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Organisation for Economic Co-operation and Development

The Organisation for Economic Co-operation and Development (OECD) is an international economic organisation of 34 countries founded in 1961 to stimulate economic progress and world trade. It is a forum of countries committed to democracy and the market economy, providing a platform to compare policy experiences, seek answers to common problems, identify good practices and co-ordinate domestic and international policies of its members.

All datasets:  A C E F H I J L M N O S W
  • A
    • July 2019
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 29 July, 2019
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      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).
    • July 2019
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 14 July, 2019
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  • C
  • E
    • April 2019
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 12 April, 2019
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      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.
    • April 2019
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 16 April, 2019
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      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.
    • April 2019
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 16 April, 2019
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      This indicator measures the fraction of any additional earnings that is lost to either higher taxes or lower benefits when an employed person increases their working hours.
    • July 2019
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 22 July, 2019
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      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.
    • October 2019
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 15 October, 2019
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    • July 2019
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 02 July, 2019
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      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.
    • July 2019
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 18 July, 2019
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      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.
    • July 2019
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 14 July, 2019
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      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.
    • July 2019
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 02 July, 2019
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      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.
    • October 2019
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 08 October, 2019
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      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.
    • October 2019
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 08 October, 2019
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  • F
    • July 2019
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 05 July, 2019
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      This table contains data on full-time and part-time employment based on a common definition of 30-usual weekly hours of work in the main job. Data are broken down by professional status - employees, total employment - sex and standardised age groups (15-24, 25-54, 55+, total). Unit of measure used - Data are expressed in thousands of persons.
    • August 2019
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 13 August, 2019
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      This dataset contains incidences and gender composition of part-time employment with standardised age groups (15-24, 25-54, 55-64, 65+, total). Part-time employment is based on national definitions.  The definition of part-time work varies considerably across OECD countries Essentially three main approaches can be distinguished: i) a classification based on the worker’s perception of his/her employment situation; ii) a cut-off (generally 30 or 35 hours per week) based on usual working hours, with persons usually working fewer hours being considered part-timers; iii) a comparable cut-off based on actual hours worked during the reference week. A criterion based on actual hours will generally yield a part-time rate higher than one based on usual hours, particularly if there are temporary reductions in working time as a result of holidays, illness, short-timing, etc. On the other hand, it is not entirely clear whether a classification based on the worker’s perception will necessarily yield estimates of part-time work that are higher or lower than one based on a fixed cut-off. In one country (France) which changed from 1981 to 1982 from a definition based on an actual hours cut-off (30 hours) to one based on the respondent’s perception, the latter criterion appeared to produce slightly higher estimates.
  • H
    • October 2019
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 08 October, 2019
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      OECD Health Data 2017 offers the most comprehensive source of comparable statistics on health and health systems across OECD countries. It is an essential tool for health researchers and policy advisors in governments, the private sector and the academic community, to carry out comparative analyses and draw lessons from international comparisons of diverse health care systems.B1:B4
  • I
    • July 2019
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 02 July, 2019
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      This table contains data on the cross-country distribution of employment by hour bands for declared hour bands, broken down by professional status - employees, total employment - sex and detailed age groups.
    • July 2019
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 05 July, 2019
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      This table contains incidences and gender composition of part-time employment with standardised (15-24, 25-54, 55-64, 65+, total) and detailed age groups. Data are further broken down by professional status - employees, total employment. Part-time employment is based on a common 30-usual-hour cut-off in the main job. Unit of measure used - Data are expressed in percentages.
    • August 2019
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 06 August, 2019
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      This table contains incidences and gender composition of part-time employment with standardised (15-24, 25-54, 55-64, 65+, total) and detailed age groups. Data are further broken down by professional status - employees, total employment. Part-time employment is based on national definitions. The definition of part-time work varies considerably across OECD countries Essentially three main approaches can be distinguished: i) a classification based on the worker's perception of his/her employment situation; ii) a cut-off (generally 30 or 35 hours per week) based on usual working hours, with persons usually working fewer hours being considered part-timers; iii) a comparable cut-off based on actual hours worked during the reference week. A criterion based on actual hours will generally yield a part-time rate higher than one based on usual hours, particularly if there are temporary reductions in working time as a result of holidays, illness, short-timing, etc. On the other hand, it is not entirely clear whether a classification based on the worker's perception will necessarily yield estimates of part-time work that are higher or lower than one based on a fixed cut-off. In one country (France) which changed from 1981 to 1982 from a definition based on an actual hours cut-off (30 hours) to one based on the respondent's perception, the latter criterion appeared to produce slightly higher estimates. Other data characteristics
    • July 2019
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 06 July, 2019
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      This datasetcontains the shares of involuntary part-time work among part-time workers and ratio of involuntary part-time work and labour force and the gender composition of involuntary part-time workers. Data are broken down by professional status - employees, total employment - sex and standardised age groups (15-24, 25-54, 55+, total).
    • April 2019
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 12 April, 2019
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    • June 2019
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 21 June, 2019
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      This table contains incidences and gender composition of temporary employment with standardized age groups (15-24, 25-54, 55-64, 65+, total). Data are further broken down by professional status - employees, total employment. Unit of measure used - Data are expressed in percentages.
    • July 2019
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 18 July, 2019
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      This table contains data on involuntary part-time workers by professional status. Data are broken down by professional status - employees, total employment - by sex and standardised age groups (15-24, 25-54, 55+, total). Involuntary part-time workers are part-timers (working less than 30-usual hours per week) because they could not find a full-time job. However, the definitions are not harmonised which hampers the comparison across countries. Unit of measure used - Data are expressed in thousands of persons
  • J
    • April 2019
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 12 April, 2019
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      Job quality refers to multiple aspects of employment that contribute to well-being of workers and represents an inherently multi-dimensional construct. Job quality database focuses on three key dimensions. These are earnings quality, labour market security and quality of the working environment.
    • April 2019
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 16 September, 2019
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      OECD calculation based on the survey of Adults Skills (PIAAC) (2012).Note : Jobs are at Risk of Automation if the likelihood of their job being automated is at-least 70%. Jobs at Risk of Significant change are those with the likelihood of their job being automated estimated at between 50% to 70%.Data for Belgium correspond to Flanders and data for the United Kingdom to England and North Ireland.
  • L
    • July 2019
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 02 July, 2019
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      This dataset contains data on employment by hour bands for usual weekly hours worked in the main job.  Standard hour bands are reported for most countries.  Actual hours of work instead of usual hours of work are only available in some countries (Japan and Korea).  Data are broken down by professional status - employees, total employment - by sex and standardised age groups (15-24, 25-54, 55+, total). Unit of measure used - Data are expressed in thousands of persons. For detailed information on labour force surveys for all countries please see the attached file : www.oecd.org/els/employmentpoliciesanddata/LFSNOTE
    • July 2019
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 06 July, 2019
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      This table contains labour force data on labour market status - population, labour force, unemployment and employment - by sex and by detailed age groups and standard age groups (15-24, 25-54, 55-64, 65+, total). Note: Population figures reported in table LFS by sex are Census-based, while the data for this table are taken from labour force surveys. Population for total age group refers to working age population (15 to 64 years).
    • August 2019
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 13 August, 2019
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      This dataset contains the age composition (as a percentage of all ages) of the population for each labour force status - labour force, employment, unemployment - by sex.
  • M
    • April 2019
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 12 April, 2019
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    • April 2019
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 16 April, 2019
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      The aim of the OECD’s new Skills for Jobs Indicators is to facilitate better adaptation to changing skill needs by making available a database of skill imbalances indicators that is comparable across countries and regularly updated. The Skill Needs Indicators provide an overview of the shortages and surpluses of skills across countries.
  • N
    • April 2019
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 12 April, 2019
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    • September 2019
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 24 September, 2019
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    • March 2019
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 27 March, 2019
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      Annual National Accounts>Detailed Tables and Simplified Accounts>7A. Labour input by activity, ISIC rev4   Unit of measure used: In persons, full-time equivalents, jobs and hours.   Statistical population: It presents employment, broken down by detailed industries according to the classification ISIC rev.4. It has been prepared from statistics reported to the OECD by Member countries in their answers to annual national accounts questionnaire.
    • September 2019
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 24 September, 2019
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      It presents population data and employment by main activity. It includes national concept data for economically active population, unemployed persons, total employment, employees and self-employed, as well as domestic concept data for total employment, employees and self-employed. The domestic concept data are available broken down by main activity. It has been prepared from statistics reported to the OECD by Member countries in their answers to annual national accounts questionnaire.
    • May 2019
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 29 May, 2019
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      Annual National Accounts
  • O
    • July 2019
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 14 July, 2019
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      This OECD inventory maps existing cross-country surveys that provide information on the characteristics of people's jobs. The information included in this inventory covers international surveys conducted since the early 1990s that are based on individuals' self-reported assessment of their current job, for 160 countries over 25 years. Survey questions are grouped into 19 indicators. For each indicator, binary codes (1 and 0) show whether indicators are available or not for the various countries and years. The inventory also provides users with detailed documentation on the questions used in the various surveys for measuring these indicators.
  • S
    • September 2019
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 17 September, 2019
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      The Short-Term Labour Market Statistics dataset contains predominantly quarterly labour statistics, and associated statistical methodological information, for the 35 OECD member countries and selected other economies. The Short-Term Labour Market Statistics dataset covers countries that compile labour statistics from sample household surveys on a monthly or quarterly basis. It is widely accepted that household surveys are the best source for labour market key statistics. In such surveys, information is collected from people living in households through a representative sample and the surveys are based on standard methodology and procedures used internationally. The subjects available cover: working age population by age; active and inactive labour force by age; employment by economic activity, by working time and by status; and, unemployment (including monthly harmonised unemployment) by age and by duration. Data is expressed in levels (thousands of persons) or rates (e.g. employment rate) where applicable.   Data are based on Labour Force Surveys and national information in this dataset is directly collected from the following sources:   ABS - Australian Bureau of Statistics (Australia) Statistics Canada (Canada) INE - Instituto Nacional de Estadísticas (Chile) CBS – Central Bureau of Statistics (Israel) Statistics Bureau (Japan) Statistics Korea (Korea) INEGI - Instituto Nacional de Estadísticas y Geografía (Mexico) Statistics New Zealand (New Zealand) BLS - Bureau of Labor Statistics (the United States) Eurostat (Austria, Belgium, the Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Luxembourg, the Netherlands, Norway, Poland, Portugal, the Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Turkey and the United Kingdom).
    • April 2019
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 12 April, 2019
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      The aim of the OECD’s new Skills for Jobs Indicators is to facilitate better adaptation to changing skill needs by making available a database of skill imbalances indicators that is comparable across countries and regularly updated. The Skill Needs Indicators provide an overview of the shortages and surpluses of skills across countries.
    • April 2019
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 12 April, 2019
      Select Dataset
      The aim of the OECD’s new Skills for Jobs Indicators is to facilitate better adaptation to changing skill needs by making available a database of skill imbalances indicators that is comparable across countries and regularly updated. The Skill Needs Indicators provide an overview of the shortages and surpluses of skills across countries.
  • W
    • April 2019
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 12 April, 2019
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      World Indicators of Skills for Employment (WISE) provide a comprehensive system of information relating to skills development. WISE presents countries with data upon which they can design skills policies and programs and monitor their impact on key outcomes, including responsiveness to current and emerging patterns of labour market demand, employability, productivity, health status, gender equity and lifelong learning.The database covers the period from 1990 to the present and consists of five inter-related domains of indicators:Contextual factors drive both the supply of and demand for skills.Skill acquisition covers investments in skills, the stock of human capital and its distribution.Skill requirements measure the demand for skills arising in the labour market.The degree of matching captures how well skills obtained through education and training correspond to the skills required in the labour market.Outcomes reflect the impact of skills on economic performance and employment and social outcomes.

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