Organisation for Economic Co-operation and Development

This dataset contains bilateral commitment data on aid in support of environment sustainability and aid to biodiversity, climate change mitigation, climate change adaptation and desertification from the Development Assistance Committee (DAC) Creditor Reporting System (CRS) database. In their reporting to the DAC CRS, donors are requested to indicate for each activity whether or not it targets environment and the Rio Conventions (biodiversity, climate change mitigation, climate change adaptation and desertification). A scoring system of three values is used, in which aid activities are "marked" as targeting environment as the "principal objective" or a "significant objective", or as not targeting the objective. The environment marker identifies activities that are "intended to produce an improvement in the physical and/or biological environment of the recipient country, area or target group concerned" or "include specific action to integrate environmental concerns with a range of development objectives through institution building and/or capacity development". A large majority of activities targeting the objectives of the Rio Conventions fall under the DAC definition of "aid to environment". The Rio markers permit their specific identification.

This database includes annual, quarterly and monthly information on carbon dioxide (CO2) emissions related to commercial passenger, freight, and general aviation flights, on both a territory and a residence basis, for 186 countries. These CO2 emissions are estimated by the OECD, based on a consistent methodology across countries. The main source used for the estimation of these CO2 emissions is a database compiled by the International Civil Aviation Organisation (ICAO) with all commercial passenger and freight flights around the world.

Data are collected from Ministries of Transport, national statistical offices and other institutions designated as the official data source. The survey used for this exercise is the "Trends in the Transport Sector". Variables collected include inland transport of goods and passengers, as well as road injury crashes. Additional information is also gathered on containers transported by rail and sea as well as short sea shipping. Data are published annually on 31 October. Data series start in 1970 and continue until the current year-1, or the most recent available data.

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.

Since the collection of 2009 data, the scope of the OECD Global Insurance Statistics questionnaire has been expanded. These changes led to the collection of key balance sheet and income statement items for direct insurance and reinsurance sectors, such as: gross claims paid, outstanding claims provision (changes), gross operating expenses, commissions, total assets, gross technical provisions (of which: unit-linked), shareholder equity, net income.

The OECD-WTO Balanced Trade in Services (BaTIS) dataset is a complete, consistent, and balanced matrix of international trade in services statistics (ITSS). It contains annual bilateral data covering 202 reporters and partners, broken down by the 12 main EBOPS2010 (BPM6) categories from 2005 to 2021. BaTIS is the result of joint efforts by the OECD and WTO. Two main features enable BaTIS to stand out as the international benchmark for any analysis on international trade in services:BaTIS is complete and consistent. At present, only about 63% of world trade in services is bilaterally specified, and the percentage is even lower for the individual service categories. The OECD-WTO methodology leverages all available official statistics and combines them with estimations and adjustments to provide users with a complete matrix covering virtually all economies in the world.BaTIS is balanced. To resolve the asymmetries between reported and mirror flows, exports and imports are reconciled by calculating a symmetry-index weighted average between the two, following a similar approach to that developed for international merchandise trade statistics. BaTIS provides 3 adjustments for each trade flow:Reported. Reflects the value officially reported by the country (where available), some values are rescaled to fit world totals.Adjusted and/or imputed. Reflects, in addition to the reported values, any adjustments made to ensure internal consistency as well as the estimations made by the OECD-WTO to fill in the gaps in the reported information.Balanced. Reflects the reconciled bilateral trade flow, where exports equal mirror imports. Official information on bilateral trade flows was collected from OECD, Eurostat, national sources as well as UNSD. The WTO-UNCTAD trade in services dataset, based on a number of primary sources complemented with estimations, was the main source for data with partner world. The BaTIS dataset can be used as a stand-alone input for economic analysis and policy-making. In addition, balanced trade in services data form an essential input to the OECD Trade in Value Added (TiVA) initiative, for which a balanced view of international trade is crucial. BaTIS is intended to be regularly updated and constantly improved as new data become available. For more information on the methodology, please refer to the working paper accompanying this dataset The OECD-WTO Balanced Trade in Services (BaTIS) For more information on balanced trade statistics, please go to the topic related OECD page Balanced trade statistics Users are encouraged to send their questions, or to signal any apparent errors, regarding this database to [email protected]://www.oecd.org/sdd

The following dataset presents seven bibliometric indicators by fields of science for the 2008-2022 period. The dataset covers OECD member and accession countries, key partners (including Brazil, China, India, Indonesia and South Africa) and other selected non-OECD countries.

Institutional coverage As a consequence of the implementation of the new OECD Global Insurance Statistics' framework, there is a break in series between 2008 and 2009 regarding life and non-life business data where composite insurance undertakings exist. Up until 2008, the insurance business is broken down between life and non-life business. As of 2009, the insurance business is broken down between the business of pure life, pure non-life and composite undertakings and composite undertakings' business is further broken down between life and non-life business. Some countries do not allow for insurance undertakings to be active in both life and non-life insurance business and therefore composite insurance undertakings do not exist in these countries. In other countries (e.g., Austria, Belgium, Hungary, Italy, Mexico, Portugal, Spain) however, the share of employment in composite insurance undertakings accounts for more than half of the whole domestic insurance sector. Therefore, to have comparable data across years for life business data (resp. non-life), one has to sum up the life (resp. non-life) business of pure life (resp. non-life) undertakings and the life (resp. non-life) business of composite undertakings as of 2009. Item coverage Business written in the reporting country on a gross and net premium basis. It contains a breakdown between domestic companies, foreign-controlled companies and branches and agencies or foreign companies.

OECD Indicators on Carbon dioxide (CO2) emissions embodied in international trade (TeCO2) are derived by combining the 2021 editions of OECD Inter-Country Input-Output (ICIO) Database and of International Energy Agency (IEA) statistics on CO2 emissions from fuel combustion. In this release of TeCO2, emissions from fuels used for international aviation and maritime transport (i.e. aviation and marine bunkers) are also considered.Production-based CO2 emissions are estimated by allocating the CO2 emissions to the 45 target industries in OECD ICIO and, to household final consumption of fuels, by both residents and non-residents.Demand-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.For more information, see TeCO2 web page: http://oe.cd/io-co2.

This indicator reports the amount of carbon emissions from fossil fuel combustion embodied in imports and exports in mega tonnes of CO2 (MtCO2) for 63 countries and 34 industries between 1995 and 2011.

The indicator reports the hypothetical amount of carbon emissions from fossil fuel combustion embodied in imports if imported goods were produced with a carbon intensity (i.e. emissions factors) equal to that of the importing country at a given time – the Equal Carbon Intensity (ECI) assumption. It covers 65 countries and 34 industries between 1995 and 2011.

Institutional coverage As a consequence of the implementation of the new OECD Global Insurance Statistics' framework, there is a break in series between 2008 and 2009 regarding life and non-life business data where composite insurance undertakings exist. Up until 2008, the insurance business is broken down between life and non-life business. As of 2009, the insurance business is broken down between the business of pure life, pure non-life and composite undertakings and composite undertakings' business is further broken down between life and non-life business. Some countries do not allow for insurance undertakings to be active in both life and non-life insurance business and therefore composite insurance undertakings do not exist in these countries. In other countries (e.g., Austria, Belgium, Hungary, Italy, Mexico, Portugal, Spain) however, the share of employment in composite insurance undertakings accounts for more than half of the whole domestic insurance sector. Therefore, to have comparable data across years for life business data (resp. non-life), one has to sum up the life (resp. non-life) business of pure life (resp. non-life) undertakings and the life (resp. non-life) business of composite undertakings as of 2009. Item coverage Commissions in the reporting country, containing a breakdown between domestic companies, foreign-controlled companies and branches and agencies of foreign companies.

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

The objective of the CRS Aid Activity database is to provide a set of readily available basic data that enables analysis on where aid goes, what purposes it serves and what policies it aims to implement, on a comparable basis for all DAC members. Data are collected on individual projects and programmes. Focus is on financial data but some descriptive information is also made available.

This database provides aggregates on official development assistance (ODA), other official flows (OOF) and flows from non-governmental organisations (NGOs) or philanthropic foundations. These statistics are shown by provider over time. To assist with the transition between systems, we provide a mapping of codes between OECD.Stat (legacy) and the OECD Data Explorer. For more information on these flows, please see the OECD’s main page on development finance statistics, including Frequently Asked Questions (FAQs) on ODA. See the Converged Statistical Reporting Directives governing these data.

As a consequence of the implementation of the new OECD Global Insurance Statistics' framework, there is a break in series between 2008 and 2009 regarding life and non-life business datawhere composite insurance undertakings exist. Up until 2008, the insurance business is broken down between life and non-life business. As of 2009, the insurance business is broken down between the business of pure life, pure non-life and composite undertakings and composite undertakings' business is further broken down between life and non-life business. Some countries do not allow for insurance undertakings to be active in both life and non-life insurance business and therefore composite insurance undertakings do not exist in these countries. In other countries (e.g., Austria, Belgium, Hungary, Italy, Mexico, Portugal, Spain) however, the share of employment in composite insurance undertakings accounts for more than half of the whole domestic insurance sector. Therefore, to have comparable data across years for life business data (resp. non-life), one has to sum up the life (resp. non-life) business of pure life (resp. non-life) undertakings and the life (resp. non-life) business of composite undertakings as of 2009. Click to collapse Item coverage Outstanding investment by direct insurance companies, classified by investment category, by the companies' nationality and by its destination (domestic or foreign). As of 2009, investment data exclude assets linked to unit-linked products sold to policyholders.

The OECD Digital STRI identifies, catalogues and quantifies barriers that affect trade in digitally enabled services across 46 countries. It provides policy makers with an evidence-based tool that helps to identify regulatory bottlenecks, design policies that foster more competitive and diversified markets for digital trade, and analyze the impact of policy reforms. The OECD Digital STRI captures cross-cutting impediments that affect all types of services traded digitally. As a stand-alone instrument, it complements the OECD Services Trade Restrictiveness Index (STRI).   STRI indices take the value from 0 to 1. Complete openness to trade and investment gives a score of zero, while being completely closed to foreign services providers yields a score of one.

The OECD Digital STRI heterogeneity indices complement the recently published Digital STRI's and presents indices of regulatory heterogeneity based on the rich information in the Digital STRI regulatory database. The indices are built from assessing – for each country pair and each measure – whether or not the countries have the same regulation. For each country pair and each sector, the indices reflect the (weighted) share of measures for which the two countries have different regulation.

Distribution of net official development assistance (ODA) is defined as geographical aid allocations. Net ODA may be distributed by income group (least developed countries, other low-income countries, lower middle-income countries, upper middle-income countries, unallocated and more advanced developing countries and territories) or by geography (sub-Saharan Africa, South and Central Asia, other Asia and Oceania, Middle East and North Africa, Latin America and the Caribbean, Europe, and unspecified). The OECD Development Assistance Committee's "List of ODA Recipients" shows developing countries and territories eligible for ODA. The list is revised every three years. It is designed for statistical purposes, not as guidance for aid distribution or for other preferential treatment. In particular, geographical aid allocations are national policy decisions and responsibilities. This indicator is measured in million USD constant prices, using 2018 as the base year.

The classification of personnel is based on functions and organises staff into four main functional categories: 1) Instructional Personnel; including two sub-groups: A. Classroom Teachers (ISCED 0-4) and Academic Staff (ISCED 5-6); and B. Teacher Aides (ISCED 0-4) and Teaching / Research Assistants (ISCED 5-6); 2) Professional Support for Students; including two sub-groups: A. Pedagogical Support (ISCED 0-4) and Academic Support (ISCED 5-6); B. Health and Social Support (ISCED 0-6); 3) Management/Quality Control/Administration; including four subgroups: A. School Level Management (ISCED 0-6); B. Higher Level Management (ISCED 0-6); C. School Level Administrative Personnel (ISCED 0-6); and D. Higher Level Administrative Personnel (ISCED 0-6); 4) Maintenance and Operations Personnel.

The Fisheries Committee (COFI) from the Trade and Agriculture Directorate (TAD) collects, on an annual basis from all its participating countries, data on landings, aquaculture production, fleet, employment in the fisheries sector, and government financial transfers. Data are collected from Fisheries Ministries, National Statistics Offices and other institution designated as an official data source. Concepts Classifications Data are collected by the OECD using the methodologies established by the Coordinating Working Party on Fishery Statistics (CWP) (www.fao.org/fishery/cwp/search/en). This inter-agency body, created in 1960 to develop common procedures and standards for the collation of fisheries statistics, provides technical advice on fishery statistical matters. Its handbook of Fishery Statistical Standards comprises definitions of the various concepts used in fishery statistics, with the exception of Government Financial Transfers which is unique to the OECD. All other statistics are based on the CWP definitions. The OECD, a partner with the CWP, additionally collects information on values for its landings and records the breakdown between the types of landings (i.e. landings in domestic ports, landings in foreign ports) data series which are not collected by the FAO. While a number of countries cover landings in a similar fashion, the same does not hold true for capacity (feet/meters, GRT/engine powers), or for employment for which both Full-time equivalents or numbers of people are used. The OECD therefore does not duplicate FAO statistics but requests complementary information to feed its analytical work.

Number of students enrolled in different education programmes by country of origin and sex.

Entrepreneurship is crucial to economic development, promoting social integration and reducing inequalities. The Gender-entrepreneurship dataset presents an original collection of indicators that measure gender equality in entrepreneurship, providing an important reference for policy insights and policy making. Data refer mainly to the self-employed, their profile, age, education and sector of activity.

Environmentally related taxes are effective policy instruments to shape relative prices of goods and services. The Environmental Related Tax Revenue Database (ERTR) categorises taxes based on their environmental relevance, constructing environmentally related tax revenue with a breakdown by tax-base category (including energy, transport, pollution, and resources) and 22 environmental domains. Note that tax-base categories are mutually exclusive, while domains are not. Therefore, one should not aggregate revenue across domains as it may lead to double counting.

Countries report expenditures by sources of funds: Governement (central, regional, local); International agencies and other foreign sources; Households and Other private entities (including firms and religious institutions and other non-profit organisations). Three types of financial transactions can be distinguished: -direct expenditure/payments on educational institutions -Intergovernmental transfers for education -Transfers to students or households and to other private entities.

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 dataset FDI by counterpart area and by economic activity, BMD4 includes inward and outward Foreign Direct Investment (FDI) flows, positions and income by partner country and by economic activity for OECD reporting economies. It is a simplified dataset with fewer breakdowns compared to the other separate datasets specifically dedicated to FDI flows, FDI positions or FDI income by counterpart area, or by economic activity. In this dataset, FDI exclude resident SPEs, when they exist (unless otherwise stated, see metadata attached at the reporting country level); and inward FDI positions are allocated to the ultimate counterpart country when available (see metadata attached at the reporting country level). Inward and outward FDI statistics in this dataset are presented on a directional basis (unless otherwise stated, see metadata attached at the reporting country level); they are measured in USD millions, in millions of national currency and as a share of total (for FDI positions only). This dataset supports FDI indicators by counterpart area and by economic activity available from the OECD Data Portal. In 2014, many countries implemented the latest international standards for Foreign Direct Investment (FDI) statistics:the OECD’s Benchmark Definition of FDI, 4th edition (BMD4); andthe IMF’s Balance of Payments and International Investment Position Manual, 6th edition (BPM6) This OECD database was launched in March 2015 which includes the data series reported by national experts according to BMD4. The data are for the most part based on balance of payments statistics published by Central Banks and Statistical Offices following the recommendations of the IMF’s BPM6 and the OECD’s BMD4. However, some of the data relate to other sources such as notifications or approvals.

This dataset FDI by counterpart area and by economic activity, BMD4 and historical BMD3 includes long time series of FDI statistics by partner country and by industry. It uses available BMD4 series and combines them with BMD3 historical series (from the unrevised OECD FDI datasets according to BMD3) as far back as 2005 and identifies breaks in series. For selected countries, breaks in series were removed as there was no significant impact of BMD4 implementation. In this dataset, inward and outward FDI flows, positions and income include resident SPEs when they exist (unless otherwise stated, see metadata attached at the reporting country level); and they are allocated to the immediate counterpart country (unless otherwise specified, see metadata attached at the reporting country level). Inward and outward FDI statistics in this datset are presented on a directional basis (unless otherwise stated, see metadata attached at the reporting country level); they are measured in USD millions, in millions of national currency and as a share of total (for FDI positions only). In 2014, many countries implemented the latest international standards for Foreign Direct Investment (FDI) statistics:the OECD’s Benchmark Definition of FDI, 4th edition (BMD4); andthe IMF’s Balance of Payments and International Investment Position Manual, 6th edition (BPM6) This OECD database was launched in March 2015 which includes the data series reported by national experts according to BMD4. The data are for the most part based on balance of payments statistics published by Central Banks and Statistical Offices following the recommendations of the IMF’s BPM6 and the OECD’s BMD4. However, some of the data relate to other sources such as notifications or approvals.

This dataset FDI flows by counterpart area, BMD4 includes inward and outward Foreign Direct Investment (FDI) flows by partner country for OECD reporting economies: Inward FDI flows by partner country measure the value of cross-border direct investment transactions received by the reporting economy during a year, by source country, enabling, for example, the identification of the major sources of FDI for a specific OECD economy in that year. Inward FDI flows are allocated to the immediate investing country. Outward FDI flows by partner country measure the value of cross-border direct investment transactions from the reporting economy during a year, by destination country or region, enabling, for example, the identification of the major destinations of FDI for a specific OECD economy in that year. Outward FDI flows are allocated to the immediate counterpart country for all OECD countries. Inward and outward FDI flows by partner country are presented according to the directional principle (unless otherwise specified in the country level metadata); they are measured in USD millions and in millions of national currency.

This dataset FDI flows by economic activity, BMD4 includes inward and outward Foreign Direct Investment (FDI) flows by economic activity for OECD reporting economies: Inward FDI flows by economic activity measure the value of cross-border direct investment transactions received by the reporting economy in a specific industry during a year, enabling, for example, the identification of the most attractive industries for FDI in each OECD economy in that year. Outward FDI flows by economic activity measure the value of cross-border direct investment transactions from the reporting economy during a year, by industry. The industry sector corresponds to the activity of the direct investment enterprise or to the activity of the direct investor (more details on the activity allocation method for outward FDI flows are indicated in the metadata information attached at the country level). Inward and outward FDI flows by economic activity are presented according to the directional principle (unless otherwise specified in the country level metadata); they are measured in USD millions and in millions of national currency. A cross-classification of inward and outward FDI flows by major ISIC4 sections and by main geographic aggregates are also available for some OECD reporting economies.

This dataset FDI flows by economic activity, BMD4 includes inward and outward Foreign Direct Investment (FDI) flows by economic activity for OECD reporting economies:Inward FDI flows by economic activity measure the value of cross-border direct investment transactions received by the reporting economy in a specific industry during a year, enabling, for example, the identification of the most attractive industries for FDI in each OECD economy in that year.Outward FDI flows by economic activity measure the value of cross-border direct investment transactions from the reporting economy during a year, by industry. The industry sector corresponds to the activity of the direct investment enterprise or to the activity of the direct investor (more details on the activity allocation method for outward FDI flows are indicated in the metadata information attached at the country level). Inward and outward FDI flows by economic activity are presented according to the directional principle (unless otherwise specified in the country level metadata); they are measured in USD millions and in millions of national currency. A cross-classification of inward and outward FDI flows by major ISIC4 sections and by main geographic aggregates are also available for some OECD reporting economies. In 2014, many countries implemented the latest international standards for Foreign Direct Investment (FDI) statistics:the OECD’s Benchmark Definition of FDI, 4th edition (BMD4); andthe IMF’s Balance of Payments and International Investment Position Manual, 6th edition (BPM6) An OECD database was launched in March 2015 which includes the data series reported by national experts according to BMD4. The data are for the most part based on balance of payments statistics published by Central Banks and Statistical Offices following the recommendations of the IMF’s BPM6 and the OECD’s BMD4. However, some of the data relate to other sources such as notifications or approvals. Historical and unrevised series of FDI flows by economic activity under the previous BMD3 methodology can be accessed in the archived dataset FDI flows by industry.

This dataset FDI income by counterpart area, BMD4 includes Foreign Direct Investment (FDI) payments and receipts by partner country for OECD reporting economies:FDI income payments (or income on inward FDI) by partner country measure the total returns within a year on direct investment stocks paid by enterprises in the reporting economy to their foreign investors, by destination countries or regions, enabling, for example, the identification of the major destinations of FDI income payments for a specific OECD economy in that year. FDI income payments are allocated to the immediate counterpart country.FDi income receipts (or income on outward FDI) by partner country measure the total returns within a year on direct investment stocks received by investors in the reporting economy from their direct investment enterprises abroad, by source countries or region, enabling, for example, the identification of the major sources of FDI income receipts for a specific OECD economy in that year.. FDI income receipts are allocated to the immediate counterpart country or region for all OECD countries. Income payments and receipts (or income on inward and outward FDI respectively) by partner country are presented according to the directional principle (unless otherwise specified in the country level metadata); they are measured in USD millions and in millions of national currency.

This dataset FDI income by economic activity, BMD4 includes Foreign Direct Investment (FDI) payments and receipts by economic activity for OECD reporting economies: FDI income payments (or income on inward FDI) by economic activity measure the total returns within a year on direct investment stocks paid by enterprises in the reporting economy, in a specific industry, to their foreign investors. FDi income receipts (or income on outward FDI) by economic activity measure the total returns within a year on direct investment stocks received by investors in the reporting economy from their direct investment enterprises abroad, by industry. The industry sector corresponds to the activity of the direct investment enterprise or to the activity of the direct investor (more details on the activity allocation method for FDI income receipts are indicated in the metadata information attached at the country level). Income payments and receipts (or income on inward and outward FDI respectively) by economic activity are presented according to the directional principle (unless otherwise specified in the country level metadata); they are measured in USD millions and in millions of national currency.

This dataset FDI income by economic activity, BMD4 includes Foreign Direct Investment (FDI) payments and receipts by economic activity for OECD reporting economies:FDI income payments (or income on inward FDI) by economic activity measure the total returns within a year on direct investment stocks paid by enterprises in the reporting economy, in a specific industry, to their foreign investors.FDi income receipts (or income on outward FDI) by economic activity measure the total returns within a year on direct investment stocks received by investors in the reporting economy from their direct investment enterprises abroad, by industry. The industry sector corresponds to the activity of the direct investment enterprise or to the activity of the direct investor (more details on the activity allocation method for FDI income receipts are indicated in the metadata information attached at the country level). Income payments and receipts (or income on inward and outward FDI respectively) by economic activity are presented according to the directional principle (unless otherwise specified in the country level metadata); they are measured in USD millions and in millions of national currency. In 2014, many countries implemented the latest international standards for Foreign Direct Investment (FDI) statistics:the OECD’s Benchmark Definition of FDI, 4th edition (BMD4); andthe IMF’s Balance of Payments and International Investment Position Manual, 6th edition (BPM6) This OECD database was launched in March 2015 which includes the data series reported by national experts according to BMD4. The data are for the most part based on balance of payments statistics published by Central Banks and Statistical Offices following the recommendations of the IMF’s BPM6 and the OECD’s BMD4. However, some of the data relate to other sources such as notifications or approvals.

This dataset FDI positions by counterpart area, BMD4 includes inward and outward Foreign Direct Investment (FDI) positions by partner country for OECD reporting economies:Inward FDI positions by partner country measure the total level of direct investment in the reporting economy at the end of the year, by source countries, enabling, for example, the identification of the major sources of FDI for a specific OECD economy. Inward FDI positions are allocated to the immediate investing country but inward FDI positions allocated to the ultimate investing country are also available for some OECD economies.Outward FDI positions by partner country measure the total level of direct investment from the reporting economy at the end of the year, by destination countries, enabling, for example, the identification of the major destinations of FDI for a specific OECD economy. Outward FDI positions are allocated to the immediate counterpart country for all OECD countries. Inward and outward FDI positions by partner country are presented according to the directional principle (unless otherwise specified in the country level metadata); they are measured in USD millions, in millions of national currency and as a share of total FDI positions. In 2014, many countries implemented the latest international standards for Foreign Direct Investment (FDI) statistics:the OECD’s Benchmark Definition of FDI, 4th edition (BMD4); andthe IMF’s Balance of Payments and International Investment Position Manual, 6th edition (BPM6) This OECD database was launched in March 2015 which includes the data series reported by national experts according to BMD4. The data are for the most part based on balance of payments statistics published by Central Banks and Statistical Offices following the recommendations of the IMF’s BPM6 and the OECD’s BMD4. However, some of the data relate to other sources such as notifications or approvals. Historical and unrevised series of FDI positions by counterpart area under the previous BMD3 methodology can be accessed in the archived dataset FDI positions by partner country.

This dataset FDI positions by economic activity, BMD4 includes inward and outward Foreign Direct Investment (FDI) positions by economic activity according to ISIC4 for OECD reporting economies:Inward FDI positions by economic activity measure the total level of direct investment in a specific industry of the reporting economy at the end of the year, enabling, for example, the identification of the most attractive industry sectors for FDI in each OECD economy.Outward FDI positions by economic activity measure the total level of direct investment from the reporting economy at the end of the year, by industry sector. The industry sector corresponds to the activity of the direct investment enterprise or to the activity of the direct investor (more details on the activity allocation method for outward FDI positions are indicated in the metadata information attached at the country level). Inward and outward FDI positions by economic activity are presented according to the directional principle (unless otherwise specified in the country level metadata); they are measured in USD millions, in millions of national currency and as a share of total FDI positions. A cross-classification of inward and outward FDI positions by major ISIC4 sections and by main geographic aggregates are also available for some OECD reporting economies. In 2014, many countries implemented the latest international standards for Foreign Direct Investment (FDI) statistics:the OECD’s Benchmark Definition of FDI, 4th edition (BMD4); andthe IMF’s Balance of Payments and International Investment Position Manual, 6th edition (BPM6) This OECD database was launched in March 2015 which includes the data series reported by national experts according to BMD4. The data are for the most part based on balance of payments statistics published by Central Banks and Statistical Offices following the recommendations of the IMF’s BPM6 and the OECD’s BMD4. However, some of the data relate to other sources such as notifications or approvals. Historical and unrevised series of FDI positions by economic activity under the previous BMD3 methodology can be accessed in the archived dataset FDI positions by industry.

The dataset Fisheries International collaboration in technology development (bilateral) provides the number of co-inventions (simple patent families) developed jointly by at least two inventors. This indicator is disaggregated by: Country - country of residence of the inventor(s), integral counted; in cases when inventors from more than two countries collaborate, this is translated into distinct bilateral relationships between country pairs. For example, if inventors from 3 countries collaborate (e.g. USA, DEU, JPN) then a unit count is assigned to 6 country pairs (USA-DEU, USA-JPN, DEU-JPN, DEU-USA, JPN-USA, JPN-DEU); in this case a country generally coordinate the project and the others are partners. Partner – country of residence of the inventor(s) who collaborate to the patent. Technology domain – the three main areas of innovation in fisheries and aquaculture, related to technology development. In detail: 1. Harvesting technology such as more effective ways to find or harvest fish and which are typically associated with improvements in catch per unit of effort (e.g. type/size of vessels and their methods of propulsion, search technologies, method of catching or harvesting fish and bringing them on board); 2.Aquaculture technology such as methods to more effectively grow fish in captivity (innovation in feeds, improving the health of aquaculture animals, etc.); 3. New products and markets such as the development of new fish products and markets (food technologies/processing such as the development of surimi as a crabmeat substitute) and the improvement of market access (secure or enlarge markets for fish products) that provides important incentives for green growth (e.g. eco-certification with fishers adopting by-catch saving technologies or modifying fishing practices and/or territorial user rights in fisheries).

The OECD FISH Unit, in collaboration with the Environment Directorate and the Directorate for Science, Technology and Innovation, has developed patent-based innovation indicators that are suitable for tracking developments in fisheries-related technologies. The search strategy for fisheries and aquaculture related technologies adopts a mixed solution with a definition of the technical field of interest in fisheries and aquaculture innovation complemented by keywords, e.g. by looking for keywords in the International Patent Classification (IPC) codes and checking manually the relevance of the results in the text of patents (in the title, the abstract, etc). Technology domains are detailed in the ANNEX attached below. The indicators allow the assessment of countries' and firms' innovative performance as well as the design of governments' fisheries, aquaculture and innovation policies.

The OECD FISH Unit, in collaboration with the Environment Directorate and the Directorate for Science, Technology and Innovation, has developed patent-based innovation indicators that are suitable for tracking developments in fisheries-related technologies. The search strategy for fisheries and aquaculture related technologies adopts a mixed solution with a definition of the technical field of interest in fisheries and aquaculture innovation complemented by keywords, e.g. by looking for keywords in the International Patent Classification (IPC) codes and checking manually the relevance of the results in the text of patents (in the title, the abstract, etc). Technology domains are detailed in the ANNEX attached below. The indicators allow the assessment of countries' and firms' innovative performance as well as the design of governments' fisheries, aquaculture and innovation policies.

The OECD FISH Unit, in collaboration with the Environment Directorate and the Directorate for Science, Technology and Innovation, has developed patent-based innovation indicators that are suitable for tracking developments in fisheries-related technologies. The search strategy for fisheries and aquaculture related technologies adopts a mixed solution with a definition of the technical field of interest in fisheries and aquaculture innovation complemented by keywords, e.g. by looking for keywords in the International Patent Classification (IPC) codes and checking manually the relevance of the results in the text of patents (in the title, the abstract, etc). Technology domains are detailed in the ANNEX attached below. The indicators allow the assessment of countries' and firms' innovative performance as well as the design of governments' fisheries, aquaculture and innovation policies.

Fisheries fleet: The FAO has a two dimensional definition, of which the OECD only uses the concept of fishing vessel. Fishery Fleet: The term "fishery fleet" or "fishery vessels" refers to mobile floating objects of any kind and size, operating in freshwater, brackishwater and marine waters which are used for catching, harvesting, searching, transporting, landing, preserving and/or processing fish, shellfish and other aquatic organisms, residues and plants. Fishing vessel: The term "fishing vessel" is used instead when the vessel is engaged only in catching operations. Gross Register Tonnage: The Gross Register Tonnage represents the total measured cubic content of the permanently enclosed spaces of a vessel, with some allowances or deductions for exempt spaces such as living quarters (1 gross register ton = 100 cubic feet = 2.83 cubic metres).

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.

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

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

The Future of Business Survey is an original source of information on small and medium-sized enterprises (SMEs). Launched in February 2016, the survey is a partnership between Facebook, OECD, and the World Bank. It provides timely information on business owners’ assessment of the current state and future outlook of their business, job creation perspectives, main business challenges and participation in international trade. Several data breakdowns are available, in particular by size of the enterprise, age, sector, trading status and gender of owners or managers.

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 part contains general information on number of insurance companies and employees within the sector.

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.

This data set contains information of The insurance industry is a major component of the economy by virtue of the amount of premiums it collects, the scale of its investment and, more fundamentally, the essential social and economic role it plays by covering personal and business risks. This annual report monitors global insurance market trends to support a better understanding of the insurance industry's overall performance and health.The OECD has collected and analysed data on insurance such as the number of insurance companies and employees, insurance premiums and investments by insurance companies dating back to the early 1980s. Over time, the framework of this exercise has expanded and now includes key balance sheet and income statement items for the direct insurance and reinsurance sector.

This data set combines the different regional publications of Revenue Statistics:Revenue Statistics 2019OECD/ATAF/AUC (2019), Revenue Statistics in Africa 2019OECD et al (2020), Revenue Statistics in Latin America and the Caribbean 2020 (LAC)Revenue Statistics in Asian and Pacific Economies 2019 Revenue Statistics The annual statistical publications present a unique set of detailed and internationally comparable tax revenue data in a common format. Its approach is based on the well-established methodology of the OECD Revenue Statistics (OECD, 2019), which has become an essential reference. It provides a conceptual framework defining which government receipts should be regarded as taxes and classifies different types of taxes. Comparable tables show revenue data by type of tax in national currency, as a percentage of GDP, and, for the different types of taxes, as a share of total taxation. Comparisons are also made with the averages of the 36 OECD economies, of the 24 LAC countries (two of which are OECD members countries° and of 26 African countries.   The data set does not replace the other publications, and users are advised to consult the regional data sets for country specific information.

Graduates are those who successfully complete an educational programme during the reference year of the data collection. One condition of a successful completion is that students should have enrolled in, and successfully completed, the final year of the corresponding educational programme, although not necessarily in the year of reference. Students who do not complete the final year of an educational programme, but later successfully complete a recognised "equivalency" examination based on knowledge learned outside of the education system, should not be counted as graduates. Successful completion is defined according to the graduation requirements established by each country: in some countries, completion occurs as a result of passing a final, curriculum-based examination or series of examinations. In other countries, completion occurs after a specific number of teaching hours has been accumulated (although completion of some or all of the course hours may also involve examinations).

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

As a consequence of the implementation of the new OECD Global Insurance Statistics' framework, there is a break in series between 2008 and 2009 regarding life and non-life business data where composite insurance undertakings exist. Up until 2008, the insurance business is broken down between life and non-life business. As of 2009, the insurance business is broken down between the business of pure life, pure non-life and composite undertakings and composite undertakings' business is further broken down between life and non-life business. Some countries do not allow for insurance undertakings to be active in both life and non-life insurance business and therefore composite insurance undertakings do not exist in these countries. In other countries (e.g., Austria, Belgium, Hungary, Italy, Mexico, Portugal, Spain) however, the share of employment in composite insurance undertakings accounts for more than half of the whole domestic insurance sector. Therefore, to have comparable data across years for life business data (resp. non-life), one has to sum up the life (resp. non-life) business of pure life (resp. non-life) undertakings and the life (resp. non-life) business of composite undertakings as of 2009. Gross claims payments in the reporting country, containing a breakdown between domestic companies, foreign-controlled companies and branches and agencies of foreign companies.

As a consequence of the implementation of the new OECD Global Insurance Statistics' framework, there is a break in series between 2008 and 2009 regarding life and non-life business data where composite insurance undertakings exist. Up until 2008, the insurance business is broken down between life and non-life business. As of 2009, the insurance business is broken down between the business of pure life, pure non-life and composite undertakings and composite undertakings' business is further broken down between life and non-life business. Some countries do not allow for insurance undertakings to be active in both life and non-life insurance business and therefore composite insurance undertakings do not exist in these countries. In other countries (e.g., Austria, Belgium, Hungary, Italy, Mexico, Portugal, Spain) however, the share of employment in composite insurance undertakings accounts for more than half of the whole domestic insurance sector. Therefore, to have comparable data across years for life business data (resp. non-life), one has to sum up the life (resp. non-life) business of pure life (resp. non-life) undertakings and the life (resp. non-life) business of composite undertakings as of 2009. This part contains gross operating expenses in the reporting country, with a breakdown between domestic companies, foreign-controlled companies and branches and agencies of foreign companies.

This dataset presents core population health indicators, covering life expectancy; mortality indicators such as causes of mortality (by ICD codes), maternal and infant mortality, avoidable (preventable+treatable) mortality; potential years of life lost; perceived health status (also disaggregated by socio-economic status and education level); low birthweight; incidence of key communicable diseases; cancer incidence; injuries in road traffic accidents; and absence from work due to illness.

ICT goods are those that are either intended to fulfil the function of information processing and communication by electronic means, including transmission and display, OR which use electronic processing to detect, measure and/or record physical phenomena, or to control a physical process. ICT goods are defined by the OECD in terms of the Harmonised System. The guiding principle for the delineation of ICT goods is that such goods must either be intended to fulfil the function of information processing and communication by electronic means, including transmission and display, OR use electronic processing to detect, measure and/or record physical phenomena, or to control a physical process.Another guiding principle was to use existing classification systems in order to take advantage of existing data sets and therefore ensure the immediate use of the proposed standard. In this case, the underlying system is the Harmonized System (HS). The HS is the only commodity classification system used on a sufficiently wide basis to support international data comparison. A large number of countries use it to classify export and import of goods, and many countries use it (or a classification derived from or linked to it) to categorise domestic outputs.The application of the ICT product definition to selection of in-scope HS categories is a somewhat subjective exercise. The fact that the HS is not built on the basis of the functionality of products makes it much more difficult. The distinction between products which fulfil those functions and products that simply embody electronics but fundamentally fulfil other functions is not always obvious.It is possible to adopt a narrow or broad interpretation of the guideline, though the OECD chose a broader interpretation, an approach which is consistent with that adopted to develop the ICT sector definition.

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 database presents the 2018 edition of OECD time-series indicators of implied R&D tax subsidy rates for OECD member countries and five non-member economies (Brazil, People's Republic of China, Romania, Russian Federation, and South Africa) over the period 2000-2018, drawing on data collected in the OECD-NESTI R&D tax incentive surveys from 2007 to 2018. The 2018 edition of RDTAXSUB contains time-series estimates that are based on headline tax credit and allowance rates, by firm size and profitability scenario. Due to limited historical data availability, the estimates are not adjusted for provisions that bound the tax benefits received by firms (e.g. ceilings, thresholds). They therefore provide an upper bound for the marginal tax subsidy implied by R&D tax relief measures across countries over time. These estimates should not be confused with separate contemporary cross-sectional OECD estimates of marginal tax subsidy rates (OECD, 2018) that compute adjusted (weighted) tax credit/allowance rates for a number of countries based on available information on the proportion of eligible R&D subject to different marginal levels of relief (see 2017).The tax subsidy rate is defined as 1 minus the B-index, a measure of the before-tax income needed by a “representative” firm to break even on USD 1 of R&D outlays (Warda, 2001). As tax component of the user cost of R&D, the B-Index is is directly linked to measures of effective marginal tax rates. Measures of tax subsidy rates such as those based on the B-index provide a convenient proxy for examining the implications of tax relief provisions. These provide a synthetic representation of the generosity of a tax system from the perspective of a generic or model type of firm for the marginal unit of R&D expenditure. To provide a more accurate representation of different scenarios, B-indices are calculated for “representative” firms according to whether they can claim tax benefits against their tax liability in the reporting period (OECD, 2013). When credits or allowances are fully refundable, the B-index of a firm in such a position is identical to the profit scenario. Carry-forwards are modelled as discounted options to claim incentives in the future, assuming a constant annual probability of returning to profit of 50% and a nominal discount rate of 10%. For general and country-specific notes on the time-series estimates of implied marginal tax subsidy rates on R&D expenditures (based on the B-index), see http://www.oecd.org/sti/rd-tax-stats-bindex-notes.pdf.

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.

Inbound tourism comprises the activities of a non-resident visitor within the country of reference.

Data source(s) used The inland fisheries 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.   Data are collected in tonnes and national currency at current values. For analytical purposes and data comparisons, value data are converted and published also in US dollars. Exchange rates are average yearly spot rates, taken from the dataset OECD Economic Outlook: Statistics and Projections. Data reported in this dataset are expressed in tonnes, in units of national currency and in US dollars. Data are recorded on a landed weight basis, i.e. the mass (or weight) of a product at the time of landing, regardless of the state in which is landed (i.e. whole, gutted, filleted, meal, etc.). For exceptions, please see the individual notes. Statistical population The statistical population is the set of countries participating in the work of the COFI, i.e. OECD members, excluding landlocked countries, with some exceptions (Czech Republic and Slovakia are included, Israel is not). The group includes also the following partner countries: Argentina, China, Colombia, Costa Rica, Indonesia, Lithuania, Peru, Philippines, Thailand and Chinese Taipei. In order to facilitate analysis and comparisons over time, historical data for OECD members have been provided over as long a period as possible, often even before a country became a member of the Organisation. Information on the membership dates of all OECD countries can be found at OECD Ratification Dates. Key statistical concept Inland fisheries include catches of fish, crustaceans, molluscs and other aquatic invertebrates (and animals), residues and seaweeds in lakes, rivers, ponds, inland canals and other land-locked water bodies. For the purpose of this questionnaire the boundary between inland and marine areas at the river mouth is left to the discretion of the national authority. Production from aquaculture installations should not be reported on this form. However, catches from fisheries that are managed by stocking should be included. The methodological reference document for fisheries and aquaculture statistics is the CWP Handbook of Fishery Statistics.

As a consequence of the implementation of the new OECD Global Insurance Statistics' framework, there is a break in series between 2008 and 2009 regarding life and non-life business data where composite insurance undertakings exist. Up until 2008, the insurance business is broken down between life and non-life business. As of 2009, the insurance business is broken down between the business of pure life, pure non-life and composite undertakings and composite undertakings' business is further broken down between life and non-life business. Some countries do not allow for insurance undertakings to be active in both life and non-life insurance business and therefore composite insurance undertakings do not exist in these countries. In other countries (e.g., Austria, Belgium, Hungary, Italy, Mexico, Portugal, Spain) however, the share of employment in composite insurance undertakings accounts for more than half of the whole domestic insurance sector. Therefore, to have comparable data across years for life business data (resp. non-life), one has to sum up the life (resp. non-life) business of pure life (resp. non-life) undertakings and the life (resp. non-life) business of composite undertakings as of 2009. Breakdown of net premiums written in the reporting country in terms of domestic risks and foreign risks, thus providing an indicator of direct cross-border operations of insurance business.

As a consequence of the implementation of the new OECD Global Insurance Statistics' framework, there is a break in series between 2008 and 2009 regarding life and non-life business data where composite insurance undertakings exist. Up until 2008, the insurance business is broken down between life and non-life business. As of 2009, the insurance business is broken down between the business of pure life, pure non-life and composite undertakings and composite undertakings' business is further broken down between life and non-life business. Some countries do not allow for insurance undertakings to be active in both life and non-life insurance business and therefore composite insurance undertakings do not exist in these countries. In other countries (e.g., Austria, Belgium, Hungary, Italy, Mexico, Portugal, Spain) however, the share of employment in composite insurance undertakings accounts for more than half of the whole domestic insurance sector. Therefore, to have comparable data across years for life business data (resp. non-life), one has to sum up the life (resp. non-life) business of pure life (resp. non-life) undertakings and the life (resp. non-life) business of composite undertakings as of 2009. Item coverage Covers business written abroad by branches, agencies and subsidiaries established abroad of domestic undertakings and includes all business written outside the country by these entities (in both OECD and non-OECD countries).

This data deals with premiums written by classes of non-life insurance for the business written in the reporting country.

Geographic coverage OECD countries, Selected African and Asian countries, Selected Latin American countries Institutional coverage The insurance industry is a key component of the economy by virtue of the amount of premiums it collects, the scale of its investment and, more fundamentally, the essential social and economic role it plays in covering personal and business risks. The "OECD Insurance Statistics" publication provides major official insurance statistics for all OECD countries. The reader will find information on the diverse activities of this industry and on international insurance market trends. The data, which are standardised as far as possible, are broken down under numerous sub-headings, and a series of indicators makes the characteristics of the national markets more readily comprehensible. This publication is an essential tool for civil servants, businessmen and academics working in the insurance field. Item coverage This part consists of tables by indicators, which reflect the most significant characteristics of the OECD insurance market. In most cases, the tables contain data of all OECD countries as well as aggregated "OECD", "EU15" (the 15 member countries of the European Union in 1995) and "NAFTA" data from 1983 to 2015, for the following categories: - life insurance, - non-life insurance - and total. The premiums amounts are converted from national currencies into US dollar. Exchange rates used are end-of-period exchanges rates for all variables valued at the end of the year, and period-average for variables representig a flow during the year.

This dataset presents patent statistics and indicators that are suitable for tracking innovation in environment-related technologies. They allow the assessment of countries and firms' innovation performance as well as the design of governments' environmental and innovation policies.

This dataset contains the number of people who graduated from an education programme by country of origin and sex.

This indicator reports the percentage of students of each country of origin over the total of international students.

Metadata by country are available in English and French. Please click on the link below: https://www.oecd.org/sdd/its/OECD-Trade-In-Services-EBOPS2010-metadata-all-countries.pdf. The aim of this dataflow International Trade in Services EBOPS 2010 (by partner country) is to collect and disseminate balance of payments data on international trade in services at the most detailed partner country level available. To the extent that countries report them, data are also broken down by type of service according to the EBOPS 2010 classification. There has been a growing interest in monitoring patterns of trade in services around the world, which is partly associated with ongoing trade negotiations and partly due to the increasing importance of services in OECD economies. It has been developed to supplement other OECD publications on trade in services to address the data needs of trade analysts. It is also an important part of OECD's programme to facilitate the implementation of the recommendations of the revised Manual on Statistics of International Trade in Services 2010. The currency unit used for all series is: Millions of US dollars or Millions of National Currency or Millions of Euros. OECD statistics contact: [email protected] http://www.oecd.org/sdd

This dataset presents official international trade statistics in fisheries products, directly sourced from the UN Comtrade Database.

This table contains figures on affiliates under foreign control by investing country in the total manufacturing, total services and total business enterprise sectors.

The units used to present data in AFA 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.

The IPP.Stat is the statistics portal of the Innovation Policy Platform containing the main available indicators relevant to a country’s innovation performance. In addition to the traditional indicators used to monitor innovation, the range of the coverage to be found in the IPP.Stat calls for the inclusion of indicators from other domains that describe the broader national and international context in which innovation occurs. Indicators are sourced primarily from the OECD and the World Bank, as well as from other sources of comparable quality. The statistics portal is still under development.

The land cover dataset provides a global assessment of land cover and land cover change to monitor pressures on ecosystems and biodiversity. By using geospatial data with high spatio-temporal resolution, it develops a set of internationally comparable indicators with a long time series from 2000 to 2020 with a five-year interval. Please see the working paper for a more complete description of the methods. The dataset has a global coverage on the national level, while on the sub-national TL2 level (i.e. large subnational regions) results are reported for all OECD countries as well as for Argentina, Brazil, China, India, Indonesia and South Africa. The following country aggregates are included: Euro area, European Union, Advanced economies, Emerging market economies, G7, G20, OECD, OECD Europe, OECD Asia Oceania, OECD Americas, the LAC region and the World. Data source(s): Copernicus Climate Change Initiative land cover data and Global Human Settlement Layer built-up area data. Contact: [email protected] Database documentation

Land resources are one of the four components of the natural environment: water, air, land and living resources. In this context land is both: a physical "milieu" necessary for the development of natural vegetation as well as cultivated vegetation;a resource for human activities.  The data presented here give information concerning land use state and changes (e.g. agricultural land, forest land).  Land area excludes area under inland water bodies (i.e. major rivers and lakes).   Arable refers to all land generally under rotation, whether for temporary crops (double-cropped areas are counted only once) or meadows, or left fallow (less than five years). These data are not meant to indicate the amount of land that is potentially cultivable.  Permanent crops are those that occupy land for a long period and do not have to be planted for several years after each harvest (e.g. cocoa, coffee, rubber). Land under vines and trees and shrubs producing fruits, nuts and flowers, such as roses and jasmine, is so classified, as are nurseries (except those for forest trees, which should be classified under "forests and other wooded land").  Arable and permanent crop land is defined as the sum of arable area and land under permanent crops.  Permanent meadows and pastures refer to land used for five years or more to grow herbaceous forage crops, either cultivated or growing wild (wild prairie or grazing land).  Forest refers to land spanning more than 0.5 hectare (0.005 km2) and a canopy cover of more than 10 percent, or trees able to reach these thresholds in situ. This includes land from which forests have been cleared but that will be reforested in the foreseeable future. This excludes woodland or forest predominantly under agricultural or urban land use and used only for recreation purposes.

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.

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. 

This dataset presents members' total use of the multilateral system i.e. both their multilateral aid ("Core contributions to") and bilateral aid channelled through ("Contributions through") multilateral organisations. These data originate from members' reporting at item-level in the CRS and are published here starting with 2011 data (item-level data for multilateral aid is not complete in CRS for earlier years).

Despite the growing importance of international trade, driven in large part by the rise of globalisation and the accompanying international fragmentation of production, the availability of statistics on price change in international merchandise trade at more graular level is still limited. To fill this data gap, the OECD has developed this new Merchandise Trade Price Index (MTPI) database using UN COMTRADE. The first release covers about 100 countries from 2011 to 2017. Indices by reporting country are available for both exports and imports, broken down by 30 products, aligned with the 2-digit level of the Classification of Products of Activity (CPA, version 2.1). Future releases are planned to expand the country coverage and the level of disaggregation.

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.

National landings in domestic ports

The Fisheries Committee (COFI) from the Trade and Agriculture Directorate (TAD) collects, on an annual basis from all its participating countries, data on landings, aquaculture production, fleet, employment in the fisheries sector, and government financial transfers. Data are collected from Fisheries Ministries, National Statistics Offices and other institution designated as an official data source. The OECD, a partner with the CWP, additionaly collects information on values for its landings and records the breakdown between the types of landings (i.e. landings in domestic ports, landings in foreign ports) data series which are not collected by the FAO. While a number of countries cover landings in a similar fashion, the same does not hold true for capacity (feet/meters, GRT/engine powers), or for employment for which both Full-time equivalents or numbers of people are used. The OECD therefore does not duplicate FAO statistics but requests complementary information to feed its analytical work.  

Official development assistance (ODA) is defined as government aid designed to promote the economic development and welfare of developing countries. Loans and credits for military purposes are excluded. Aid may be provided bilaterally, from donor to recipient, or channelled through a multilateral development agency such as the United Nations or the World Bank. Aid includes grants, "soft" loans and the provision of technical assistance. The OECD maintains a list of developing countries and territories; only aid to these countries counts as ODA. The list is periodically updated and currently contains over 150 countries or territories. 

In this version, seven GVCs indicators are presented for 59 economies (34 OECD and 23 non-OECD economies, plus the "rest of the world" and the European Union) for 18 industries in the years 1995, 2000, 2005, 2008 and 2009. The indicators are calculated based on the five global input-output matrices of the TiVA database. More details on the aggregation and specific country notes can be downloaded at http://www.oecd.org/sti/ind/input-outputtables.htm and http://oe.cd/gvc/.

This OECD inventory maps existing cross-country surveys that provide information on the characteristics of people's jobs. The information included in this inventory covers international surveys conducted since the early 1990s that are based on individuals' self-reported assessment of their current job, for 160 countries over 25 years. Survey questions are grouped into 19 indicators. For each indicator, binary codes (1 and 0) show whether indicators are available or not for the various countries and years. The inventory also provides users with detailed documentation on the questions used in the various surveys for measuring these indicators.

The OECD Science, Technology and Industry Outlook reviews key trends in STI policies and performance in OECD countries and major emerging economies. It is published every two years and draws on a unique international policy survey conducted by the OECD - with more than 45 countries involved in 2014 - and the latest OECD work on STI policy analysis and measurement. Following an overview of the recent STI global landscape, key current policy issues are discussed across a series of thematic policy profiles. Country profiles report the STI performance of individual countries and the most recent national policy developments.

The Agricultural Outlook has been prepared as a joint report by the Organisation for Economic Co-operation and Development (OECD) and the Food and Agriculture Organization (FAO) of the United Nations. The report provides a ten year forward looking, assessment of trends and prospects in the major temperate-zone agricultural commodity markets of biofuels, cereals, oilseeds and oilseed products, sugar, meat, fish and sea food, dairy products, cotton, roots and tubers and pulses. It is published annually, in the middle of the second quarter, as part of a continuing effort to promote informed discussion of emerging market and policy issues. The data used to develop the projections underlying the assessment are those available as of January 2023. The projections and assessments provided in the report are the result of close co-operation between the OECD and FAO Secretariats and national experts with a jointly developed modelling system, based on the AGLINK-COSIMO model, used to facilitate consistency in the projections. The data series for the projections are drawn from OECD and FAO databases. For the most part information in these databases has been taken from national statistical sources. For further details on particular series, enquiries should be directed to the OECD and FAO Secretariats.

Outbound tourism comprises the activities of residents of a given country travelling to and staying in places outside their country of residence.

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.

This table contains figures on the activity of affiliates located abroad by host country in the total manufacturing sector or in the total business sector. The units used to present data in AFA 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.

This dataset presents patent statistics and indicators that are suitable for tracking innovation in environment-related technologies. They allow the assessment of countries and firms' innovation performance as well as the design of governments' environmental and innovation policies.

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.

Patent counts (including total patents, estimated total patents for latest years): EPO, USPTO, PCT and Patent Families indicators are presented for technology domains defined by the OECD or by WIPO

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 database provides a set of indicators that reflect the level and structure of central government support for business R&D; in form of R&D; tax incentives and direct funding across OECD member countries and ten non-member economies (Argentina, Brazil, Bulgaria, Croatia, Cyprus, People's Republic of China, Romania, Russian Federation, and South Africa). This includes time-series indicators of tax expenditures for R&D;, based on the latest 2017 OECD data collection on tax incentive support for R&D; expenditures that was completed in July 2017. These estimates of the cost of R&D; tax relief have been combined with data on direct R&D; funding, as compiled by National Statistical Offices based on reports from firms, in order to provide a more complete picture of government efforts to promote business R&D.; The latest indicators and information on R&D; tax incentives also feature on the dedicated OECD website Measuring R&D; tax incentives.Tax expenditures are deviations from a benchmark tax system (OECD, 2010) and countries use different national benchmarks. Available estimates typically reflect the sum of foregone tax revenues – on an accruals basis – and refunds where applicable, with no or minimal adjustments for behavior effects. Some countries only report claims realised in a given year (cash basis), while others report losses to government on an accrual basis, excluding claims referring to earlier periods and including claims for current R&D; to be used in the future. For general and country-specific notes on the estimates of government tax relief for R&D; expenditures (GTARD), see http://www.oecd.org/sti/rd-tax-stats-gtard-notes.pdfThe sources for the other indicators (direct funding of BERD, BERD and GDP) include the OECD databases on Main Science and Technology Indicators and Eurostat R&D; statistics.

Revenue Statistics in Asian Countries is a joint publication by the OECD Centre for Tax Policy and Administration and the OECD Development Centre. It presents detailed, internationally comparable data on tax revenues for seven Asian economies, two of which (Korea and Japan) are OECD members. Its approach is based on the well-established methodology of the OECD Revenue Statistics (OECD, 2015), which has become an essential reference source for OECD member countries. Comparisons are also made with the average for OECD economies. Comparable tables show revenue data by type of tax in national currency and US dollars, as a percentage of GDP, and, for the different types of taxes, as a share of total taxation. Detailed country tables show information in national currency values

The OECD STRI is a unique, evidence-based tool that collects information on services trade restrictions across 22 services sectors. The project has two distinct but complementary instruments: a services trade regulatory database and a services trade restrictiveness index. Based on the qualitative information in the database, composite indices quantify the identified restrictions across five standard policy categories, with values between zero and one. Complete openness to trade and investment gives a score of zero, while being completely closed to foreign services providers yields a score of one.

The aim of the OECD’s new Skills for Jobs Indicators is to facilitate better adaptation to changing skill needs by making available a database of skill imbalances indicators that is comparable across countries and regularly updated. The Skill Needs Indicators provide an overview of the shortages and surpluses of skills across countries.

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.

Excess capacity is one of the main challenges facing the global steel sector. The OECD Steelmaking Capacity database contains data on crude steelmaking capacity by economy and provides researchers and policymakers with an important tool for analysing steel capacity developments.

The OECD STRI heterogeneity indices complement the existing STRI's and presents indices of regulatory heterogeneity based on the rich information in the STRI regulatory database. The indices are built from assessing – for each country pair and each measure – whether or not the countries have the same regulation. For each country pair and each sector, the indices reflect the (weighted) share of measures for which the two countries have different regulation.

The OECD indicators of employment protection are synthetic indicators of the strictness of regulation on dismissals and the use of temporary contracts. For each year, indicators refer to regulation in force on the 1st of January. Version 1 of the indicator of strictness of employment protection - individual and collective dismissals (regular contracts) - does not incorporate all the data items of version 3 and, in particular, does not incorporate regulation of collective dismissals. You should only use version 1 if you need data for years for which neither version 2 nor 3 are available.

The OECD indicators of employment protection are synthetic indicators of the strictness of regulation on dismissals and the use of temporary contracts. For each year, indicators refer to regulation in force on the 1st of January. For more information and full methodology, see www.oecd.org/employment/protection.

The OECD indicators of employment protection are synthetic indicators of the strictness of regulation on dismissals and the use of temporary contracts. For each year, indicators refer to regulation in force on the 1st of January. For more information and full methodology, see www.oecd.org/employment/protection.

The OECD indicators of employment protection legislation evaluate the regulations on the dismissal of workers on regular contracts and the hiring of workers on temporary contracts. They cover both individual and collective dismissals.The indicators have been compiled using the Secretariat’s own reading of statutory laws, collective bargaining agreements and case law as well as contributions from officials from OECD member countries and advice from country experts

For data aligned to Finance, the year shown is the calendar year. For data aligned to personnel, the year shown is the year in which the end of the school year falls (e.g. 2002 refers to the school year 2001/2002), with the exceptions of Korea where the year refers to the year in which the school year begins and Australia and New Zealand where the school academic year corresponds to the calendar year.

Survey on Monitoring the Paris Declaration. The dataset contains data as reported by donors and national co-ordinators in participating partner countries. The dataset includes all quantitative data collected through the 2006, 2008 and 2011 Surveys.

The OECD Sustainable Ocean Economy Database synthesizes available ocean-related datasets and indicators from across the Organisation to improve their discoverability and comparability. The database brings together relevant indicators from the Environment Directorate (ENV), the Trade and Agriculture Directorate (TAD), the Centre for Entrepreneurship, SMEs, Regions and Cities (CFE), the International Transport Forum (ITF), the International Energy Agency (IEA), and others.

This indicator reports the import-weighted applied tariffs on environmentally related goods as defined in the Combined List of Environmental Goods (CLEG) in percentage points for all countries between 2003 and 2016.

Because of the limited availability of official statistics on national supply-use and input-output tables in recent years – reflecting the fact that these are only typically available at best two or three years after the reference period to which they refer – TiVA indicators for the most recent years, as displayed in this dataset, are estimated using now-casting techniques. The approach (described in more detail in the accompanying methodological note) in essence estimates national input-output tables by projecting relationships observed in the latest TiVA benchmark year (currently 2011) into nowcast years (currently 2012-2014) but constrained to official estimates of gross output and value-added by industry and national accounts main aggregates of demand and trade, and supplemented by bilateral trade statistics, all of which are available throughout the nowcast period. Importantly, the projections of relationships in 2011 into 2012 are determined using a volume approach, to account for possible distortions that might be introduced – by for example differential price movements in imports and domestic production – if projections were made using nominal relationships. These estimates are then reflated into current prices, and simultaneously balanced – consistent with official volume and current price estimates of trade, demand and activity – to arrive at a balanced national input-output table in 2012, in nominal terms as well as in prices of 2011. Estimates for 2013 and 2014 are calculated in the same manner but using, respectively, the 2012 and 2013 relationships as the starting point.

Official Development Financing (ODF), measured for recipient countries only, is defined as the sum of their receipts of bilateral ODA, concessional and non-concessional resources from multilateral sources, and bilateral other official flows made available for reasons unrelated to trade, in particular loans to refinance debt.

This table presents export/import information by enterprise size class and partner country.

This dataset shows the number of exporters and importers and their associated trade values for a selected set of partner countries and zones, broken down by three economic sectors: industry, trade and repair and other sectors. Total values for the wide economy are also displayed.Recommended uses and limitations EU countries break down trade data into Intra- and extra- EU zones, whereas non EU countries report their Total trade. Trade values have been aggregated for EU countries and Total (Intra-EU plus Extra-EU) trade flows are displayed, whereas Intra and Extra-EU data expressed in term of number of enterprises cannot be summed up, because of possible double-counting (same enterprise can be trader in both intra- and extra- EU trade). Data have been collected in ISIC revision 3 from 2003 up to 2007 and in ISIC revision 4 as from reference year 2008. Time series are affected by this change in classification, and thus data are displayed into two separate databases.

This indicator reports the amount of exports and imports of environmentally-related goods as defined in the Combined List of Environmental Goods (CLEG) in current USD for all countries between 2003 and 2016.

The TiVA database contains a range of indicators measuring the value added content of international trade flows and final demand. The indicators are derived from the 2016 version of OECD's Inter-Country Input-Output (ICIO) Database.  The ICIO has been constructed from various national and international data sources all drawn together and balanced under constraints based on official (SNA93) National Accounts by economic activity and National Accounts main aggregates.  Underlying sources used are notably:  • National supply and use tables (SUTs)  • National and harmonised Input-Output Tables • Bilateral trade in goods by industry and end-use category (BTDIxE) and  • Bilateral trade in services.  Compared to the old versions of the TiVA database, this current version includes two more countries, Morocco and Peru. The data are presented for all years from 1995 to 2011. The industry breakdown remains the same. 

The Trade in Value Added (TiVA) database consists of a set of measures that aim to provide better insights into global production networks and supply chains than is possible with conventional trade statistics.   The Origin of value added in final demand presented here, is derived from the latest version of OECD’s Inter-Country Input-Output (ICIO) database and provides estimates of final demand in country c for industry i final goods and services, broken down by the value added originating from source industry j in source country s.   In other words, it reveals how the value of final demand goods and services consumed within a country is an accumulation of value generated by many industries in many countries.   For a description of the method used for calculating these estimates using the ICIO   Domestic value added origin is shown where source country s = c and, for convenience, also represented by source country = “DXD: Domestic”.   From this data cube, a range of final demand-based measures can be derived including those in TiVA principal indicators cube such as: • Domestic value added embodied in foreign final demand, FFD_DVA and related partner shares FFD_DVApSH. • Foreign value added embodied in domestic final demand, DFD_FVA and related partner shares DFD_FVApSH.

The Trade-in Value Added (TiVA) database consists of a set of measures that aim to provide better insights into global production networks and supply chains than is possible with conventional trade statistics.   The Origin of value-added in gross imports presented here is derived from the latest version of OECD’s Inter-Country Input-Output (ICIO) database. It provides estimates of gross imports by country c of goods and services from industry in partner country/region p broken down by value-added originating from source country/region s.   In other words, the four dimensions link the imports of country c to the value-added from source country s embodied in the exports of industry in the exporting country p - thus revealing how the value of a country’s gross imports of intermediate and final products from a particular partner is an accumulation of value generated by many countries.   For a description of the method used for calculating these estimates, using the ICIO   From this data cube, a range of gross imports-based measures can be derived including the following found in the main TiVA indicators database: • Total gross imports by industry, IMGR (c, i): set exporting country p = World and source country s = World. • Domestic value-added content of gross imports by partner and industry, IMGR_DVA (c, i, p): set source country s = importing country c. • Share of IMGR_DVA in relation to IMGR: IMGR_DVAsh (c, i, p).   Note that the same value-added originating from source country s can be present in the gross imports of more than one importing country c (as embodied value-added, from upstream production, may cross national borders many times). In general, therefore, these estimates should be viewed from the perspective of an importing country c.

The indicator reports the amount of exports and imports of waste and scrap as defined in Kellenberg (2012) in current USD and in kilograms for all countries between 2003 and 2016.

World Indicators of Skills for Employment (WISE) provide a comprehensive system of information relating to skills development. WISE presents countries with data upon which they can design skills policies and programs and monitor their impact on key outcomes, including responsiveness to current and emerging patterns of labour market demand, employability, productivity, health status, gender equity and lifelong learning.The database covers the period from 1990 to the present and consists of five inter-related domains of indicators:Contextual factors drive both the supply of and demand for skills.Skill acquisition covers investments in skills, the stock of human capital and its distribution.Skill requirements measure the demand for skills arising in the labour market.The degree of matching captures how well skills obtained through education and training correspond to the skills required in the labour market.Outcomes reflect the impact of skills on economic performance and employment and social outcomes.