stat, only published applications for "patents of invention" are considered (i.e. excluding utility models, petty patents, etc.).
The relevant patent documents are identified using search strategies for environment-related technologies (ENV-TECH) which were developed specifically for this purpose. They allow identifying technologies relevant to environmental management, water-related adaptation and climate change mitigation. An aggregate category labelled "selected environment-related technologies" includes all of the environmental domains presented here.
The OECD Environment Directorate, in collaboration with the Directorate for Science, Technology and Innovation, has developed patent-based innovation indicators that are suitable for tracking developments in environment-related technologies. The indicators allow the assessment of countries' and firms' innovative performance as well as the design of governments' environmental and innovation policies.
The patent statistics presented here are constructed using data extracted from the Worldwide Patent Statistical Database (PATSTAT) of the European Patent Office (EPO) using algorithms developed by the OECD. Consistent with other patent statistics provided in OECD.Stat, only published applications for "patents of invention" are considered (i.e. excluding utility models, petty patents, etc.).
The relevant patent documents are identified using search strategies for environment-related technologies (ENV-TECH) which were developed specifically for this purpose. They allow identifying technologies relevant to environmental management, water-related adaptation and climate change mitigation. An aggregate category labelled "selected environment-related technologies" includes all of the environmental domains presented here.
The OECD Environment Directorate, in collaboration with the Directorate for Science, Technology and Innovation, has developed patent-based innovation indicators that are suitable for tracking developments in environment-related technologies. The indicators allow the assessment of countries' and firms' innovative performance as well as the design of governments' environmental and innovation policies.
The patent statistics presented here are constructed using data extracted from the Worldwide Patent Statistical Database (PATSTAT) of the European Patent Office (EPO) using algorithms developed by the OECD. Consistent with other patent statistics provided in OECD.Stat, only published applications for "patents of invention" are considered (i.e. excluding utility models, petty patents, etc.).
The relevant patent documents are identified using search strategies for environment-related technologies (ENV-TECH) which were developed specifically for this purpose. They allow identifying technologies relevant to environmental management, water-related adaptation and climate change mitigation. An aggregate category labelled "selected environment-related technologies" includes all of the environmental domains presented here.
CLIMAT is a code for reporting monthly climatological data assembled at land‐based meteorological surface observation sites to data centers. CLIMAT‐coded messages contain information on several meteorological variables that are important to monitor characteristics, changes, and variability of climate. Usually these messages are sent and exchanged via the Global Telecommunication System (GTS) of the World Meteorological Organization (WMO). The world is divided by WMO in 9 regions and Mozambique is in region I and also hosts a CBS Lead Centre.
Region 1 has 354 RBCN stations from 28 countries, where 82 stations are part of GCOS and which are overseen by Mozambique. The monitoring results for these stations are shown from January to December 2012, some countries sent all CLIMAT reports, other countries sent some, and there countries did not send even one CLIMAT report.
We can see that a lot of countries concentrate more on GSN Stations, sending more data from only those stations. The more efficient countries, which send between 90 to 100% of CLIMAT reports, are: CANARY ISLANDS, ST.HELENA ISLANDS, MARTIN DE VIVIES (ILE AMSTERDAM), ILES CROZET AND ILES KERGUELEN. The countries that do not send any CLIMAT reports are: BURUNDI, BOTWANA, DJIBOUTI, ERITREA, LESOTHO, MALAWI, RWANDA, SOMALIA, UGANDA AND SWAZILAND. In Mozambique, we have had difficulties in sending CLIMAT reports for circulation via the GTS and we have used as an alternative the German Meteorological Service, DWD.
Data source(s) used: Persons convicted for crime with irrevocable judgement: Survey on convicted persons for felony and misdemeanor with irrevocable judgement, type of crimes and misdemeanors committed, main features of the convicted persons and of the sentence.
Other data characteristics: The number of persons convicted by type of crime is calculated on the most serious crime committed. The number of convicted persons by final judgement and the number of crimes is available with reference to two types of classifications. An analytical classification including about 470 items of crime, and a synthetic one, where the individual items are hierarchically grouped in 130 items broadly reflecting the Titles, Books and Sections of the Italian Penal Code. The complementary legislation is organized grouping the type of crimes by subject. The analytical classification of the types of crimes committed is given only in Italian language, as many crimes present in the Italian legislation do not have an exact match in the laws of other Countries. The literal translation of this classification is not available because the meaning of the legal terms translated into English could lead to misleading interpretations. An international classification is not available yet. "Number of concurrent crimes" means the total number of crimes committed by the convicted person. "1" means that the offender committed only one kind of crime. "2", "3", "4 and over" mean that, in addition to the most serious crime evident in the table, the offender committed other crimes.
Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings.
Persons outside the labour force comprise all persons of working age who, during the specified reference period, were not in the labour force (that is, were not employed or unemployed). The working-age population is commonly defined as persons aged 15 years and older, but this varies from country to country. The series is part of the ILO estimates and is harmonized to account for differences in national data and scope of coverage, collection and tabulation methodologies as well as for other country-specific factors. For more information, refer to the ILO estimates and projections methodological note.
Persons outside the labour force comprise all persons of working age who, during the specified reference period, were not in the labour force (that is, were not employed or unemployed). 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 labour force estimates and projections methodological paper.
citation:
Gibney, Mark, Linda Cornett, Reed Wood, Peter Haschke, and Daniel Arnon. 2016. The Political Terror Scale 1976-2015. Date Retrieved, from the Political Terror Scale website: http://www.politicalterrorscale.org.
Political Terror Scale Levels
1 - Countries under a secure rule of law, people are not imprisoned for their views, and torture is rare or exceptional. Political murders are extremely rare.
2 - There is a limited amount of imprisonment for nonviolent political activity. However, few persons are affected, torture and beatings are exceptional. Political murder is rare.
3 - There is extensive political imprisonment, or a recent history of such imprisonment. Execution or other political murders and brutality may be common. Unlimited detention, with or without a trial, for political views is accepted.
4 - Civil and political rights violations have expanded to large numbers of the population. Murders, disappearances, and torture are a common part of life. In spite of its generality, on this level terror affects those who interest themselves in politics or ideas.
5 - Terror has expanded to the whole population. The leaders of these societies place no limits on the means or thoroughness with which they pursue personal or ideological goals.
Data cited at: OECD/SWAC (2018), Africapolis (database), www.africapolis.org (accessed 05 February 2019); (FR):OCDE/CSAO (2018), Africapolis (base de données), www.africapolis.org (consultée le 05 février 2019).
The Population Register contains information on all persons who have resided in Greenland after 1 January 1977.
The purpose of the register is to be the basis for population statistics, and to supplement other personal information with basic information about each person, like address and family relations.The Population register is updated with information from CPR (Administrative Population Register) where the following information is retrieved:
name, gender, age, place of birth, citizenship, marital status, reference to mother, father and spouse, address of residence and more.
According to §13 of the Act on Greenland Statistics, no person-related information is disclosed from the register, except for personal numbers, randomly drawn for surveys
The total population comprises persons of all ages who were living in the country during the reference period, regardless of residency status or citizenship. Data for 1990-2015 are estimates while 2016-2030 data are projections. The dataset was updated as of July 2017, the source for the population figures used is World Population Prospects: The 2017 Revision and the rural urban distribution population source is World Urbanization Prospects: The 2014 Revision issued by the United Nations.
The total population comprises persons of all ages who were living in the country during the reference period, regardless of residency status or citizenship. Data for 1990-2015 are estimates while 2016-2030 data are projections. The dataset was updated as of July 2017, the source for the population figures used is World Population Prospects: The 2017 Revision issued by the United Nations.
The total population comprises persons of all ages who were living in the country during the reference period, regardless of residency status or citizenship. Data for 1990-2015 are estimates while 2016-2030 data are projections. The dataset was updated as of July 2017, the source for the population figures used is World Population Prospects: The 2017 Revision and the rural urban distribution population source is World Urbanization Prospects: The 2014 Revision issued by the United Nations.
Data cited at: The World Bank https://datacatalog.worldbank.org/
Topic: Population Estimates And Projections
Publication: https://datacatalog.worldbank.org/dataset/population-estimates-and-projections
License: http://creativecommons.org/licenses/by/4.0/
This database presents population and other demographic estimates and projections from 1960 to 2050. They are disaggregated by age-group and sex and covers more than 200 economies.
This dataset covers the topics of Urban population and proportion of urban population living in slum area across countries & regions for the year of 1990-2014
The FAOSTAT Population module contains time series data on population, by sex and urban/rural. The series consist of both estimates and projections for different periods as available from the original sources, namely:
1. Population data refers to the World Population Prospects: The 2015 Revision from the UN Population Division.
2. Urban/rural population data refers to the World Urbanization Prospects: The 2014 Revision from the UN Population Division.
Long term series estimates and projections from 1961 to 2050. http://www.un.org/en/development/desa/population/
This is part of a supporting dataset for Lester R. Brown, Full Planet, Empty Plates: The New Geopolitics of Food Scarcity (New York: W.W. Norton & Company, 2012).
This dataset presents activities in support of development from philanthropic foundations since 2009, including bilateral activities and core contributions to multilateral organisations. Bilateral activities from this dataset can also be found in the Creditor Reporting System (CRS) database. Collecting data on private philanthropy for development is work in progress, which may explain break in series for some foundations.
This dataset contains OAPEC: Organization of Arab Petroleum Exporting Countries crude oil production and crude oil reserves by country
Data Cited at - https://kapsarc.opendatasoft.com
CropsCrop statistics are recorded for 173 products, covering the following categories: Crops Primary, Fibre Crops Crop statistics are recorded for 173 products, covering the following categories: Crops Primary, Fibre Crops Primary, Cereals, Coarse Grain, Citrus Fruit, Fruit, Jute & Jute-like Fibres, Oilcakes Equivalent, Oil crops Primary, Pulses, Roots and Tubers, Treenuts and Vegetables and Melons. Data are expressed in terms of area harvested, production quantity, yield and seed quantity. The objective is to comprehensively cover production of all primary crops for all countries and regions in the world.
Cereals: Area and production data on cereals relate to crops harvested for dry grain only. Cereal crops harvested for hay or harvested green for food, feed or silage or used for grazing are therefore excluded. Area data relate to harvested area. Some countries report sown or cultivated area only; however, in these countries the sown or cultivated area does not differ significantly in normal years from the area actually harvested, either because practically the whole area sown is harvested or because the area surveys are conducted around the harvest period.
Vegetables, total (including melons): Data relate to vegetable crops grown mainly for human consumption. Crops such as cabbages, pumpkins and carrots, when explicitly cultivated for animal feed, are therefore excluded. Statistics on vegetables are not available in many countries, and the coverage of the reported data differs from country to country. In general, it appears that the estimates refer to crops grown in field and market gardens mainly for sale, thus excluding crops cultivated in kitchen gardens or small family gardens mainly for household consumption.
Fruit, total (excluding melons): Data refer to total production of fresh fruit, whether finally used for direct consumption for food or feed, or processed into different products: dry fruit, juice, jam, alcohol, etc. Generally, production data relate to plantation crops or orchard crops grown mainly for sale. Data on production from scattered trees used mainly for home consumption are not usually collected. Production from wild plants, particularly berries, which is of some importance in certain countries, is generally disregarded by national statistical services. Therefore, the data for the various fruits and berries are rather incomplete. Bananas and plantains: Figures on bananas refer, as far as possible, to all edible fruit-bearing species of the genus Musa except Musa paradisiaca, commonly known as plantain. Unfortunately, several countries make no distinction in their statistics between bananas and plantains and publish only overall estimates. When this occurs and there is some indication or assumption that the data reported refer mainly to bananas, the data are included. The production data on bananas and plantains reported by the various countries are also difficult to compare because a number of countries report in terms of bunches, which generally means that the stalk is included in the weight. Dates, plantains and total grapes are included in the “total fruit” aggregated figures, while olives are excluded.
Treenuts: Production of nuts (including chestnuts) relates to nuts in the shell or in the husk. Statistics are very scanty and generally refer only to crops for sale. In addition to the kind of nuts shown separately, production data include all other treenuts mainly used as dessert or table nuts, such as pecan nuts, pili nuts, sapucaia nuts and macadamia nuts. Nuts mainly used for flavouring beverages are excluded as are masticatory and stimulant nuts and nuts used mainly for the extraction of oil or butter, including areca/betel nuts, cola nuts, illipe nuts, karite nuts, coconuts, tung nuts, oilpalm nuts etc.
http://www.fao.org/faostat/en/#data/QCCrops processedThe dataset covers the following commodities: Beer of barley; Cotton lint; Cottonseed; Margarine, short; Molasses; Oil, coconut (copra); Oil, cottonseed; Oil, groundnut; Oil, linseed; Oil, maize; Oil, olive, virgin; Oil, palm; Oil, palm kernel; Oil, rapeseed; Oil, safflower; Oil, sesame; Oil, soybean; Oil, sunflower; Palm kernels; Sugar Raw Centrifugal; Wine.
http://www.fao.org/faostat/en/#data/QD
USDA Production, Supply and Distribution dataset contains current and historical official USDA data on production, supply and distribution of agricultural commodities for the United States and key producing and consuming countries.
This indicator conveys the proportion of children (defined as persons aged 5 to 17) involved in child labour, as well as the proportion of children involved in employment and the proportion of children involved in hazardous work. Children in employment include all those children who are engaged in any activity falling within the System of National Accounts' production boundary. Child labour is a subgroup of child employment, and it refers to children engaged in prohibited work or in types of work that should be eliminated given that they are injurious, negative or socially or morally undesirable according to national and international standards. More specifically, child labour comprises all children engaged in hazardous work, all children engaged in worst forms of child labour other than hazardous work, and employment below the minimum working age, excluding, where applicable, light work performed by children over the age of 13. For further information, see the SDG Indicators Metadata Repository.
Data cited at: The Quality of Government Institute; Teorell, Jan, Stefan Dahlberg, Sören Holmberg, Bo Rothstein, Natalia Alvarado Pachon & Richard Svensson. 2018. The Quality of Government Standard Dataset, version Jan18. University of Gothenburg: The Quality of Government Institute, http://www.qog.pol.gu.se doi:10.18157/QoGStdJan18
In the QoG Standard TS dataset, data from 1946 to 2018 is included and the unit of analysis is country-year (e.g. Sweden-1946, Sweden-1947 and so on).
Data cited at: Refugee Processing Center
FY - Fiscal Years have been used (since October until September). Data for 2017 include the last available values.
The Refugee Processing Center (RPC) is operated by the U.S Department of State (DOS) Bureau of Population, Refugees, and Migration (PRM) in the Rosslyn section of Arlington, Virginia USA.
At the RPC and at Resettlement Support Centers (RSCs), an interactive computer system called the Worldwide Refugee Admissions Processing System (WRAPS) is used to process and track the movement of refugees from various countries around the world to the U.S. for resettlement under the U.S. Refugee Admissions Program (USRAP).
Fiscal years 2008 through 2019 as of june 30,2019.
. Fiscal Years 2008 through 2019 as of june 30,2019.
EU-countries in Eastern Europe are transferred from group 2 to group 1 from the time of membership in the EU.
2003 Q2: 115 Estonia, 124 Latvia, 131 Polen, 136 Litauen, 146 Slovenia, 152 Hungary, 157 Slovakia, 158 Czech Republic
2007 Q1: 113 Bulgaria, 133 Romania.
EU-countries in Eastern-Europe are transferred from group2 to group 1 from the time of membership of the EU:
2004 k2: 115 Estonia, 124 Latvia, 131 Poland, 136 Lithuania, 146 Slovenia, 152 Hungary, 157 Slovakia, 158 Czech Republic.
2007 k1: 113 Bulgaria, 133 Romania.
Countries transferred from group 2 to group 1 from the time of membership of the EU:
2004 k2: 126 Malta, 500 Cyprus.
Asia includes Turkey and Cypus.
Figures updated December 5, 2018.
There is a break in the time series on registered unemployed among immigrants from Q4 2018, so the figures are not directly comparable with previous years.
country background
Serbia and Montenegro
The name changed from Yugoslavia to Serbia and Montenegro 14 February 2003.
Data cited at: The World Bank https://datacatalog.worldbank.org/
Topic: Remittance Prices Worldwide
Publication: https://datacatalog.worldbank.org/dataset/remittance-prices-worldwide
License: http://creativecommons.org/licenses/by/4.0/
Provides data on the cost of sending and receiving relatively small amounts of money from one country to another. Data cover 365 "country corridors" worldwide, from 48 remittance sending countries to 105 receiving countries.
Data source(s) used: Migration and calculation of foreign resident population and structure by citizenship The survey allows the calculation of the demographic balance of the foreign resident population and gives the amount of foreign residents for each year. Foreign resident population is represented by individuals who do not have Italian citizenship having usual residence in Italy. It is calculated for each municipality on December 31st of each year that follows the population Census, adding to the foreign population enumerated by the census the foreign population inflows and outflows recorded during each calendar year
Other data characteristics: Data subject to change for reconstruction after the last Population census
Data on agricultural land-use are valuable for conducting studies on a various perspectives concerning agricultural production, food security and for deriving cropping intensity among others uses. Indicators derived from the land-use categories can also elucidate the environmental sustainability of countries’ agricultural practices. FAOSTAT Land-use statistics contain a wide range of information on variables that are significant for: understanding the structure of a country’s agricultural sector; making economic plans and policies for food security; deriving environmental indicators, including those related to investment in agriculture and data on gross crop area and net crop area which are useful for policy formulation and monitoring. Land-use resources sub-domain covers: Country area (including area under inland water bodies), Land area (excluding area under inland water bodies), Agricultural area, Arable land and Permanent crops, Arable land, Permanent crops, Permanent meadows and pastures, Forest area, Other land and Area equipped for irrigation. Detailed information on sub-categories: Temporary crops, Temporary meadows and pastures, Fallow land (temporary: less than 5 years), Permanent meadows and pastures cultivated and naturally grown and Organic land. Data are available from 1961 to 2009 for more than 200 countries and areas. Forest area: Global Forest Resource Assessment 2010 (FRA 2010) is the main source of forest area data in FAOSTAT. Data were provided by countries for years 1990, 2000, 2005 and 2010. Data for intermediate years were estimated for FAO using linear interpolation and tabulation. Some of the most interesting data for economists is found in this domain. The national distribution of land, among arable land, pastures and other lands, as well as the importance of irrigation are just some of the interesting data sets.
Greenhouse gas (GHG) emissions from rice cultivation consist of methane gas from the anaerobic decomposition of organic matter in paddy fields. 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) and the IPCC 2000 Good Practice Guidance and Uncertainty Management in National GHG Inventories (http://www.ipcc-nggip.iges.or.jp/public/gp/english/). 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. Implied emission factor for CH4 and activity data are also provided.
Restrictions apply: https://www.scimagojr.com/aboutus.php
Citation: SCImago, (n.d.). SJR — SCImago Journal & Country Rank [Portal]. Retrieved Date you Retrieve, from http://www.scimagojr.com
SCImago Journal Rank: It expresses the average number of weighted citations received in the selected year by the documents published in the selected journal in the three previous years, --i.e. weighted citations received in year X to documents published in the journal in years X-1, X-2 and X-3.
H Index: The h index expresses the journal's number of articles (h) that have received at least h citations. It quantifies both journal scientific productivity and scientific impact and it is also applicable to scientists, countries, etc.
Total Documents: Output of the selected period. All types of documents are considered, including citable and non citable documents.
Total Cites (3years): Number of citations received in the seleted year by a journal to the documents published in the three previous years, --i.e. citations received in year X to documents published in years X-1, X-2 and X-3. All types of documents are considered.
Self Cites: Number of journal's self-citations in the selected year to its own documents published in the three previous years, --i.e. self-citations in year X to documents published in years X-1, X-2 and X-3. All types of documents are considered.
Cited Documents: Number of documents cited at least once in the three previous years, --i.e. years X-1, X-2 and X-3
Cites per Document (2 years): Average citations per document in a 2 year period. It is computed considering the number of citations received by a journal in the current year to the documents published in the two previous years, --i.e. citations received in year X to documents published in years X-1 and X-2.
ILO modelled estimates. The labour income share in GDP is the ratio, in percentage, between total labour income and gross domestic product (a measure of total output), both provided in nominal terms. Labour income includes the compensation of employees and part of the income of the self-employed. Self-employed workers earn from both their work and capital ownership. Total compensation of employees refers to the remuneration, in cash or in kind, payable by an enterprise to an employee in return for work done by the latter during the accounting period. The labour income of self-employed is imputed on the basis of a statistical analysis of employees of similar characteristics. The labour income share after accounting for the labour income of the self-employed is often referred to as the adjusted labour income share in GDP. For more information, refer to the general methodological note. For full metadata please download the data from the bulk download site. For further information, see the SDG Indicators Metadata Repository.
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.
Full Name: Activities of U.S. Multinational Enterprises (MNEs), Selected Data for Majority-Owned Foreign Affiliates in All Countries in which Investment was Reported.
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).
The figures are shown in four different measures:millions of United States dollarspercentages of the world totalannual percentage changes (growth rates)shares of each service category in total services.
Sources:
UNCTAD, WTO and ITC secretariats’ calculations, based on:IMF, Balance of Payments StatisticsEurostat, online databaseOECD, OECD.StatUN DESA Statistics Division, UN Service Trade Statistical DatabaseOther international and national sourcesUNCTAD-WTO estimates.
This Dataset presents annual statistics on total international trade in services by individual country, geographical region and economic groups, expressed in millions of dollars. Percentages of the world total and the annual percentage changes are also indicated. International trade in total commercial services is included as a memo item. The data presented are the result of the common work of UNCTAD, World Trade Organization (WTO) and International Trade Center (ITC), and are published simultaneously by the three organizations.
Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings.
The series is part of the ILO estimates and is harmonized to account for differences in national data and scope of coverage, collection and tabulation methodologies as well as for other country-specific factors. For more information, refer to the ILO estimates and projections methodological note.
Restrictions Apply: https://www.sipri.org/about/terms-and-conditions
Citation: Information from the Stockholm International Peace Research Institute (SIPRI), https://www.sipri.org/databases/milex
The SIPRI Military Expenditure Database contains consistent time series on the military spending of countries for the period 1949–2018. The database is updated annually, which may include updates to data for any of the years included in the database.
Military expenditure in local currency at current prices is presented according to both the financial year of each country and according to calendar year, calculated on the assumption that, where financial years do not correspond to calendar years, spending is distributed evenly through the year. Figures in constant (2017) and current US $, as a share of GDP and per capita are presented according to calendar year. Figures as a share of government expenditure are presented according to financial year.
The availability of data varies considerably by country, but for a majority of countries that were independent at the time, data is available from at least the late 1950s. Estimates for regional military expenditure have been extended backwards depending on availability of data for countries in the region, but no estimates for total world military expenditure are available before 1988 due to the lack of data for the Soviet Union.
The SIGI is built on 27 innovative variables measuring discriminatory social institutions, which are grouped into 4 dimensions: discrimination in the family, restricted physical integrity, restricted access to productive and financial resources, and restricted civil liberties.Lower values indicate lower levels of discrimination in social institutions: the SIGI ranges from 0% for no discrimination to 100% for very high discrimination.
Data cited at: Social Progress Index https://www.socialprogress.org/download
The Social Progress Index is a new way to define the success of our societies. It is a comprehensive measure of real quality of life, independent of economic indicators. The Social Progress Index is designed to complement, rather than replace, economic measures such as GDP.
Each year, Social Progress Imperative conducts a comprehensive review of all indicators included in the Social Progress Index framework to check data updates (which frequently include retroactive revisions) and whether new indicators have been published that are well-suited to describing social progress concepts. Such a review necessitates a recalculation of previously published versions of the Social Progress Index, as any removal or additions of indicators to the framework or changes due to retroactive revisions in data from the original data sources prevent comparability between previously published versions of the Social Progress Index and the 2019 Social Progress Index. Therefore, using the 2019 Social Progress Index framework and methodology, we provide comparable historical data for additional five years of the Social Progress Index, from 2014 to 2018.
This indicator is a proxy for the availability of health care. It represents the percentage of the population without access to health care due to the absence of the health workforce. The threshold for having a sufficient health workforce is 41.1 health workers per 10 000 population. A higher figure indicates worse availability. Note that this indicator reflects the supply side of availability, in this case the availability of human resources is at a level that guarantees at least basic, but universal, access. To estimate access to the services of skilled medical professionals (physicians, nursing and midwifery personnel), it uses as a proxy the relative difference between the density of these health workers in a given country (number per 10 000 population) and its median value in countries with a low level of vulnerability (defined according to the structure of employment and levels of poverty).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%. The same principle applies to the staff access deficit indicator, for which the 2014 median in low-vulnerability countries was 41.1. 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.
Statistical Capacity Indicator has three dimensions:
a). Statistical Methodology b). Source data and c). Periodicity and timeliness.
For each dimension, a country is scored against specific criteria, using information available from the World Bank, IMF, UN, UNESCO, and WHO. A composite score for each dimension is calculated by adding criteria scores, ranges from 0 to 1, and multiplying by 10. And an overall score combining all three dimensions are derived for each country on a scale of 0-100 by taking average of these three dimensions. A score of 100 indicates that the country meets all the criteria.
The first dimension, statistical methodology, Countries are evaluated against a set of criteria such as use of an updated national accounts base year, use of the latest BOP manual, external debt reporting status, subscription to IMF’s Special Data Dissemination Standard, and enrolment data reporting to UNESCO.
The second dimension, source data, reflects whether a country conducts data collection activities in line with internationally recommended periodicity, and whether data from administrative systems are available and reliable for statistical estimation purposes. Specifically, the criteria used are the periodicity of population and agricultural censuses, the periodicity of poverty and health related surveys, and completeness of vital registration system coverage.
The third dimension, periodicity and timeliness, looks at the availability and periodicity of key socioeconomic indicators, of which nine are MDG indicators. This dimension attempts to measure the extent to which data are made accessible to users through transformation of source data into timely statistical outputs. Criteria used include indicators on income poverty, child and maternal health, HIV/AIDS, primary completion, gender equality, access to water and GDP growth.
This dataset provides data on literacy rates, primary and secondary school attendance rates access to improved water and sanitation, household access to electricity, and household ownership of radio and television. Unlike other datasets, notably the World Bank’s World Development Indicators (WDI), this dataset provides data at the subnational level, specifically the first administrative district level. Furthermore, the data is comparable both within and across countries. This subnational level of data allows for assessment of education and household characteristics at a more relevant level for allocation of resources and targeting development interventions.
These tables offer a summary of obligations and disbursements in current and constant dollars by funding agency, funding account, and country from 2001 to the most recent year.
These tables offer a summary of obligations and disbursements in current and constant dollars by implementing agency and country from 2001 to the most recent year.
These tables offer a summary of obligations and disbursements in current and constant dollars by U.S. Government (USG) sector and country from 2001 to the most recent year.
Location:-Visitors are coming from mentioned location.(World is total of all visitors)
Nationality:-Nationality of visitors, whether the nationality of the visitors is same as the location or they belong to different nationality.(Under this dimension "Total" represents total number of visitors by their nationality)
Ports:-Tourist arrival via ports.
Data Cited at - Sachs, J., Schmidt-Traub, G., Kroll, C., Lafortune, G., Fuller, G. (2019): Sustainable Development Report 2019. New York:
Bertelsmann Stiftung and Sustainable Development Solutions Network (SDSN).
The 2019 SDG Index and Dashboards report presents a revised and updated assessment of countries’ distance to achieving the Sustainable Development Goals (SDGs). It includes detailed SDG Dashboards to help identify implementation priorities for the SDGs. The report also provides a ranking of countries by the aggregate SDG Index of overall performance.
The Sustainable Development Goals Report 2019 reviews progress in the third year of implementation of the 2030 Agenda presenting an overview with charts and info-graphics of highlights of the 17 Goals, followed by chapters that focus in more depth on the Goals under review at the high-level political forum in July 2018. This report follows the recently published report of the United Nations Secretary-General on "Progress towards the Sustainable Development Goals" (E/2018/64), both of which are based on the global indicator framework developed by the Inter-Agency and Expert Group on SDG Indicators (IAEG-SDGs) and agreed by the General Assembly in July 2017 in resolution 71/313.
The launch of The Sustainable Development Goals Report 2018 is accompanied by the Global SDG Indicators Database, which presents country level data and global and regional aggregates compiled through the UN System and other international organizations.
UNECE Clearing House on Migration Statistics is a platform for data exchange on migration statistics for countries of Eastern Europe, Caucasus and Central Asia (EECCA) established with the purpose of improving the understanding migration processes and the systems of measuring migration in the region. The data are presented as submitted by national statistical offices.
For more information about the methodology of producing statistics on international migration in EECCA region, please refer to the UNECE Handbook on the Use of Administrative Sources and Sample Surveys to Measure International Migration in CIS Countries and the documentation of UNECE Workshops on Migration Statistics.
Country: Armenia
Data source: 2001, 2011 - population and housing census; 2015 and onwards - administrative source.
Country: Azerbaijan
Data source: population and housing census.
Country: Belarus
The sum of the age groups does not correspond to the ''Total'' since age was unknown for a number of persons.
Data source: population and housing census.
Country: Georgia
Data source: 2002 - population and housing census; 2011 and onwards - administrative source.
Country: Kyrgyzstan
Data source: population and housing census.
Country: Moldova, Republic of
''Other'' includes a number of migrants for which the country is unknown.
Data source: population register.
Country: Russian Federation
In 2010, the sum of the age groups does not correspond to the ''Total'' since the age was unknown for a number of persons.
Data source: 2010 - population and housing census.
Country: Ukraine
In 2001, the sum of the age groups does not correspond to the ''Total'' since age was unknown for a number of persons. The population count does not include the territory of the Autonomous Republic of Crimea and the city of Sevastopol. The General Assembly has addressed the status of the Autonomous Republic of Crimea and the city of Sevastopol in resolution 68/262 of 27 March 2014.
Data source: 2001 - population and housing census; 2011 and onwards - the annual estimate of the number of permanent residents as of January 1 carried out by the State Statistics Committee.
UNECE Clearing House on Migration Statistics is a platform for data exchange on migration statistics for countries of Eastern Europe, Caucasus and Central Asia (EECCA) established with the purpose of improving the understanding migration processes and the systems of measuring migration in the region. The data are presented as submitted by national statistical offices.
For more information about the methodology of producing statistics on international migration in EECCA region, please refer to the UNECE Handbook on the Use of Administrative Sources and Sample Surveys to Measure International Migration in CIS Countries and the documentation of UNECE Workshops on Migration Statistics.
Country: Armenia
Data source: 2001, 2011 - population and housing census; 2015 and onwards - administrative source.
Country: Azerbaijan
Data source: population and housing census.
Country: Belarus
The sum of the age groups does not correspond to the ''Total'' since age was unknown for a number of persons.
Data source: population and housing census.
Country: Georgia
Data source: 2002 - population and housing census; 2011 and onwards - administrative source.
Country: Kyrgyzstan
Data source: population and housing census.
Country: Moldova, Republic of
''Other'' includes a number of migrants for which the country is unknown.
Data source: population register.
Country: Russian Federation
In 2010, the sum of the age groups does not correspond to the ''Total'' since the age was unknown for a number of persons.
Data source: 2010 - population and housing census.
Country: Tajikistan
Data source: 2000, 2010 - population and housing census; 2011-2014 - source unspecified.
Country: Ukraine
In 2001, the sum of the age groups does not correspond to the ''Total'' since age was unknown for a number of persons. The population count does not include the territory of the Autonomous Republic of Crimea and the city of Sevastopol. The General Assembly has addressed the status of the Autonomous Republic of Crimea and the city of Sevastopol in resolution 68/262 of 27 March 2014.
Data source: 2001 - population and housing census; 2011 and onwards - the annual estim