China

With the release of the 2016 Global Manufacturing Competitiveness Index (GMCI), Deloitte Touche Tohmatsu Limited (Deloitte Global) and the Council on Competitiveness (the Council) in the US build upon the GMCI research, with prior studies published in 2010 and 2013. The results of the 2016 study clearly show the ongoing influence manufacturing has on driving global economies. From its influence on infrastructure development, job creation, and contribution to gross domestic product (GDP) on both an overall and per capita basis, a strong manufacturing sector creates a clear path toward economic prosperity.

This dataset and predefined summary tables are a complement to the report Agricultural Policy Monitoring and Evaluation 2017, which monitors agricultural policy developments in 35 OECD member countries, 6 non-OECD EU member states and 11 emerging economies: Brazil, China, Colombia, Costa Rica, Indonesia, Kazakhstan, Russia, the Philippines, South Africa, Ukraine and Viet Nam. The OECD uses a comprehensive system for measuring and classifying support to agriculture - the Producer and Consumer Support Estimates (PSEs and CSEs) and related indicators. They provide insight into the increasingly complex nature of agricultural policy and serve as a basis for OECD’s work on agricultural policies. 

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.

Chapter C includes indicators that are a mixture of outcome indicators, policy levers and context indicators. Internationalisation of education and progression rates are, for instance, outcome measures to the extent that they indicate the results of policies and practices at the classroom, school and system levels. But they can also provide contexts for establishing policy by identifying areas where policy intervention is necessary, for example, to address issues of inequity.

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

Accuracy of annual economic forecasts of international organisations - European Commission, IMF, OECD, World Bank, UN LINK

This indicator aims to capture the share of persons in the labour force protected through a contributory pension scheme (with benefits guaranteed but not currently being received). It provides information about the proportion of the labour force that will receive an old age pension once reaching pensionable age. This right to income security in old age is guaranteed by the prior payment of premiums or contributions, i.e. before the occurrence of the insured contingency.

This indicator aims to capture the share of persons in the labour force protected through a contributory pension scheme (with benefits guaranteed but not currently being received). It provides information about the proportion of the labour force that will receive an old age pension once reaching pensionable age. This right to income security in old age is guaranteed by the prior payment of premiums or contributions, i.e. before the occurrence of the insured contingency.

This indicator aims to capture the share of persons of working age protected through a contributory pension scheme (with benefits guaranteed but not currently being received). It provides information about the proportion of the working-age population that will receive an old age pension once reaching pensionable age. This right to income security in old age is guaranteed by the prior payment of premiums or contributions, i.e. before the occurrence of the insured contingency.

This indicator aims to capture the share of persons of working age protected through a contributory pension scheme (with benefits guaranteed but not currently being received). It provides information about the proportion of the working-age population that will receive an old age pension once reaching pensionable age. This right to income security in old age is guaranteed by the prior payment of premiums or contributions, i.e. before the occurrence of the insured contingency.

Activities of U.S. MNEs: Majority-Owned Foreign Affiliates, Selected Indicators, 2016.

FAA Aerospace Forecasts: Economic Tables

Agency Revenue By Government Donor for assessed revenue type

This dataset provides data for foreign tourist arrivals distributed by age  group.

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 - CBS Notes: Financial Year 2016/17, 2017/18, 2018/19 have been considered as 2017, 2018, 2019 respectively

Notes: Financial Years 2016/17, 2017/18, 2018/19 have been considered as 2017, 2018, 2019 respectively. Data Source - IGS

Data Source - PSD Notes: Financial Year 2016/17, 2017/18, 2018/19 have been considered as 2017, 2018, 2019 respectively.

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

The 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.

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

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

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.

Data cited at: The World Bank https://datacatalog.worldbank.org/ Topic: All The Ginis Dataset Publication: https://datacatalog.worldbank.org/dataset/all-ginis-dataset License: http://creativecommons.org/licenses/by/4.0/   This dataset includes combined and standardized Gini data from eight original sources: Luxembourg Income Study (LIS), Socio-Economic Database for Latin America (SEDLAC), Survey of Living Conditions (SILC) by Eurostat, World Income Distribution (WYD; the full data set is available here), World Bank Europe and Central Asia dataset, World Institute for Development Research (WIDER), World Bank Povcal, and Ginis from individual long-term inequality studies (just introduced in this version).

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).

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

The OECD's ANalytical Business Enterprise Research and Development (ANBERD) database presents annual data on Research and Development (R&D) expenditures by industry and was developed to provide analysts with comprehensive data on business R&D expenditures. The ANBERD database incorporates a number of estimations that build upon and extend national submissions of business enterprise R&D data by industry (main activity/industry orientation). The current version of the ANBERD database presents OECD countries' and selected non-member economies' business expenditure on R&D since 1987, broken down across 100 manufacturing and service industry groups. The reported data follow the International Standard Industrial Classification, Revision 4 (ISIC Rev. 4) and are expressed in national currencies as well as in US dollars at Purchasing Power Parity (PPP), both at current and constant prices.   Main activity and industry orientation: The 2015 Frascati Manual practice is to report BERD on an enterprise basis. The main economic activity of an enterprise is usually defined as that which accounts for most of its economic outputs; this may be identified directly from sales or indirectly proxied (such as by numbers of personnel devoted to different activities). This determines the industry in which the enterprise, and any BERD it carries out, is classified. As such, all BERD of a diversified enterprise (i.e. one with multiple lines of business) is allocated to the same industry, that of its main activity. This enables, as far as possible, the alignment and compatability of BERD data with other economic statistics (e.g. value added broken down by industry). In addition, the Frascati Manual also recommends reporting BERD by industry orientation, whereby the statistical unit’s R&D is distributed across the various lines of business to which it relates. In a few countries, hybrid approaches are followed and reported as main activity data. As an example, some countries primarily follow the main activity approach but redistribute the R&D of large diversified firms across the economic activities to which it relates. This can affect interpretation of the data and resulting statistics. There are also important differences between countries in the treatment of R&D undertaken by firms in the service sector but closely associated (though not necessarily contractually) with manufacturing firms. Industrial research institutes, largely funded by the manufacturing industries they serve, are the most frequent examples. With the implementation of the 2015 Frascati Manual, such hybrid data will be phased out in favour of a strict main activity approach. Countries still reporting hybrid data are flagged in the ANBERD country notes.

Angola trade with selected countries by commodity 04 HS, 2013

ITTO's Annual Review and Assessment of the World Timber Situation compiles the most up-to-date and reliable international statistics available on global production and trade of timber, with an emphasis on the tropics. It also provides information on trends in forest area, forest management and the economies of ITTO member countries. Data cited at: ITTO Biennial review statistics: https://www.itto.int/biennal_review/

Annual Temperature Anomaly timeseries for China, India, and the United States.

The Japan Apparel Industrial Association

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

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%)    

Title: Area / Production And Productivity of Major Fruits and Vegetables Producing Countries In The World   Description: The data refers to data on Area, Production and Productivity of major Fruits and Vegetables producing Countries in the World from 2009-10 to 2010-11 (such as China, India, Brazil, USA, Italy, Spain, Mexico, Indonesia, Philippines, Iran, Turkey and Others).   Released Under: National Data Sharing and Accessibility Policy (NDSAP)   Contributor: Ministry of Statistics and Programme Implementation   Keywords: area; Brazil; China; Horticulture; Iran; Italy; major; Mexico; Philippines; Spain; Turkey; USA; Donesia   Group: Horticulture   Sectors: Statistics; Horticulture; Agriculture   Note: Source: Data taken from Statistical Year Book-2013, CSO; NA: Not Available.

Title: Area / Population And Income Of G-20 Countries   Description: The data refers to for G-20 countries details on area, population, gross domestic product (GDP), domestic product per capita income and purchasing power from 2010 to 2012.   Released Under: National Data Sharing and Accessibility Policy (NDSAP)   Contributor: Ministry of Statistics and Programme Implementation   Keywords: GDP; domestic; product; Capita; Gross; Purchasing; Power   Group: India and The World   Sectors: Statistics; All   Note: Original Data Source: IMF staff estimates and published in Statistical Year Book-2014 by MoSPI.

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/

Ecuador: Arrival and Departure Statistics

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

Data cited at GLOBAL CORRUPTION BAROMETER 2015/16/17by 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/research/gcb/gcb_2015_16/0 In the most extensive survey of its kind, we spoke to 21,861 people in 16 countries, regions and territories across the Asia Pacific region between July 2015 and January 2017 about their perceptions and experiences of corruption. The survey results show a great diversity in the corruption risks across the region, but in every country surveyed there is scope for improved approaches to corruption prevention.

The data does not differentiate if airplane is used for GA or commercial operations. In 2006, the CAA stopped publishing the number of registered aircraft by weight in favor of classes.In 2012, the CAA began publishing aircraft registry statistics by aircraft class.

ASTI collects primary time-series data on agricultural research capacity and spending levels through national survey rounds in over 80 low-and middle-income countries. Data collection is carried out by country focal points, who distribute survey forms to all agencies known to conduct agricultural research in a given country, including government, nonprofit, and higher education agencies. Private-for profit sector coverage is limited, and hence excluded from this dataset. More detailed country- and regional-level data on agricultural research capacity, investment, and outputs are available 

Automotive Industry, 2014

The International Organization of Motor Vehicle Manufacturers was founded in Paris in 1919. It is known as the “Organisation Internationale des Constructeurs d’Automobiles” (OICA). This dataset contains figures related to auto production, sales and usage.

Note: Values for the variables under the parent "LMCA European Monthly Sales Report" and "LMCA Global Monthly Sales Report" are getting updated every month. 2019 annual data is Year-to-Date value.

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

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

This table presents data on average monthly earnings converted to a common currency. Data in U.S. dollars are converted from local currency using exchange rates, while data in constant 2011 U.S. dollars are converted using 2011 purchasing power parities (PPPs)   Dataset splitted into below datasets:-   Local Currency (Total) - https://knoema.com/EAR_TEAR_NOC_NB   Local Currency (Men) - https://knoema.com/EAR_MEAR_NOC_NB   Local Currency (Women) - https://knoema.com/EAR_FEAR_NOC_NB   Constant 2011 PPP $ (Total) - https://knoema.com/EAR_4MPT_NOC_NB   Constant 2011 PPP $ (Men) - https://knoema.com/EAR_4MPM_NOC_NB   Constant 2011 PPP $ (Women) - https://knoema.com/EAR_4MPW_NOC_NB

The concept of earnings, as applied in wages statistics, relates to gross remuneration in cash and in kind paid to employees, as a rule at regular intervals, for time worked or work done together with remuneration for time not worked, such as annual vacation, other type of paid leave or holidays. This indicator is presented in terms of the average monthly earnings per employee, in local currency.

U.S. Direct Investment Abroad: Balance of Payments and Direct Investment Position 

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

Balance of payment, quarterly by items, receipts/expenditure, country and time

Annual statistics of Malaysia's balance of payment as time series 1947 - 2017.

BOPSY Global Tables aggregate country data by major balance of payments components and by international investment position (IIP) data for (i) Net IIP and (ii) Total Assets and Total Liabilities. Data for countries, country groups, and the world are provided. In addition to data reported by countries as shown in BOPSY, balance of payments data are provided for international organizations in BOPSY Global Tables. The BOPSY Global Tables include, in addition to reported data, data derived in a few instances indirectly from published sources.

Data cited at: Barro-Lee  

Data cited at: Barro-Lee

Sugar Data of United States

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 : https://www.bis.org/statistics/index.htm

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

Data cited at: Havocscope

Allowable limit for alcohol for countries across the world.

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

Symbols used Titles / circulation Circulation in thousands1) 1)The discrepancy in the data on circulation in the column 'total' is dueto rounding to thousands.

The Energy Outlook explores the forces shaping the global energy transition out to 2040 and the key uncertainties surrounding that transition. The Outlook considers a number of different scenarios. These scenarios are not predictions of what is likely to happen or what BP would like to happen. Rather, they explore the possible implications of different judgments and assumptions by considering a series of “what if” experiments. The scenarios consider only a tiny sub-set of the uncertainty surrounding energy markets out to 2040; they do not provide a comprehensive description of all possible future outcomes. For ease of explanation, much of the Outlook is described with reference to the ‘Evolving Transition’ scenario. But that does not imply that the probability of this scenario is higher than the others. Indeed, the multitude of uncertainties means the probability of any one of these scenarios materializing exactly as described is negligible. The Energy Outlook is produced to aid BP’s analysis and decision-making, and is published as a contribution to the wider debate. But the Outlook is only one source among many when considering the future of global energy markets. BP considers the scenarios in the Outlook, together with a range of other analysis and information, when forming its long-term strategy.

BP Energy Outlook Charts Data Pack - 2019 edition The Energy Outlook considers different aspects of the energy transition and the key issues and uncertainties these raise.   In all the scenarios considered, world GDP more than doubles by 2040 driven by increasing prosperity in fast-growing developing economies. In the Evolving transition (ET) scenario this improvement in living standards causes energy demand to increase by around a third over the Outlook, driven by India, China and Other Asia which together account for two-thirds of the increase. Despite this increase in energy demand, around two-thirds of the world’s population in 2040 still live in countries where average energy consumption per head is relatively low, highlighting the need for ‘more energy’. Energy consumed within industry and buildings accounts for around three-quarters of the increase in energy demand. Growth in transport demand slows sharply relative to the past, as gains in vehicle efficiency accelerate. The share of passenger vehicle kilometres powered by electricity increases to around 25% by 2040, supported by the growing importance of fully-autonomous cars and shared-mobility services.

The BP Statistical Review of World Energy has provided high-quality, objective and globally consistent data on world energy markets. The Review is one of the most widely respected and authoritative publications in the field of energy economics, used for reference by the media, academia, world governments and energy companies. A new edition is published every June. Historical data from 1965 for many sections.

Brasil: produção de aço

Data cited at: https://cameroon.opendataforafrica.org/gfuiizc Breeding and Fishing, 2013

The Times Higher Education Emerging Economies University Rankings 2018 includes only institutions in countries classified by the FTSE as “advanced emerging”, “secondary emerging” or “frontier”. its fifth year, the 2018 ranking parts with the ‘BRICS’ acronym in the title to recognise the strength and potential of a diverse range of emerging economies. The rankings use the same 13 performance indicators as the THE World University Rankings to judge institutions on their teaching, research, knowledge transfer and international outlook. But they are recalibrated to reflect the development priorities of universities in emerging economies. The performance indicators are grouped into five areas: 1. Teaching (the learning environment) 2. Research (volume, income and reputation) 3. Citations (research influence) 4. International outlook (staff, students and research) 5. Industry income (knowledge transfer) Note: The ranking of institutions, after 200, have been given in range like 201-250 and 251-300. The rank has been taken as 201, 202, 203……..250 as the same order as they appear in the source.

Data are provided in million national currency (for the euro zone, pre-EMU euro or EUR), million current PPP USD and million constant USD (2000 prices and PPPs). Variables collected This table presents research and development (R&D) expenditure statistics performed in the business enterprise sector by industry according to the International Standard Industrial Classification (ISIC) revision 3.1. and by type of costs (current expenditure, capital expenditure). Data at the industry level are presented beginning 1987, year when most of the countries converted from ISIC rev.2 to the current ISIC rev. 3 classification. This breakdown between industries is, in principle, made at the enterprise level, although some countries are able to break down R&D data for multi product enterprises between their main lines of business. National statistical regulations prevent publication of results where there are very few firms in the given category, hence the many gaps in the tables. Depending on the country, R&D institutes serving enterprises are either classified with the industry concerned, or grouped under “Research and Development” (ISIC rev.3.1, Division 73). When these R&D institutes are classified with the industry served, the evaluation of R&D in these industries is more complete and more comparable between countries for the industries concerned. This results, however, in an underestimation of the percentage of BERD performed by the service sector as compared with other countries. The Frascati Manual recommendation concerning data on R&D by industry is to report BERD on an enterprise basis (see FM section 3.4). When this is interpreted strictly, all the BERD of a diversified enterprise will be allocated to the industrial class of its principal activity. In circumstances where a few large firms dominate R&D spending in several areas, this can and does lead to underestimates of R&D associated with the secondary activities of the firms. Overall, R&D is therefore overestimated for some industries and underestimated for others. However, not all countries follow a strict enterprise basis for allocating R&D expenditures to industrial classes. Some countries make a disaggregation of the R&D of their largest, diversified firms into a number of different activities. In other countries, the enterprise approach has been abandoned and data are reported on a product field basis. This is why two classification criteria for BERD by industry are included in the table “BERD by industry” (see the variable CLASSIFICATION CRITERIA: Main activity or Product field) depending on which approach is more closely followed by each country (only a few countries currently collect these data both ways and are therefore included according to both criteria). However, this table “BERD by industry and type of costs” and the preceding one “BERD by industry and source of funds” present data for only one of the criteria, depending on the country.

Data are provided in million national currency (for the euro zone, pre-EMU euro or EUR), million current PPP USD and million constant USD (2005 prices and PPPs). Variables collected This table presents research and development (R&D) expenditure statistics performed in the business enterprise sector by industry according to the International Standard Industrial Classification (ISIC) revision 3.1. Data at the industry level are presented beginning 1987, year when most of the countries converted from ISIC rev.2 to the current ISIC rev. 3 classification. This breakdown between industries is, in principle, made at the enterprise level, although some countries are able to break down R&D data for multi product enterprises between their main lines of business. National statistical regulations prevent publication of results where there are very few firms in the given category, hence the many gaps in the tables. Depending on the country, R&D institutes serving enterprises are either classified with the industry concerned, or grouped under “Research and Development” (ISIC rev.3.1, Division 73). When these R&D institutes are classified with the industry served, the evaluation of R&D in these industries is more complete and more comparable between countries for the industries concerned. This results, however, in an underestimation of the percentage of BERD performed by the service sector as compared with other countries. The Frascati Manual recommendation concerning data on R&D by industry is to report BERD on an enterprise basis (see FM section 3.4). When this is interpreted strictly, all the BERD of a diversified enterprise will be allocated to the industrial class of its principal activity. In circumstances where a few large firms dominate R&D spending in several areas, this can and does lead to underestimates of R&D associated with the secondary activities of the firms. Overall, R&D is therefore overestimated for some industries and underestimated for others. However, not all countries follow a strict enterprise basis for allocating R&D expenditures to industrial classes. Some countries make a disaggregation of the R&D of their largest, diversified firms into a number of different activities. In other countries, the enterprise approach has been abandoned and data are reported on a product field basis. This is why two classification criteria for BERD by industry are included in this view of “BERD by industry” (see the variable CLASSIFICATION CRITERIA: Main activity or Product field) depending on which approach is more closely followed by each country (only a few countries currently collect these data both ways and are therefore included according to both criteria)). The two tables that follow, “BERD by industry and source of funds” and “BERD by industry and type of costs” present data for only one of the criteria, depending on the country.

Data are provided in million national currency (for the euro zone, pre-EMU euro or EUR), million current PPP USD and million constant USD (2005 prices and PPPs). Variables collected This table presents research and development (R&D) expenditure statistics performed in the business enterprise sector by industry according to the International Standard Industrial Classification (ISIC) revision 3.1. and by source of funds (business enterprise, government, other national funds, and funds from abroad). Data at the industry level are presented beginning 1987, year when most of the countries converted from ISIC rev.2 to the current ISIC rev. 3 classification. This breakdown between industries is, in principle, made at the enterprise level, although some countries are able to break down R&D data for multi product enterprises between their main lines of business. National statistical regulations prevent publication of results where there are very few firms in the given category, hence the many gaps in the tables. Depending on the country, R&D institutes serving enterprises are either classified with the industry concerned, or grouped under “Research and Development” (ISIC rev.3.1, Division 73). When these R&D institutes are classified with the industry served, the evaluation of R&D in these industries is more complete and more comparable between countries for the industries concerned. This results, however, in an underestimation of the percentage of BERD performed by the service sector as compared with other countries. The Frascati Manual recommendation concerning data on R&D by industry is to report BERD on an enterprise basis (see FM section 3.4). When this is interpreted strictly, all the BERD of a diversified enterprise will be allocated to the industrial class of its principal activity. In circumstances where a few large firms dominate R&D spending in several areas, this can and does lead to underestimates of R&D associated with the secondary activities of the firms. Overall, R&D is therefore overestimated for some industries and underestimated for others. However, not all countries follow a strict enterprise basis for allocating R&D expenditures to industrial classes. Some countries make a disaggregation of the R&D of their largest, diversified firms into a number of different activities. In other countries, the enterprise approach has been abandoned and data are reported on a product field basis. This is why two classification criteria for BERD by industry are included in the table “BERD by industry” (see the variable CLASSIFICATION CRITERIA: Main activity or Product field) depending on which approach is more closely followed by each country (only a few countries currently collect these data both ways and are therefore included according to both criteria). However, this table “BERD by industry and source of funds” and the one that follows, “BERD by industry and type of costs” present data for only one of the criteria, depending on the country.

This table presents research and development (R&D) statistics on personnel in the business enterprise sector. Measured in full-time equivalent are the number of total R&D personnel and researchers in the business enterprise sector by industry according to the International Standard Industrial Classification (ISIC) revision 3.1. Data at the industry level are presented beginning 1987, year when most of the countries converted from ISIC rev.2 to the current ISIC rev. 3 classification.

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.

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

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

The National Energy Board’s (NEB) Energy Futures series explores how possible energy pathways might unfold for Canadians over the long term. The report employs economic and energy models to make projections based on a certain set of assumptions given what we know today about technology, energy and climate policies, human behavior, and the structure of the economy. Readers of this analysis should consider the projections as a baseline to support ongoing discussions of Canada’s energy future. This analysis is not a prediction of what will take place, nor does it aim to show how specific goals, such as Canada’s climate targets, will be achieved. Canada’s Energy Future 2018: Supply and Demand Projections to 2040 (EF2018) considers four different Cases: • The Reference Case is based on a current economic outlook, a moderate view of energy prices and technological improvements, and climate and energy policies announced at the time of analysis. • The High and Low Price Cases consider the impact of uncertain commodity prices on the Canadian energy system. • The Technology Case pushes past the policy and technology boundaries specific to the Reference Case and includes greater global climate policy action and low carbon technology adoption. It provides one potential view of what a faster transition enabled by stronger long-term carbon policy, faster uptake of technologies such as electric vehicles, and lower cost of renewable would mean for Canada’s energy future.

For the location "Puerto Rico" data is available from 1990.

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."

data as of April 14, 2019

Capital Importation into Nigeria

OICA Car Production Statistics 1999-2018 contains world motor vehicle production statistics, obtained from national trade organisations, OICA members or correspondents. Passenger cars are motor vehicles with at least four wheels, used for the transport of passengers, and comprising no more than eight seats in addition to the driver's seat. Commercial vehicles include light commercial vehicles, heavy trucks, coaches and buses.

World: Car Sales by Country 2017

Indicators in the OECD database on Carbon dioxide (CO2) emissions embodied in international trade are derived by combining the 2015 version of OECD's Inter-Country Input-Output (ICIO) Database with International Energy Agency (IEA) statistics on CO2 emissions from fuel combustion. Production-based CO2 emissions are estimated by allocating the IEA CO2 emissions to the 34 target industries in OECD ICIO and, to final demand for fuels, by both residents and non-residents. Consumption-based CO2 emissions are calculated by multiplying the intensities of the production-based emissions (c) with the global Leontief inverse (I-A)(-1) and global final demand matrix (Y) from OECD ICIO, taking the column sums of the resulting matrix and adding residential and private road emissions (FNLC), i.e. direct emissions from final demand: colsum [ diag(c) (I-A)(-1) Y ] + FNLC. The ICIO system includes discrepancies in the trade data (referred to as DISC). Emissions allocated to DISC are made explicit (e.g. in indicator FD_CO2). This ensures that global CO2 production equals global CO2 consumption.

In summary, this database provides estimates of regional and global net carbon fluxes, on a year-by-year basis from 1850 through 2005, resulting from changes in land use (such as harvesting of forest products and clearing for agriculture), taking into account not only the initial removal and oxidation of the carbon in the vegetation, but also subsequent regrowth and changes in soil carbon. The net flux of carbon to the atmosphere from changes in land use from 1850 to 2005 was modeled as a function of documented land-use change and changes in aboveground and belowground carbon following changes in land use.

Japan: Shipping Statistics Handbook, Harbour

A case of occupational injury is the case of a worker incurring an occupational injury as a result of an occupational accident. An occupational injury that is fatal is the result of an occupational accident where death occurred within one year from the day of the accident. Data are disaggregated by economic activity according to the latest version of the International Standard Industrial Classification of All Economic Activities (ISIC) available for that year. Economic activity refers to the main activity of the establishment in which a person worked during the reference period and does not depend on the specific duties or functions of the person's job, but on the characteristics of the economic unit in which this person works.

A case of occupational injury is the case of a worker incurring an occupational injury as a result of an occupational accident. An occupational injury that is fatal is the result of an occupational accident where death occurred within one year from the day of the accident.

A case of non-fatal occupational injury is the case of a worker incurring an occupational injury as a result of an occupational accident not leading to death. The non-fatal occupational injury entails a loss of working time. Data are disaggregated by economic activity according to the latest version of the International Standard Industrial Classification of All Economic Activities (ISIC) available for that year. Economic activity refers to the main activity of the establishment in which a person worked during the reference period and does not depend on the specific duties or functions of the person's job, but on the characteristics of the economic unit in which this person works.

A case of non-fatal occupational injury is the case of a worker incurring an occupational injury as a result of an occupational accident not leading to death. The non-fatal occupational injury entails a loss of working time.

Catches of fish, crustaceans, molluscs and other aquatic organisms by species and fishing area for EU and associated countries (in live weight equivalent of the landings).

Eurostat Dataset Id:fish_ca_00 Catches of fish, crustaceans, molluscs and other aquatic organisms by species and fishing area for EU and associated countries (in live weight equivalent of the landings). The concepts and definitions used in the compilation of catch statistics are those laid down by the Coordinating Working Party on Fishery Statistics (CWP), of which Eurostat is one of the member organizations. These concepts and definitions have been in force since the late 1950's and are applied uniformly worldwide by the CWP and by the national authorities reporting to its member organizations. Therefore, though the quality of the data varies from country to country (being in many cases a function of the general characteristics of the national fishing industry), there is a high degree of comparability between countries and over time.  Nominal catch  The data refer to the catch of freshwater, brackish water and marine species of fish, crustaceans, molluscs and other aquatic animals and plants, killed, caught, trapped or collected for all commercial, industrial, recreational and subsistence purposes. In view of the importance of recreational fishing regarding some stocks and for certain countries, as well as the difficulty of distinguishing between recreational and subsistence fishing, the data should include the catches from recreational fisheries as well. However, it is recognised that certain countries are unable to supply the data for recreational fisheries. The catches are expressed in the live weight equivalent of the landings. As such they exclude all quantities caught but not landed (for example: discarded fish, fish consumed on board). The unit used is generally the metric ton. Data for marine mammals (e.g. whales) and certain other animals (e.g. crocodiles) are expressed in the number caught. The nominal catch data are normally derived from the landed quantities of the fishery products. For this purpose, the landed weight is converted to the live weight equivalent (nominal catch) by the application of factors. Species: All species for which catches are reported to international organizations are included in the Eurostat's database. They are identified by the internationally assigned three letter identifier (e.g. COD = Atlantic cod, PLE = European plaice) according to the FAO ASFIS (Aquatic Sciences and Fishery Information System) list of Species for Fishery Statistics Purposes. Fishing areas/regions: The catches are sub-divided by the area in which they occur. The methodologies vary from country to country depending on the nature of their fishing industries. Basic documentation used in collecting the data from EU fisheries are fishing log-books, landings declarations and sales notes used in the management of catch quota and market management systems within the Common Fisheries Policy. The methodologies used by EEA member countries have been described in the Eurostat publication "Fisheries: The collection and compilation of fish catch and landing statistics in member countries of the European Economic Area". Those used by the New Member States are described in a working document "Fisheries: The collection and compilation of fishery statistics in European Union Candidate Countries"

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.

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

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.

China : Natural Gas Price

For relevant topic data, please refer: For latest data, please refer: Legal Entities by Three Strata of IndustryChina: General Survey, Number of Legal Entities by Three Strata of Industry https://knoema.com/NBSCN_A_A010301China: Employment and Wages - Number of Employed Persons by Three Strata of Industry https://knoema.com/NBSCN_A_A0402 Province Data China Province Data: Employment and Wages - Number of Employed Persons in Urban Units by Sector https://knoema.com/NBSCN_P_A_A0401China Province Data: Employment and Wages - Number of Engaged Persons in Private Enterprises and Self-employed Individuals by Sector https://knoema.com/NBSCN_P_A_A0402China Province Data: Employment and Wages - Number of Engaged Persons in Urban Private Enterprises and Self-employed Individuals by Sector https://knoema.com/NBSCN_P_A_A0403 Self-employed Individuals by Sector China: Employment and Wages - Number of Self-employed Individuals https://knoema.com/NBSCN_A_A040DChina Province Data: Employment and Wages - Number of Self-employed Individuals https://knoema.com/NBSCN_P_A_A0405China: Employment and Wages - Number of Engaged Persons in Private Enterprises and Self-employed Individuals by Sector https://knoema.com/NBSCN_A_A040AChina Province Data: Employment and Wages - Number of Engaged Persons in Private Enterprises and Self-employed Individuals by Sector https://knoema.com/NBSCN_P_A_A0402China: Employment and Wages - Number of Engaged Persons in Urban Private Enterprises and Self-employed Individuals by Sector https://knoema.com/NBSCN_A_A040BChina Province Data: Employment and Wages - Number of Engaged Persons in Urban Private Enterprises and Self-employed Individuals by Sector https://knoema.com/NBSCN_P_A_A040

For National data, please refer: China: Industry - Output of Industrial Products https://knoema.com/NBSCN_A_A0E0H