Organisation for Economic Co-operation and Development

Databasepublished September 2011These tables are a complement to the report Agricultural Policy Monitoring and Evaluation 2011 : OECD COUNTRIES AND EMERGING ECONOMIES. They comprise the summary of agricultural support estimates for OECD countries.More detailed data by country and documentation can be found in the full dataset (Excel Format) available at :

Databasepublished September 2011These tables are a complement to the report Agricultural Policy Monitoring and Evaluation 2011 : OECD COUNTRIES AND EMERGING ECONOMIES. They comprise the summary of agricultural support estimates for OECD countries.More detailed data by country and documentation can be found in the full dataset (Excel Format) available at :

Databasepublished September 2011These tables are a complement to the report Agricultural Policy Monitoring and Evaluation 2011 : OECD COUNTRIES AND EMERGING ECONOMIES. They comprise the summary of agricultural support estimates for OECD countries.More detailed data by country and documentation can be found in the full dataset (Excel Format) available at :

Databasepublished September 2011These tables are a complement to the report Agricultural Policy Monitoring and Evaluation 2011 : OECD COUNTRIES AND EMERGING ECONOMIES. They comprise the summary of agricultural support estimates for OECD countries.More detailed data by country and documentation can be found in the full dataset (Excel Format) available at :

Databasepublished September 2011These tables are a complement to the report Agricultural Policy Monitoring and Evaluation 2011 : OECD COUNTRIES AND EMERGING ECONOMIES. They comprise the summary of agricultural support estimates for OECD countries.More detailed data by country and documentation can be found in the full dataset (Excel Format) available at :

Databasepublished September 2011These tables are a complement to the report Agricultural Policy Monitoring and Evaluation 2011 : OECD COUNTRIES AND EMERGING ECONOMIES. They comprise the summary of agricultural support estimates for OECD countries.More detailed data by country and documentation can be found in the full dataset (Excel Format) available at :

Databasepublished September 2011These tables are a complement to the report Agricultural Policy Monitoring and Evaluation 2011 : OECD COUNTRIES AND EMERGING ECONOMIES. They comprise the summary of agricultural support estimates for OECD countries.More detailed data by country and documentation can be found in the full dataset (Excel Format) available at :

Databasepublished : September 2012These 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.More detailed data by country and documentation can be found in the full dataset (Excel Format) available at :

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.More detailed data by country and documentation can be found in the full dataset (Excel Format) available at :

This dataset contains the main results of the 2017 Eurostat-OECD PPP comparison for the 49 countries that participated in the 2017 round of the Eurostat-OECD Purchasing Power Parity (PPP) Programme. Colombia and Costa Rica participated for the first time. Please note that time series for PPPs for GDP, actual individual consumption and household final consumption for these two countries in the National Accounts databases are still based on the 2011 ICP results until April 2020 (release of ICP 2017 results).

The breakdown of transactions and positions by counterpart sector enriches the information as included in the financial accounts and balance sheets by providing insight into the relationships between institutional sectors within an economy as well as between residents and non-residents. For a given financial instrument it is possible to trace the creditor-debtor relations between institutional sectors and with the rest of the world.

The breakdown of transactions and positions by counterpart sector enriches the information as included in the financial accounts and balance sheets by providing insight into the relationships between institutional sectors within an economy as well as between residents and non-residents. For a given financial instrument it is possible to trace the creditor-debtor relations between institutional sectors and with the rest of the world.

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

This indicator measures the income of selected jobless families that claim Guaranteed Minimum Income (GMI) benefits. Values are expressed both in national currency and as a percentage of the median disposable income in the country. When the country's poverty line is defined as a fixed percentage of the median disposable income, the normalization of GMI amounts in terms of the median disposable income allows measuring the gap between benefit entitlements and the poverty line. For instance, if the poverty threshold is 50% of the median disposable income, a value of the indicator of 30% means that benefit entitlements are 20 percentage points below the poverty line.

AEO (African Economic Outlook) Country Notes Tables, 2015

AEO (African Economic Outlook) Statistical Annex, 2015

Produced by the OECD Sahel and West Africa Club, Africapolis.org is the only comprehensive and standardised geospatial database on cities and urbanisation dynamics in Africa. Combining demographic sources, satellite and aerial imagery and other cartographic sources, it is designed to enable comparative and long-term analyses of urban dynamics - covering 7 500 agglomerations in 50 countries. Bulk download is available from below link

Data is available for the 17 countries covered by the SWAC/OECD (Benin, Burkina Faso, Cabo Verde, Chad, Cote d'Ivoire, The Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Mali, Mauritania, Niger, Nigeria, Senegal, Sierra Leone and Togo).   Agglomerated population: Population of urban agglomerations above 10 000 inhabitants.

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.

Destination of Official Development Assistance Disbursements. Geographical breakdown by donor, recipient and for some types of aid (e.g. grant, loan, technical co-operation) on a disbursement basis (i.e. actual expenditures). The data cover flows from bilateral and multilateral donors which focus on flows from DAC member countries and the EU Institutions.

This dataset is used to report the tying status of bilateral ODA commitments. Members have agreed that administrative costs and technical co-operation expenditure should be disregarded in assessing the percentages of tied, partially untied and untied aid. These items have not been included in the data reported in this data set.

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

This 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”.

This dataset describes the emissions to the atmosphere released as a result of production and consumption processes. The productive activities are broken down by economic activity and the household consumption activities are broken down by purpose (transport, heating, and other activities). Air emissions include emissions of individual greenhouse gases (GHG) as well as air pollutants such as sulphur dioxide, nitrogen oxides, and particulate matter. The emissions are also aggregated (using equivalence factors) to report on environmental pressures: global warming potential, acidifying gases and ozone precursors.

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

AITRAW = All in average income tax rates at average wage OECD Taxing Wages. Taxing Wages provides unique information on income tax paid by workers and social security contributions levied on employees and their employers in OECD countries. In addition, this annual publication specifies family benefits paid as cash transfers. Amounts of taxes and benefits are detailed program by program, for eight household types which differ by income level and household composition. Results reported include the marginal and effective tax burden for one- and two-earner families, and total labour costs of employers.

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

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

The dataset includes a detailed breakdown of Investment funds, Insurance companies and Pension funds, and Other forms of institutional savings, as institutional sectors. This finer breakdown by type of investors has been established with reference to the System of National Accounts (SNA), when possible. Within Investment funds, one distinguishes Open-end companies, further broken down into Money market funds and Other mutual funds, and Closed-end companies, of which Real estate funds. Within Insurance companies and pension funds one distinguishes Insurance companies, further broken down into Life insurance companies and Non-life insurance companies, and Autonomous pension funds. Financial assets included correspond to the assets requested in the previous database on Institutional Investors, i.e. Currency and deposits, Securities other than shares, Loans, Shares and other equities and Other financial assets. Moreover, Total non-financial assets are also included. While the sub-classification of the above financial assets corresponds to SNA93, a further breakdown between assets issued by residents and assets issued by non-residents is reported.

The “ALFS Summary tables” dataset is a subset of the Annual Labour Force Statistics database which presents annual labour force statistics and broad population series for 34 OECD member countries plus Brazil, Columbia and Russian Federation and 4 geographical areas (Major Seven, Euro area, European Union and OECD-Total). Data are presented in thousands of persons, in percentage or as indices with base year 2010=100. This dataset contains estimates from the OECD Secretariat for the latest years when countries did not provide data. These estimates are necessary to compile aggregated statistics for the geographical areas for a complete span of time. Since 2003, employment data by sector for the United States are compiled following the North American Industrial Classification System (NAICS); therefore they are not strictly comparable with other countries’ data. Euro area and European Union data were extracted from Eurostat (LFS Series, Detailed annual survey results in New Cronos). Euro area refer to Euro area with 17 countries (geo = ea17). European Union refers to European Union with 27 countries (geo = eu27).

Annual Purchasing Power Parities and exchange rates: 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.

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 concept used is the total number of hours worked over the year divided by the average number of people in employment. The data are intended for comparisons of trends over time; they are unsuitable for comparisons of the level of average annual hours of work for a given year, because of differences in their sources. Part-time workers are covered as well as full-time workers. The series on annual hours actually worked per person in total employment presented in this table for all 34 OECD countries are consistent with the series retained for the calculation of productivity measures in the OECD Productivity database (www.oecd.org/statistics/productivity/compendium). However, there may be some differences for some countries given that the main purpose of the latter database is to report data series on labour input (i.e. total hours worked) and also because the updating of databases occur at different moments of the year. Hours Hours actually worked per person in employment are according to National Accounts concepts for 18 countries: Austria, Canada, the Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Italy, Korea, the Netherlands, Norway, the Slovak Republic, Spain, Sweden, Switzerland and Turkey. OECD estimates for Belgium, Ireland, Luxembourg and Portugal for annual hours worked are based on the European Labour Force Survey, as are estimates for dependent employment only for Austria, Estonia, Greece, the Slovak Republic and Slovenia. The table includes labour-force-survey-based estimates for the Russian Federation.countries: For further details and country specfic notes see: www.oecd.org/employment/outlook and www.oecd.org/employment/emp/ANNUAL-HOURS-WORKED.pdf

This dataset contains data on average annual wages per full-time and full-year equivalent employee in the total economy.  Average annual wages per full-time equivalent dependent employee are obtained by dividing the national-accounts-based total wage bill by the average number of employees in the total economy, which is then multiplied by the ratio of average usual weekly hours per full-time employee to average usually weekly hours for all employees.   Average wages are converted in USD PPPs using 2017 USD PPPs for private consumption and are deflated by a price deflator for private final consumption expenditures in 2017 prices.   Real compensation per employee (instead of real wages) are considered for Chile, Iceland, Mexico and New Zealand.

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

This table contains data on the average duration of unemployment by sex and standardised age groups (15-19, 15-24, 20-24, 25-54, 55+, total). Data are expressed in months.

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

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) .

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

This dataset present the Balanced Trade in Services (BaTIS) dataset published by OECD. It is a complete, consistent and balanced matrix of international trade in services statistics (ITSS). It contains annual bilateral data covering 202 reporters and partners, broken down by the 12 main EBOPS2010 (BPM6) categories.

The Benefits and Wages series addresses the complicated interactions of tax and benefit systems for different family types and labour market situations. The series is a valuable tool used to compare the different benefits made available to those without work and those with different levels of in-work income for OECD countries and EU countries. The main social policy areas are as follows: taxes and social security contributions due on earnings and benefits, unemployment benefits, social assistance, family benefits, housing benefits, and in-work benefits. OECD Work Incentive and Income adequacy indicators, country specific files, the tax-benefit models and the tax benefit calculator, including detailed descriptions of all cash benefits available to those in and out of work as well as the taxes they were liable to pay are available on Benefits and Wages: OECD Indicators   Unit of measure used: Estonia: 2011 - EUR; 2010; 2009; 2008; 2007; 2006; 2005 -EEK Slovak Republic: 2010; 2009 - EUR; 2008; 2007; 2006; 2005 -SKK

This dataset presents a set of bibliometric indicators calculated using Elsevier's Scopus Custom Data, Version 5.2019; and Scimago Journal Rankings are used to calculate the top 10% most cited scientific publications.Elsevier assigns each journal in Scopus to one or more subjects using its All Science and Journal Classification (ASJC). There are 27 main fields comprising 334 subjects in the classification.This dataset is a small sample of the bibliometric indicators calculated using the Scopus Database. Different indicators will be presented in the Scoreboard platform (forthcoming).The following indicators are presented:FPUBS_NBFRAC: Total number of scientific publications, fractional counts. Publications are attributed to countries on the basis of the authors' institutional affiliations. Publications were fractionalised by contributing units; so that reported figures add up to the total number of publications (each document has the same weight). Fractional counts can be aggregated.TOP10FPUBS_NBFRAC: Total number of 10% top-cited scientific publications, fractional counts. The top 10% most cited documents is an indicator of excellence. This rate indicates the amount (in percentages) of a unit's scientific output that is included into the group of the 10% of the most cited papers in their respective scientific fields. It is a measure of high quality of research output of a unit, in this case the country. The indicator of scientific excellence is calculated at the document level using fractional counts. Documents organized by document type, ASJC field and year - are sorted in descending order based on the number of citations received. A threshold of 10% most cited documents is calculated for each category. Only documents with a fixed number of citations above the threshold are included. Documents with the same number of citations as the threshold are sorted according to the Scimago Journal Rankings (SJR) value of the journal in which they were published; those with the highest scores are selected. The citation window is based on the whole period, so top 10% most cited documents for the reference year uses whole period citations. No citation window is imposed as citation-based indicators are calculated on the basis of comparisons with documents published in the same year. The world average is 10% for the period.TOP10_X: The top 10% most cited documents is an indicator of excellence. This rate indicates the amount (in percentages) of a unit's scientific output that is included into the group of the 10% of the most cited papers in their respective scientific fields. The world average is 10% for the period.INTL_X: Percentage of scientific publications involving international collaboration. International collaboration refers to publications co-authored among institutions in different countries. Estimates are computed for each country by counting documents for which the set of listed affiliations includes at least one address within the country and one outside. Single-authored documents with multiple affiliations in different countries count as international collaboration.ISCORR_X: Percentage of corresponding/leading author scientific publications. Percentage corresponding or leading author documents, i.e. the share of scientific output where a domestic author is listed as a corresponding author. The scientific leadership indicator helps interpret the role of a given country in collaboration activities. The scientific leadership indicator shows the share of scientific output (in this case, documents by authors from a given country) where an author from this country is listed as leading author.NORMCIT_X: Normalised citation score, ratio of actual versus expected publications, percentage. Because of the differences in citation patterns of the fields and document types, field-based normalisation is required. The measure of normalised citation impact shows the relationship of the unit's average citation to the world's average, the unit is the country. The indicator is derived as the ratio between the average number of citations received by the documents published by authors affiliated to an institution in a given country and the world average of citations, over the same time period, by document type and subject area. Its value indicates how many times above (or below) the document has been cited above (or below) world average. For example, a value of 80 means the country is cited 20% below world average in that field and 130 means the country is cited 30% above average for that domain. The normalisation of citation values is item-oriented, i.e. carried out at the level of the individual document. If an article has been published in a journal allocated to more than one subject area, a mean value of the areas is calculated. The world average is 100.SPEC_I: The Relative Specialisation Index measures relative specialisation. This index is calculated by dividing all papers in a field from country a by the total production in all fields for country a, this proportion is then divided by the same proportion calculated at the world level. The world average is 1. A threshold has been set and the index has only been calculated for countries with 50 or more documents.

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 OECD broadband database provides access to a range of broadband-related statistics gathered by the OECD. Policymakers must examine a range of indicators which reflect the status of individual broadband markets in the OECD. Source - https://www.oecd.org/digital/broadband/broadband-statistics/

The OECD broadband portal provides access to a range of broadband-related statistics gathered by the OECD. Policy makers must examine a range of indicators which reflect the status of individual broadband markets. The OECD broadband speed tests by country show the official measurements of actual access network broadband speed. The OECD broadband map shows national broadband statistics in OECD countries. Mobile broadband penetration has risen to 85.4% in the OECD area, meaning more than four wireless subscriptions for every five inhabitants, according to data for June 2015 released by the OECD .

The Burkina Faso Gender, Institutions and Development Database (Burkina Faso-GID) provides researchers and policymakers with key data at the national and subnational levels on gender-based discrimination in social institutions. This data helps analyse women’s empowerment and understand gender gaps in other key areas of development. Covering the 13 regions of the country, the Burkina Faso-GID contains comprehensive information on social norms, attitudes and both perceived and actual practices that discriminate against women and girls.

The Burkina Faso-SIGI is a composite indicator measuring discriminatory social institutions. It is built on 46 innovative variables which are grouped into 5 sub-indices: discrimination in the family, restricted physical and moral integrity, son preference, restricted access to resources and assets and restricted civil liberties. The Burkina Faso-SIGI and its sub-indices range from 0, for no discrimination, to 1, for very high discrimination.

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

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

Data are provided in million national currency (for the euro zone, pre-EMU euro or EUR), million current PPP USD and million constant USD (2010 prices and PPPs). Variables collected This table presents research and development (R&D) expenditure statistics performed in the business enterprise sector. Data include total business enterprise intramural expenditure on R&D by size class and source of funds.

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

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.

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

OECD Indicators on Carbon dioxide (CO2) emissions embodied in international trade (TeCO2) are derived by combining the 2021 editions of OECD Inter-Country Input-Output (ICIO) Database and of International Energy Agency (IEA) statistics on CO2 emissions from fuel combustion. In this release of TeCO2, emissions from fuels used for international aviation and maritime transport (i.e. aviation and marine bunkers) are also considered.Production-based CO2 emissions are estimated by allocating the CO2 emissions to the 45 target industries in OECD ICIO and, to household final consumption of fuels, by both residents and non-residents.Demand-based CO2 emissions are calculated by multiplying the intensities of the production-based emissions (c) with the global Leontief inverse (I-A)(-1) and global final demand matrix (Y) from OECD ICIO, taking the column sums of the resulting matrix and adding residential and private road emissions (FNLC), i.e. direct emissions from final demand: colsum [ diag(c) (I-A)(-1) Y ] + FNLC.For more information, see TeCO2 web page: http://oe.cd/io-co2.

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

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

Note:  The updates and revisions for the OECD Central Government Debt Database have been suspended. This dataset is no longer updated. For more info, please read http://stats.oecd.org/Index.aspx?DataSetCode=GOV_DEBT   Statistical population The focus of this dataset is to provide comprehensive quantitative information on marketable and non-marketable central government debt instruments in all OECD member countries. The coverage of the data is limited to central government debt issuance and excludes therefore state and local government debt and social security funds.

CGPITRT: Central government personal income tax rates and threshold   This table reports statutory central government personal income tax rates for wage income plus the taxable income thresholds at which these statutory rates apply. The table also reports basic/standard tax allowances, tax credits and surtax rates. The information is applicable to a single person without dependents. The threshold, tax allowance and tax credit amounts are expressed in national currencies Tapered means that the tax relief basic amount is reduced with increasing income Further explanatory notes may be found in the Explanatory Annex This data represents part of the data presented within the Excel file “Personal income tax rates and thresholds for central governments - Table I.1”. The Data for 1981 to 1999 is not included here within as not all the data for these years is either available, or can be verified. The OECD tax database provides comparative information on a range of tax statistics - tax revenues, personal income taxes, non-tax compulsory payments, corporate and capital income taxes and taxes on consumption - that are levied in the 34 OECD member countries.” Tax policy Analysis homepage OECD Tax Database Taxing Wages Dissemination format(s) This data is also presented through the OECD Tax database webpage. OECD Tax Database

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

The OECD Science, Technology and Industry Outlook 2012 presents, in a series of country profiles, the main features, strengths and weaknesses of national STI systems and major recent changes in national STI policy. The statistical dimension of the country profiles has drawn on the work and empirical research conducted by the OECD on the measurement of innovation and the development of internationally comparable STI indicators for policy analysis.   

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.

This dataset provides information on the level of public equipment installed by countries to managed and abate water pollution. It shows the percentage of national population connected to "public" sewerage networks and related treatment facilities, and the percentage of national population connected to "public" wastewater treatment plants, and the degree of treatment. Connected here means actually connected to a wastewater plants through a public sewage network. When analysing these data, it should be kept in mind that the optimal connection rate is not necessarily 100%. It may vary among countries and depends on geographical features and on the spatial distribution of habitats. The interpretation of those data should take into account some variations in countries' definitions, as reflected in metadata. Data source(s): Joint OECD/Eurostat questionnaire on Inland Waters. Data for non-OECD countries is sourced from UNSD (https://unstats.un.org/unsd/envstats/country_files)

OECD Economic Outlook, June 2022.

This dataset presents the Consolidated financial balance sheets by economic sector (Quarterly table 0710), according to SNA 2008 methodology. It comprises all flows, which record, by type of financial instruments, the financial transactions between institutional sectors.

This dataset presents the Consolidated financial transactions by economic sector (Quarterly table 0610), according to SNA 2008 methodology. It comprises all flows, which record, by type of financial instruments, the financial transactions between institutional sectors.

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).

Note: CPA data for 2018 and 2019 are projections from the 2016 Survey on Forward Spending Plans. Country Programmable Aid (CPA), outlined in our Development Brief  and also known as “core” aid, is the portion of aid donors programme for individual countries, and over which partner countries could have a significant say. CPA is much closer than ODA to capturing the flows of aid that goes to the partner country, and has been proven in several studies to be a good proxy of aid recorded at country level. CPA was developed in 2007 in close collaboration with DAC members. It is derived on the basis of DAC statistics and was retroactively calculated from 2000 onwards

The 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.

This dataset contains three earnings-dispersion measures - ratio of 9th-to-1st, 9th-to-5th and 5th-to-1st - where ninth, fifth (or median) and first deciles are upper-earnings decile limits, unless otherwise indicated, of gross earnings of full-time dependent employees. The dataset also includes series on: the incidence of low-paid workers defined as the share of full-time workers earning less than two-thirds of gross median earnings of all full-time workers; the incidence of high of high-paid workers defined as the share of full-time workers earning more than one-and-half time gross median earnings of all full-time workers; gender wage gap unadjusted and defined as the difference between median wages of men and women relative to the median wages of men.

Note: Update to this data are available only to OECD subscribers. Please visit: https://stats.oecd.org/Index.aspx?DataSetCode=REG_DEMO_TL3 National values have been computed based on regional estimates. For this reason, it is possible that in some cases these values differ from national statistics

This dataset traces net changes in terms of volume in the growing stock of standing wood on forest land. Forest depletion and growth describe balances or imbalances in different types of forests. The intensity of use of forest resources reflects various forest management methods and their sustainability. These data should be read in connection with other indicators, in particular land use changes and forest quality (species diversity, forest degradation), and be complemented with data on forest management practices and protection measures. Please bear in mind that definitions and estimation methods vary for some countries.

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

The data are on cash basis. The figures exclude local government revenues as the data are not available. Heading 5212: In ECLAC data, property tax on motor vehicles is classified in category 4000. Source: Subsecretaría de Ingresos Públicos, Ministry of Economy and Production.

Data are on a fiscal year basis beginning 1st April. From 1990, data are on accrual basis. The figures for different groups of taxes are reported on different reporting bases, namely: * Social security contributions (heading 2000) : in principle accrual basis, * Central government taxes : accrual basis (revenues accrued during the fiscal year plus cash receipts collected before the end of May (the end of April until 1977), * Local government taxes : accrual basis (due to be paid during the fiscal year and cash receipts collected before the end of May). The Japanese authorities take the view that the Enterprise tax (classified in 1100 and 1200) and the Mineral product tax (classified in 5121) should be classified in heading 6000 since under articles 72 and 519 of the Local Tax Law these taxes are regarded as levies on the business or mining activity itself. Heading 2000 includes some unidentifiable voluntary contributions. Heading 2300: Includes contibutions to the National pension, National Health Insurance and the Farmer's pension fund. Contributions to the Farmer's pension fund are not available for the years before 1999. Heading 4100: Municipal property tax, includes Prefectural property tax from 1990 to 1994 because data is not available to provide a breakdown. Heading 5121: Municipal tobacco tax, includes Prefectural tobacco tax from 1990 to 1994 because data is not available to provide a breakdown. Heading 5121: In sub-item Petroleum and coal tax, the data before 2003 refer to petroleum tax.

From 1981 the figures take into account the classification procedures set out in the OECD Interpretative Guide. Consequently they are not completely comparable with the figures for earlier years though the amounts involved are quite small. Heading 1000: Includes a tax on property 'Contribucion Rustica' which would be more appropriately classified in 4110, and the 'Licencia fiscal industrial and professionales' which, because it is a tax levied by reference to the size of the firm, energy input, etc, would be more appropriately classified in 6000. The data necessary to provide a breakdown is not available. All subdivisions are estimated. Heading 2300: Contributions paid by self-employed were shown under heading 2100 until 1982. Heading 4100: Most of these receipts fall under 4110. Heading 4400: In 1988 revenues from taxes on legal Acts issued by Autonomous Communities (Local) are included in 4400. Heading 5121 comprises certain local levies which may include non-tax revenues.

Revenue Statistics in Latin America and the Caribbean Revenue Statistics in Latin America and the Caribbean 2020 is a joint publication by the Organisation for Economic Co-operation and Development (OECD) Centre for Tax Policy and Administration, the OECD Development Centre, the United Nations Economic Commission for Latin America and the Caribbean (UN-ECLAC), the Inter-American Center of Tax Administrations (CIAT), the Inter-American Development Bank (IDB) and with the support of the European Union Regional Facility for Development in Transition for Latin America and the Caribbean (LAC). It presents detailed, internationally comparable data on tax revenues for 26 Latin American and Caribbean economies, three of which are OECD members. Colombia was not an OECD Member at the time of preparation of this publication. Accordingly, Colombia does not appear in the list of OECD Members and is not included in the zone aggregates. Revenue Statistics in Latin America and the Caribbean’s approach is based on the well-established methodology of the OECD Revenue Statistics database, which is an essential reference source for OECD member countries. Comparisons are also made with the average tax indicators for OECD economies. Comparable tables show total tax revenue data and by tax as a percentage of GDP, and, for the different types of taxes, as a share of total taxation. Detailed country tables show information in national currency values.

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

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

This table contains data on discouraged workers who are persons not in the labour force who believe that there is no work available due to various reasons and who desire to work. Data are broken down by sex and standardised age groups (15-24, 15-64, 25-54, 55-64, 65+, total).

Graduates/new entrants in each educational field as a percentage of the sum of graduates/new entrants in all fields.

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

Distribution of teachers by gender and different age groups.

1. ccTLDs stands for country code top-level domains. 2. gTLDs - stands for generic top-level domains.

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

Tables show earmarked grants classified into the 10 functions (or policy areas) for which they are disbursed. Functions are the same as used in the Classification of Functions of Government (COFOG) by the System of National Accounts. A 'miscellaneous' category has been added to these 10 functions to allow for situations where a precise breakdown by function is not available.

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.

Latest Version is available here: https://knoema.com/OECDEO2020DEC/economic-outlook-no-108-december-2020   Economic Outlook No 107 (EO107) 2/2   Given the unusual level of uncertainty caused by the Covid-19 pandemic, this Economic Outlook (EO107) presents two scenarios for each country and economy – one scenario in which a second outbreak occurs in most economies towards the end of this year (double-hit scenario) and an alternative scenario where the second outbreak is avoided (single-hit scenario).Furthermore, only a limited number of series is made available compared to past editions.   This data set presents the double-hit scenario.   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 selected 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.   The database contains annual data (for all variables) and quarterly figures (for a subset of variables). 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 the 4 June 2020.   Concerning the aggregation of world trade, a new composition has been introduced, since projections are now made for the major non-OECD economies. Thus, besides OECD and the OECD euro area, the following new regions are available: Dynamic Asian Economies (Chinese Taipei, Hong Kong, Malaysia, the Philippines, Singapore, Thailand, Vietnam); Oil Producers (Azerbaijan, Kazakhstan, Turkmenistan, Brunei, Timor-Leste, Bahrain, Iran, Iraq, Kuwait, Libya, Oman, Qatar, Saudi Arabia, United Arab Emirates, Yemen, Ecuador, Trinidad and Tobago, Venezuela, Algeria, Angola, Chad, Rep. of Congo, Equatorial Guinea, Gabon, Nigeria, Sudan); with the remaining countries in a residual 'Rest of the World' group.

Latest version is available here: https://knoema.com/OECDEO2020DEC/economic-outlook-no-108-december-2020   Economic Outlook No 107 (EO107) 1/2   Given the unusual level of uncertainty caused by the Covid-19 pandemic, this Economic Outlook (EO107) presents two scenarios for each country and economy – one scenario in which a second outbreak occurs in most economies towards the end of this year (double-hit scenario) and an alternative scenario where the second outbreak is avoided (single-hit scenario).Furthermore, only a limited number of series is made available compared to past editions.   This data set presents the single-hit scenario.   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 selected 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.   The database contains annual data (for all variables) and quarterly figures (for a subset of variables). 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 the 4 June 2020.   Concerning the aggregation of world trade, a new composition has been introduced, since projections are now made for the major non-OECD economies. Thus, besides OECD and the OECD euro area, the following new regions are available: Dynamic Asian Economies (Chinese Taipei, Hong Kong, Malaysia, the Philippines, Singapore, Thailand, Vietnam); Oil Producers (Azerbaijan, Kazakhstan, Turkmenistan, Brunei, Timor-Leste, Bahrain, Iran, Iraq, Kuwait, Libya, Oman, Qatar, Saudi Arabia, United Arab Emirates, Yemen, Ecuador, Trinidad and Tobago, Venezuela, Algeria, Angola, Chad, Rep. of Congo, Equatorial Guinea, Gabon, Nigeria, Sudan); with the remaining countries in a residual 'Rest of the World' group.

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.

The OECD Economic Outlook analyses the major economic trends over the coming 2 to 3 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 this Outlook provides a unique tool 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 rates and exchange rates, the balance of payments, government and of households, and government debt. For the non-OECD regions, foreign trade and current account series are available. The database contains annual and quarterly data for the historical period and for the projection period. For this latter period, quarterly data are available for the G7 countries, and the OECD regions, while annual data are available for all OECD countries and for non-OECD regions. Quarterly series are seasonally adjusted. Variables are defined in such a way that they are as homogenous as possible over the countries. Breaks in underlying series are corrected as far as possible. Sources for the historical data are publications of national statistical agencies and OECD statistical publications such as the Quarterly National Accounts, the Annual National Accounts, the Annual Labour Force Statistics and the Main Economic Indicators.

The OECD Economic Outlook analyses the major economic trends over the coming 2 to 3 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 this Outlook provides a unique tool 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 rates and exchange rates, the balance of payments, government and of households, and government debt. For the non-OECD regions, foreign trade and current account series are available. The database contains annual and quarterly data for the historical period and for the projection period. For this latter period, quarterly data are available for the G7 countries, and the OECD regions, while annual data are available for all OECD countries and for non-OECD regions. Quarterly series are seasonally adjusted. Variables are defined in such a way that they are as homogenous as possible over the countries. Breaks in underlying series are corrected as far as possible. Sources for the historical data are publications of national statistical agencies and OECD statistical publications such as the Quarterly National Accounts, the Annual National Accounts, the Annual Labour Force Statistics and the Main Economic Indicators.

Current data with full Archive version is available here: https://knoema.com/OECDEO2020DEC/economic-outlook-no-108-december-2020  

Current data with full Archive version is available here: https://knoema.com/OECDEO2020DEC/economic-outlook-no-108-december-2020

 The OECD Economic Outlook analyses the major economic trends over the coming 2 to 3 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. The database contains annual for the projection period. 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 statistical publications such as the Quarterly National Accounts, the Annual National Accounts, the Labour Force Statistics and the Main Economic Indicators. The cut-off date for information used in the compilation of the projections was the 29 May 2015. Concerning the aggregation of world trade, a new composition has been introduced, since projections are now made for the major non-OECD economies. Thus, besides OECD and the OECD euro area, the following new regions are available: Dynamic Asian Economies (Chinese Taipei, Hong Kong, Malaysia, the Philippines, Singapore, Thailand, Vietnam); Oil Producers (Azerbaijan, Kazakhstan, Turkmenistan, Brunei, Timor-Leste, Bahrain, Iran, Iraq, Kuwait, Libya, Oman, Qatar, Saudi Arabia, United Arab Emirates, Yemen, Ecuador, Trinidad and Tobago, Venezuela, Algeria, Angola, Chad, Rep. of Congo, Equatorial Guinea, Gabon, Nigeria, Sudan); with the remaining countries in a residual 'Rest of the World' group.

This table contains data on economic short-time workers by professional status (employees or total employment). Economic short-time workers comprise workers who are working less than usual due to business slack, plant stoppage, or technical reasons. However, the definitions are not harmonised which hampers the comparison across countries. Data are broken down professional status - employees, total employment - by sex and by standardised age groups (15-24, 25-54, 55+, total).

The OECD, in cooperation with the EU, has developed a harmonised definition of urban areas which overcomes previous limitations linked to administrative definitions (OECD, 2012). According to this definition an urban area is a functional economic unit characterised by densely inhabited “city core” and “commuting zone” whose labour market is highly integrated with the core. The Metropolitan database provides indicators of 649 OECD metropolitan areas identified in 33 OECD countries and the functional urban areas of Colombia. Comparable values of population, GDP, employment, and other indicators are presented.

The data presented in this table are addressing one of the seven quality of life dimensions that have been identified within the framework of Education and Social Outcomes. The seven dimensions are: Health status, Work-life balance, Social connections, Civic engagement and governance, Environment, Personal safety and Subjective well-being.

This dataset presents internationally comparable data on education and earnings, by educational attainment, age and gender as published in OECD Education at a Glance 2022. For trend data, Education at a Glance 2022 includes data for 2005 and 2010-2020 (or years with available 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.

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

Average number of teachers by sex and age.

Average number of teachers by sex and type of institution.

 This indicator measures the proportion of earnings that are lost to either higher taxes or lower benefit entitlements when a jobless person takes up employment. It is commonly referred to as "Participation Tax Rate (PTR)" as it measures financial disincentives to participate in the labour market.

Related data is available here: https://knoema.com/PTRCCSA/ptrs-for-parents-claiming-guaranteed-minimum-income-gmi-benefits-and-using-childcare-services This indicator measures the proportion of earnings that are lost to either higher taxes, lower benefits or childcare costs when a parent with young children takes up full-time employment and requires use of centre-based childcare services.

The Pensions at a Glance indicators, covering all 34 OECD countries, are designed to show future entitlements for workers who entered the labour market in 2008 and spend their entire working lives under the same set of rules. The results presented here include all mandatory pension schemes for private-sector workers, regardless of whether they are public or private.

These tables are a complement to the report Agricultural Policy Monitoring and Evaluation 2011 : OECD COUNTRIES AND EMERGING ECONOMIES. They comprise the summary of agricultural support estimates for OECD countries.

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.

These tables are a complement to the report Agricultural Policy Monitoring and Evaluation 2011 : OECD COUNTRIES AND EMERGING ECONOMIES. They comprise the summary of agricultural support estimates for OECD countries.

2011 G) Emerging Economies: Consumer Support Estimate by Country These tables are a complement to the report Agricultural Policy Monitoring and Evaluation 2011 : OECD COUNTRIES AND EMERGING ECONOMIES. They comprise the summary of agricultural support estimates for OECD countries. NPC: Nominal Protection Coefficient. NAC: Nominal Assistance Coefficient.

These tables are a complement to the report Agricultural Policy Monitoring and Evaluation 2011 : OECD COUNTRIES AND EMERGING ECONOMIES. They comprise the summary of agricultural support estimates for OECD countries. TSE : Total support estimate.

2011 E) Emerging Economies: Producer Support Estimate by Country These tables are a complement to the report Agricultural Policy Monitoring and Evaluation 2011 : OECD COUNTRIES AND EMERGING ECONOMIES. They comprise the summary of agricultural support estimates for OECD countries. NPC: Nominal Protection Coefficient. NAC: Nominal Assistance Coefficient.  

These tables are a complement to the report Agricultural Policy Monitoring and Evaluation 2011 : OECD COUNTRIES AND EMERGING ECONOMIES. They comprise the summary of agricultural support estimates for OECD countries.

This dataset provides selected information on national emissions of air pollutants: man-made emissions of sulphur oxides (SOx), nitrogen oxides (NOx), particulate matter (PM), carbon monoxide (CO) and volatile organic compounds (VOC). Categories presented are based on the NFR (nomenclature for reporting) 2014 classification. 

Compared to men, women are less likely to work full-time, more likely to be employed in lower-paid occupations, and less likely to progress in their careers. As a result gender pay gaps persist and women are more likely to end their lives in poverty. This data looks at how many men and women are in paid work, who works full-time, and how having children and growing older affect women’s work patterns and earnings differently to men’s. It looks at how women bear the brunt of domestic and family responsibilities, even when working full-time. It also considers the benefits for businesses of keeping skilled women in the workplace, and encouraging them to sit on company boards. It looks at women’s representation in parliaments, judicial systems, and the senior civil service. It examines male and female employment in the wake of the crisis, and how women tend to be confined to the most vulnerable categories within the informal sector in developing countries.

Employment, participation rates: population aged 15-64; Unemployment rate: active population aged 15-64.   Rates as defined by the International Labour Organization.

This table contains a distribution of workers by job tenure intervals. Data are broken down by professional status - employees, self-employed, total employment – sex, five-year and broad age groups (15-24, 25-54, 55-64, 15-64, total, etc.). Job tenure is measured by the length of time workers have been in their current or main job or with their current employer. This information is valuable for estimating the degree of fluidity in the labour market and in identifying the areas of economic activity where the turnover of labour is rapid or otherwise. Data are so far reported for a number of European countries and will be expanded to cover a greater number of countries. Unit of measure used - Data are expressed years. Example: 1.5 = 1 year and 6 months.

This dataset contains the tenure composition (as a percentage of all job tenures). Data are broken down by professional status - employees and total employment - sex, five-year and broad age groups (15-24, 25-54, 55-64, 15-64, total, etc.). Geographic coverageIn 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.

Job tenure is measured by the length of time workers have been in their current or main job or with their current employer. This information is valuable for estimating the degree of fluidity in the labour market and in identifying the areas of economic activity where the turnover of labour is rapid or otherwise. Data are so far reported for a number of European countries and will be expanded to cover a greater number of countries.

This table contains data on permanent and temporary workers based on the type of work contract of their main job. Data are further broken down by professional status - employees, total employment - by sex and by standardised age groups (15-19, 15-24, 20-24, 25-54, 55-64, 65+, total). Unit of measure used - Data are expressed in thousands of persons.

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

Number of students by level of education, adjusted to the financial year. When financial year, school year and calendar year differs, adjustments are made to ease comparison.

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 age and sex.

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

This indicator examines the share of students by gender, programme orientation, mode of study and type of institution over the total number of students.

Number of students enrolled in different education programmes by type of institution and sex.

Number of students by level of education, adjusted to the financial year. When financial year, school year and calendar year differs, adjustments are made to ease comparison.

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

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

Dataset provides information on selected economic aspects of environmental management. It includes tables on expenditure, which help to identify the financial consequences of environmental policies: public and private pollution abatement and control expenditure; public research and development financing for environmental protection; official development assistance, including aid in support of environment. Dataset also includes data concerning revenues from environmentally-related taxes.

Note: Update to this data are available only to OECD subscribers. Please visit: https://stats.oecd.org/Index.aspx?DataSetCode=REG_ENV_TL3. National values have been computed based on regional estimates. For this reason, it is possible that in some cases these values differ from national statistics.

As countries are increasingly using a wide range of policy measures to address agri-environmental issues, indicators provide crucial information to monitor and analyse the effects of those policies on the environment. They can also help the understanding and analysis of the environmental effects of future policy scenarios and agricultural projections. To help improve measurement of the environmental performance of agriculture, OECD has established a set of agri-environmental indicators, with development of the indicators in cooperation with Eurostat and FAO. These indicators inform policy makers and society on the state and trends in agri-environmental conditions, and can provide a valuable aid to policy analysis.

The OECD Environmental Policy Stringency Index (EPS) is a country-specific and internationally-comparable measure of the stringency of environmental policy. Stringency is defined as the degree to which environmental policies put an explicit or implicit price on polluting or environmentally harmful behaviour. The index ranges from 0 (not stringent) to 6 (highest degree of stringency). The index covers 28 OECD and 6 BRIICS countries for the period 1990-2012. The index is based on the degree of stringency of 14 environmental policy instruments, primarily related to climate and air pollution.

Data is expressed in million national currency.   Environmental protection (EP) includes all activities and actions which have as their main purpose the prevention, reduction and elimination of pollution as well as any other degradation of the environment. The scope of environmental protection expenditure is defined according to the Classification of Environmental Protection Activities (CEPA 2000). CEPA distinguishes nine environmental domains.   The Environmental Protection Expenditure Account (EPEA) is a monetary description of environmental protection activities in accordance with the System of Environmental-economic Accounting (SEEA) central framework. It is coherent with the European System of Accounts (ESA 2010) which applies to national accounts and related satellite accounts. Because the full EPEA framework is quite expensive in terms of resources to be set up, this EPEA module significantly simplifies the full framework while it still allows compiling a measure of environmental protection expenditure for the whole economy comparable with national accounts aggregates.   This data focuses on the production and uses of environmental protection services. Output of these services can be output of market, non-market and ancillary activities. EPEA is directly linked to the three definitions of GDP, the production measure, the expenditure measure and the income measure of GDP.   EPEA covers (1) expenditure on EP products by resident units; (2) expenditure related to the production of EP products, including the gross capital formation, and (3) transactions related to the financing of EP expenditure. It covers both the supply and demand side. Demand equals supply: Final consumption + Gross fixed capital formation (GFCF for characteristic environmental activities) + Exports - Imports = Output - Intermediate consumption + VAT plus taxes less subsidies on products That is, the final uses of a product equal the supply of that product. The terms can be reorganised as follows: Final consumption + GFCF + Intermediate consumption = Output + Imports - Exports + VAT plus taxes less subsidies on products   The left side is the sought sum of expenditure on EP products by resident units. The right side proposes an alternative calculation approach, which indeed EPEA follows instead of the left side approach. There are several reasons for this choice, including that (a) output is simpler to measure than final consumption, intermediate consumption and capital formation (capital formation in EP products is rare; one instance is soil decontamination leading to land improvement); (b) imports and exports are small; and (c) output is also relevant by itself for analysis of production.

Unit of measure used Environmental protection (EP) includes all purposeful activities directly aimed at the prevention, reduction and elimination of pollution or any other degradation of the environment resulting from production or consumption processes. The scope of Environmental Protection is defined according to the Classification of Environmental Protection Activities (CEPA), which distinguishes nine different environmental domains. Activities such as energy and material saving are only included to the extent that they mainly aim at environmental protection. An important example is recycling which is included only to the extent that it constitutes a substitute for waste management. Excluded are: (i) activities that, while beneficial to the environment, primarily satisfy technical needs or health and safety requirements for the protection of the workplace. (ii) expenditure linked to mobilisation of natural resources (e.g., water supply). (iii) calculated cost items such as depreciation (consumption of fixed capital) or the cost of capital as this questionnaire only records actual outlays. (iv) payments of interest, fines and penalties for non-compliance with environmental regulations or compensations to third parties etc., as they are not directly linked with an environmental protection activity. Environmental Protection Expenditure can be evaluated both according to the abater principle and the financing principle. This distinction makes it possible to aggregate different sectors and industries without double counting. Expenditure according to the abater principle (EXP I), includes all expenditure that the sector has for measures they themselves execute. Any economic benefits directly linked with the environmental protection activities (Receipts from by-products) are deducted in order to calculate the net amount of money spent by the sector for their own activities. The financing principle (EXP II) measures how much money a particular sector (directly) contributes to overall environmental protection activities, wherever they are executed. This means that the part of EXP I that was directly financed by others (through subsidies or revenues received) should be deducted, while the part of EXP I in other sectors that this sector finances directly (through subsidies or fees paid) should be added. The framework is based on double entry bookkeeping, where each activity and expenditure item has an abater (producer) and a financing side. This means that much expenditure by specialised producers is financed by the users of their services, mainly business sector and households. This will be recorded as Revenues for the Specialised producers (Table 4), and fees/purchases in Business and Households (Tables 2 and 3). Specialised producers include the production of environmental protection services by public and private corporations or quasi-corporations for the use of other units, mainly financed by the users of these services. These are mainly activities within ISIC Rev. 4/NACE Rev. 2 division and classes 37, 38.1, 38.2 and 39 such as: 37 Sewerage, 38.1 Waste collection, 38.2 Waste treatment and disposal, 39  Remediation activities and other waste management services. This sector is the sum of two components: a) Public specialised producers: All corporations and quasi-corporations that are subject to control by government units. Control is defined as the ability to determine general corporate policy by choosing appropriate directors, if necessary (Table 4A). b) Private specialised producers: All corporations and quasi-corporations that are not subject to control by government units (Table 4B). Specialised producers could also include for example the activities of e.g. volunteer environmental organisations or secondary environmental activities. These should be entered along with a footnote describing the coverage. CEPA domains: a column "pollution abatement and control" (PAC) has been kept in the questionnaire to ensure continuity with earlier data series.

EAMFP growth measures the residual growth in the joint production of both the desirable and the undesirable outputs that cannot be explained by changes in the consumption of factor inputs (including labour, produced capital and natural capital). Therefore, for a given growth of input use, EAMFP increases when GDP increases or when pollution decreases. As part of the growth accounting framework underlying the EAMFP indicator, the growth contribution of natural capital and growth adjustment for pollution abatement indicators are derived: Growth contribution of natural capital - measures to what extent a country's growth in output is attributable to natural resource use; Growth adjustment for pollution abatement - measures to what extent a country's GDP growth should be corrected for pollution abatement efforts - adding what has been undervalued due to resources being diverted to pollution abatement, or deducing the ‘excess' growth which is generated at the expense of environmental quality.

This dataset presents the data collected from OECD and partner economies on environmentally related tax revenue accounts with a breakdown by tax-base category and industrial activity. The data collection follows the OECD methodological guidelines for compiling Environmentally Related Tax Revenue (ERTR) accounts in line with the System of Environmental-Economic Accounting - Central Framework (SEEA-CF). The OECD ERTR accounts are consistent with the existing data collection by Eurostat. Nevertheless, in an effort to enhance the policy relevance of this SEEA module, the OECD approach goes slightly further and includes several additional revenue categories: Taxes levied on greenhouse gas (GHG) emissions are explicitly reported in two sub-categories: an energy related part (recorded as an energy tax) and a non-energy related part, such as certain GHG emissions related to landfills or agriculture (recorded as a pollution tax). Four "memo items" (i.e. information items that do not change the total) are included: (i) Certain land taxes; (ii) Taxes on oil and natural gas extraction; (iii) Taxes on the resource rent; (iv) Elevated value added taxes levied on environmentally related tax-bases. The dataset covers OECD member countries, accession candidates and selected partner economies since the year 1995. For EU countries, it includes the information on ERTR accounts reported to Eurostat.

National currencies are converted in United States dollars (USD) using IMF monthly average conversion rates.

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

Air pollution is considered one of the most pressing environmental and health issues across OECD countries and beyond. According to the World Health Organisation (WHO), exposure to fine particulate matter (PM2.5) has potentially the most significant adverse effects on health compared to other pollutants. PM2.5 can be inhaled and cause serious health problems including both respiratory and cardiovascular disease, having its most severe effects on children and elderly people. Exposure to PM2.5 has been shown to considerably increase the risk of heart disease and stroke in particular. For these reasons, population exposure to (outdoor or ambient) PM2.5 has been identified as an OECD Green Growth headline indicator. The underlying PM2.5 concentrations estimates are taken from van Donkelaar et al. (2016). They have been derived using satellite observations and a chemical transport model, calibrated to global ground-based measurements using Geographically Weighted Regression at 0.01° resolution. The underlying population data, Gridded Population of the World, version 4 (GPWv4) are taken from the Socioeconomic Data and Applications Center (SEDAC) at the NASA. The underlying boundary geometries are taken from the Global Administrative Unit Layers (GAUL) developed by the FAO, and the OECD Territorial Classification, when available. The current version of the database presents much more variation with respect to the previous one. The reason is that the underlying concentration estimates previously included smoothed multi-year averages and interpolations; while in the current version annual concentration estimates are used. Establishing trends of pollution exposure should be done with care, especially at smaller output areas, as their inputs (e.g. underlying data and models) can change from year to year. We recommend using a 3-year moving average for visualisation.

Air pollution is considered one of the most pressing environmental and health issues across OECD countries and beyond. According to the World Health Organisation (WHO), exposure to fine particulate matter (PM2.5) has potentially the most significant adverse effects on health compared to other pollutants. PM2.5 can be inhaled and cause serious health problems including both respiratory and cardiovascular disease, having its most severe effects on children and elderly people. Exposure to PM2.5 has been shown to considerably increase the risk of heart disease and stroke in particular. For these reasons, population exposure to (outdoor or ambient) PM2.5 has been identified as an OECD Green Growth headline indicator. The underlying PM2.5 concentrations estimates are taken from van Donkelaar et al. (2016). They have been derived using satellite observations and a chemical transport model, calibrated to global ground-based measurements using Geographically Weighted Regression at 0.01° resolution. The underlying population data, Gridded Population of the World, version 4 (GPWv4) are taken from the Socioeconomic Data and Applications Center (SEDAC) at the NASA. The underlying boundary geometries are taken from the Global Administrative Unit Layers (GAUL) developed by the FAO, and the OECD Territorial Classification, when available. The current version of the database presents much more variation with respect to the previous one. The reason is that the underlying concentration estimates previously included smoothed multi-year averages and interpolations; while in the current version annual concentration estimates are used. Establishing trends of pollution exposure should be done with care, especially at smaller output areas, as their inputs (e.g. underlying data and models) can change from year to year. We recommend using a 3-year moving average for visualization.

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

This dataset FDI flows main aggregates, BMD4 is updated every quarter and includes annual and quarterly aggregate Foreign Direct Investment (FDI) flows for OECD member countries and for non-OECD G20 countries (Argentina, Brazil, China, India, Indonesia, Saudi Arabia and South Africa), which are included in Balance of Payments (BOP) accounts. FDI flows record the value of cross-border transactions related to direct investment during a given period of time, usually a quarter or a year, and consist of equity transactions, reinvestment of earnings, and intercompany debt transactions.

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.

This dataset FDI positions by counterpart area, BMD4 includes inward and outward Foreign Direct Investment (FDI) positions by partner country for OECD reporting economies.  Inward and outward FDI positions by partner country are presented according to the directional principle (unless otherwise specified in the country level metadata); they are measured in USD millions, in millions of national currency and as a share of total FDI positions.

This dataset FDI positions by economic activity, BMD4 includes inward and outward Foreign Direct Investment (FDI) positions by economic activity according to ISIC4 for OECD reporting economies.

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.

Chapter B includes indicators that are either policy levers or antecedents to policy, 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.

National Accounts - Volume IIIb - Financial Balance Sheets - Stocks, which record the stocks of financial assets and liabilities by institutional sectors, at the end of the accounting period, and are presented in two tables: Balance sheets for financial assets and liabilities, consolidated and Balance sheets for financial assets and liabilities, non consolidated.

Data are compiled either by Central banks, or Statistical National Institutes. EU data are validated and provided by the European Central Bank whereas non-EU data are provided by national institutions. The sectors for which information is presented are: Total economy (S1) - Non-financial corporations (S11) - Financial corporations (S12) and its sub-sectors (S121 to S125) - General government (S13) and its sub-sectors (1311 to S1314) - Households (S14) - Non-profit institutions serving households (S15) Rest of the world (S2)

The financial indicators in this dataset are derived from OECD countries’ financial accounts (transactions): they give a picture of the short-term behavior of institutional sectors. They comprise for instance: Net financial transactions of the general government, as a percentage of Gross Domestic Product (GDP), which corresponds to the general government deficit; Transactions in financial assets of Households and NPISHs, as a percentage of Households Gross Disposable Income (GDI); Transactions in liabilities of Households and NPISHs, as a percentage of GDI.

The financial indicators in this dataset are constructed from OECD countries’ financial balance sheets (stocks): these ratios are considered as relevant to analyse the position and performance of the various institutional sectors. They comprise for instance: Financial net worth of Households and NPISHs, as a percentage of GDI; Non-financial corporations debt to equity ratio; Private sector debt; Leverage of the banking sector; General government debt, as a percentage of GDP.

This dataset presents internationally comparable data on expenditure per student on educational institutions at sub-national level. Expenditure per student on educational institutions at a particular level of education is calculated by dividing total expenditure on educational institutions at that level by the corresponding full-time equivalent enrolment. Expenditure per student are converted into equivalent USD by using national purchasing power parities (PPPs) for GDP. Further work on the cost of living at subnational level is required to fully adjust expenditure per student available in this dataset.

National Accounts - Volume IIIa - Financial Accounts - Flows, which record, by type of financial instruments, the financial transactions between institutional sectors, and are presented in two tables: Financial accounts, consolidated and Financial accounts, non-consolidated.

The Financial account, which is the second accumulation account, records financial flows: it indicates the types of financial instruments utilized by the different institutional sectors to acquire financial assets or incur liabilities. Data are compiled either by Central banks, or Statistical National Institutes. EU data are validated and provided by the European Central Bank whereas non-EU data are provided by national institutions.

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.

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

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

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

The OECD food waste dataset is a compilation of available data related to food loss and food waste for 32 countries. The period covered may vary across different countries depending on data availability (globally ranging from 1993 to 2013). Several types of sources have been used: international organisations, government and national statistic institutes, OECD delegations, academic studies and private sector or>>/governmental analytical reports. When available, detailed information on sources is provided in the "variable def. and sources" (eg. references to an academic article or a government website).

The number of students enrolled refers to the count of students studying in the reference period. Each student enrolled in the education programmes covered by the corresponding category is counted once and only once. National data collection systems permitting, the statistics reflect the number of students enrolled at the beginning of the school / academic year. Preferably, the end (or near-end) of the first month of the school / academic year is chosen (special arrangements are made for part-year students who may not start studies at the beginning of the school year). Students are classified as foreign students (non-citizens) if they are not citizens of the country in which the data are collected. While pragmatic and operational, this classification is inappropriate for capturing student mobility because of differing national policies regarding the naturalisation of immigrants. Countries that have lower propensity to grant permanent residence to its immigrant populations are likely to report second generation immigrants as foreign students. Therefore, for student mobility and bilateral comparisons, interpretations of data based on the concept of foreign students should be made with caution. Students are classified as international students if they left their country of origin and moved to another country for the purpose of study. Depending on country-specific immigration legislation, mobility arrangements, and data availability, international students may be defined as students who are not permanent or usual residents of their country of study or alternatively as students who obtained their prior education in a different country, including another EU country.

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.

ARGENTINA: GENERAL METADATA Data documentation General notes The fiscal year in Argentina coincides with the calendar year. The Republic of Argentina is a federal country comprising twenty-three provinces and one autonomous city (Autonomous City of Buenos Aires - CABA), which is the capital city of the country. Methodological notes This inventory includes monetary transfers made by the National Administration to finance the current expenditures of different stakeholders related to the energy sector, including state-owned companies, private companies and trust funds. It also includes tax expenditures related to taxes on fuels (motor gasoline, diesel fuel and compressed natural gas). These are calculated by the government as revenue foregone when a tax treatment other than the one established in more general terms in the tax laws, or a different tax treatment to substitute goods, is provided (Ministerio de Hacienda y Finanzas Públicas, 2016).

AUSTRALIA: GENERAL METADATA Data documentation General notes The fiscal year in Australia runs from 1July to 30June. Following OECD convention, data are allocated to the starting calendar year so that data covering the period July2005 to June2006 are allocated to 2005. Australia being a federal country, the data also cover the following states and territories: the Australian Capital Territory (ACT), New South Wales (NSW), the Northern Territory (NT), Queensland (QLD), South Australia (SA), Tasmania (TAS), Victoria (VIC), and Western Australia (WA). Certain features of Australia’s tax system that indirectly support the production of fossil fuels apply to the mining sector as a whole. While the OECD’s approach to support for fossil fuels stresses the importance of specificity [1], the present inventory considers those measures that apply to mining in general to be specific enough to warrant their inclusion in the database. In the absence of data on the actual sector distribution of the usage of these measures, as in other countries, the OECD has estimated based on relative levels of output or exploration expenditure the share of the usage that relates to fossil-fuel extraction, as opposed to the share relating to the extraction of other minerals (e.g. gold or zinc). This estimation should not be interpreted, however, as reflecting the views of the responsible governments. [2] Because 90% of Australia’s power generation uses fossil fuels (and coal in particular), and taking into account the fact that the country does not trade electricity with other economies, measures directly supporting the use of electricity in Australia are here treated as indirect support for fossil fuels. Since Victoria is the only state where recoverable brown coal reserves are available, all state-level measures in place in other states and benefitting coal have been allocated to hard coal only. [ABS Mineral Account (1996) and Minerals Council of Australia (2015) Coal Hard Facts] Notes relating to Producer Support Estimates The offshore extraction of oil and natural gas in Australia is subject to a particular tax regime combining a resource tax and the regular corporate income tax. The former, the Petroleum Resource Rent Tax (PRRT), was introduced with the Petroleum Resource Rent Tax Assessment Act of 1987. It is project-based and applies to taxable profits at the rate of 40%. [3] Rules under the PRRT allow for the full deduction of exploration, development, and decommissioning expenditures in the year in which they are incurred. Financing costs are, however, not deductible for PRRT purposes. Unclaimed deductions can be carried forward and compounded every year at varying rates. Some of these deductions can also be transferred to other projects within the same company or group. The general corporate income-tax rate in Australia is 30% and deductions are allowed for PRRT and royalty payments, business expenses, and exploration costs incurred for mining (including coal) and oil and gas extraction. Some expenses related to mine rehabilitation and the decommissioning of offshore platforms are also deductible for income-tax purposes. Because the tax is not ring-fenced, losses from one project can be deducted against the profits of another. The immediate write-off of expenditures of a capital nature (including exploration and development expenditures) is normally considered under the tax systems of many countries to amount to a preferential tax treatment. The reason is that in calculating taxable profits in most income-tax systems, capital expenses are amortised over the period to which they contribute to earnings. Allowing these types of expenditure to be written-off in full in the year in which they are incurred therefore provides companies with a benefit akin to a zero-interest loan from the government since it delays the collection of taxes. A present-value calculation would thus show a positive transfer from the government to the companies benefitting from such provisions. However, when combined with a provision preventing companies from deducting interest costs and other financing charges, the immediate write-off of expenditures of a capital nature may not be considered preferential tax treatment. This is because this particular combination of tax provisions may approximate what is known as "cash-flow" taxation. Cash-flow tax systems can be theoretically equivalent to the more common imputed-income tax systems where the objective is to levy a neutral business tax. For that reason, measures such as the expensing of exploration and development costs may not be preferential tax provisions in the particular case of Australia’s PRRT. [4] In recent years, the Australian Government has enacted legislation that modified the country’s resource-taxation regime considerably. Changes include the extension of the PRRT regime to most onshore and offshore oil and natural-gas projects, and the introduction and subsequent repeal of the short-lived Mineral Resource Rent Tax (MRRT), which only applied from 2012 through 2013 and sought to tax the profits from the extraction of coal and iron ore at a 30% rate. Footnotes [1] Article2 of the WTO’s Agreement on Subsidies and Countervailing Measures (SCM) also stresses the importance of "specificity" in determining whether a particular measure falls under the scope of the agreement. [2] An estimated allocation based on gross-output shares or exploration expenditure by mineral is used here to provide readers with a sense of the magnitudes involved. Since these allocations are not from government sources and are based on general volume and value ratios, they might not always correlate well with actual distributions, if such information were available. These assumptions have been made by the OECD and should not be interpreted as reflecting the views of the responsible government. [3] Some offshore areas like the North West Shelf were, until recently, still subject to the previous royalty and crude-oil excise regime, or to production-sharing contracts. However, legislation enacted by the Australian Government now provides for the extension of the PRRT regime to all onshore and offshore oil and gas projects by 1July2012, with the exception of the Joint Petroleum Development Area in the Timor Sea which remains subject to production-sharing contracts with Timor-Leste under the Timor Sea Treaty. [4] See Box5 in OECD (2015).

AUSTRIA: GENERAL METADATA Data documentation General notes The fiscal year in Austria coincides with the calendar year. Amounts prior to 1999 are expressed as "euro-fixed series", meaning that this inventory applies the fixed EMU conversion rate (EUR 1 = ATS 13.7603) to data initially expressed in Austrian Schilling (ATS). In the case of the support measure AUT_te_04 ("Energy-Tax Refund to Energy-Intensive Industries"), the conversion into EUR was already made by the Federal Ministry of Finance. Annual estimates were found on the website of the Federal Ministry of Finance or provided directly by the Federal Ministry of Finance (for years prior to 2004).

BELGIUM: GENERAL METADATA Data documentation General notes The fiscal year in Belgium coincides with the calendar year. Following OECD convention, amounts prior to 1999 are expressed as "euro-fixed series", meaning that this inventory applies the fixed EMU conversion rate (EUR 1 = BEF 40.339) to data initially expressed in the Belgian Franc (BEF). Producer Support Estimate Belgium supported the production of hard coal until 1992, at which time the last mine still in operation was closed. Since then, it has not supported the production of any fossil fuel.

BRAZIL: GENERAL METADATA Data documentation General notes Brazil is a federation comprising 27sub-national jurisdictions [1] that possess a certain degree of freedom in setting prices for energy generation, transmission, and distribution. For this reason, there may be variations between federal and regional authorities in the adoption and implementation of energy-related policies. A few regional spending programmes provide reductions in the ICMS tax for transactions involving diesel fuel used in public transport. A cursory review of these regional policies suggests that the overall value of sub-national support for fossil fuels is much less significant than that of federal support. Brazil’s fiscal year coincides with the calendar year. Methodological note A large part of support to fossil fuels in non-OECD economies takes the form of price controls or regulations benefitting final consumers. In many cases, this occurs through the government mandating state-owned oil and gas companies to charge lower retail prices, which lowers the revenues these companies collect through their sales of fuel. This sometimes results in the government subsequently intervening to compensate state-owned oil and gas companies for the losses they incurred in the downstream sector due to the regulated prices, with this compensation taking many forms. Some governments choose, for example, to compensate national oil and gas companies through targeted tax concessions (e.g., VAT exemptions) or equity injections. This inventory focusses on the direct budgetary transfers and tax expenditures that encourage the production or consumption of fossil fuels, including those benefitting national oil and gas companies. For this reason, some of the measures classified here under "Producer Support Estimate" may have been introduced by governments with a view to compensating domestic, vertically integrated oil and gas companies for the lower prices they are required to charge at the retail level, resulting in these measures being connected to some extent to consumer support. Producer Support Estimate Upstream operators of oil and natural-gas concession blocks in Brazil are subject to additional specific government levies - notably royalties, a signature bonus, a special participation tax, and an area-retention tax. Royalties take the form of a monthly tax applied on sales revenue at a rate of 10%, which can be reduced in exceptional cases to 5%. Special participation is a quarterly tax applied on sales revenue at a rate that varies from 10% to 40%, which is adjusted for certain high-volume or high-profit-margin fields. The signature bonus is an amount included by oil and gas producers in their concession bids, and which is payable to the ANP upon signature of the concession agreement. Lastly, the area-retention tax is an annual tax set by the ANP during the bidding round, the rate of which depends on the size and the geological characteristics of the field. Under this latter tax, upstream operators of concession blocks located onshore must pay the owners of the land where they operate an amount determined by the ANP, usually in the range of 0.5% to 1% of the value of production. Under concession agreements, upstream operators are also required to carry out domestic R&D investments equal to at least 1% of their gross revenues. Readers are advised that some fiscal measures related to oil and natural-gas production may not constitute tax expenditures under an alternative baseline where resource taxes (or production taxes) vary with market conditions and production costs. This inventory uses the annual amounts of tax expenditures as reported by the Federal Revenue Secretariat of Brazil. Footnotes [1] The Federative Republic ofBrazil currently consists of 26 states and one federal district.

CANADA: GENERAL METADATA Data documentation General notes The fiscal year in Canada runs from 1 April to 31 March. Following OECD convention, data are allocated to the starting calendar year so that data covering the period April 2005 to March 2006 are allocated to 2005. Canada being a federal country, the data also cover the following provinces and territories: Alberta (AB), British Columbia (BC), Manitoba (MB), New Brunswick (NB), Newfoundland and Labrador (NL), Nova Scotia (NS), Ontario (ON), Prince Edward Island (PE), Quebec (QC), Saskatchewan (SK), and Yukon Territory (YT). [1] The inventory includes a number of provincial tax expenditures within resource royalty systems. These are included because they are explicitly defined as quantified departures from the general royalty rules. As noted in Chapter 2 of OECD (2015), however, it is important that such measures, including their objectives and impacts, be considered (in a parallel way with income-tax and consumption-tax measures) within the context of the broader royalty system of which they form a part. Certain features of Canada’s tax system that indirectly support the production of fossil fuels - including coal and oil sands - apply to the mining sector as a whole. While the OECD definition of support to fossil fuels stresses specificity as a requisite [2], the present inventory considers those measures that apply to mining in general to be specific enough to warrant their inclusion in the database. In the absence of data on the actual sector distribution of the usage of these measures, as in other countries, the OECD has estimated based on relative output levels the share of the usage that relates to fossil-fuel extraction, as opposed to the share relating to the extraction of other minerals (e.g. uranium). This should not be interpreted, however, as reflecting the views of the responsible governments. [3] Notes relating to Producer Support Estimates in the Province of Quebec The province of Quebec does not currently produce fossil fuels on a significant scale, though some companies are actively exploring for oil in the Gaspe Peninsula and around the Anticosti Island. Exploration efforts are also concentrating on the province’s potential for shale gas, mostly in the south (e.g. Basses-Terres du Saint-Laurent). The refundable tax credit for resources (Crédit d’impôt remboursable relatif aux ressources) was introduced in March 2001 by the government of Quebec and provides eligible mining companies operating in the province with a refundable tax credit for up to 38.75% of qualifying exploration expenditure. [4] Qualifying exploration expenditure includes those expenses made with respect to oil and natural-gas, and which attract an additional 50% deduction for tax purposes. While this measure benefits some companies engaged in the exploration for fossil fuels in Quebec, exploration expenditure in the province remains heavily oriented towards non-energy minerals. This measure is therefore not deemed specific enough to warrant inclusion in the present inventory, which would not preclude its inclusion at a later stage should fossil-fuel exploration further increase in scale. Footnotes [1] The inventory does not include at this stage Nunavut and the Northwest Territories. [2] Article 2 of the WTO’s Agreement on Subsidies and Countervailing Measures (SCM) also stresses the importance of "specificity" in determining whether a particular measure falls under the scope of the agreement. [3] An estimated allocation based on gross-output shares is used here to provide readers with a sense of the magnitudes involved. Since these allocations are not from government sources and are based on general volume and value ratios, they might not always correlate well with actual distributions, if such information were available. These assumptions have been made by the OECD and should not be interpreted as reflecting the views of the responsible government. [4] The amount of credit that can be claimed depends on whether taxpayers are also engaged in the extraction of minerals or hydrocarbons, and on the region in which they operate (e.g. the Great North). This measure is not compatible with flow-through shares.

SWITZERLAND: GENERAL METADATA Data documentation General notes The fiscal year in Switzerland coincides with the calendar year.

CHILE: GENERAL METADATA Data documentation General notes The Chilean tax system relies on the use of the UTM (Unidad Tributaria Mensual). The UTM is a unit of account used exclusively for tax purposes. Its exchange rate vis-à-vis the Chilean peso is adjusted monthly on the basis of the consumer price index, thereby keeping its real value more or less constant.

CHINA: GENERAL METADATA Data documentation General notes China’s publically available budget data lack the specificity of many OECD countries. Where available, figures provided as part of the official budget cycle typically group expenses at the highest level for overall categories, while estimates of the revenue foregone due to tax expenditures are rarely provided. Official announcements, especially those related to overall programme budgets, are often made via reports issued by official news agencies of the People’s Central Government (Xinhua, CCTV) or the Chinese Communist Party (People’s Daily). Media sources (Caixin, Caijing and other financial websites) also occasionally reveal budget support levels or estimates of tax expenditures. The sources are typically the Central Government or relevant ministries, firms themselves, or top company officials. News sources will also occasionally compare these statements with other official documents they were able to obtain in order to arrive at broader estimates. These documents are often featured on official government websites of the Central People’s Government or the Ministry of Finance (MOF). Sources are clearly stated in this database, including whether the amounts have been initially provided or subsequently cited by government websites or by official media sources. The same caveats apply to amounts listed as "government grants" (in English-language reports) or either "government support" or "subsidies" (in Chinese-language reports), notably in the annual reports that state energy firms provide to major stock markets (Shanghai; Hong-Kong, China; and New York) as part of their disclosure requirements for listed subsidiaries. Sometimes these numbers specify the source and use of funds, but they are rarely as specific as would be preferred in order to fully allocate the amounts of stated subsidies to the measures listed below or other relevant programmes. Estimates for budgetary support and tax expenditures are allocated to the various support categories and programmes to the best extent possible given available information. In view of the limited availability of the data, assumptions are sometimes necessary to assess the allocation and, in some cases, the amount of support. Those assumptions are clearly stated where applicable. Methodological note A large part of support to fossil fuels in non-OECD countries (and in a few member countries such as Mexico) takes the form of price controls or regulations benefitting final consumers. In many cases, this occurs through the government mandating state-owned oil and gas companies to charge lower retail prices, thereby lowering the revenues these companies collect through sales of fuel. This often results in the government subsequently intervening to compensate state-owned oil and gas companies for the losses they incurred in the downstream sector due to the regulated prices, with this compensation taking many forms. Some governments choose, for example, to compensate national oil and gas companies through targeted tax concessions (e.g., VAT exemptions) or equity injections. This inventory focusses on the direct budgetary transfers and tax expenditures that encourage the production or consumption of fossil fuels, including those benefitting national oil and gas companies. For this reason, some of the measures classified here under "Producer Support Estimate" may have been introduced by governments with a view to compensating domestic, vertically integrated oil and gas companies for the lower prices they are required to charge at the retail level, resulting in these measures being connected to some extent to consumer support. Estimates of the support directly conferred to final consumers by regulated prices are available from the International Energy Agency (IEA), which estimates these induced transfers as part of its annual World Energy Outlook publication. Readers are therefore advised not to add together the OECD and IEA estimates given the significant risk of overlap and double-counting this involves.

COLOMBIA: GENERAL METADATA Data documentation General notes Colombia’s fiscal year matches the calendar year.

CZECH REPUBLIC: GENERAL METADATA Data documentation General notes The fiscal year in the Czech Republic coincides with the calendar year. Consumer support estimates were provided directly by the Ministry of Environment, the Ministry of Finance, and the Ministry of Industry and Trade. Measures pertaining to the restructuring of the country’s coal-mining industry and associated environmental liabilities are taken from a study included in the Mineral Commodity Summaries of the Czech Republic (Czech Geological Survey - Geofond, 2010) that was published by the Ministry of Industry and Trade: "Eliminating negative consequences of mining in the Czech Republic" - main methods and financial resources" (Kaštovský and Platzek, 2010). Notes relating to the General Services Support Estimate Since 1991, the Czech Republic has not supported the production or consumption of coal. The state retains, however, an obligation to deal with the social, health, and environmental liabilities associated with past mining activity. The government transferred these obligations to two state-owned enterprises, DIAMO, s.p. and Palivový kombinát Ústí, s.p., which acquired the assets of the closed mining companies. These state-owned enterprises receive government subsidies for the activities they carry out. Since measures financed through these subsidy payments do not act to increase current production or consumption of coal, they are all allocated to the GSSE. Restructuring the coal-mining industry and remediating the negative environmental consequences of mining are conducted in several different ways and using several different financial resources (Kaštovský and Platzek, 2010). Besides the measures reported in this inventory, mining companies have since 1994 been required to set up two reserve funds: a financial reserve for remediation and reclamation of all plots of land affected by mining, and a financial reserve for alleviating material damage caused by mining (e.g. land subsidence).

GERMANY: GENERAL METADATA Data documentation The fiscal year in Germany coincides with the calendar year. Following OECD convention, amounts prior to 1999 are expressed as "euro-fixed series", in which the fixed EMU conversion rate (EUR 1 EUR = DEM 1.956) was applied to data initially expressed in the Deutsche Mark (DEM). In a few cases [1], the conversion into EUR was already made in official government documents. Germany being a federal country, the data also cover the states (Länder) that are still producing hard coal (North Rhine Westphalia, NW) and those that were producing hard coal until recently (Saarland, SR). Also included are payments for the rehabilitation of lignite-mining sites in eastern Germany (see DEU_dt_13) made by the Federal Government and the states of Saxony (SN), Brandenburg (BR), Saxony Anhalt (ST), and Thüringen (TH). Producer Support Estimate Hard-coal mining in Germany has traditionally attracted support for geological, historical, and political reasons. Since production of hard coal remains largely uneconomic, most mines are due to close by 2018 when government support to the industry is planned to be removed. Over the years, production of hard coal has been scaled back through numerous government initiatives. In the 1990s, the industry underwent various capacity-adjustment plans. Funding for these programmes was usually provided jointly by the coal-mining Land and the Federal Government, with the former accounting for about two-thirds of total payments. Hard-coal production has generally been supported through a combination of debt-relief schemes, mining-royalty concessions, reduced pension contributions for miners and provisions guaranteeing demand for the hard coal produced (see Combined Aids in North Rhine-Westphalia). In accordance with the EU’s state-aid rules, the Federal Government does not provide any more assistance to coal-mining under article 5-3 (current production aid). In preparation for the closure of mines, most of the support is now provided in the form of early-retirement funding for coal miners. Footnotes [1] This applies to the support measures for which the source is Landtag des Saarlandes (2005).

DENMARK: GENERAL METADATA Data documentation General notes Denmark’s fiscal year coincides with the calendar year. Producer Support Estimate Income derived from oil and natural-gas production is subject to various taxes and fees: the regular corporate income tax; the hydrocarbon tax (a specific tax on income derived from oil and gas production); royalties and compensatory payments; and profit sharing. Payments under the corporate tax are deductible from the hydrocarbon tax base. In addition, the oil pipeline tariff and compensatory fee can be offset against the hydrocarbon tax, but not against the corporate tax base. As of 2014, the corporate income tax amounts to 24.5%. However, in 2013 the Danish parliament passed two bills that will reduce the corporate income tax rate to 22% by 2016. Until January2014, the hydrocarbon tax regime differentiated between "old" licences granted before January2004 and "new" licences granted since 1 January 2004. For old licences, hydrocarbon income was subject to a 70% tax rate, but licensees were allowed to offset 25% of their capital expenditure (CAPEX) against their hydrocarbon tax bill over a period of ten years. For new licences, the hydrocarbon income tax was set at 52% and the allowance was granted for 5% of CAPEX over six years. From January2014 on, this differentiation is now abolished and old licences are treated under the same tax terms as new ones.

SPAIN: GENERAL METADATA Data documentation General notes The fiscal year in Spain coincides with the calendar year. Following OECD convention, amounts prior to 1999 appear as "euro-fixed series" where fixed EMU conversion rate (EUR 1EUR = ESP 166.386) were applied to data initially expressed in Spanish Peseta (ESP).