Organisation for Economic Co-operation and Development

Residential Property Prices Indices (RPPIs) – also named House price indices (HPIs), are index numbers that measure the prices of residential properties over time. RPPIs are key statistics not only for citizens and households across the world, but also for economic and monetary policy makers. They can help, for example, to monitor potential macroeconomic imbalances and the risk exposure of the household and financial sectors. This dataset covers the OECD member countries and some non-member countries. In addition to the nominal RPPIs it contains information on real house prices, rental prices, and the ratios of nominal prices to rents and to disposable household income per capita. This dataset contains quarterly and annual statistics for each country. Annual figures refer to the average of the year.

This table presents Gross Domestic Product (GDP) and its components according to the expenditure approach. In the expenditure approach, the main components of GDP are: final consumption expenditure of households and non-profit institutions serving households (NPISH) plus final consumption expenditure of General Government plus gross fixed capital formation (or investment) plus net trade (exports minus imports). Data is presented for each country in national currency as well as in euros for the European Union and the euro area. Data is also presented converted to US dollars using both purchasing power parities and exchange rates. In this table, the presentation of GDP and its components is on a country-by-country basis. Users are recommended to select one country (or area) at a time in the ‘Reference area’ filter. The default view shows all transactions (GDP and components) for the selected area, but it is possible to select specific components and sub-components of GDP using the ‘Transaction’ filter. The sector to which the selected transactions relate will be shown in the 'Institutional sector' filter, and further options (if applicable) will be shown under the ‘Financial instruments and non-financial assets’ filter. It is also possible to select current prices, chain linked volumes etc using the ‘Price base’ filter (the default view is current prices). The table shows OECD countries and selected economies, as well as the OECD total, OECD Europe, European Union and euro area. These can be selected using the ‘Reference area’ filter. We are working on an issue with rows that appear empty but are in fact not applicable. These indicators were presented in the previous dissemination system in the SNA_TABLE1 dataset. See ANA Changes for information on changes in methodology: ANA Changes Explore also the GDP and non-financial accounts webpage: GDP and non-financial accounts webpage OECD statistics contact: [email protected]

This table presents Gross Domestic Product (GDP) and its components according to the income approach. In the income approach, GDP is measured as the sum of compensation of employees (wages and salaries paid to employees and their employers’ social contributions), plus gross operating surplus (business profits) and gross mixed income (profits of the self-employed), plus taxes on production and imports less subsidies. The presentation is on a country-by-country basis. Users are recommended to select one country (or area) at a time in the ‘Reference area’ filter. Data is presented for each country in national currency as well as in euros for the European Union and the euro area. It is also presented converted to US dollars using exchange rates. The table shows OECD countries and selected economies, as well as the OECD total, OECD Europe, European Union and euro area. These indicators were presented in the previous dissemination system in the SNA_TABLE1 dataset. See ANA Changes for information on changes in methodology: ANA Changes Explore also the GDP and non-financial accounts webpage: GDP and non-financial accounts webpage OECD statistics contact: [email protected]

This table presents Gross Domestic Product (GDP) and its components according to the output approach. In the output approach, GDP is measured as the sum of gross value added (GVA) of all economic activities plus taxes less subsidies on products. This table includes breakdowns of GVA by type of economic activity according to Revision 4 of the International Standard Industrial Classification of All Economic Activities (ISIC). The presentation is on a country-by-country basis. Users are recommended to select one country (or area) at a time in the ‘Reference area’ filter. Data is presented for each country in national currency as well as in euros for the European Union and the euro area. Data is also presented converted to US dollars using exchange rates. It is also possible to select current prices, chain linked volumes etc using the ‘Price base’ filter (the default view is current prices). The table shows OECD countries and selected economies, as well as the OECD total, OECD Europe, European Union and euro area. These can be selected using the ‘Reference area’ filter. These indicators were presented in the previous dissemination system in the SNA_TABLE1 dataset. See ANA Changes for information on changes in methodology: ANA Changes Explore also the GDP and non-financial accounts webpage: GDP and non-financial accounts webpage OECD statistics contact: [email protected]

This table presents the different transactions and balances to get from Gross Domestic Product (GDP) to net lending/net borrowing. It includes national disposable income (gross and net), consumption of fixed capital and net saving. The presentation is on a country-by-country basis. Users are recommended to select one country (or area) at a time in the ‘Reference area’ filter. Data is presented for each country in national currency as well as in euros for the European Union and the euro area, but you can also ask for it to be presented in US dollars converted with Purchasing Power Parities (PPPs) and exchange rates using the ‘Unit of measure’ filter. It is also possible to select chain linked volume and previous year price measures using the ‘Price base’ filter (the default view is current prices ). The indicators were presented in the previous dissemination system in the SNA_TABLE2 dataset. See ANA Changes for information on changes in methodology: ANA Changes Explore also the GDP and non-financial accounts webpage: GDP and non-financial accounts webpage OECD statistics contact: [email protected]

This table shows annual Purchasing Power Parities (PPPs) for Gross Domestic Product (GDP), household final consumption expenditure and actual individual consumption. It also shows exchange rates (annual averages and end of period), sourced from the International Monetary Fund's database on International Financial Statistics. Final consumption expenditure is the expenditure of resident households on consumption goods or services, while individual consumption is the sum of household consumption plus the individual (not collective) consumption of the non-profit institutions serving households (NPISH) and General Government sectors. These indicators were presented in the previous dissemination system in the SNA_TABLE4 dataset. For further information on (PPPs) please check the following link: Purchasing Power Parities and more precisely FAQ 5: Purchasing power parities OECD statistics contact: [email protected]

This dataset contains statistics on Consumer Price Indices by COICOP 1999 divisions, including national CPIs, Harmonised Indices of Consumer Prices (HICPs) and their associated weights and contributions to national year-on-year inflation. For countries for which data are already available according to the COICOP 2018 classification statistics on Consumer Price Indices can be found in dataset Consumer price indices (CPIs, HICPs), COICOP 2018. The data series presented have been chosen as the most relevant prices statistics for which comparable data across countries is available. Data are available monthly for all the countries except for Australia and New Zealand (quarterly data).

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.

This indicator presents internationally comparable data regarding the labour force status and the educational attainment level by the National Educational Attainment Categories (NEAC) as reported by the labour force survey (LFS) and published in OECD Education at a Glance 2017. For trend data, the Education at a Glance Database includes data from 1981 to 2016 (or years with available data).

The nature of expenditure distinguishes between current and capital expenditure. The resource category refers to service provider (public institutions, government-dependent private institutions, and independent private institutions, i.e. both educational and other institutions). These expenditure figures are intended to represent the total cost of services provided by each type of institution, without regard to sources of funds (whether they are public or private).

All entities that provide funds for education, either initially or as final payers, are classified as either governmental (public) sources or non-governmental (private) sources, the sole exception being "international agencies and other foreign sources", which are treated as a separate category. There are three types of financial transactions: Direct expenditure on educational institutions; Transfers to students or households and to other private entities; and Households' expenditure on education outside educational institutions.

These indicators on expenditure on education are published in chapter C of Education at a Glance, which covers financial and human resources invested in education.They are either policy levers or provide context information on education systems, or sometimes both. For example, expenditure per student is a key policy measure that most directly affects the individual learner, as it acts as a constraint on the learning environment in schools and learning conditions in the classroom.The data set “educational finance indicators” provides the main indicators computed for three levels of education : primary, secondary and post-secondary non-tertiary levels combined; tertiary level; and primary to tertiary levels combined. Other datasets provide more breakdowns for each specific indicator.

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.

This dataset FDI main aggregates, BMD4 is updated every quarter and includes quarterly and annual aggregate inward and outward Foreign Direct Investment (FDI) flows, positions and income for OECD reporting economies and for non-OECD G20 countries (Argentina, Brazil, China, India, Indonesia, Saudi Arabia and South Africa). It is a simplified dataset with fewer breakdowns compared to the other separate datasets specifically dedicated to FDI flows, FDI positions or FDI income aggregates. In this dataset, FDI statistics are presented on directional basis only (unless otherwise specified, see metadata attached at the reporting country level) and resident Special Purpose Entities (SPEs), when they exist, are excluded (unless otherwise stated, see metadata attached at the reporting country level). FDI aggregates are measured in USD millions, in millions of national currency and as a share of GDP.

This dataset FDI positions main aggregates, BMD4 is updated every quarter and includes annual aggregate Foreign Direct Investment (FDI) positions for OECD member countries and for non-OECD G20 countries (Argentina, Brazil, China, India, Indonesia, Saudi Arabia and South Africa), which are included in International Investment Position (IIP) accounts. FDI positions record the total level of direct investment at a given point in time, usually the end of a quarter or of a year.

The FDI Regulatory Restrictiveness Index (FDI Index) measures statutory restrictions on foreign direct investment across 22 economic sectors. It gauges the restrictiveness of a country’s FDI rules by looking at the four main types of restrictions on FDI: 1) Foreign equity limitations; 2) Discriminatory screening or approval mechanisms; 3) Restrictions on the employment of foreigners as key personnel and 4) Other operational restrictions, e.g. restrictions on branching and on capital repatriation or on land ownership by foreign-owend enterprises. Restrictions are evaluated on a 0 (open) to 1 (closed) scale. The overall restrictiveness index is the average of sectoral scores. The discriminatory nature of measures, i.e. when they apply to foreign investors only, is the central criterion for scoring a measure. State ownership and state monopolies, to the extent they are not discriminatory towards foreigners, are not scored. The FDI Index is not a full measure of a country’s investment climate. A range of other factors come into play, including how FDI rules are implemented. Entry barriers can also arise for other reasons, including state ownership in key sectors. A country’s ability to attract FDI will be affected by others factors such as the size of its market, the extent of its integration with neighbours and even geography among other. Nonetheless, FDI rules can be a critical determinant of a country’s attractiveness to foreign investors.

Financial Indicators aim to capture in quantitative terms an important but heterogeneous and fast evolving area. Key factors driving this change are: globalisation of the financial markets; maturing of national financial markets and therefore the structure of these markets required to service their needs; increased sophistication of the actors in these markets; rapid technological change; and evolving regulatory frameworks. Financial institutions react and adapt to these conditions by changing their strategies; by specialising, by diversifying or concentrating their activities, and by extending through mergers and acquisitions. As a consequence, there is almost constant evolution in the institutional structures in which financial markets operate.   OECD statistics contact   Statistics and Data Directorate

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

This dataset contains statistics on Consumer Price Indices - all items, for G20 countries and for the G20 as a whole.  The G20 area consists of the following economies: Argentina, Australia, Brazil, Canada, China, France, Germany, India, Indonesia, Italy, Japan, Korea, Mexico, the Russian Federation, Saudi Arabia, South Africa, Türkiye, the United Kingdom, the United States, the African Union and the European Union. 

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.

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

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

Tourism GDP corresponds to the part of GDP generated by all industries in response to internal tourism consumption. A further distinction must be made between direct tourism GDP and indirect tourism GDP. Put simply, tourism direct GDP is generated by industries directly in contact with visitors, while indirect tourism GDP is generated by industries supplying inputs to industries directly in contact with the visitors. The Tourism Staellite Account (TSA) Framework limits its recommendations to the evaluation of direct tourism GDP.

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.

Main Economic Indicators (MEI) provides a wide range of indicators on recent economic developments in the 35 OECD member countries and 15 non-member countries. The indicators published in MEI have been prepared by national statistical agencies primarily to meet the requirements of users within their own country. In most instances, the indicators are compiled in accordance with international statistical guidelines and recommendations. However, national practices may depart from these guidelines, and these departures may impact on comparability between countries.

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.

This dataset presents trends in amounts of municipal waste generated (including household waste), and the treatment and disposal method used. The amount of waste generated in each country is related to the rate of urbanisation, the types and pattern of consumption, household revenue and lifestyles.

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 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.

Annual National Accounts>General Government Accounts>750. General Government Debt-Maastricht   Unit of measure used: National currency; current prices. Expressed in millions   Statistical population: Government debt as defined in the Maastricht Treaty (Source : Eurostat). Available for Europeans countries only. In the Protocol on the excessive deficit procedure annexed to the Maastricht Treaty, government debt is defined as the debt of the whole general government sector: gross, consolidated and nominal value (face value). It excludes the other accounts payable (AF.7), as well as, if they exist, insurance technical reserve (AF.6).

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.

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.

The OECD Pensions at a Glance Database has been developed in order to serve a growing need for pensions indicators. It includes reliable and internationally comparable statistics on public and mandatory and voluntary pensions. It covers OECD countries and aims to cover all G20 countries. Pensions at a Glance reviews and analyses the pension measures enacted or legislated in OECD countries for workers entering the labour market at age 22 in the specified year. It provides an in-depth review of the first layer of protection of the elderly, first-tier pensions across countries and provideds a comprehensive selection of pension policy indicators for all OECD and G20 countries. OECD Pensions at a Glance - OECD and G20 indicators

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: each variable in the dataset is made publicly available as soon as it is updated in the sources databases. The productivity database contains data on labour productivity both measured using employment or hours worked and the compenents of capital and labour inputs. The productivity database in levels, in growth rates and by industry contains annual data, while the database on productivity and unit labour costs are quarterly estimates. Further information for all datasets and the methodology may be found in the attached file OECD-Productivity-Statistics-Database-metadata

This dataset presents reference statistics (GDP, Total Government Expenditure, deflators, etc.) that are used to calculate some of the indicators on educational expenditure included in the indicators dataset.

Student-teacher ratio refers to the average number of students per teacher, while average class size is the average number of students in a classroom.

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.