Organisation for Economic Co-operation and Development

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

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

commitment is a firm written obligation by a government or official agency, backed by the appropriation or availability of the necessary funds, to provide resources of a specified amount under specified financial terms and conditions and for specified purposes for the benefit of a recipient country or a multilateral agency. Members unable to comply with this definition should explain the definition that they use. -- Commitments are considered to be made at the date a loan or grant agreement is signed or the obligation is otherwise made known to the recipient (e.g. in the case of budgetary allocations to overseas territories, the final vote of the budget should be taken as the date of commitment). For certain special expenditures, e.g. emergency aid, the date of disbursement may be taken as the date of commitment. -- Bilateral commitments comprise new commitments and additions to earlier commitments, excluding any commitments cancelled during the same year. Cancellations and reductions in the year reported on of commitments made in earlier years are reported in the CRS, but not in the DAC questionnaire. -- In contrast to bilateral commitments, commitments of capital subscriptions, grants and loans to multilateral agencies should show the sum of amounts which are expected to be disbursed before the end of the next year and amounts disbursed in the year reported on but not previously reported as a commitment. For capital subscriptions in the form of notes payable at sight, enter the expected amount of deposits of such notes as the amount committed.

This dataset contains commitment data (since 2002) and disbursement data (since 2009) on aid in support of gender equality from the CRS database. In their reporting to the Development Assistance Committee (DAC) Creditor Reporting System (CRS), donors are requested to indicate for each activity whether or not it targets gender equality as one of its policy objectives. To qualify as “gender equality focussed,” an activity must explicitly promote gender equality and women’s empowerment. An activity can either target gender equality as its “principal objective” or as a “significant objective”. A “principal” score (2) is assigned if gender equality was an explicit objective of the activity and fundamental to its design - i.e. the activity would not have been undertaken without this objective. A “significant” score (1) is assigned if gender equality was an important, but secondary, objective of the activity - i.e. it was not the principal reason for undertaking the activity. A “not targeted” score (0) is assigned if, after being screened against the gender equality policy marker, an activity is not found to target gender equality. Activities assigned a “principal objective” score should not be considered better than activities assigned a “significant objective” score, as donors that mainstream gender equality - and thus integrate it into their projects across a range of sectors - are more likely to allocate the marker score “significant” to their aid activities. The gender equality marker allows an approximate quantification of aid flows that target gender equality as a policy objective. In marker data presentations the figures for principal and significant objectives should be shown separately and the sum referred to as the “estimate” or “upper bound” of gender equality-focussed aid. An activity can have more than one principal or significant objective. Therefore, total amounts targeting the different objectives should not be added-up to avoid double-counting. Policy markers seek information on the donor’s policy objectives which can be best assessed at the design stage of projects. This is why policy markers are applied to commitments. Policy marker data on a disbursement basis can also be compiled, but it is important to note that this does not mean the policy objectives of projects under implementation would have been re-assessed. Rather, the disbursements are linked to the qualitative information on the original commitment through project identifiers. Consequently, a project marked as gender equality focussed at the commitment stage will be flagged as gender equality focussed throughout its lifetime, unless the qualitative information was changed. Activity-level gender equality marker data that underlie the aggregate figures presented in this dataset are available for consultation and download: see “Export”, “Related files”.

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

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.

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

STAN Bilateral Trade Database by Industry and End-use category (BTDIxE) provides values of imports and exports (as well as re-imports and re-exports) of goods broken down by industrial sectors and by end-use categories. BTDIxE was designed to extend the old BTD database which provided bilateral trade in goods by industry only.  BTDIxE allows, for example, insights into the patterns of trade in intermediate goods between countries to track global production networks and supply chains, and it helps to address policy issues such as trade in value added and trade in tasks.  The database presents estimates of bilateral flows of goods from 1990 to the latest available year, i.e. 2018; the latest year shown is subject to the availability of underlying product-based annual trade statistics.  Reporters are the OECD member countries and a large number of non-OECD economies, including the BRIICS: Brazil, the Russian Federation, India, Indonesia, People's Republic of China and South Africa; other selected G20 and Asian economies; and major African and Latin American nations.  It should be noted that starting from mid-2012, the OECD and the United Nations agreed to centralise the data collection and processing procedures within UNSD Comtrade.  The list of partners covers the OECD countries, more than a hundred of non-member economies as well as the partners "World", "Rest of the World" and "Unspecified". The partner "Total foreign trade" corresponds to the flows with partner "World" excluding intra-country flows. Trade flows are divided into economic activities based on the Revision 4 of ISIC and nine end-use categories including capital goods, intermediate goods and household consumption.

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

Statistical population: CLIs are calculated for 33 OECD countries (Iceland is not included), 6 non-member economies and 8 zone aggregates. A country CLI comprises a set of component series selected from a wide range of key short-term economic indicators.   CLIs, reference series data (see below) and standardised business and consumer confidence indicators are presented in various forms.   Recommended uses and limitations: The composite leading indicator is a times series, formed by aggregating a variety of component indicators which show a reasonably consistent relationship with a reference series (e.g. industrial production IIP up to March 2012 and since then the reference series is GDP) at turning points. The OECD CLI is designed to provide qualitative information on short-term economic movements, especially at the turning points, rather than quantitative measures. Therefore, the main message of CLI movements over time is the increase or decrease, rather than the amplitude of the changes. The OECD’s headline indicator is the amplitude adjusted CLI. In practice, turning points in the de-trended reference series have been found about 4 to 8 months (on average) after the signals of turning points had been detected in the headline CLI.

The 'Consumer Price Indices (CPIs)' contains all data that was previously contained in three different datasets: 'Consumer Prices', 'National Consumer Price Indices (CPIs) by COICOP divisions' and 'Harmonised Indices of Consumer Prices (HICPs) by COICOP divisions'. The 'Consumer Price Indices (CPIs)' dataset contains predominantly monthly statistics, and associated statistical methodological information, for the 36 OECD member countries and for some non-member countries. The ‘Consumer Price Indices (CPIs)' dataset contains statistics on Consumer Price Indices including national CPIs, Harmonised Indices of Consumer Prices (HICPs) and their associated weights and contributions to national annual inflation. The data series presented have been chosen as the most relevant prices statistics for which comparable data across countries is available. In all cases, a lot of effort has been made to ensure that the data are internationally comparable across all countries presented and that all the subjects have good historical time-series’ data to aid with analysis. Data are available monthly for all the countries except for Australia and New Zealand (quarterly data).

The country statistical profiles provide a broad selection of indicators, illustrating the demographic, economic, environmental and social developments, for all OECD members. The dataset also covers the five key partner economies with which the OECD has developed an enhanced engagement program with (Brazil, China, India, Indonesia and South Africa) ,accession countries (Colombia, Costa Rica and Lithuania) , Peru and the Russian Federation. The user can easily compare indicators across all countries. Total fertility rates - Unit of measure used: Number of children born to women aged 15 to 49

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.

Tourism can be regarded as a social, cultural and economic phenomenon related to the movement of people outside their usual place of residence.

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.Other commentsThe Task Force on Statistics of International Trade in Services maintains a matrix summarising the status of the trade in services data collection performed by International Organisations. The table displays links to the databases as well as update timetables, availability of metadata, availability of bilateral data, and other important characteristics.

The OECD Economic Outlook analyses the major economic trends over the coming 2 years. It provides in-depth coverage of the main economic issues and the policy measures required to foster growth in each member country. Forthcoming developments in major non-OECD economies are also evaluated in detail. Each edition of the Outlook provides a unique resource to keep abreast of world economic developments. The OECD Economic Outlook database is a comprehensive and consistent macroeconomic database of the OECD economies, covering expenditures, foreign trade, output, labour markets, interest and exchange rates, balance of payments and government debt. For the non-OECD regions, foreign trade and current account series are available. Variables are defined in such a way that they are as homogenous as possible for the countries covered. Breaks in underlying series are corrected as far as possible. Sources for the historical data are publications of national statistical agencies and OECD data bases such as Quarterly National Accounts, Annual National Accounts, Labour Force Statistics and Main Economic Indicators. The cut-off date for information used in the compilation of the projections was June 1, 2023. The aggregation of world trade takes into account the projections made for the main non-OECD economies. Thus, besides OECD and the OECD euro area, the following regions are available: Dynamic Asian Economies (Chinese Taipei, Hong Kong, Malaysia, the Philippines, Singapore, Thailand, Vietnam); Oil Producers (Algeria, Angola, Azerbaijan Bahrain, Brunei, Chad, Rep. of Congo, Ecuador, Equatorial Guinea, Gabon, Iran, Iraq, Kazakhstan, Kuwait, Libya, Nigeria, Oman, Qatar, Saudi Arabia, Sudan, Timor-Leste, Trinidad and Tobago, Turkmenistan, United Arab Emirates, Yemen, Venezuela); with the remaining countries in a residual 'Rest of the World' group.

These tables are a complement to the report Agricultural Policy Monitoring and Evaluation 2012 : OECD COUNTRIES. They comprise the summary of agricultural support estimates for OECD countries.

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.

Enrolment rate per age is the percentage of students enrolled in each type of institution over the total of students

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

In view of the strong demand for cross-national indicators on the situation of families and children, the OECD Family Database was developed to provide cross-national indicators on family outcomes and family policies across the OECD countries, its enhanced engagement partners and EU member states. The database brings together information from various national and international databases, both from within the OECD and from external organisations. The database classifies indicators into four main dimensions: (i) structure of families, (ii) labour market position of families, (iii) public policies for families and children and (iv) child outcomes. Detailed information on the definitions, sources and methods used in the construction of the database can be found on the OECD Family Database webpage.

FDI data are based on statistics provided by 35 OECD member countries and by Lithuania. BMD4: OECD Benchmark Definition of Foreign Direct Investment - 4th Edition

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 Fisheries R&D expenditures dataset contains the budgetary expenditures in research and development on total budgetary FSE. Three variables are presented in this dataset:  • R&D expenditures - they are budgetary expenditures that finance research and development activities related to fisheries, irrespective of the institution (private or public, ministry, university, research centre or fisher group) or where they take place, the nature of research (scientific, institutional, etc.), or its purpose. The focus is on research and development expenditures on applied research related to the fisheries sector. Social-sciences research related to fisheries is included. It is also included data dissemination when associated primarily with research and development (knowledge generation), e.g. reports from research and databases developed as an adjunct to research. •FISHERIES SUPPORT ESTIMATE - Budgetary - it is the annual monetary value of gross transfers from taxpayers to fishers arising from policy measures that support fisheries, regardless of their nature, objectives or impacts. Data on FSE are collected by the Fisheries Committee (COFI) from the Trade and Agriculture Directorate (TAD) on an annual basis from all its participating countries. Data are provided by Fisheries Ministries, National Statistics Offices and other institution designated as an official data source. The original financial data is collected in national currency at current values; they are converted and published also in US dollars, for analytical purposes and to allow data comparisons. • Share of R&D expenditures on FSE - it is the share of budgetary research and development expenditures on total budgetary FSE. Please notice that total budgetary FSE is defined ‘net’, i.e. it is adjusted for costs incurred by fishers in order to receive the support. Whenever these costs are of significant amount, total budgetary FSE becomes remarkably low or negative. The corresponding share of research and development expenditures turns into a percentage exceptionally high or negative.

The OECD Fisheries Support Estimates (FSE) database is intended to be the best source of information on fisheries policies in OECD members and participating non-OECD economies.   It is designed to monitor and quantify developments in fisheries policy, to establish a common basis for policy dialogue among countries, and to provide economic data to assess the effectiveness and efficiency of policies.   These tables report country programmes data aggregated according to the main categories presented in the FSE Manual.   More detailed documentation on country programmes can be found in country-level metadata; more data on country programmes can be found in the full dataset (Excel Format - link provided below). Statistics are organized in pivot tables to make possible cross-country comparisons and to filter disaggregated policy-level data by policy implementation criteria and country.   The FSE 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 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.

FDI statistics cover all entities in an FDI relationship. An FDI relationship is established when an investor in one country acquires 10% or more of the voting power in a business enterprise in another country. The investor is also called a direct investor or a parent and the business enterprise is called a direct investment enterprise or an affiliate. The 10 percent criteria is used to establish that the direct investor has a significant degree of influence over the operations of the direct investment enterprise.   The FDI population includes affiliates that are directly and indirectly owned by the parent. In direct ownership, the parent owns the 10% or more voting power itself. In indirect ownership, the parent controls an affiliate that in turn owns 10 percent or more of the voting power in another enterprise.   The FDI population also includes enterprises that are not in a direct investment relationship themselves but have a direct investor in common. Called fellow enterprises, they are included because, even though there is no direct investment relationship between the two, any transactions between them likely resulted from the influence that their common direct investor has on both of their operations.

Key statistical concept Although there are clear definitions for all the terms used in this survey, countries might have different methodologies to calculate tonne-kilometer and passenger-kilometers. Methods could be based on traffic or mobility surveys, use very different sampling methods and estimating techniques which could affect the comparability of their statistics. Also, if the definition on road fatalities is very clear and well applied by most countries, this is not the case for road injuries. Indeed, not only countries might have different definitions but the important underreporting of road injuries in most countries can distort analysis based on these data.

Date is taken as per country metadata, and which is not having any metadata date is considered as 2023

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.

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.

This dataset presents trends in man-made emissions of major greenhouse gases and emissions by gas.   Data refer to total emissions of CO2 (emissions from energy use and industrial processes, e.g. cement production), CH4 (methane emissions from solid waste, livestock, mining of hard coal and lignite, rice paddies, agriculture and leaks from natural gas pipelines), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulphur hexafluoride (SF6) and nitrogen trifluoride (NF3). Data exclude indirect CO2.   Intensities (per unit of GDP and per capita) as well as index are calculated on gross direct emissions excluding emissions or removals from land-use, land-use change and forestry (LULUCF).   The GDP used to calculate intensities is expressed in USD at 2010 prices and PPPs.

Productivity is a key driver of economic growth and changes in living standards. Labour productivity growth implies a higher level of output for unit of labour input (hours worked or persons employed). This can be achieved if more capital is used in production or through improved overall efficiency with which labour and capital are used together, i.e., higher multifactor productivity growth (MFP). Productivity is also a key driver of international competitiveness, e.g. as measured by Unit Labour Costs (ULC).   The OECD Productivity Database aims at providing users with the most comprehensive and the latest productivity estimates. The update cycle is on a rolling basis, i.e. each variable in the dataset is made publicly available as soon as it is updated in the sources databases. However, some time lag may arise which affects individual series and/or countries for two reasons: first, hours worked data from the OECD Employment Outlook are typically updated less frequently than the OECD Annual National Accounts Database; second, source data for capital services are typically available in annual national accounts later than source data for labour productivity and ULCs.   Note to users: The OECD Productivity Database accounts for the methodological changes in national accounts' statistics, such as the implementation of the System of National Accounts 2008 (2008 SNA) and the implementation of the international industrial classification ISIC Rev.4. These changes had an impact on output, labour and capital measurement. For Chile, China, Colombia, India, Japan, Turkey and the Russian Federation the indicators are in line with the System of National Accounts 1993 (1993 SNA); for all other countries, the indicators presented are based on the 2008 SNA

Cancer follow up has been given for the range of 5 years. The highest range has been considered as for this period, for example 1995-2000 is considered as 2000.

OECD Health Data 2016 offers the most comprehensive source of comparable statistics on health and health systems across OECD countries. It is an essential tool for health researchers and policy advisors in governments, the private sector and the academic community, to carry out comparative analyses and draw lessons from international comparisons of diverse health care systems.

OECD Health Data 2017 offers the most comprehensive source of comparable statistics on health and health systems across OECD countries. It is an essential tool for health researchers and policy advisors in governments, the private sector and the academic community, to carry out comparative analyses and draw lessons from international comparisons of diverse health care systems.B1:B4

OECD Health Data 2017 offers the most comprehensive source of comparable statistics on health and health systems across OECD countries. It is an essential tool for health researchers and policy advisors in governments, the private sector and the academic community, to carry out comparative analyses and draw lessons from international comparisons of diverse health care systems.

OECD Health Data 2016 offers the most comprehensive source of comparable statistics on health and health systems across OECD countries. It is an essential tool for health researchers and policy advisors in governments, the private sector and the academic community, to carry out comparative analyses and draw lessons from international comparisons of diverse health care systems.

Tourism can be regarded as a social, cultural and economic phenomenon related to the movement of people outside their usual place of residence.

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.

These data are part of a larger database, hosted on a different website, which includes both quantitative and qualitative data, as well as graphs.

Patents are a key measure of innovation output, as patent indicators reflect the inventive performance of countries, regions, technologies, firms, etc. They are also used to track the level of diffusion of knowledge across technology areas, countries, sectors, firms, etc., and the level of internationalisation of innovative activities. Patent indicators can serve to measure the output of R&D, its productivity, structure and the development of a specific technology/industry. Among the few available indicators of technology output, patent indicators are probably the most frequently used. The relationship between patents as an intermediate output resulting from R&D inputs has been investigated extensively. Patents are often interpreted as an output indicator; however, they could also be viewed as an input indicator, as patents are used as a source of information by subsequent inventors. Like any other indicator, patent indicators have many advantages and disadvantages. The advantages of patent indicators are : patents have a close link to invention; patents cover a broad range of technologies on which there are sometimes few other sources of data; the contents of patent documents are a rich source of information (on the applicant, inventor, technology category, claims, etc.); and patent data are readily available from patent offices. However, patents are subject to certain drawbacks: the value distribution of patents is skewed as many patents have no industrial application (and hence are of little value to society) whereas a few are of substantial value; many inventions are not patented because they are not patentable or inventors may protect the inventions using other methods, such as secrecy, lead time, etc.; the propensity to patent differs across countries and industries; differences in patent regulations make it difficult to compare counts across countries; and changes in patent law over the years make it difficult to analyse trends over time.

Unit of measure used: Thousands   OECD countries seldom have tools specifically designed to measure the inflows and outflows of the foreign population, and national estimates are generally based either on population registers or residence permit data. This note is aimed at describing more systematically what is measured by each of the sources used.   Flows derived from population registers   Population registers can usually produce inflow and outflow data for both nationals and foreigners. To register, foreigners may have to indicate possession of an appropriate residence and/or work permit valid for at least as long as the minimum registration period. Emigrants are usually identified by a stated intention to leave the country, although the period of (intended) absence is not always specified.   When population registers are used, departures tend to be less well recorded than arrivals. Indeed, the emigrant who plans to return to the host country in the future may be reluctant to inform about his departure to avoid losing rights related to the presence on the register. Registration criteria vary considerably across countries (as the minimum duration of stay for individuals to be defined as immigrants ranges from three months to one year), which poses major problems of international comparison. For example, in some countries, register data cover a portion of temporary migrants, in some cases including asylum seekers when they live in private households (as opposed to reception centres or hostels for immigrants) and international students.   Flows derived from residence and/or work permits   Statistics on permits are generally based on the number of permits issued during a given period and depend on the types of permits used. The so-called “settlement countries” (Australia, Canada, New Zealand and the United States) consider as immigrants persons who have been granted the right of permanent residence. Statistics on temporary immigrants are also published in this database for these countries since the legal duration of their residence is often similar to long-term migration (over a year). In the case of France, the permits covered are those valid for at least one year (excluding students). Data for Italy and Portugal include temporary migrants.   Another characteristic of permit data is that flows of nationals are not recorded. Some flows of foreigners may also not be recorded, either because the type of permit they hold is not tabulated in the statistics or because they are not required to have a permit (freedom of movement agreements). In addition, permit data do not necessarily reflect physical flows or actual lengths of stay since: i) permits may be issued overseas but individuals may decide not to use them, or delay their arrival; ii) permits may be issued to persons who have in fact been resident in the country for some time, the permit indicating a change of status, or a renewal of the same permit.   Permit data may be influenced by the processing capacity of government agencies. In some instances a large backlog of applications may build up and therefore the true demand for permits may only emerge once backlogs are cleared.   Flows estimated from specific surveys   Ireland provides estimates based on the results of Quarterly National Household Surveys and other sources such as permit data and asylum applications. These estimates are revised periodically on the basis of census data. Data for the United Kingdom are based on a survey of passengers entering or exiting the country by plane, train or boat (International Passenger Survey). One of the aims of this survey is to estimate the number and characteristics of migrants. The survey is based on a random sample of approximately one out of every 500 passengers. The figures were revised significantly following the latest census in each of these two countries, which seems to indicate that these estimates do not constitute an “ideal” source either. Australia and New Zealand also conduct passenger surveys which enable them to establish the length of stay on the basis of migrants’ stated intentions when they enter or exit the country.

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

The Key Economic Indicators (KEI) database contains monthly and quarterly statistics (and associated statistical methodological information) for all OECD member countries and for a selection of non-member countries on a wide variety of economic indicators, namely: quarterly national accounts, industrial production, composite leading indicators, business tendency and consumer opinion surveys, retail trade, consumer and producer prices, hourly earnings, employment/unemployment, interest rates, monetary aggregates, exchange rates, international trade and balance of payments.

The productivity and income estimates presented in this dataset are mainly based on GDP, population and employment data from the OECD Annual National Accounts. Hours worked are sourced from the OECD Annual National Accounts, the OECD Employment Outlook and national sources. The OECD Productivity Database aims at providing users with the most comprehensive and the latest productivity estimates. The update cycle is on a rolling basis, i.e. each variable in the dataset is made publicly available as soon as it is updated in the sources databases. However, timely data issues may arise and affect individual series and/or individual countries. In particular, annual hours worked estimates from the OECD Employment Outlook are typically updated less frequently (once a year, in the summer) than series of hours worked from the OECD Annual National Accounts.

This table contains labour force data on labour market status - population, labour force, unemployment and employment - by sex and by detailed age groups and standard age groups (15-24, 25-54, 55-64, 65+, total). Note: Population figures reported in table LFS by sex are Census-based, while the data for this table are taken from labour force surveys. Population for total age group refers to working age population (15 to 64 years).

This dataset contains the age composition (as a percentage of all ages) of the population for each labour force status - labour force, employment, unemployment - by sex.

The OECD Long-Term Baseline Scenario is a projection of some major economic variables beyond the short-term horizon of the OECD Economic Outlook. It covers all OECD economies, non-OECD G20 economies and key partners. The projection horizon is currently 2060. For the historical period and the short-run projection horizon, the series are consistent with those of the OECD Economic Outlook number in the dataset title. The definitions, sources and methods are also the same, except where noted explicitly (such as coverage of the non-OECD and world aggregates). For more details on the methodology, please see Boxes 1 to 3 in The Long View: Scenarios for the World Economy to 2060 and the references therein.The baseline scenario is a projection conditional on a number of assumptions, notably that countries do not carry out institutional and policy reforms. It is used as a reference point to illustrate the potential impact of structural reforms in alternative scenarios, such as those discussed in The Long View: Scenarios for the World Economy to 2060. The data for these alternative scenarios are not available here but can be obtained on request by writing to [email protected].

This biannual publication provides a set of indicators that reflect the level and structure of the efforts undertaken by OECD member countries and seven non-member economies (Argentina, People's Republic of China, Romania, Russian Federation, Singapore, South Africa, Chinese Taipei) in the field of science and technology. These data include final or provisional results as well as forecasts established by government authorities. The indicators cover the resources devoted to research and development, patent families, technology balance of payments and international trade in R&D-intensive industries. Also presented are the underlying economic series used to calculate these indicators. Indicators on R&D expenditures, budgets and personnel are derived from the OECD's Research and Development Statistics (RDS) database, which is based on the data reported to OECD and Eurostat in the framework of a co-ordinated collection. The sources for the other indicators include the OECD databases on Activities of Multinational Enterprises (AMNE), on Bilateral Trade in Goods by Industry and End-use Category database (BTDIxE), on Patents and on Technological Balance of Payments (TBP). The R&D data used in this publication have been collected and presented in line with the standard OECD methodology for R&D statistics as laid out in the OECD "Frascati Manual". The 2002 edition of the manual has now been superseded by the 2015 edition. The revised guidelines and definitions are in the course of being implemented and are not expected to change the main indicators significantly although some terminology changes will occur. This edition of MSTI has been compiled in accordance with the 2002 Frascati Manual; these changes will be made in a coming edition as R&D surveys move to the new standard.   2018 values are estimated value.

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).

Database published : June 2020 

Database published : June 2020 

The International Trade (MEI) dataset contains predominantly monthly merchandise trade statistics, and associated statistical methodological information, for all OECD member countries and for all non-OECD G20 economies and the EU.   The dataset itself contains international trade statistics measured in billions of United States dollars (USD) for: Exports, Imports, Balance. In all cases a lot of effort has been made to ensure that the data are internationally comparable across all countries presented and that all the subjects have good historical time-series’ data to aid with analysis.

The Financial Statistics dataset contains predominantly monthly statistics, and associated statistical methodological information, for the 36 OECD member countries and some selected other countries. The dataset itself contains financial statistics on 4 separate subjects: Monetary Aggregates, Interest Rates, Exchange Rates, and Share Prices. The data series presented within these subjects have been chosen as the most relevant financial statistics for which comparable data across countries is available. In all cases a lot of effort has been made to ensure that the data are internationally comparable across all countries presented and that all the subjects have good historical time-series’ data to aid with analysis. All data are available monthly, and are presented as either an index (where the year 2015 is the base year) or as a level depending on which measure is seen as the most appropriate and/or useful in the economic analysis context.

It presents gross capital formation, gross fixed capital formation, changes in inventories and acquisition less disposals of valuables broken down by detailed industries according to the classification ISIC rev.4. Gross fixed capital formation is also available broken down by type of assets. It has been prepared from statistics reported to the OECD by Member countries in their answers to annual national accounts questionnaire. This questionnaire is designed to collect internationally comparable data according to the 1993 SNA. Unit of measure used - In national currency, in current prices and constant prices (national base year, previous year prices and OECD base year i.e. 2005). Expressed in millions. For the Euro area countries, the data in national currency for all years are calculated using the fixed conversion rates against the euro.

It presents fixed assets by activity according to the classification ISIC rev.3 and by type of product and by type of assets.  It has been prepared from statistics reported to the OECD by Member countries in their answers to annual national accounts questionnaire. In national currency, in current prices and constant prices (national base year and OECD base year i.e. 2010). Expressed in millions. For the Euro area countries, the data in national currency for all years are calculated using the fixed conversion rates against the euro.

It presents the balance sheets for non financial assets by institutional sectors, for both produced assets (fixed assets, inventories, valuables) and non-produced assets (tangible and intangible).  It has been prepared from statistics reported to the OECD by Member countries in their answers to annual national accounts questionnaire. This questionnaire is designed to collect internationally comparable data according to the 1993 SNA. Unit of measure used - In national currency expressed in millions. For the Euro area countries, the data in national currency for all years are calculated using the fixed conversion rates against the euro.

This dataset presents information using an "indicator" approach, focusing on cross-country comparisons. The aim is to make the accounts more accessible and informative, whilst taking the opportunity to present the conceptual underpinning  and comparability issues of each of the indicators presented. The range of indicators is set deliberately wide to reflect the richness of the national accounts dataset and to encourage users of economic statistics to refocus some of the spotlight that is often placed on GDP to other important economic indicators, which may better respond to their needs. Indeed many users themselves have been instrumental in this regard. The report of the Commission on the Measurement of Economic Performance and Social Progress (Stiglitz-Sen-Fitoussi Commission) is but one notable example. That is not to undermine the importance of GDP, which arguably remains the most important measure of total economic activity, but other measures may better reflect other aspects of the economy. For example, net national income may be a more appropriate measure of income available to citizens in countries with large outflows of property income, and household adjusted disposable income per capita may be a better indicator of the material well-being of citizens. But certainly from a data perspective more can and remains to be done. The Stiglitz-Sen-Fitoussi Commission for example highlights the pressing need for the provision, by official statistics institutes, of more detailed information that better describes the distributional aspects of activity, especially income, and the need to build on the national accounts framework to address issues such as non-market services produced by households or leisure. It is hoped that by producing a publication such as this and thereby raising awareness, the momentum from this and other initiatives will be accelerated. The publication itself will pick up new indicators in the future as they become available at the OECD.

It presents the whole set of non financial accounts, from the production account to the acquisitions of non-financial assets accounts. For general government sector, property income, other current transfers and capital transfers are consolidated..

It presents the whole set of non financial accounts, from the production account to the acquisitions of non-financial assets accounts. For general government sector, property income, other current transfers and capital transfers are consolidated.. It has been prepared from statistics reported to the OECD by Member countries in their answers to the new version of the annual national accounts questionnaire. This questionnaire is designed to collect internationally comparable data according to the 1993 SNA.

It presents gross capital formation, gross fixed capital formation, changes in inventories and acquisition less disposals of valuables broken down by detailed industries. Gross fixed capital formation is also available broken down by type of assets. It has been prepared from statistics reported to the OECD by Member countries in their answers to annual national accounts questionnaire. This questionnaire is designed to collect internationally comparable data according to the 1993 SNA. Unit of measure used - In national currency, in current prices and constant prices (national base year, previous year prices and OECD base year i.e. 2010). Expressed in millions. For the Euro area countries, the data in national currency for all years are calculated using the fixed conversion rates against the euro.

It presents the different transactions and balances to get from the GDP to the net lending/net borrowing. Therefore, it includes, in particular, national disposable income (gross and net), consumption of fixed capital as well as net saving.

It presents the final consumption expenditure of households broken down by the COICOP (Classification of Individual Consumption According to Purpose) classification and by durability.  It has been prepared from statistics reported to the OECD by Member countries in their answers to annual national accounts questionnaire. This questionnaire is designed to collect internationally comparable data according to the 1993 SNA. In national currency, in current prices and constant prices (national base year, previous year prices and OECD base year i.e. 2010). Expressed in millions. For the Euro area countries, the data in national currency for all years are calculated using the fixed conversion rates against the euro.

It provides a faithful image, to the greatest extent possible, of the aggregates and balances of the general government sector in the SNA 1993 conceptual framework. In addition, it brings to light two relevant aggregates that do not belong to this conceptual frame work: the Total Revenue and the Total Expenditure of the general government sector. Unit of measure used - National currency; current prices. Expressed in millions.

It provides a breakdown of government expenditure according to their function. To meet this end, economic flows of expenditure must be aggregated according to the Classification of the Functions of Government (COFOG).

It presents the three approaches of the GDP: expenditure based, output based and income based. It has been prepared from statistics reported to the OECD by Member countries in their answers to annual national accounts questionnaire. This questionnaire is designed to collect internationally comparable data according to the 1993 SNA.

It presents population data and employment by main activity. It includes national concept data for economically active population, unemployed persons, total employment, employees and self-employed, as well as domestic concept data for total employment, employees and self-employed. The domestic concept data are available broken down by main activity. It has been prepared from statistics reported to the OECD by Member countries in their answers to annual national accounts questionnaire.

It presents simplified non-financial accounts, from the gross value added to the net lending/net borrowing. In this table, the total economy is broken down in three main institutional sectors: corporations, general government, households and non-profit institutions serving households. It has been prepared from statistics reported to the OECD by Member countries in their answers to annual national accounts questionnaire. This questionnaire is designed to collect internationally comparable data according to the 1993 SNA. Unit of measure used - In national currency, in current prices. Expressed in millions. For the Euro area countries, the data in national currency for all years are calculated using the fixed conversion rates against the euro.

It provides a faithful image, to the greatest extent possible, of the aggregates and balances of the general government sector Data are also available, for most countries, for the sub-sectors of general government.

Statistical population: Its presents output, intermediate consumption and the gross value added and its components, in particular compensation of employees and gross operating surplus and mixed income, broken down by detailed industries. It has been prepared from statistics reported to the OECD by Member countries in their answers to annual national accounts questionnaire. Data presented in this table will not be updated after summer 2010. Data reported to the OECD by countries in their answers to the annual national accounts questionnaire are now available on theme Industry and Services, Structural Analysis (STAN) Databases. Unit of measure used: In national currency, in current prices and constant prices (national base year, previous year prices and OECD base year i.e. 2010). Expressed in millions. For the Euro area countries, the data in national currency for all years are calculated using the fixed conversion rates against the euro.

National landings in domestic ports

The OECD Digital Economy Outlook 2020 examines trends and analyses emerging opportunities and challenges in the digital economy. It highlights how OECD countries and partner economies are taking advantage of information and communication technologies (ICTs) and the Internet to meet their public policy objectives. Through comparative evidence, it informs policy makers of regulatory practices and policy options to help maximise the potential of the digital economy as a driver for innovation and inclusive growth.   This third edition of the OECD Digital Economy Outlook provides a holistic overview of converging trends, policy developments and data on both the supply and demand sides of the digital economy. It illustrates how the digital transformation is affecting economies and societies. Finally, it provides a special focus on how the COVID-19 pandemic is amplifying opportunities and challenges from the digital transformation.

Other official flows are official sector transactions which do not meet the ODA criteria, e.g.:  i.) Grants to developing countries for representational or essentially commercial purposes;  ii.) Official bilateral transactions intended to promote development but having a grant element of less than 25 per cent;  iii.) Official bilateral transactions, whatever their grant element, that are primarily export-facilitating in purpose. This category includes by definition export credits extended directly to an aid recipient by an official agency or institution ("official direct export credits");  iv.) The net acquisition by governments and central monetary institutions of securities issued by multilateral development banks at market terms;  v.) Subsidies (grants) to the private sector to soften its credits to developing countries [see Annex 3, paragraph A3.5.iv)b)];  vi.) Funds in support of private investment.

This table contains figures on the activity of affiliates located abroad by host country in the total manufacturing, total services and total business enterprise sectors. The units used to present data in AMNE are millions of national currency for monetary variables and units for the other variables. Monetary variables are in current prices. Euro-area countries: national currency data is expressed in euro beginning with the year of entry into the Economic and Monetary Union (EMU). For years prior to the year of entry into EMU, data have been converted from the former national currency using the appropriate irrevocable conversion rate. This presentation facilitates comparisons within a country over time and ensures that the historical evolution is preserved. Please note, however, that pre-EMU euro are a notional unit and should not be used to form area aggregates or to carry out cross-country comparisons.

The International Transport Forum collects data on transport statistics on annual basis from all its Member countries. Data are collected from Transport Ministries, statistical offices and other institution designated as official data source. Variables collected are inland transport of goods (T-km), of passengers (P-km) and road injury accidents. Additional information is also gathered on containers transported by rail and sea (Tons and TEU) as well as short sea shipping data (T-km).

Patents are a key measure of innovation output, as patent indicators reflect the inventive performance of countries, regions, technologies, firms, etc. They are also used to track the level of diffusion of knowledge across technology areas, countries, sectors, firms, etc., and the level of internationalisation of innovative activities. Patent indicators can serve to measure the output of R&D, its productivity, structure and the development of a specific technology/industry. The relationship between patents as an intermediate output resulting from R&D inputs has been investigated extensively. Patents are often interpreted as an output indicator; however, they could also be viewed as an input indicator, as patents are used as a source of information by subsequent inventors. Like any other indicator, patent indicators have many advantages and disadvantages. The advantages of patent indicators are :patents have a close link to invention;patents cover a broad range of technologies on which there are sometimes few other sources of data;the contents of patent documents are a rich source of information (on the applicant, inventor, technology category, claims, etc.); andpatent data are readily available from patent offices. However, patents are subject to certain drawbacks:the value distribution of patents is skewed as many patents have no industrial application (and hence are of little value to society) whereas a few are of substantial value;many inventions are not patented because they are not patentable or inventors may protect the inventions using other methods, such as secrecy, lead time, etc.;the propensity to patent differs across countries and industries;differences in patent regulations make it difficult to compare counts across countries; andchanges in patent law over the years make it difficult to analyse trends over time. 

This dataset contains the number of people by sex and age group per country.

Private transactions are those undertaken by firms and individuals resident in the reporting country.

The OECD Productivity Database aims at providing users with the most comprehensive and the latest productivity estimates. The update cycle is on a rolling basis, i.e. each variable in the dataset is made publicly available as soon as it is updated in the OECD Annual National Accounts database. However, timely data issues may arise and affect individual series and/or individual countries. Sectors differ from each other with respect to their productivity growth. Understanding the drivers of productivity growth at the total economy level requires an understanding of the contribution of each sector. Data of real gross value added, labour compensation, hours worked and employment are sourced from the OECD Annual National Accounts.

This dataset contains Purchasing Power Parities (PPPs) for all OECD countries. PPPs are the rates of currency conversion that eliminate the differences in price levels between countries. Per capita volume indices based on PPP converted data reflect only differences in the volume of goods and services produced. Comparative price levels are defined as the ratios of PPPs to exchange rates. They provide measures of the differences in price levels between countries. The PPPs are given in national currency units per US dollar. The price levels and volume indices derived using these PPPs have been rebased on the OECD average. Per capita volume indices should not be used to rank countries as PPPs are statistical constructs rather than precise measures. Minor differences between countries should be interpreted with caution.

This table contains research and development (R&D) expenditure statistics on current domestic R&D and gross domestic R&D expenditures by sector of performance (business enterprise, government, higher education, private non-profit, and total intramural) and by type of R&D within each sector (basic research, applied research, experimental development, non-specified, and total activity). Unit of measure used - Data are provided in million national currency (for the euro zone, pre-EMU euro or EUR), million current PPP USD and million constant USD (2005 prices and PPPs).

The Regional database contains annual data from 1995 to the most recent available year (generally 2022 for demographic and labor market data, 2021 for regional accounts, innovation and social statistics). 

The Regional database contains annual data from 1995 to the most recent available year (generally 2018 for demographic and labor market data, 2017 for regional accounts, innovation and social statistics). The data collection is undertaken by the Center for Entrepreneurship, SMEs, Regions and Cities (CFE). Statistics are collected through an annual questionnaire sent to the delegates of the Working Party on Territorial Indicators (WPTI), and via downloads from the web-sites of National Statistical Offices and Eurostat

The Regional Database contains annual data from 1995 to the most recent available year. The data collection is undertaken by the Directorate of Public Governance and Territorial Development, within the Regional Development Policy division (GOV/RDP). Statistics are collected through an annual questionnaire sent to the delegates of the Working Party on Territorial Indicators (WPTI), and through access to the web-sites of National Statistical Offices and Eurostat. The WPTI is responsible for developing regional (subnational) and urban statistics and providing analysis to support policy evaluations. The Regional Database includes statistics on the regional distribution of resources, regional disparities, and how regions contribute to national growth and the well-being of society. Under this framework, the Regional Database is one of the pillars for providing indicators to the publication OECD Regions at a Glance (link).

The Regional Database contains annual data from 1995 to the most recent available year (generally 2016 for demographic, 2015 for labor market data and 2014 for regional accounts, innovation and social statistics).

The Regional Database contains annual data from 1995 to the most recent available year (generally 2014 for demographic and labour market data, 2013 for regional accounts, innovation and social statistics).   In any analytical study conducted at sub-national levels, the choice of the territorial unit is of prime importance. The territorial grids (TL2 and TL3) used in this database are officially established and relatively stable in all member countries, and are used by many as a framework for implementing regional policies. This classification - which, for European countries, is largely consistent with the Eurostat classification - facilitates greater comparability of regions at the same territorial level. The differences with the Eurostat NUTS classification concern Belgium, Greece and the Netherlands where the NUTS 2 level correspond to the OECD TL3 and Germany where the NUTS1 corresponds to the OECD TL2 and the OECD TL3 corresponds to 97 spatial planning regions (Groups of Kreise). For the United Kingdom the Eurostat NUTS1 corresponds to the OECD TL2. Due to limited data availability, labour market indicators in Canada are presented for a different grid (groups of TL3 regions). Since these breakdowns are not part of the OECD official territorial grids, for the sake of simplicity they are labelled as Non Official Grids (NOG).

The Registered Unemployment and Job Vacancies dataset is a subset of the Short-Term Labour Situation database, which contains predominantly monthly statistics, and associated statistical methodological information, for the 34 OECD member countries and for selected other economies. There are basically two sources for unemployment statistics: labour force surveys and administrative data. Surveys are based on standard methodology and procedures used all over the world while administrative data are subject to national legislations which evolve through time. Consequently registered unemployment data are not comparable across countries. The relationship between survey and registered unemployment is not the same for all countries. Number of registered unemployed persons and registered unemployment rates are presented here because they are monthly and quickly available after their reference period. The job vacancies data provides estimates of the number of unfilled job vacancies across national economies. Series give an indication of the labour demand while the unemployment is linked with the labour supply.

Residential Property Prices Indices (RPPIs) – also named House price indices (HPIs), are index numbers that measure the price of residential properties over time. RPPIs are key statistics not only for citizens and households across the world, but also for economic and monetary policy makers. Among their professional uses, they serve, for example, to monitor macroeconomic imbalances and risk exposure of the financial sector. This dataset covers the 34 OECD member countries and some non-member countries. Please note that not all RPPIs are available for all countries. For instance, the RPPI at the most aggregate level for the United States only covers single-family dwellings, not all types of dwellings as it is the case for most other OECD countries. This dataset presents, for each country, the RPPI that is available at the most aggregate level. It mainly contains quarterly statistics. The dataset called “Residential Property Price Indices (RPPIs) – Complete dataset” contains the full list of available RPPIs. The dataset called “Analytical house price indicators” contains, in addition to nominal RPPIs, information on real house prices, rental prices and the ratios of nominal prices to rents and to disposable household income per capita. The datasets “Analytical house price indicators” and “Residential Property Price Indices (RPPIs) – Headline Indicators” do not refer to the same price indices for Brazil, Canada, China, Germany, the United States and the Euro area. These differences are further documented in country-specific metadata. For the United States, the series used in “Analytical house price indicators” is included in the dataset called “Residential Property Price Indices (RPPIs) – Complete database”, but is not the headline indicator. For all other countries, non-seasonally adjusted price indices in both datasets are identical in the period in which they overlap.For all other countries, non-seasonally adjusted price indices in both datasets are identical on the overlapping period.

Trade in services drives the exchange of ideas, know-how and technology. It helps firms cut costs, increase productivity, participate in global value chains and boost competitiveness. Consumers benefit from lower prices and greater choice. However, international trade in services is often impeded by trade and investment barriers and domestic regulations. The Service Trade Restrictions Index (STRI) helps identify which policy measures restrict trade. It provides policy makers and negotiators with information and measurement tools to open up international trade in services and negotiate international trade agreements. It can also help governments identify best practice and then focus their domestic reform efforts on priority sectors and measures. The STRI indices take the value from 0 to 1, where 0 is completely open and 1 is completely closed. They are calculated on the basis of information in the STRI database which reports regulation currently in force.

Input-Output tables describe the sale and purchase relationships between producers and consumers within an economy. They can be produced by illustrating flows between the sales and purchases (final and intermediate) of industry outputs or by illustrating the sales and purchases (final and intermediate) of product outputs. The OECD Input-Output database is presented on the former basis, reflecting in part the collection mechanisms for many other data sources such as research and development Research and Development expenditure data, employment statistics, pollution data, energy consumption, which are in the main collected by enterprise or by establishment, and thus according to industry classifications.

Input-Output tables describe the sale and purchase relationships between producers and consumers within an economy. They can be produced by illustrating flows between the sales and purchases (final and intermediate) of industry outputs or by illustrating the sales and purchases (final and intermediate) of product outputs. The OECD Input-Output database is presented on the former basis, reflecting in part the collection mechanisms for many other data sources such as research and development Research and Development expenditure data, employment statistics, pollution data, energy consumption, which are in the main collected by enterprise or by establishment, and thus according to industry classifications.

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

This dataset presents internationally comparable data on (full-time) salaries of teachers and school heads in public institutions at pre-primary, primary and general (lower and upper) secondary education. Actual salaries are displayed by level of education and are also available by age and gender. Data also include other statistics related to salaries of teachers.

This dataset presents internationally comparable data on (full-time) salaries of teachers and school heads in public institutions at pre-primary, primary and general (lower and upper) secondary education. Statutory salaries are displayed by level of education, Data also include other statistics related to salaries of teachers.

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.

Total Official Flows: the sum of Official Development Assistance (ODA) and Other Official Flows (OOF) represents the total (gross or net) disbursements by the official sector at large to the recipient country shown.

Total Receipts, Net: in addition to Official Development Assistance, this heading includes in particular: other official bilateral transactions which are not concessional or which, even though they have concessional elements, are primarily trade facilitating in character (i.e., "Other Official Flows''); changes in bilateral long-term assets of the private non-monetary and monetary sectors, in particular guaranteed export credits, private direct investment, portfolio investment and, to the extent they are not covered in the preceding headings, loans by private banks. Flows from the multilateral sector which are not classified as concessional are also included here.

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.

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 lack of common definitions and practices to measure transport infrastructure spending hinders comparisons between countries and spending options. Data for road and rail infrastructure are the most comprehensive while data on sea port and airport spending are less detailed in coverage and definition. While our survey covers all sources of financing a number of countries exclude private spending, including Japan and India. Around 65% of countries report data on urban spending while for the remaining countries data on spending in this area are missing. Indicators such as the share of GDP needed for investment in transport infrastructure, depend on a number of factors, such as the quality and age of existing infrastructure, maturity of the transport system, geography of the country and transport-intensity of its productive sector. Caution is therefore required when comparing investment data between countries. However, data for individual countries and country groups are consistent over time and useful for identifying underlying trends and changes in levels of spending, especially for inland transport infrastructure. These issues of definitions and methods are addressed in a companion report Understanding the Value of Transport Infrastructure – Guidelines for macro-level measurement of spending and assets (ITF/OECD2013) that aims to improve the international collection of related statistics.

This data set is a combination of three tables, 1. Good Transport- Inland freight 2. Passenger transport 3. Transport Safety- Road injury accidents- Road CausalitiesThe geographical area covered is the ITF member countries.The International Transport Forum collects data on transport statistics on annual basis from all its Member countries. Data are collected from Transport Ministries, statistical offices and other institution designated as official data source.TEU (Twenty-foot Equivalent Unit): a statistical unit based on an ISO container of 20 foot length (6.10 m) to provide a standardised measure of containers of various capacities and for describing the capacity of container ships or terminals. one 20 Foot ISO container equals 1 TEU.