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丹麦

  • Monarch:Margrethe II
  • 首相:Mette Frederiksen
  • 首都:Copenhagen
  • 语言:Danish, Faroese, Greenlandic (an Inuit dialect), German (small minority) note: English is the predominant second language
  • 政府
  • 国家统计局
  • 人口,人口:5,797,446 (2018)
  • 面积,平方公里:41,990
  • 人均国内生产总值,美元:60,596 (2018)
  • GDP,目前美元十亿美元:351.3 (2018)
  • 基尼系数:No data
  • 经商容易度排名:3

employment

所有数据集:  A B C D E F G H I J L M N O P Q R S T U W Y
  • A
    • 八月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 27 八月, 2019
      选择数据集
      Harmonised data on accidents at work are collected in the framework of the administrative data collection 'European Statistics on Accidents at Work (ESAW)', on the basis of a methodology developed first in 1990. An accident at work is defined as 'a discrete occurrence in the course of work which leads to physical or mental harm'. The data include only fatal and non-fatal accidents involving more than 3 calendar days of absence from work. If the accident does not lead to the death of the victim it is called a 'non-fatal' (or 'serious') accident. A fatal accident at work is defined as an accident which leads to the death of a victim within one year of the accident. The variables collected  on accidents at work include: Economic activity of the employer and size of the enterpriseEmployment status, occupation, age, sex and nationality of victimGeographical location, date and time of the accidentType of injury, body part injured and the severity of the accident (number of full calendar days during which the victim is unfit for work excluding the day of the accident, permanent incapacity or death within one year of the accident).Variables on causes and circumstances of the accident: workstation, working environment, working process, specific physical activity, material agent of the specific physical activity, deviation and material agent of deviation, contact - mode of injury and material agent of contact - mode of injury. The national ESAW sources are the declarations of accidents at work, either to the accident insurance of the national social security system, a private insurance for accidents at work or to other relevant national authorities (labour inspection etc.). As an exception, accident data for the Netherlands are based on survey data. On the Eurostat website, ESAW data are disseminated in two sections: 'Main Indicators' and 'Details by economic sector (NACE Rev2, 2008 onwards)'. Depending on the table, data are broken down by: economic activity (NACE 'main sectors' (1 digit code) or more detailed NACE divisions (2 digit codes)); the occupation of the victim (ISCO-08 code); country; severity of the accident, sex, age, employment status, size  of the enterprise, body part injured and type of injury. The data is presented in form of numbers, percentages, incidence rates and standardised incidence rates of non-fatal and fatal accidents at work, either for EU aggregates, countries or certain breakdowns by dimensions such as age, sex etc. Numbers correspond to a simple count of all non-fatal and fatal accidents for the entirety or certain breakdowns of the data;Percentages represent shares of breakdowns;The incidence rate of non-fatal or fatal accidents at work is the number of serious or fatal accidents per 100,000 persons in employment;The standardised incidence rates of non-fatal or fatal accidents at work aim to eliminate differences in the structures of countries' economies (see section 20.6 Adjustment for more details). The incidence rate indicates the relative importance of non-fatal or fatal accidents at work in the working population. For both types of accidents at work the numerator is the number of accidents that occurred during the year. The denominator is the reference population (i.e. the number of persons in employment) expressed in 100,000 persons. The reference population (or number of persons in employment) related to the national ESAW reporting system is provided by the Member States, either from administrative sources related to accidents at work or from the EU Labour Force Survey (LFS).
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 15 十月, 2019
      选择数据集
      Harmonised data on accidents at work are collected in the framework of the administrative data collection 'European Statistics on Accidents at Work (ESAW)', on the basis of a methodology developed first in 1990. An accident at work is defined as 'a discrete occurrence in the course of work which leads to physical or mental harm'. The data include only fatal and non-fatal accidents involving more than 3 calendar days of absence from work. If the accident does not lead to the death of the victim it is called a 'non-fatal' (or 'serious') accident. A fatal accident at work is defined as an accident which leads to the death of a victim within one year of the accident. The variables collected  on accidents at work include: Economic activity of the employer and size of the enterpriseEmployment status, occupation, age, sex and nationality of victimGeographical location, date and time of the accidentType of injury, body part injured and the severity of the accident (number of full calendar days during which the victim is unfit for work excluding the day of the accident, permanent incapacity or death within one year of the accident).Variables on causes and circumstances of the accident: workstation, working environment, working process, specific physical activity, material agent of the specific physical activity, deviation and material agent of deviation, contact - mode of injury and material agent of contact - mode of injury. The national ESAW sources are the declarations of accidents at work, either to the accident insurance of the national social security system, a private insurance for accidents at work or to other relevant national authorities (labour inspection etc.). As an exception, accident data for the Netherlands are based on survey data. On the Eurostat website, ESAW data are disseminated in two sections: 'Main Indicators' and 'Details by economic sector (NACE Rev2, 2008 onwards)'. Depending on the table, data are broken down by: economic activity (NACE 'main sectors' (1 digit code) or more detailed NACE divisions (2 digit codes)); the occupation of the victim (ISCO-08 code); country; severity of the accident, sex, age, employment status, size  of the enterprise, body part injured and type of injury. The data is presented in form of numbers, percentages, incidence rates and standardised incidence rates of non-fatal and fatal accidents at work, either for EU aggregates, countries or certain breakdowns by dimensions such as age, sex etc. Numbers correspond to a simple count of all non-fatal and fatal accidents for the entirety or certain breakdowns of the data;Percentages represent shares of breakdowns;The incidence rate of non-fatal or fatal accidents at work is the number of serious or fatal accidents per 100,000 persons in employment;The standardised incidence rates of non-fatal or fatal accidents at work aim to eliminate differences in the structures of countries' economies (see section 20.6 Adjustment for more details). The incidence rate indicates the relative importance of non-fatal or fatal accidents at work in the working population. For both types of accidents at work the numerator is the number of accidents that occurred during the year. The denominator is the reference population (i.e. the number of persons in employment) expressed in 100,000 persons. The reference population (or number of persons in employment) related to the national ESAW reporting system is provided by the Member States, either from administrative sources related to accidents at work or from the EU Labour Force Survey (LFS).
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 19 十月, 2019
      选择数据集
      Harmonised data on accidents at work are collected in the framework of the administrative data collection 'European Statistics on Accidents at Work (ESAW)', on the basis of a methodology developed first in 1990. An accident at work is defined as 'a discrete occurrence in the course of work which leads to physical or mental harm'. The data include only fatal and non-fatal accidents involving more than 3 calendar days of absence from work. If the accident does not lead to the death of the victim it is called a 'non-fatal' (or 'serious') accident. A fatal accident at work is defined as an accident which leads to the death of a victim within one year of the accident. The variables collected  on accidents at work include:Economic activity of the employer and size of the enterpriseEmployment status, occupation, age, sex and nationality of victimGeographical location, date and time of the accidentType of injury, body part injured and the severity of the accident (number of full calendar days during which the victim is unfit for work excluding the day of the accident, permanent incapacity or death within one year of the accident).Variables on causes and circumstances of the accident: workstation, working environment, working process, specific physical activity, material agent of the specific physical activity, deviation and material agent of deviation, contact - mode of injury and material agent of contact - mode of injury. The national ESAW sources are the declarations of accidents at work, either to the accident insurance of the national social security system, a private insurance for accidents at work or to other relevant national authorities (labour inspection etc.). As an exception, accident data for the Netherlands are based on survey data. On the Eurostat website, ESAW data are disseminated in two sections: 'Main Indicators' and 'Details by economic sector (NACE Rev2, 2008 onwards)'. Depending on the table, data are broken down by: economic activity (NACE 'main sectors' (1 digit code) or more detailed NACE divisions (2 digit codes)); the occupation of the victim (ISCO-08 code); country; severity of the accident, sex, age, employment status, size  of the enterprise, body part injured and type of injury. The data is presented in form of numbers, percentages, incidence rates and standardised incidence rates of non-fatal and fatal accidents at work, either for EU aggregates, countries or certain breakdowns by dimensions such as age, sex etc.Numbers correspond to a simple count of all non-fatal and fatal accidents for the entirety or certain breakdowns of the data;Percentages represent shares of breakdowns;The incidence rate of non-fatal or fatal accidents at work is the number of serious or fatal accidents per 100,000 persons in employment;The standardised incidence rates of non-fatal or fatal accidents at work aim to eliminate differences in the structures of countries' economies (see section 20.6 Adjustment for more details). The incidence rate indicates the relative importance of non-fatal or fatal accidents at work in the working population. For both types of accidents at work the numerator is the number of accidents that occurred during the year. The denominator is the reference population (i.e. the number of persons in employment) expressed in 100,000 persons. The reference population (or number of persons in employment) related to the national ESAW reporting system is provided by the Member States, either from administrative sources related to accidents at work or from the EU Labour Force Survey (LFS).
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 19 十月, 2019
      选择数据集
      Harmonised data on accidents at work are collected in the framework of the administrative data collection 'European Statistics on Accidents at Work (ESAW)', on the basis of a methodology developed first in 1990. An accident at work is defined as 'a discrete occurrence in the course of work which leads to physical or mental harm'. The data include only fatal and non-fatal accidents involving more than 3 calendar days of absence from work. If the accident does not lead to the death of the victim it is called a 'non-fatal' (or 'serious') accident. A fatal accident at work is defined as an accident which leads to the death of a victim within one year of the accident. The variables collected  on accidents at work include:Economic activity of the employer and size of the enterpriseEmployment status, occupation, age, sex and nationality of victimGeographical location, date and time of the accidentType of injury, body part injured and the severity of the accident (number of full calendar days during which the victim is unfit for work excluding the day of the accident, permanent incapacity or death within one year of the accident).Variables on causes and circumstances of the accident: workstation, working environment, working process, specific physical activity, material agent of the specific physical activity, deviation and material agent of deviation, contact - mode of injury and material agent of contact - mode of injury. The national ESAW sources are the declarations of accidents at work, either to the accident insurance of the national social security system, a private insurance for accidents at work or to other relevant national authorities (labour inspection etc.). As an exception, accident data for the Netherlands are based on survey data. On the Eurostat website, ESAW data are disseminated in two sections: 'Main Indicators' and 'Details by economic sector (NACE Rev2, 2008 onwards)'. Depending on the table, data are broken down by: economic activity (NACE 'main sectors' (1 digit code) or more detailed NACE divisions (2 digit codes)); the occupation of the victim (ISCO-08 code); country; severity of the accident, sex, age, employment status, size  of the enterprise, body part injured and type of injury. The data is presented in form of numbers, percentages, incidence rates and standardised incidence rates of non-fatal and fatal accidents at work, either for EU aggregates, countries or certain breakdowns by dimensions such as age, sex etc.Numbers correspond to a simple count of all non-fatal and fatal accidents for the entirety or certain breakdowns of the data;Percentages represent shares of breakdowns;The incidence rate of non-fatal or fatal accidents at work is the number of serious or fatal accidents per 100,000 persons in employment;The standardised incidence rates of non-fatal or fatal accidents at work aim to eliminate differences in the structures of countries' economies (see section 20.6 Adjustment for more details). The incidence rate indicates the relative importance of non-fatal or fatal accidents at work in the working population. For both types of accidents at work the numerator is the number of accidents that occurred during the year. The denominator is the reference population (i.e. the number of persons in employment) expressed in 100,000 persons. The reference population (or number of persons in employment) related to the national ESAW reporting system is provided by the Member States, either from administrative sources related to accidents at work or from the EU Labour Force Survey (LFS).
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 19 十月, 2019
      选择数据集
      Harmonised data on accidents at work are collected in the framework of the administrative data collection 'European Statistics on Accidents at Work (ESAW)', on the basis of a methodology developed first in 1990. An accident at work is defined as 'a discrete occurrence in the course of work which leads to physical or mental harm'. The data include only fatal and non-fatal accidents involving more than 3 calendar days of absence from work. If the accident does not lead to the death of the victim it is called a 'non-fatal' (or 'serious') accident. A fatal accident at work is defined as an accident which leads to the death of a victim within one year of the accident. The variables collected  on accidents at work include:Economic activity of the employer and size of the enterpriseEmployment status, occupation, age, sex and nationality of victimGeographical location, date and time of the accidentType of injury, body part injured and the severity of the accident (number of full calendar days during which the victim is unfit for work excluding the day of the accident, permanent incapacity or death within one year of the accident).Variables on causes and circumstances of the accident: workstation, working environment, working process, specific physical activity, material agent of the specific physical activity, deviation and material agent of deviation, contact - mode of injury and material agent of contact - mode of injury. The national ESAW sources are the declarations of accidents at work, either to the accident insurance of the national social security system, a private insurance for accidents at work or to other relevant national authorities (labour inspection etc.). As an exception, accident data for the Netherlands are based on survey data. On the Eurostat website, ESAW data are disseminated in two sections: 'Main Indicators' and 'Details by economic sector (NACE Rev2, 2008 onwards)'. Depending on the table, data are broken down by: economic activity (NACE 'main sectors' (1 digit code) or more detailed NACE divisions (2 digit codes)); the occupation of the victim (ISCO-08 code); country; severity of the accident, sex, age, employment status, size  of the enterprise, body part injured and type of injury. The data is presented in form of numbers, percentages, incidence rates and standardised incidence rates of non-fatal and fatal accidents at work, either for EU aggregates, countries or certain breakdowns by dimensions such as age, sex etc.Numbers correspond to a simple count of all non-fatal and fatal accidents for the entirety or certain breakdowns of the data;Percentages represent shares of breakdowns;The incidence rate of non-fatal or fatal accidents at work is the number of serious or fatal accidents per 100,000 persons in employment;The standardised incidence rates of non-fatal or fatal accidents at work aim to eliminate differences in the structures of countries' economies (see section 20.6 Adjustment for more details). The incidence rate indicates the relative importance of non-fatal or fatal accidents at work in the working population. For both types of accidents at work the numerator is the number of accidents that occurred during the year. The denominator is the reference population (i.e. the number of persons in employment) expressed in 100,000 persons. The reference population (or number of persons in employment) related to the national ESAW reporting system is provided by the Member States, either from administrative sources related to accidents at work or from the EU Labour Force Survey (LFS).
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 19 十月, 2019
      选择数据集
      Harmonised data on accidents at work are collected in the framework of the administrative data collection 'European Statistics on Accidents at Work (ESAW)', on the basis of a methodology developed first in 1990. An accident at work is defined as 'a discrete occurrence in the course of work which leads to physical or mental harm'. The data include only fatal and non-fatal accidents involving more than 3 calendar days of absence from work. If the accident does not lead to the death of the victim it is called a 'non-fatal' (or 'serious') accident. A fatal accident at work is defined as an accident which leads to the death of a victim within one year of the accident. The variables collected  on accidents at work include:Economic activity of the employer and size of the enterpriseEmployment status, occupation, age, sex and nationality of victimGeographical location, date and time of the accidentType of injury, body part injured and the severity of the accident (number of full calendar days during which the victim is unfit for work excluding the day of the accident, permanent incapacity or death within one year of the accident).Variables on causes and circumstances of the accident: workstation, working environment, working process, specific physical activity, material agent of the specific physical activity, deviation and material agent of deviation, contact - mode of injury and material agent of contact - mode of injury. The national ESAW sources are the declarations of accidents at work, either to the accident insurance of the national social security system, a private insurance for accidents at work or to other relevant national authorities (labour inspection etc.). As an exception, accident data for the Netherlands are based on survey data. On the Eurostat website, ESAW data are disseminated in two sections: 'Main Indicators' and 'Details by economic sector (NACE Rev2, 2008 onwards)'. Depending on the table, data are broken down by: economic activity (NACE 'main sectors' (1 digit code) or more detailed NACE divisions (2 digit codes)); the occupation of the victim (ISCO-08 code); country; severity of the accident, sex, age, employment status, size  of the enterprise, body part injured and type of injury. The data is presented in form of numbers, percentages, incidence rates and standardised incidence rates of non-fatal and fatal accidents at work, either for EU aggregates, countries or certain breakdowns by dimensions such as age, sex etc.Numbers correspond to a simple count of all non-fatal and fatal accidents for the entirety or certain breakdowns of the data;Percentages represent shares of breakdowns;The incidence rate of non-fatal or fatal accidents at work is the number of serious or fatal accidents per 100,000 persons in employment;The standardised incidence rates of non-fatal or fatal accidents at work aim to eliminate differences in the structures of countries' economies (see section 20.6 Adjustment for more details). The incidence rate indicates the relative importance of non-fatal or fatal accidents at work in the working population. For both types of accidents at work the numerator is the number of accidents that occurred during the year. The denominator is the reference population (i.e. the number of persons in employment) expressed in 100,000 persons. The reference population (or number of persons in employment) related to the national ESAW reporting system is provided by the Member States, either from administrative sources related to accidents at work or from the EU Labour Force Survey (LFS).
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 19 十月, 2019
      选择数据集
      Harmonised data on accidents at work are collected in the framework of the administrative data collection 'European Statistics on Accidents at Work (ESAW)', on the basis of a methodology developed first in 1990. An accident at work is defined as 'a discrete occurrence in the course of work which leads to physical or mental harm'. The data include only fatal and non-fatal accidents involving more than 3 calendar days of absence from work. If the accident does not lead to the death of the victim it is called a 'non-fatal' (or 'serious') accident. A fatal accident at work is defined as an accident which leads to the death of a victim within one year of the accident. The variables collected  on accidents at work include:Economic activity of the employer and size of the enterpriseEmployment status, occupation, age, sex and nationality of victimGeographical location, date and time of the accidentType of injury, body part injured and the severity of the accident (number of full calendar days during which the victim is unfit for work excluding the day of the accident, permanent incapacity or death within one year of the accident).Variables on causes and circumstances of the accident: workstation, working environment, working process, specific physical activity, material agent of the specific physical activity, deviation and material agent of deviation, contact - mode of injury and material agent of contact - mode of injury. The national ESAW sources are the declarations of accidents at work, either to the accident insurance of the national social security system, a private insurance for accidents at work or to other relevant national authorities (labour inspection etc.). As an exception, accident data for the Netherlands are based on survey data. On the Eurostat website, ESAW data are disseminated in two sections: 'Main Indicators' and 'Details by economic sector (NACE Rev2, 2008 onwards)'. Depending on the table, data are broken down by: economic activity (NACE 'main sectors' (1 digit code) or more detailed NACE divisions (2 digit codes)); the occupation of the victim (ISCO-08 code); country; severity of the accident, sex, age, employment status, size  of the enterprise, body part injured and type of injury. The data is presented in form of numbers, percentages, incidence rates and standardised incidence rates of non-fatal and fatal accidents at work, either for EU aggregates, countries or certain breakdowns by dimensions such as age, sex etc.Numbers correspond to a simple count of all non-fatal and fatal accidents for the entirety or certain breakdowns of the data;Percentages represent shares of breakdowns;The incidence rate of non-fatal or fatal accidents at work is the number of serious or fatal accidents per 100,000 persons in employment;The standardised incidence rates of non-fatal or fatal accidents at work aim to eliminate differences in the structures of countries' economies (see section 20.6 Adjustment for more details). The incidence rate indicates the relative importance of non-fatal or fatal accidents at work in the working population. For both types of accidents at work the numerator is the number of accidents that occurred during the year. The denominator is the reference population (i.e. the number of persons in employment) expressed in 100,000 persons. The reference population (or number of persons in employment) related to the national ESAW reporting system is provided by the Member States, either from administrative sources related to accidents at work or from the EU Labour Force Survey (LFS).
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 19 十月, 2019
      选择数据集
      Harmonised data on accidents at work are collected in the framework of the administrative data collection 'European Statistics on Accidents at Work (ESAW)', on the basis of a methodology developed first in 1990. An accident at work is defined as 'a discrete occurrence in the course of work which leads to physical or mental harm'. The data include only fatal and non-fatal accidents involving more than 3 calendar days of absence from work. If the accident does not lead to the death of the victim it is called a 'non-fatal' (or 'serious') accident. A fatal accident at work is defined as an accident which leads to the death of a victim within one year of the accident. The variables collected  on accidents at work include:Economic activity of the employer and size of the enterpriseEmployment status, occupation, age, sex and nationality of victimGeographical location, date and time of the accidentType of injury, body part injured and the severity of the accident (number of full calendar days during which the victim is unfit for work excluding the day of the accident, permanent incapacity or death within one year of the accident).Variables on causes and circumstances of the accident: workstation, working environment, working process, specific physical activity, material agent of the specific physical activity, deviation and material agent of deviation, contact - mode of injury and material agent of contact - mode of injury. The national ESAW sources are the declarations of accidents at work, either to the accident insurance of the national social security system, a private insurance for accidents at work or to other relevant national authorities (labour inspection etc.). As an exception, accident data for the Netherlands are based on survey data. On the Eurostat website, ESAW data are disseminated in two sections: 'Main Indicators' and 'Details by economic sector (NACE Rev2, 2008 onwards)'. Depending on the table, data are broken down by: economic activity (NACE 'main sectors' (1 digit code) or more detailed NACE divisions (2 digit codes)); the occupation of the victim (ISCO-08 code); country; severity of the accident, sex, age, employment status, size  of the enterprise, body part injured and type of injury. The data is presented in form of numbers, percentages, incidence rates and standardised incidence rates of non-fatal and fatal accidents at work, either for EU aggregates, countries or certain breakdowns by dimensions such as age, sex etc.Numbers correspond to a simple count of all non-fatal and fatal accidents for the entirety or certain breakdowns of the data;Percentages represent shares of breakdowns;The incidence rate of non-fatal or fatal accidents at work is the number of serious or fatal accidents per 100,000 persons in employment;The standardised incidence rates of non-fatal or fatal accidents at work aim to eliminate differences in the structures of countries' economies (see section 20.6 Adjustment for more details). The incidence rate indicates the relative importance of non-fatal or fatal accidents at work in the working population. For both types of accidents at work the numerator is the number of accidents that occurred during the year. The denominator is the reference population (i.e. the number of persons in employment) expressed in 100,000 persons. The reference population (or number of persons in employment) related to the national ESAW reporting system is provided by the Member States, either from administrative sources related to accidents at work or from the EU Labour Force Survey (LFS).
    • 十一月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 10 十一月, 2019
      选择数据集
      The section 'LFS series - detailed quarterly survey results' reports detailed quarterly results going beyond the EU-LFS main aggregates, which have a separate data domain and some methodological differences. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 10 十月, 2019
      选择数据集
      The indicator is defined as the percentage of the population in a given age group who are economically active. According to the definitions of the International Labour Organisation (ILO) people are classified as employed, unemployed and economically inactive for the purposes of labour market statistics. The economically active population (also called labour force) is the sum of employed and unemployed persons. Inactive persons are those who, during the reference week, were neither employed nor unemployed. The indicator is based on the EU Labour Force Survey.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 10 十月, 2019
      选择数据集
      The indicator is defined as the percentage of the population aged 15-64 who are economically active. According to the definitions of the International Labour Organisation (ILO) people are classified as employed, unemployed and economically inactive for the purposes of labour market statistics. The economically active population (also called labour force) is the sum of employed and unemployed persons. Inactive persons are those who, during the reference week, were neither employed nor unemployed. The indicator is based on the EU Labour Force Survey.
    • 十一月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 10 十一月, 2019
      选择数据集
      The section 'LFS series - detailed quarterly survey results' reports detailed quarterly results going beyond the EU-LFS main aggregates, which have a separate data domain and some methodological differences. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 31 十月, 2019
      选择数据集
      The section 'LFS series - detailed annual survey results' reports annual results from the EU-LFS. While LFS is a quarterly survey, it is also possible to produce annual results. There are several ways of doing it, see section '18.5 Data compilation' below for details. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metadata. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 31 十月, 2019
      选择数据集
      The section 'LFS series - detailed annual survey results' reports annual results from the EU-LFS. While LFS is a quarterly survey, it is also possible to produce annual results. There are several ways of doing it, see section '18.5 Data compilation' below for details. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metadata. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 01 十一月, 2019
      选择数据集
      The source for the regional labour market information down to NUTS level 2 is the EU Labour Force Survey (EU-LFS). This is a quarterly household sample survey conducted in all Member States of the EU and in EFTA and Candidate countries.  The EU-LFS survey follows the definitions and recommendations of the International Labour Organisation (ILO). To achieve further harmonisation, the Member States also adhere to common principles when formulating questionnaires. The LFS' target population is made up of all persons in private households aged 15 and over. For more information see the EU Labour Force Survey (lfsi_esms, see paragraph 21.1.).  The EU-LFS is designed to give accurate quarterly information at national level as well as annual information at NUTS 2 regional level and the compilation of these figures is well specified in the regulation. Microdata including the NUTS 2 level codes are provided by all the participating countries with a good degree of geographical comparability, which allows the production and dissemination of a complete set of comparable indicators for this territorial level. At present the transmission of the regional labour market data at NUTS 3 level has no legal basis. However many countries transmit NUTS 3 figures to Eurostat on a voluntary basis, under the understanding that they are not for publication with such detail, but for aggregation in few categories per country, i.e., metropolitan regions and urban-rural typology. Most of the NUTS 3 data are based on the LFS while some countries transmit data based on registers, administrative data, small area estimation and other reliable sources.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 27 十月, 2019
      选择数据集
      The source for the regional labour market information down to NUTS level 2 is the EU Labour Force Survey (EU-LFS). This is a quarterly household sample survey conducted in all Member States of the EU and in EFTA and Candidate countries.  The EU-LFS survey follows the definitions and recommendations of the International Labour Organisation (ILO). To achieve further harmonisation, the Member States also adhere to common principles when formulating questionnaires. The LFS' target population is made up of all persons in private households aged 15 and over. For more information see the EU Labour Force Survey (lfsi_esms, see paragraph 21.1.).  The EU-LFS is designed to give accurate quarterly information at national level as well as annual information at NUTS 2 regional level and the compilation of these figures is well specified in the regulation. Microdata including the NUTS 2 level codes are provided by all the participating countries with a good degree of geographical comparability, which allows the production and dissemination of a complete set of comparable indicators for this territorial level. At present the transmission of the regional labour market data at NUTS 3 level has no legal basis. However many countries transmit NUTS 3 figures to Eurostat on a voluntary basis, under the understanding that they are not for publication with such detail, but for aggregation in few categories per country, i.e., metropolitan regions and urban-rural typology. Most of the NUTS 3 data are based on the LFS while some countries transmit data based on registers, administrative data, small area estimation and other reliable sources.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 27 十月, 2019
      选择数据集
      The source for the regional labour market information down to NUTS level 2 is the EU Labour Force Survey (EU-LFS). This is a quarterly household sample survey conducted in all Member States of the EU and in EFTA and Candidate countries.  The EU-LFS survey follows the definitions and recommendations of the International Labour Organisation (ILO). To achieve further harmonisation, the Member States also adhere to common principles when formulating questionnaires. The LFS' target population is made up of all persons in private households aged 15 and over. For more information see the EU Labour Force Survey (lfsi_esms, see paragraph 21.1.).  The EU-LFS is designed to give accurate quarterly information at national level as well as annual information at NUTS 2 regional level and the compilation of these figures is well specified in the regulation. Microdata including the NUTS 2 level codes are provided by all the participating countries with a good degree of geographical comparability, which allows the production and dissemination of a complete set of comparable indicators for this territorial level. At present the transmission of the regional labour market data at NUTS 3 level has no legal basis. However many countries transmit NUTS 3 figures to Eurostat on a voluntary basis, under the understanding that they are not for publication with such detail, but for aggregation in few categories per country, i.e., metropolitan regions and urban-rural typology. Most of the NUTS 3 data are based on the LFS while some countries transmit data based on registers, administrative data, small area estimation and other reliable sources.
    • 八月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 09 八月, 2019
      选择数据集
      'Statistics on high-tech industry and knowledge-intensive services' (sometimes referred to as simply 'high-tech statistics') comprise economic, employment and science, technology and innovation (STI) data describing manufacturing and services industries or products traded broken down by technological intensity. The domain uses various other domains and sources of  Eurostat's official statistics (CIS, COMEXT, HRST, LFS, PATENT, R&D and SBS) and its coverage is therefore dependent on these other primary sources. Two main approaches are used in the domain to identify technology-intensity: the sectoral approach and the product approach. A third approach is used for data on high-tech and biotechnology patents aggregated on the basis of the International Patent Classification (IPC) 8th edition (see summary table in Annex 1 for which approach is used by each type of data). The sectoral approach: The sectoral approach is an aggregation of the manufacturing industries according to technological intensity (R&D expenditure/value added) and based on the Statistical classification of economic activities in the European Community (NACE)  at 2-digit level. The level of R&D intensity served as a criterion of classification of economic sectors into high-technology, medium high-technology, medium low-technology and low-technology industries. Services are mainly aggregated into knowledge-intensive services (KIS) and less knowledge-intensive services (LKIS) based on the share of tertiary educated persons at NACE 2-digit level. The sectoral approach is used for all indicators except data on high-tech trade and patents. Note that due to the revision of the NACE from NACE Rev. 1.1 to NACE Rev. 2 the definition of high-technology industries and knowledge-intensive services has changed in 2008. For high-tech statistics it means that two different definitions (one according NACE Rev. 1.1 and one according NACE Rev. 2) are used in parallel and the data according to both NACE versions are presented in separated tables depending on the data availability. For example as the LFS provides the results both by NACE Rev. 1.1 and NACE Rev. 2, all the table using this source have been duplicated to present the results by NACE Rev. 2 from 2008. For more details, see both definitions of high-tech sectors in Annex 2 and 3. Within the sectoral approach, a second classification was created , named Knowledge Intensive Activities KIA) and based on the share of tertiary educated people in each sectors of industries and services according to NACE at 2-digit level and for all EU28 Member States. A threshold was applied to judge sectors as knowledge intensive. In contrast to first sectoral approach mixing two methodologies, one for manufacturing industries and one for services, the KIA classification is based on one methodology for all the sectors of industries and services covering even public sector activities. The aggregations in use are Total Knowledge Intensive Activities (KIA) and Knowledge Intensive Activities in Business Industries (KIABI). Both classifications are made according to NACE Rev. 1.1 and NACE Rev. 2 at 2- digit level. Note that due to revision of the NACE Rev.1.1 to NACE Rev. 2 the list of Knowledge Intensive Activities has changed as well, the two definitions are used in parallel and the data are shown in two separate tables. NACE Rev.2 collection includes data starting from 2008 reference year. For more details please see the definitions in Annex 7 and 8. The product approach: The product approach was created to complement the sectoral approach and it is used for data on high-tech trade. The product list is based on the calculations of R&D intensity by groups of products (R&D expenditure/total sales). The groups classified as high-technology products are aggregated on the basis of the Standard International Trade Classification (SITC). The initial definition was built based on SITC Rev.3 and served to compile the high-tech product aggregates until 2007. With the implementation in 2007 of the new version of SITC Rev.4, the definition of high-tech groups was revised and adapted according to new classification. Starting from 2007 the Eurostat presents the trade data for high-tech groups aggregated based on the SITC Rev.4. For more details, see definition of high-tech products in Annex 4 and 5. High-tech patents: High-tech patents are defined according to another approach. The groups classified as high-tech patents are aggregated on the basis of the International Patent Classification (IPC 8th edition). Biotechnology patents are also aggregated on the basis of the IPC 8th edition. For more details, see the aggregation list of high-tech and biotechnology patents in Annex 6. The high-tech domain also comprises the sub-domain Venture Capital Investments: data are provided by INVEST Europe (formerly named the European Private Equity and Venture Capital Association EVCA). More details are available in the Eurostat metadata under Venture capital investments. Please note that for paragraphs where no metadata for regional data has been specified, the regional metadata is identical to the metadata provided for the national data.
    • 八月 2014
      来源: Eurostat
      上传者: Knoema
      访问日期: 28 十一月, 2015
      选择数据集
      The Structure of Earnings Survey (SES) is a 4-yearly survey which provides EU-wide harmonised structural data on gross earnings, hours paid and annual days of paid holiday leave, which are collected every four years under Council Regulation (EC) No 530/1999 concerning structural statistics on earnings and on labour costs, and Commission Regulation (EC) No 1738/2005 amending Regulation (EC) No 1916/2000 as regards the definition and transmission of information on the structure of earnings. The objective of this legislation is so that National Statistical Institutes (NSIs) provide accurate and harmonised data on earnings in EU Member States and other countries for policy-making and research purposes. The SES 2010 provides detailed and comparable information on relationships between the level of hourly, monthly and annual remuneration, personal characteristics of employees (sex, age, occupation, length of service, highest educational level attained, etc.) and their employer (economic activity, size and economic control of the enterprise). Regional data is also available for some countries and regional metadata is identical to that provided for national data.
    • 一月 2017
      来源: Eurostat
      上传者: Knoema
      访问日期: 05 二月, 2017
      选择数据集
      The Structure of Earnings Survey (SES) is a 4-yearly survey which provides EU-wide harmonised structural data on gross earnings, hours paid and annual days of paid holiday leave, which are collected every four years under Council Regulation (EC) No 530/1999 concerning structural statistics on earnings and on labour costs, and Commission Regulation (EC) No 1738/2005 amending Regulation (EC) No 1916/2000 as regards the definition and transmission of information on the structure of earnings. The objective of this legislation is so that National Statistical Institutes (NSIs) provide accurate and harmonised data on earnings in EU Member States and other countries for policy-making and research purposes. The SES 2010 provides detailed and comparable information on relationships between the level of hourly, monthly and annual remuneration, personal characteristics of employees (sex, age, occupation, length of service, highest educational level attained, etc.) and their employer (economic activity, size and economic control of the enterprise). Regional data is also available for some countries and regional metadata is identical to that provided for national data.
    • 七月 2019
      来源: Organisation for Economic Co-operation and Development
      上传者: Knoema
      访问日期: 29 七月, 2019
      选择数据集
      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).
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 11 十月, 2019
      选择数据集
      The folder 'population by educational attainment level (edat1)' presents data on the highest level of education successfully completed by the individuals of a given population. The folder 'transition from education to work (edatt)' covers data on young people neither in employment nor in education and training – NEET, early leavers from education and training and the labour status of young people by years since completion of highest level of education. The data shown are calculated as annual averages of quarterly EU Labour Force Survey data (EU-LFS). Up to the reference year 2008, the data source (EU-LFS) is, where necessary, adjusted and enriched in various ways, in accordance with the specificities of an indicator, including the following: correction of the main breaks in the LFS series,estimation of the missing values, i.e. in case of missing quarters, annual results and EU aggregates are estimated using adjusted quarterly national labour force survey data or interpolations of the EU-LFS data with reference to the available quarter(s). Details on the adjustments are available in CIRCABC. The adjustments are applied in the following online tables: Population by educational attainment level (edat1)- Population by educational attainment level, sex and age (%) - main indicators (edat_lfse_03) - Population aged 25-64 by educational attainment level, sex and NUTS 2 regions (%) (edat_lfse_04) - Population aged 30-34 by educational attainment level, sex and NUTS 2 regions (%) (edat_lfse_12) (Other tables shown in the folder 'population by educational attainment level (edat1)' are not adjusted and therefore the results in these tables might differ).Young people by educational and labour status (incl. neither in employment nor in education and training - NEET) (edatt0) – all tablesEarly leavers from education and training (edatt1) – all tablesLabour status of young people by years since completion of highest level of education (edatt2) – all tables  LFS ad-hoc module data available in the folder 'transition from education to work (edatt)' are not adjusted.
    • 七月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 01 八月, 2019
      选择数据集
    • 七月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 01 八月, 2019
      选择数据集
    • 七月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 01 八月, 2019
      选择数据集
    • 三月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 23 三月, 2019
      选择数据集
    • 九月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 02 十月, 2019
      选择数据集
      Labour cost statistics provide a comprehensive and detailed picture of the level, structure and short-term development of labour costs in the different sectors of economic activity in the European Union and certain other countries. All statistics are based on a harmonised definition of labour costs. Structural information on labour costs is collected through four-yearly Labour Cost Surveys (LCS), which provides details on the level and structure of labour cost data, hours worked and hours paid. LCS results are available for the reference years 2000, 2004, 2008 and 2012. All EU Member States together with Norway and Iceland (2004 onwards), Turkey and Macedonia (2008), as well as Serbia (2012) participated in the LCS. As far as available data and confidentiality rules permit, all variables and proportions are further broken down by enterprise size category, economic activity and region (for larger countries only). The data are collected by the National Statistical Institutes in most cases on the basis of stratified random samples of enterprises or local units, restricted in most countries to units with at least 10 employees. The stratification is based on economic activity, size category and region (where appropriate). Regional metadata is identical to the metadata provided for national data. Some countries also complement the survey results with administrative data. Monetary variables are expressed in EUR, national currencies (for non-euro-area countries) and Purchasing Power Standards (PPS). Labour costs are quoted in total per year, per month and per hour, as well as per capita and per full-time equivalents (FTE). Information on staff, hours worked and hours paid is quoted in aggregate and separately for full- and part-time employees.
    • 七月 2018
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 17 七月, 2018
      选择数据集
      This table presents data on average monthly earnings converted to a common currency. Data in U.S. dollars are converted from local currency using exchange rates, while data in constant 2011 U.S. dollars are converted using 2011 purchasing power parities (PPPs)   Dataset splitted into below datasets:-   Local Currency (Total) - https://knoema.com/EAR_TEAR_NOC_NB   Local Currency (Men) - https://knoema.com/EAR_MEAR_NOC_NB   Local Currency (Women) - https://knoema.com/EAR_FEAR_NOC_NB   Constant 2011 PPP $ (Total) - https://knoema.com/EAR_4MPT_NOC_NB   Constant 2011 PPP $ (Men) - https://knoema.com/EAR_4MPM_NOC_NB   Constant 2011 PPP $ (Women) - https://knoema.com/EAR_4MPW_NOC_NB
    • 七月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 01 八月, 2019
      选择数据集
    • 七月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 01 八月, 2019
      选择数据集
    • 七月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 01 八月, 2019
      选择数据集
    • 八月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 15 八月, 2019
      选择数据集
      The section 'LFS series - detailed annual survey results' reports annual results from the EU-LFS. While LFS is a quarterly survey, it is also possible to produce annual results. There are several ways of doing it, see section '20.5 Data compilation' below for details. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 八月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 15 八月, 2019
      选择数据集
      The section 'LFS series - detailed annual survey results' reports annual results from the EU-LFS. While LFS is a quarterly survey, it is also possible to produce annual results. There are several ways of doing it, see section '20.5 Data compilation' below for details. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 三月 2014
      来源: Eurostat
      上传者: Knoema
      访问日期: 26 十一月, 2015
      选择数据集
      The ad hoc module on work organisation and working time arrangements was included in the Labour Force Survey (LFS) and carried out in 2004. For a detailed evaluation of the ad hoc module see the publication, Eurostat (2006):"Final report of the task force for evaluating the 2004 LFS ad hoc module on work organisation and working time arrangements". The data cover all employees aged 25-49. The totals for the EU-25 referred to in the text and included in the figures and tables exclude Bulgaria and Romania since the data relate to period before these two joined the EU. There are no data for Bulgaria, the Czech Republic, Latvia, Lithuania, Poland, Sweden, and Croatia. In addition, there are no data in the following cases: a) Working arrangements by household circumstances: Denmark, Finland, Sweden, Norway and Switzerland; b) Employees working weekends and at night and convenience for personal life situation: Germany, Netherlands, Romania, Sweden, Norway and Switzerland.
    • 三月 2014
      来源: Eurostat
      上传者: Knoema
      访问日期: 26 十一月, 2015
      选择数据集
      The ad hoc module on work organisation and working time arrangements was included in the Labour Force Survey (LFS) and carried out in 2004. For a detailed evaluation of the ad hoc module see the publication, Eurostat (2006):"Final report of the task force for evaluating the 2004 LFS ad hoc module on work organisation and working time arrangements". The data cover all employees aged 25-49. The totals for the EU-25 referred to in the text and included in the figures and tables exclude Bulgaria and Romania since the data relate to period before these two joined the EU. There are no data for Bulgaria, the Czech Republic, Latvia, Lithuania, Poland, Sweden, and Croatia. In addition, there are no data in the following cases: a) Working arrangements by household circumstances: Denmark, Finland, Sweden, Norway and Switzerland; b) Employees working weekends and at night and convenience for personal life situation: Germany, Netherlands, Romania, Sweden, Norway and Switzerland.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 30 十月, 2019
      选择数据集
      The source for the regional labour market information down to NUTS level 2 is the EU Labour Force Survey (EU-LFS). This is a quarterly household sample survey conducted in all Member States of the EU and in EFTA and Candidate countries.  The EU-LFS survey follows the definitions and recommendations of the International Labour Organisation (ILO). To achieve further harmonisation, the Member States also adhere to common principles when formulating questionnaires. The LFS' target population is made up of all persons in private households aged 15 and over. For more information see the EU Labour Force Survey (lfsi_esms, see paragraph 21.1.).  The EU-LFS is designed to give accurate quarterly information at national level as well as annual information at NUTS 2 regional level and the compilation of these figures is well specified in the regulation. Microdata including the NUTS 2 level codes are provided by all the participating countries with a good degree of geographical comparability, which allows the production and dissemination of a complete set of comparable indicators for this territorial level. At present the transmission of the regional labour market data at NUTS 3 level has no legal basis. However many countries transmit NUTS 3 figures to Eurostat on a voluntary basis, under the understanding that they are not for publication with such detail, but for aggregation in few categories per country, i.e., metropolitan regions and urban-rural typology. Most of the NUTS 3 data are based on the LFS while some countries transmit data based on registers, administrative data, small area estimation and other reliable sources.
    • 八月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 14 八月, 2019
      选择数据集
      The section 'LFS series - detailed annual survey results' reports annual results from the EU-LFS. While LFS is a quarterly survey, it is also possible to produce annual results. There are several ways of doing it, see section '20.5 Data compilation' below for details. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 十一月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 06 十一月, 2019
      选择数据集
      The section 'LFS series - detailed quarterly survey results' reports detailed quarterly results going beyond the EU-LFS main aggregates, which have a separate data domain and some methodological differences. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 26 十月, 2019
      选择数据集
      The section 'LFS series - detailed quarterly survey results' reports detailed quarterly results going beyond the EU-LFS main aggregates, which have a separate data domain and some methodological differences. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 七月 2019
      来源: Organisation for Economic Co-operation and Development
      上传者: Knoema
      访问日期: 14 七月, 2019
      选择数据集
  • B
    • 五月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 23 五月, 2019
      选择数据集
      Data given in this domain are collected on a yearly basis by the National Statistical Institutes or Ministries and are based on the annual Eurostat Model Questionnaires on ICT (Information and Communication Technologies) usage and e-commerce in enterprises. Large part of the data collected are used in the context of the follow up of the Digital Single Market process (Monitoring the Digital Economy & Society 2016-2021). This conceptual framework follows the 2011 - 2015 benchmarking framework, the i2010 Benchmarking Framework and the eEurope 2005 Action Plan. The aim of the European ICT usage surveys is to collect and disseminate harmonised and comparable information on the use of Information and Communication Technologies in enterprises and e-commerce at European level. Coverage: The characteristics to be provided are drawn from the following list of subjects: ICT systems and their usage in enterprises,use of the Internet and other electronic networks by enterprises,e-commerce,e-business processes and organisational aspects,ICT competence in the enterprise and the need for ICT skills,barriers to the use of ICT, the Internet and other electronic networks, e-commerce and e-business processes,ICT security and trust,access to and use of the Internet and other network technologies for connecting objects and devices (Internet of Things),access to and use of technologies providing the ability to connect to the Internet or other networks from anywhere at any time (ubiquitous connectivity),use of Big data analysis,use of 3D printing,use of robotics.Breakdowns:by size class,by NACE categories,by region (until 2010)
    • 三月 2019
      来源: World Bank
      上传者: Knoema
      访问日期: 20 三月, 2019
      选择数据集
      Data cited at: The World Bank https://datacatalog.worldbank.org/ Topic: Jobs Publication: https://datacatalog.worldbank.org/dataset/jobs License: http://creativecommons.org/licenses/by/4.0/   The World Bank Jobs Statistics Over 150 indicators on labor-related topics, covering over 200 economies from 1990 to present.
  • C
    • 四月 2019
      来源: Organisation for Economic Co-operation and Development
      上传者: Knoema
      访问日期: 16 四月, 2019
      选择数据集
    • 十月 2018
      来源: Eurostat
      上传者: Knoema
      访问日期: 03 十一月, 2018
      选择数据集
      The indicator presents the average compensation of employee received by hour worked, expressed in euro. It is calculated by dividing national accounts data on compensation of employees for the total economy, which include wages and salaries as well as employers' social contributions, by the total number of hours worked by all employees (domestic concept). The indicator is based on European national accounts.
    • 十一月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 12 十一月, 2019
      选择数据集
    • 十一月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 12 十一月, 2019
      选择数据集
    • 七月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 01 八月, 2019
      选择数据集
      Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. The series is part of the ILO estimates and is harmonized to account for differences in national data and scope of coverage, collection and tabulation methodologies as well as for other country-specific factors. For more information, refer to the ILO estimates and projections methodological note.
    • 十月 2009
      来源: Eurostat
      上传者: Knoema
      访问日期: 28 十一月, 2015
      选择数据集
      The Continuing Vocational Training Survey (CVTS) collects information on enterprises’ investment in the continuing vocational training of their staff. Continuing vocational training (CVT) refers to education or training measures or activities which are financed in total or at least partly by the enterprise (directly or indirectly). Part financing could include the use of work-time for the training activity as well as financing of training equipment. Information available from the CVTS is grouped around the following topics: Training/non-training enterprisesParticipation in continuing vocational trainingPlanning and assessment of continuing vocational trainingCosts of continuing vocational training coursesTime spent on continuing vocational training courses Four waves of the CVTS have been carried out by now: CVTS 1 – reference year 1993CVTS 2 – reference year 1999CVTS 3 – reference year 2005CVTS 4 – reference year 2010 The domain "Vocational training in enterprises (trng_cvts)" presents data for 2010 and 2005 which are comparable between the two waves. 2005 data which are not comparable with 2010 data are shown in the folder "Continuing vocational training - reference year 2005 (trng_cvts3)" and 1999 data are available in the folder "Continuing vocational training - reference year 1999 (trng_cvts2)". Both folders can be found in the domain "Past series (trng_h)". The first survey (CVTS 1) was carried out in the then 12 Member States of the European Union. CVTS 1 was of pioneering nature and due to lack of comparability with the following waves data are not available in Eurostat's online database but main results are available here. The next CVTS is due for reference year 2015.
    • 十月 2009
      来源: Eurostat
      上传者: Knoema
      访问日期: 28 十一月, 2015
      选择数据集
      The Continuing Vocational Training Survey (CVTS) collects information on enterprises’ investment in the continuing vocational training of their staff. Continuing vocational training (CVT) refers to education or training measures or activities which are financed in total or at least partly by the enterprise (directly or indirectly). Part financing could include the use of work-time for the training activity as well as financing of training equipment. Information available from the CVTS is grouped around the following topics: Training/non-training enterprisesParticipation in continuing vocational trainingPlanning and assessment of continuing vocational trainingCosts of continuing vocational training coursesTime spent on continuing vocational training courses Four waves of the CVTS have been carried out by now: CVTS 1 – reference year 1993CVTS 2 – reference year 1999CVTS 3 – reference year 2005CVTS 4 – reference year 2010 The domain "Vocational training in enterprises (trng_cvts)" presents data for 2010 and 2005 which are comparable between the two waves. 2005 data which are not comparable with 2010 data are shown in the folder "Continuing vocational training - reference year 2005 (trng_cvts3)" and 1999 data are available in the folder "Continuing vocational training - reference year 1999 (trng_cvts2)". Both folders can be found in the domain "Past series (trng_h)". The first survey (CVTS 1) was carried out in the then 12 Member States of the European Union. CVTS 1 was of pioneering nature and due to lack of comparability with the following waves data are not available in Eurostat's online database but main results are available here. The next CVTS is due for reference year 2015.
    • 十月 2009
      来源: Eurostat
      上传者: Knoema
      访问日期: 28 十一月, 2015
      选择数据集
      The Continuing Vocational Training Survey (CVTS) collects information on enterprises’ investment in the continuing vocational training of their staff. Continuing vocational training (CVT) refers to education or training measures or activities which are financed in total or at least partly by the enterprise (directly or indirectly). Part financing could include the use of work-time for the training activity as well as financing of training equipment. Information available from the CVTS is grouped around the following topics: Training/non-training enterprisesParticipation in continuing vocational trainingPlanning and assessment of continuing vocational trainingCosts of continuing vocational training coursesTime spent on continuing vocational training courses Four waves of the CVTS have been carried out by now: CVTS 1 – reference year 1993CVTS 2 – reference year 1999CVTS 3 – reference year 2005CVTS 4 – reference year 2010 The domain "Vocational training in enterprises (trng_cvts)" presents data for 2010 and 2005 which are comparable between the two waves. 2005 data which are not comparable with 2010 data are shown in the folder "Continuing vocational training - reference year 2005 (trng_cvts3)" and 1999 data are available in the folder "Continuing vocational training - reference year 1999 (trng_cvts2)". Both folders can be found in the domain "Past series (trng_h)". The first survey (CVTS 1) was carried out in the then 12 Member States of the European Union. CVTS 1 was of pioneering nature and due to lack of comparability with the following waves data are not available in Eurostat's online database but main results are available here. The next CVTS is due for reference year 2015.
    • 二月 2011
      来源: Eurostat
      上传者: Knoema
      访问日期: 28 十一月, 2015
      选择数据集
      The Continuing Vocational Training Survey (CVTS) collects information on enterprises’ investment in the continuing vocational training of their staff. Continuing vocational training (CVT) refers to education or training measures or activities which are financed in total or at least partly by the enterprise (directly or indirectly). Part financing could include the use of work-time for the training activity as well as financing of training equipment. Information available from the CVTS is grouped around the following topics: Training/non-training enterprisesParticipation in continuing vocational trainingPlanning and assessment of continuing vocational trainingCosts of continuing vocational training coursesTime spent on continuing vocational training courses Four waves of the CVTS have been carried out by now: CVTS 1 – reference year 1993CVTS 2 – reference year 1999CVTS 3 – reference year 2005CVTS 4 – reference year 2010 The domain "Vocational training in enterprises (trng_cvts)" presents data for 2010 and 2005 which are comparable between the two waves. 2005 data which are not comparable with 2010 data are shown in the folder "Continuing vocational training - reference year 2005 (trng_cvts3)" and 1999 data are available in the folder "Continuing vocational training - reference year 1999 (trng_cvts2)". Both folders can be found in the domain "Past series (trng_h)". The first survey (CVTS 1) was carried out in the then 12 Member States of the European Union. CVTS 1 was of pioneering nature and due to lack of comparability with the following waves data are not available in Eurostat's online database but main results are available here. The next CVTS is due for reference year 2015.
    • 十月 2009
      来源: Eurostat
      上传者: Knoema
      访问日期: 28 十一月, 2015
      选择数据集
      The Continuing Vocational Training Survey (CVTS) collects information on enterprises’ investment in the continuing vocational training of their staff. Continuing vocational training (CVT) refers to education or training measures or activities which are financed in total or at least partly by the enterprise (directly or indirectly). Part financing could include the use of work-time for the training activity as well as financing of training equipment. Information available from the CVTS is grouped around the following topics: Training/non-training enterprisesParticipation in continuing vocational trainingPlanning and assessment of continuing vocational trainingCosts of continuing vocational training coursesTime spent on continuing vocational training courses Four waves of the CVTS have been carried out by now: CVTS 1 – reference year 1993CVTS 2 – reference year 1999CVTS 3 – reference year 2005CVTS 4 – reference year 2010 The domain "Vocational training in enterprises (trng_cvts)" presents data for 2010 and 2005 which are comparable between the two waves. 2005 data which are not comparable with 2010 data are shown in the folder "Continuing vocational training - reference year 2005 (trng_cvts3)" and 1999 data are available in the folder "Continuing vocational training - reference year 1999 (trng_cvts2)". Both folders can be found in the domain "Past series (trng_h)". The first survey (CVTS 1) was carried out in the then 12 Member States of the European Union. CVTS 1 was of pioneering nature and due to lack of comparability with the following waves data are not available in Eurostat's online database but main results are available here. The next CVTS is due for reference year 2015.
    • 四月 2019
      来源: United Nations Economic Commission for Europe
      上传者: Knoema
      访问日期: 07 五月, 2019
      选择数据集
      Source: UNECE Statistical Database, compiled from national official sources. Definition: Couple: A couple is defined as a man and woman living as a married couple, a registered couple or a couple who lives in a consensual union. Two persons are considered as partners in a consensual union when they have usual residence in the same household, are not married to each other and have a marriage-like relationship to each other. Data refer to couples where both partners are in the age range 25-49. Data are reported according to the age of the youngest child of the couple. Children living outside the household are not considered. Part-time/full-time: A part-time worker is an employed person whose normal hours of work are less than those of comparable full-time workers. In most countries, the distinction between part-time and full-time work is based on self-declaration. In a few countries, work is defined as part-time when the hours usually worked are below a fixed threshold. Not working: Both inactive and unemployed persons are considered as not working. General note: Data come from the Labour Force Survey (LFS) unless otherwise specified. .. - data not available Country: Austria Break in methodlogy (2004): Break in series due to change in data collection procedure. Country: Austria Change in definition (1980): Data refer to the livelihood concept Country: Austria Reference period (1980): Data refer to 1984 Country: Austria Data below the threshold of 3 000 persons are not published, while caution should be taken in interpreting data below the threshold of 6 000 persons. Country: Belgium Break in methodology (2012): From 2012, data explicitely include couples living in a consensual union. Country: Belgium Change in definition (2005 - 2015): A child is considered as a person below 17 who lives in the household whatever the relation to the reference person may be. Country: Canada Data refer to women aged 25-49 and men aged 15+. Data for No child refers to no child under the age of 16. Child aged more than 6 refers to child aged 6 to 15. Data are annual averages. Country: Canada Data do not cover the three northern territories (Yukon, Northwest and Nunavuk) Country: Croatia Data given for 2013 onwards are calibrated according to the results of the Census 2011 and are not fully comparable with data given for previous years. Country: Denmark Change in definition (1980 - 2006): Data do not cover couples where one or both members are self employed Country: Denmark Reference period (1980): Data refer to 1986 Country: Finland Data do not include children aged 17+. Data for child aged more than 6 refers to child aged more than 7 and child aged up to 6 refers to child aged 0-6 years (including 6). Country: France Reference area: Metropolitan France. Country: Germany Break in methodlogy (2005): Until 2004, data refer to one reporting week. From 2005 data are annual average figures. Country: Greece Data refer to annual averages. Country: Hungary Change in definition (2000 - 2013): Data refer to couples where both members are in the age range 15-74. Women not working include also those on maternity leave. Couples with youngest child aged 6 refer to couples with youngest child aged 6-16. Country: Hungary Reference period (2000 - 2013): Data refer to 2nd Quarter of each year. Country: Israel Break in methodlogy (2000): In 1998: 1) Changes in the weighting method; 2) Transition to the 1995 Population Census estimates; See explanations: http://www.cbs.gov.il/www/publications/saka_change/tch_e.pdf Country: Israel Break in methodlogy (2001): Changes in the weighting method. See explanations: http://www.cbs.gov.il/www/saka_y/e_intro_f1_comparison-mimi.f Country: Israel Break in methodlogy (2009): 1) Update of the definition of the civilian labour force characteristics; 2) Transition to the 2008 Population Census estimates. See explanations: http://www.cbs.gov.il/publications11/1460/pdf/intro05_e.pdf Country: Israel Break in methodlogy (2012): 1) Transitiom from a quarterly to a monthly LFS; 2) Changes in the definitions of labour force characteristics (including compulsory and permanent military service into labour force). See explanations: http://www.cbs.gov.il/publications/labour_survey04/labour_f--orce_survey/answer_question_e_2012.pdf Country: Israel Change in definition (1995): 1) Update of the definitions of labour force characteristics; 2) Changes in the Standard Industrial Classification of Economic Activities; See explanations: http://www.cbs.gov.il/www/publications/saka_change/tch_e.pdf Country: Israel Change in definition: from 2000 data for All couples include homosexual couples and couples where one partner is working but with unknown working hours. Child aged up to 6 refers to child under the age of 5. Child aged more than 6 refers to child aged 5 to 17. Country: Israel Territorial change (1995 onwards): Data do not cover couples living in kibbutzim, in institutions and living outside localities (Bedouins in the South and others) Country: Italy Break in methodlogy (2004): From 2004, there is a break in series due to change in survey and data collection procedure (continuous survey). Country: Latvia Change in definition (2010 - 2012): Couples with youngest child aged 6 and above& 39; - youngest child aged 6-16 years. Country: Luxembourg Change in definition (2001): Data do not include couples (with or without children) living with other persons. Full-time workers are those who usually work 35 hours or more per week, part-time workers are those who usually work less than 35 hours per week. Country: Portugal Data from 2011 onwards are not directly comparable with data for the previous years due to new data collection methods used in the Portuguese Labour Force Survey series. Estimates below 2 250 individuals are not shown due to high coefficients of variation. Country: Romania Break in methodology (2002): Due to the revision of the definitions and the coverage, the data series of 2002-2012 are not perfectly comparable with data series of previous years. Break in series starting with year 2013. For years 2014 onward data were estimated using the resident population. For year 2013 data were estimated based on revised population figures (resident population) in accordance to the 2011 Census results. Country: Romania Reference period (1995): Data for 1995 refers to March 1995. Country: Spain Data refer only to children of the reference person in the household. Data are annual average of the four quarters of the year. Data include persons working abroad as full time workers. Country: Sweden Reference period (1990): Data refer to 1991. Country: Switzerland Break in methodlogy (2010): From 2010, data based on sample survey of the resident permanent population 15 years and older (part of the annual combined census). Before 2000, data based on traditional census (full field enumeration). Data for 2010 and onwards are not fully comparable with those of 2000 and earlier. Country: Switzerland From 2010 onwards the sum of the data for the different work patterns of couples does not equal the total of all couples (the sum of the percentage isn’t equal to 100%) because of missing data. Country: United Kingdom Change in definition (2000 - 2013): Data refer to & 39;couple families& 39; and not & 39;couple households& 39;. Country: United States Data refer to married couples aged 16+. Full-time workers are those who usually work 35 hours or more per week, part-time workers are those who usually work less than 35 hours per week.
    • 六月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 26 六月, 2019
      选择数据集
      Statistics on culture cover many aspects of economic and social life. According to the Europe 2020 strategy, the role of culture is crucial for achieving the goal of a "smart, sustainable and inclusive" growth. Employment in cultural sector statistics aim at investigating on the dimension of the contribution of cultural employment to the overall employment. Cultural employment statistics are derived from data on employment based on the results of the European Labour Force Survey (see EU-LFS metadata) that is the main source of information about the situation and trends on the labour market in the European Union. The final report of the European Statistical System Network on Culture (ESS-Net Culture Report 2012, in particular pp. 129-226) deals with the methodology applied to cultural statistics, including the scope of the 'cultural economic activities' and 'cultural occupations' based on two reference classifications: the NACE classification (‘Nomenclature générale des Activités économiques dans les Communautés Européennes’) which classifies the employer’s main activity, andthe ISCO classification(‘International Standard Classification of Occupations’) which classifies occupations. Results from the EU-LFS allow to characterize cultural employment by different variables such as gender, age, employment status, working time, educational attainment, permanency of jobs by cross-tabulating ISCO and NACE cultural codes as defined in the ESS-Net Culture Report 2012 (Annex 3 – Table 26 and Annex 4 – Table 27).
    • 十一月 2018
      来源: Eurostat
      上传者: Knoema
      访问日期: 23 十一月, 2018
      选择数据集
      Culture statistics cover many aspects of economic and social life. According to the Europe 2020 strategy, the role of culture is crucial for achieving the goal of a "smart, sustainable and inclusive" growth. Statistics on cultural employment show the contribution of cultural employment to the overall employment and present different characteristics of the employment in this field of economy. Cultural employment statistics are derived from data on employment based on the results of the European Labour Force Survey (see EU-LFS metadata) that is the main source of information about the situation and trends on the labour market in the European Union. The final report of the European Statistical System Network on Culture (ESS-net Culture report 2012, in particular pp. 129-226) deals with the methodology applied to cultural statistics, including the scope of the 'cultural economic activities' and 'cultural occupations' based on two reference classifications: the NACE classification (‘Nomenclature générale des Activités économiques dans les Communautés Européennes’) which classifies the employer’s main activity, andthe ISCO classification (‘International Standard Classification of Occupations’) which classifies occupations. Results from the EU-LFS allow to characterize cultural employment by some core social variables (sex, age, educational attainment) and by selected labour market characteristics (self-employment, full-time work, permanent jobs and persons with one job only), by cross-tabulating ISCO and NACE cultural codes as defined in the ESS-net Culture report 2012 (Annex 3 – Table 26 and Annex 4 – Table 27). In 2016, an extension of the cultural scope was agreed upon by the Working Group 'Culture statistics' and implemented after in cultural employment statistics for reference years 2011 onwards. The publication "Culture statistics - 2016 edition" from the "Statistical books" series was based on the previous scope. Previous scope data are available here, for reference years 2008-2015: cultural employment by sexcultural employment by agecultural employment by educational attainmentcultural employment by NACE rev. 2
  • D
    • 十月 2013
      来源: Eurostat
      上传者: Knoema
      访问日期: 28 十一月, 2015
      选择数据集
    • 十月 2013
      来源: Eurostat
      上传者: Knoema
      访问日期: 28 十一月, 2015
      选择数据集
    • 十一月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 05 十一月, 2019
      选择数据集
    • 三月 2018
      来源: Eurostat
      上传者: Knoema
      访问日期: 27 三月, 2018
      选择数据集
      Dispersion of regional employment rates (total, females, males) measures the regional (NUTS level 2) differences in employment within countries and groups of countries (EU-25, euro area). The dispersion is expressed by the coefficient of variation of employment rates of the age group 15-64. It is zero when the employment rates in all regions are identical, and it will rise if there is an increase in the differences between employment rates among regions. Employment rate of the age group 15-64 represents employed persons aged 15-64 as a percentage of the population of the same age group. The source for the regional labour market information down to NUTS level 2 is the EU Labour Force Survey (EU-LFS). This is a quarterly household sample survey conducted in all Member States of the EU and in EFTA and Candidate countries.  The EU-LFS survey follows the definitions and recommendations of the International Labour Organisation (ILO). To achieve further harmonisation, the Member States also adhere to common principles when formulating questionnaires. The LFS' target population is made up of all persons in private households aged 15 and over. For more information see the EU Labour Force Survey (lfsi_esms, see paragraph 21.1.).  Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 01 十一月, 2019
      选择数据集
      Structural business statistics (SBS) describes the structure, conduct and performance of economic activities, down to the most detailed activity level (several hundred economic sectors). SBS are transmitted annually by the EU Member States on the basis of a legal obligation from 1995 onwards.   SBS covers all activities of the business economy with the exception of agricultural activities and personal services and the data are provided by all EU Member States, Norway and Switzerland, some candidate and potential candidate countries. The data are collected by domain of activity (annex) : Annex I - Services, Annex II - Industry, Annex III - Trade and Annex IV- Constructions and by datasets. Each annex contains several datasets as indicated in the SBS Regulation. The majority of the data is collected by National Statistical Institutes (NSIs) by means of statistical surveys, business registers or from various administrative sources. Regulatory or controlling national offices for financial institutions or central banks often provide the information required for the financial sector (NACE Rev 2 Section K / NACE Rev 1.1 Section J). Member States apply various statistical methods, according to the data source, such as grossing up, model based estimation or different forms of imputation, to ensure the quality of SBSs produced. Main characteristics (variables) of the SBS data category: Business Demographic variables (e.g. Number of enterprises)"Output related" variables (e.g. Turnover, Value added)"Input related" variables: labour input (e.g. Employment, Hours worked); goods and services input (e.g. Total of purchases); capital input (e.g. Material investments) All SBS characteristics are published on Eurostat’s website by tables and an example of the existent tables is presented below: Annual enterprise statistics: Characteristics collected are published by country and detailed on NACE Rev 2 and NACE Rev 1.1 class level (4-digits). Some classes or groups in 'services' section have been aggregated.Annual enterprise statistics broken down by size classes: Characteristics are published by country and detailed down to NACE Rev 2 and NACE Rev 1.1 group level (3-digits) and employment size class. For trade (NACE Rev 2 and NACE Rev 1.1 Section G) a supplementary breakdown by turnover size class is available.Annual regional statistics: Four characteristics are published by NUTS-2 country region and detailed on NACE Rev 2 and NACE Rev 1.1 division level (2-digits) (but to group level (3-digits) for the trade section). More information on the contents of different tables: the detail level and breakdowns required starting with the reference year 2008 is defined in Commission Regulation N° 251/2009. For previous reference years it is included in Commission Regulations (EC) N° 2701/98 and amended by Commission Regulation N°1614/2002 and Commission Regulation N°1669/2003. Several important derived indicators are generated in the form of ratios of certain monetary characteristics or per head values. A list with the available derived indicators is available below in the Annex.
  • E
    • 三月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 22 三月, 2019
      选择数据集
      In 2011, the European Union Labour Force Survey (EU-LFS) included an ad hoc module (AHM) on employment of disabled people. The module consisted of 11 variables dealing with: Health problems and difficulties in basic activities;Limitations in work caused by health problems/difficulties in basic activities;Special assistance needed or used by people with health problems/difficulties in basic activities;Limitation in work because of other reasons. On the basis of how the module was operationalised, the following two main definitions for disability were considered for presenting the results: Disabled persons = People having a basic activity difficulty (such as seeing, hearing, walking, communicating);Disabled persons = People having a work limitation caused by a longstanding health condition and/or a basic activity difficulty. 32 countries have implemented this module: the EU 28 Member States plus Turkey, Iceland, Norway and Switzerland. The Norwegian data are not disseminated because the AHM questionnaire in Norway only partly complies with the Commission Regulation (EU) No 317/2010 and consequently, the data are incomplete and partly comparable. Missing values, don't know and refusal answers are not considered in the calculations. It means the indicators have been worked out on the respondents and validated answers only.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 11 十月, 2019
      选择数据集
      The folder 'population by educational attainment level (edat1)' presents data on the highest level of education successfully completed by the individuals of a given population. The folder 'transition from education to work (edatt)' covers data on young people neither in employment nor in education and training – NEET, early leavers from education and training and the labour status of young people by years since completion of highest level of education. The data shown are calculated as annual averages of quarterly EU Labour Force Survey data (EU-LFS). Up to the reference year 2008, the data source (EU-LFS) is, where necessary, adjusted and enriched in various ways, in accordance with the specificities of an indicator, including the following:correction of the main breaks in the LFS series,estimation of the missing values, i.e. in case of missing quarters, annual results and EU aggregates are estimated using adjusted quarterly national labour force survey data or interpolations of the EU-LFS data with reference to the available quarter(s). Details on the adjustments are available in CIRCABC. The adjustments are applied in the following online tables:Population by educational attainment level (edat1) - Population by educational attainment level, sex and age (%) - main indicators (edat_lfse_03) - Population aged 25-64 by educational attainment level, sex and NUTS 2 regions (%) (edat_lfse_04) - Population aged 30-34 by educational attainment level, sex and NUTS 2 regions (%) (edat_lfse_12) (Other tables shown in the folder 'population by educational attainment level (edat1)' are not adjusted and therefore the results in these tables might differ).Young people by educational and labour status (incl. neither in employment nor in education and training - NEET) (edatt0) – all tablesEarly leavers from education and training (edatt1) – all tablesLabour status of young people by years since completion of highest level of education (edatt2) – all tables LFS ad-hoc module data available in the folder 'transition from education to work (edatt)' are not adjusted.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 11 十月, 2019
      选择数据集
      The folder 'population by educational attainment level (edat1)' presents data on the highest level of education successfully completed by the individuals of a given population. The folder 'transition from education to work (edatt)' covers data on young people neither in employment nor in education and training – NEET, early leavers from education and training and the labour status of young people by years since completion of highest level of education. The data shown are calculated as annual averages of quarterly EU Labour Force Survey data (EU-LFS). Up to the reference year 2008, the data source (EU-LFS) is, where necessary, adjusted and enriched in various ways, in accordance with the specificities of an indicator, including the following:correction of the main breaks in the LFS series,estimation of the missing values, i.e. in case of missing quarters, annual results and EU aggregates are estimated using adjusted quarterly national labour force survey data or interpolations of the EU-LFS data with reference to the available quarter(s). Details on the adjustments are available in CIRCABC. The adjustments are applied in the following online tables:Population by educational attainment level (edat1) - Population by educational attainment level, sex and age (%) - main indicators (edat_lfse_03) - Population aged 25-64 by educational attainment level, sex and NUTS 2 regions (%) (edat_lfse_04) - Population aged 30-34 by educational attainment level, sex and NUTS 2 regions (%) (edat_lfse_12) (Other tables shown in the folder 'population by educational attainment level (edat1)' are not adjusted and therefore the results in these tables might differ).Young people by educational and labour status (incl. neither in employment nor in education and training - NEET) (edatt0) – all tablesEarly leavers from education and training (edatt1) – all tablesLabour status of young people by years since completion of highest level of education (edatt2) – all tables LFS ad-hoc module data available in the folder 'transition from education to work (edatt)' are not adjusted.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 11 十月, 2019
      选择数据集
      Population by educational attainment level presents data on the highest level of education successfully completed by the individuals of a given population. Transition from education to work covers data on young people neither in employment nor in education and training – NEET, early leavers from education and training and the labour status of young people by years since completion of highest level of education. The data shown are calculated as annual averages of quarterly EU Labour Force Survey data (EU-LFS). Up to the reference year 2008, the data source (EU-LFS) is, where necessary, adjusted and enriched in various ways, in accordance with the specificities of an indicator, including the following:correction of the main breaks in the LFS series,estimation of the missing values, i.e. in case of missing quarters, annual results and EU aggregates are estimated using adjusted quarterly national labour force survey data or interpolations of the EU-LFS data with reference to the available quarter(s). Details on the adjustments are available in CIRCABC. The adjustments are applied in the following online tablesPopulation by educational attainment level (edat1)   - Population with lower secondary education attainment by sex and age (edat_lfse_05) - Population with upper secondary education attainment by sex and age (edat_lfse_06) - Population with tertiary education attainment by sex and age (edat_lfse_07) - Population with upper secondary or tertiary education attainment by sex and age (edat_lfse_08) - Population aged 25-64 with lower secondary education attainment by sex and NUTS 2 regions (edat_lfse_09) - Population aged 25-64 with upper secondary education attainment by sex and NUTS 2 regions (edat_lfse_10) - Population aged 25-64 with tertiary education attainment by sex and NUTS 2 regions (edat_lfse_11) - Population aged 30-34 with tertiary education attainment by sex and NUTS 2 regions (edat_lfse_12) - Population aged 25-64 with upper secondary or tertiary education attainment by sex and NUTS 2 regions (edat_lfse_13) (Other tables shown in the folder 'population by educational attainment level (edat1)' are not adjusted and therefore the results in these tables might differ).Young people by educational and labour status (incl. neither in employment nor in education and training - NEET) (edatt0) – all tablesEarly leavers from education and training (edatt1) – all tablesLabour status of young people by years since completion of highest level of education (edatt2) – all tables  LFS ad-hoc module data available in the folder 'transition from education to work (edatt)' are not adjusted.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 11 十月, 2019
      选择数据集
      The folder 'population by educational attainment level (edat1)' presents data on the highest level of education successfully completed by the individuals of a given population. The folder 'transition from education to work (edatt)' covers data on young people neither in employment nor in education and training – NEET, early leavers from education and training and the labour status of young people by years since completion of highest level of education. The data shown are calculated as annual averages of quarterly EU Labour Force Survey data (EU-LFS). Up to the reference year 2008, the data source (EU-LFS) is, where necessary, adjusted and enriched in various ways, in accordance with the specificities of an indicator, including the following:correction of the main breaks in the LFS series,estimation of the missing values, i.e. in case of missing quarters, annual results and EU aggregates are estimated using adjusted quarterly national labour force survey data or interpolations of the EU-LFS data with reference to the available quarter(s). Details on the adjustments are available in CIRCABC. The adjustments are applied in the following online tables:Population by educational attainment level (edat1)   - Population by educational attainment level, sex and age (%) - main indicators (edat_lfse_03) - Population aged 25-64 by educational attainment level, sex and NUTS 2 regions (%) (edat_lfse_04) - Population aged 30-34 by educational attainment level, sex and NUTS 2 regions (%) (edat_lfse_12) (Other tables shown in the folder 'population by educational attainment level (edat1)' are not adjusted and therefore the results in these tables might differ).Young people by educational and labour status (incl. neither in employment nor in education and training - NEET) (edatt0) – all tablesEarly leavers from education and training (edatt1) – all tablesLabour status of young people by years since completion of highest level of education (edatt2) – all tables  LFS ad-hoc module data available in the folder 'transition from education to work (edatt)' are not adjusted.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 11 十月, 2019
      选择数据集
      The folder 'population by educational attainment level (edat1)' presents data on the highest level of education successfully completed by the individuals of a given population. The folder 'transition from education to work (edatt)' covers data on young people neither in employment nor in education and training – NEET, early leavers from education and training and the labour status of young people by years since completion of highest level of education. The data shown are calculated as annual averages of quarterly EU Labour Force Survey data (EU-LFS). Up to the reference year 2008, the data source (EU-LFS) is, where necessary, adjusted and enriched in various ways, in accordance with the specificities of an indicator, including the following:correction of the main breaks in the LFS series,estimation of the missing values, i.e. in case of missing quarters, annual results and EU aggregates are estimated using adjusted quarterly national labour force survey data or interpolations of the EU-LFS data with reference to the available quarter(s). Details on the adjustments are available in CIRCABC. The adjustments are applied in the following online tables:Population by educational attainment level (edat1)   - Population by educational attainment level, sex and age (%) - main indicators (edat_lfse_03) - Population aged 25-64 by educational attainment level, sex and NUTS 2 regions (%) (edat_lfse_04) - Population aged 30-34 by educational attainment level, sex and NUTS 2 regions (%) (edat_lfse_12) (Other tables shown in the folder 'population by educational attainment level (edat1)' are not adjusted and therefore the results in these tables might differ).Young people by educational and labour status (incl. neither in employment nor in education and training - NEET) (edatt0) – all tablesEarly leavers from education and training (edatt1) – all tablesLabour status of young people by years since completion of highest level of education (edatt2) – all tables  LFS ad-hoc module data available in the folder 'transition from education to work (edatt)' are not adjusted.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 30 十月, 2019
      选择数据集
      The source for the regional labour market information down to NUTS level 2 is the EU Labour Force Survey (EU-LFS). This is a quarterly household sample survey conducted in all Member States of the EU and in EFTA and Candidate countries.  The EU-LFS survey follows the definitions and recommendations of the International Labour Organisation (ILO). To achieve further harmonisation, the Member States also adhere to common principles when formulating questionnaires. The LFS' target population is made up of all persons in private households aged 15 and over. For more information see the EU Labour Force Survey (lfsi_esms, see paragraph 21.1.).  The EU-LFS is designed to give accurate quarterly information at national level as well as annual information at NUTS 2 regional level and the compilation of these figures is well specified in the regulation. Microdata including the NUTS 2 level codes are provided by all the participating countries with a good degree of geographical comparability, which allows the production and dissemination of a complete set of comparable indicators for this territorial level. At present the transmission of the regional labour market data at NUTS 3 level has no legal basis. However many countries transmit NUTS 3 figures to Eurostat on a voluntary basis, under the understanding that they are not for publication with such detail, but for aggregation in few categories per country, i.e., metropolitan regions and urban-rural typology. Most of the NUTS 3 data are based on the LFS while some countries transmit data based on registers, administrative data, small area estimation and other reliable sources.
    • 一月 2017
      来源: Eurostat
      上传者: Knoema
      访问日期: 25 一月, 2017
      选择数据集
      The source for regional typology statistics are regional indicators at NUTS level 3 published on the Eurostat website or existing in the Eurostat production database. The structure of this domain is as follows: - Metropolitan regions (met)    For details see http://ec.europa.eu/eurostat/web/metropolitan-regions/overview - Maritime policy indicators (mare)    For details see http://ec.europa.eu/eurostat/web/maritime-policy-indicators/overview - Urban-rural typology (urt)    For details see http://ec.europa.eu/eurostat/web/rural-development/overview
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 30 十月, 2019
      选择数据集
      The source for the regional labour market information down to NUTS level 2 is the EU Labour Force Survey (EU-LFS). This is a quarterly household sample survey conducted in all Member States of the EU and in EFTA and Candidate countries.  The EU-LFS survey follows the definitions and recommendations of the International Labour Organisation (ILO). To achieve further harmonisation, the Member States also adhere to common principles when formulating questionnaires. The LFS' target population is made up of all persons in private households aged 15 and over. For more information see the EU Labour Force Survey (lfsi_esms, see paragraph 21.1.).  The EU-LFS is designed to give accurate quarterly information at national level as well as annual information at NUTS 2 regional level and the compilation of these figures is well specified in the regulation. Microdata including the NUTS 2 level codes are provided by all the participating countries with a good degree of geographical comparability, which allows the production and dissemination of a complete set of comparable indicators for this territorial level. At present the transmission of the regional labour market data at NUTS 3 level has no legal basis. However many countries transmit NUTS 3 figures to Eurostat on a voluntary basis, under the understanding that they are not for publication with such detail, but for aggregation in few categories per country, i.e., metropolitan regions and urban-rural typology. Most of the NUTS 3 data are based on the LFS while some countries transmit data based on registers, administrative data, small area estimation and other reliable sources.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 12 十月, 2019
      选择数据集
      The source for regional typology statistics are regional indicators at NUTS level 3 published on the Eurostat website or existing in the Eurostat production database. The structure of this domain is as follows: - Metropolitan regions (met)    For details see http://ec.europa.eu/eurostat/web/metropolitan-regions/overview - Maritime policy indicators (mare)    For details see http://ec.europa.eu/eurostat/web/maritime-policy-indicators/overview - Urban-rural typology (urt)    For details see http://ec.europa.eu/eurostat/web/rural-development/overview
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 30 十月, 2019
      选择数据集
      The source for the regional labour market information down to NUTS level 2 is the EU Labour Force Survey (EU-LFS). This is a quarterly household sample survey conducted in all Member States of the EU and in EFTA and Candidate countries.  The EU-LFS survey follows the definitions and recommendations of the International Labour Organisation (ILO). To achieve further harmonisation, the Member States also adhere to common principles when formulating questionnaires. The LFS' target population is made up of all persons in private households aged 15 and over. For more information see the EU Labour Force Survey (lfsi_esms, see paragraph 21.1.).  The EU-LFS is designed to give accurate quarterly information at national level as well as annual information at NUTS 2 regional level and the compilation of these figures is well specified in the regulation. Microdata including the NUTS 2 level codes are provided by all the participating countries with a good degree of geographical comparability, which allows the production and dissemination of a complete set of comparable indicators for this territorial level. At present the transmission of the regional labour market data at NUTS 3 level has no legal basis. However many countries transmit NUTS 3 figures to Eurostat on a voluntary basis, under the understanding that they are not for publication with such detail, but for aggregation in few categories per country, i.e., metropolitan regions and urban-rural typology. Most of the NUTS 3 data are based on the LFS while some countries transmit data based on registers, administrative data, small area estimation and other reliable sources.
    • 四月 2019
      来源: Organisation for Economic Co-operation and Development
      上传者: Knoema
      访问日期: 12 四月, 2019
      选择数据集
      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.
    • 四月 2019
      来源: Organisation for Economic Co-operation and Development
      上传者: Knoema
      访问日期: 16 四月, 2019
      选择数据集
      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.
    • 四月 2019
      来源: Organisation for Economic Co-operation and Development
      上传者: Knoema
      访问日期: 16 四月, 2019
      选择数据集
      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.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 26 十月, 2019
      选择数据集
    • 十月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 24 十月, 2019
      选择数据集
    • 八月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 15 八月, 2019
      选择数据集
      'Statistics on high-tech industry and knowledge-intensive services' (sometimes referred to as simply 'high-tech statistics') comprise economic, employment and science, technology and innovation (STI) data describing manufacturing and services industries or products traded broken down by technological intensity. The domain uses various other domains and sources of  Eurostat's official statistics (CIS, COMEXT, HRST, LFS, PATENT, R&D and SBS) and its coverage is therefore dependent on these other primary sources. Two main approaches are used in the domain to identify technology-intensity: the sectoral approach and the product approach. A third approach is used for data on high-tech and biotechnology patents aggregated on the basis of the International Patent Classification (IPC) 8th edition (see summary table in Annex 1 for which approach is used by each type of data). The sectoral approach: The sectoral approach is an aggregation of the manufacturing industries according to technological intensity (R&D expenditure/value added) and based on the Statistical classification of economic activities in the European Community (NACE)  at 2-digit level. The level of R&D intensity served as a criterion of classification of economic sectors into high-technology, medium high-technology, medium low-technology and low-technology industries. Services are mainly aggregated into knowledge-intensive services (KIS) and less knowledge-intensive services (LKIS) based on the share of tertiary educated persons at NACE 2-digit level. The sectoral approach is used for all indicators except data on high-tech trade and patents. Note that due to the revision of the NACE from NACE Rev. 1.1 to NACE Rev. 2 the definition of high-technology industries and knowledge-intensive services has changed in 2008. For high-tech statistics it means that two different definitions (one according NACE Rev. 1.1 and one according NACE Rev. 2) are used in parallel and the data according to both NACE versions are presented in separated tables depending on the data availability. For example as the LFS provides the results both by NACE Rev. 1.1 and NACE Rev. 2, all the table using this source have been duplicated to present the results by NACE Rev. 2 from 2008. For more details, see both definitions of high-tech sectors under Annexes section. Within the sectoral approach, a second classification was created , named Knowledge Intensive Activities KIA) and based on the share of tertiary educated people in each sectors of industries and services according to NACE at 2-digit level and for all EU28 Member States. A threshold was applied to judge sectors as knowledge intensive. In contrast to first sectoral approach mixing two methodologies, one for manufacturing industries and one for services, the KIA classification is based on one methodology for all the sectors of industries and services covering even public sector activities. The aggregations in use are Total Knowledge Intensive Activities (KIA) and Knowledge Intensive Activities in Business Industries (KIABI). Both classifications are made according to NACE Rev. 1.1 and NACE Rev. 2 at 2- digit level. Note that due to revision of the NACE Rev.1.1 to NACE Rev. 2 the list of Knowledge Intensive Activities has changed as well, the two definitions are used in parallel and the data are shown in two separate tables. NACE Rev.2 collection includes data starting from 2008 reference year. For more details please see the definitions under Annexes section. The product approach: The product approach was created to complement the sectoral approach and it is used for data on high-tech trade. The product list is based on the calculations of R&D intensity by groups of products (R&D expenditure/total sales). The groups classified as high-technology products are aggregated on the basis of the Standard International Trade Classification (SITC). The initial definition was built based on SITC Rev.3 and served to compile the high-tech product aggregates until 2007. With the implementation in 2007 of the new version of SITC Rev.4, the definition of high-tech groups was revised and adapted according to new classification. Starting from 2007 the Eurostat presents the trade data for high-tech groups aggregated based on the SITC Rev.4. . For more details, see definition of high-tech products under Annexes section. High-tech patents: High-tech patents are defined according to another approach. The groups classified as high-tech patents are aggregated on the basis of the International Patent Classification (IPC 8th edition). Biotechnology patents are also aggregated on the basis of the IPC 8th edition. For more details, see the aggregation list of high-tech and biotechnology patents under Annexes section. The high-tech domain also comprises the sub-domain Venture Capital Investments: data are provided by INVEST Europe (formerly named the European Private Equity and Venture Capital Association EVCA). More details are available in the Eurostat metadata under Venture capital investments. Please note that for paragraphs where no metadata for regional data has been specified, the regional metadata is identical to the metadata provided for the national data.
    • 八月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 13 八月, 2019
      选择数据集
    • 八月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 15 八月, 2019
      选择数据集
      'Statistics on high-tech industry and knowledge-intensive services' (sometimes referred to as simply 'high-tech statistics') comprise economic, employment and science, technology and innovation (STI) data describing manufacturing and services industries or products traded broken down by technological intensity. The domain uses various other domains and sources of  Eurostat's official statistics (CIS, COMEXT, HRST, LFS, PATENT, R&D and SBS) and its coverage is therefore dependent on these other primary sources. Two main approaches are used in the domain to identify technology-intensity: the sectoral approach and the product approach. A third approach is used for data on high-tech and biotechnology patents aggregated on the basis of the International Patent Classification (IPC) 8th edition (see summary table in Annex 1 for which approach is used by each type of data). The sectoral approach: The sectoral approach is an aggregation of the manufacturing industries according to technological intensity (R&D expenditure/value added) and based on the Statistical classification of economic activities in the European Community (NACE)  at 2-digit level. The level of R&D intensity served as a criterion of classification of economic sectors into high-technology, medium high-technology, medium low-technology and low-technology industries. Services are mainly aggregated into knowledge-intensive services (KIS) and less knowledge-intensive services (LKIS) based on the share of tertiary educated persons at NACE 2-digit level. The sectoral approach is used for all indicators except data on high-tech trade and patents. Note that due to the revision of the NACE from NACE Rev. 1.1 to NACE Rev. 2 the definition of high-technology industries and knowledge-intensive services has changed in 2008. For high-tech statistics it means that two different definitions (one according NACE Rev. 1.1 and one according NACE Rev. 2) are used in parallel and the data according to both NACE versions are presented in separated tables depending on the data availability. For example as the LFS provides the results both by NACE Rev. 1.1 and NACE Rev. 2, all the table using this source have been duplicated to present the results by NACE Rev. 2 from 2008. For more details, see both definitions of high-tech sectors under Annexes section. Within the sectoral approach, a second classification was created , named Knowledge Intensive Activities KIA) and based on the share of tertiary educated people in each sectors of industries and services according to NACE at 2-digit level and for all EU28 Member States. A threshold was applied to judge sectors as knowledge intensive. In contrast to first sectoral approach mixing two methodologies, one for manufacturing industries and one for services, the KIA classification is based on one methodology for all the sectors of industries and services covering even public sector activities. The aggregations in use are Total Knowledge Intensive Activities (KIA) and Knowledge Intensive Activities in Business Industries (KIABI). Both classifications are made according to NACE Rev. 1.1 and NACE Rev. 2 at 2- digit level. Note that due to revision of the NACE Rev.1.1 to NACE Rev. 2 the list of Knowledge Intensive Activities has changed as well, the two definitions are used in parallel and the data are shown in two separate tables. NACE Rev.2 collection includes data starting from 2008 reference year. For more details please see the definitions under Annexes section. The product approach: The product approach was created to complement the sectoral approach and it is used for data on high-tech trade. The product list is based on the calculations of R&D intensity by groups of products (R&D expenditure/total sales). The groups classified as high-technology products are aggregated on the basis of the Standard International Trade Classification (SITC). The initial definition was built based on SITC Rev.3 and served to compile the high-tech product aggregates until 2007. With the implementation in 2007 of the new version of SITC Rev.4, the definition of high-tech groups was revised and adapted according to new classification. Starting from 2007 the Eurostat presents the trade data for high-tech groups aggregated based on the SITC Rev.4. . For more details, see definition of high-tech products under Annexes section. High-tech patents: High-tech patentsare defined according to another approach. The groups classified as high-tech patents are aggregated on the basis of the International Patent Classification (IPC 8th edition). Biotechnology patents are also aggregated on the basis of the IPC 8th edition. For more details, see the aggregation list of high-tech and biotechnology patents under Annexes section. The high-tech domain also comprises the sub-domain Venture Capital Investments: data are provided by INVEST Europe (formerly named the European Private Equity and Venture Capital Association EVCA). More details are available in the Eurostat metadata under Venture capital investments. Please note that for paragraphs where no metadata for regional data has been specified, the regional metadata is identical to the metadata provided for the national data.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 01 十一月, 2019
      选择数据集
      The Human Resources in Science and Technology (HRST) domain provides data on stocks and flows (where flows in turn are divided into job-to-job mobility and education inflows). Stocks and flows are the main statistics for HRST. Their methodologies interlink and are therefore presented together in one single metadata-file. This metadata-file is duplicated in the structure of Eurostat's online database, while statistics for stocks and flows are found in separate folders. Several breakdowns are available for stocks and flows indicators: sex, age, region, sector of economic activity, occupation, educational attainment, fields of education, although not all combinations are possible. The data on stocks and job-to-job mobility are obtained from the European Union Labour Force Survey (EU LFS). The National Statistical Institutes are responsible for conducting the surveys and forwarding the results to Eurostat. The data on education inflows are obtained from Eurostat's Education database and in turn obtained via the UNESCO/OECD/Eurostat questionnaire on education. The National Statistical Institutes are responsible for conducting the surveys, compiling the results and forwarding the results to Eurostat. Please note that for paragraphs where no metadata for regional data has been specified, the regional metadata is identical to the metadata provided for the national data.
    • 八月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 09 八月, 2019
      选择数据集
      The Human Resources in Science and Technology (HRST) domain provides data on stocks and flows (where flows in turn are divided into job-to-job mobility and education inflows). Stocks and flows are the main statistics for HRST. Their methodologies interlink and are therefore presented together in one single metadata-file. This metadata-file is duplicated in the structure of Eurostat's online database, while statistics for stocks and flows are found in separate folders. Several breakdowns are available for stocks and flows indicators: sex, age, region, sector of economic activity, occupation, educational attainment, fields of education, although not all combinations are possible. The data on stocks and job-to-job mobility are obtained from the European Union Labour Force Survey (EU LFS). The National Statistical Institutes are responsible for conducting the surveys and forwarding the results to Eurostat. The data on education inflows are obtained from Eurostat's Education database and in turn obtained via the UNESCO/OECD/Eurostat questionnaire on education. The National Statistical Institutes are responsible for conducting the surveys, compiling the results and forwarding the results to Eurostat. Please note that for paragraphs where no metadata for regional data has been specified, the regional metadata is identical to the metadata provided for the national data.
    • 七月 2014
      来源: Eurostat
      上传者: Knoema
      访问日期: 28 十一月, 2015
      选择数据集
      In 2011, the European Union Labour Force Survey (EU-LFS) included an ad hoc module (AHM) on employment of disabled people. The module consisted of 11 variables dealing with: Health problems and difficulties in basic activities;Limitations in work caused by health problems/difficulties in basic activities;Special assistance needed or used by people with health problems/difficulties in basic activities;Limitation in work because of other reasons. On the basis of how the module was operationalised, the following two main definitions for disability were considered for presenting the results: Disabled persons = People having a basic activity difficulty (such as seeing, hearing, walking, communicating);Disabled persons = People having a work limitation caused by a longstanding health condition and/or a basic activity difficulty. 32 countries have implemented this module: the EU 28 Member States plus Turkey, Iceland, Norway and Switzerland. The Norwegian data are not disseminated because the AHM questionnaire in Norway only partly complies with the Commission Regulation (EU) No 317/2010 and consequently, the data are incomplete and partly comparable. Missing values, don't know and refusal answers are not considered in the calculations. It means the indicators have been worked out on the respondents and validated answers only.
    • 三月 2009
      来源: Eurostat
      上传者: Knoema
      访问日期: 31 十二月, 2015
      选择数据集
      The tables presented in the topic of active population cover the total population for 31 countries (for more information on received tables and geographic coverage, see "2001 Census Round - Tables Received" in the Annex at the bottom of the page). The level of completeness of the tables depends largely on the availability of data at the respective national statistical institutes. There are four ways of collecting census data, namely: - the traditional method of using census questionnaires (exhaustive census); - the method of using registers and/or other administrative sources; - a combination of registers and/or other administrative sources and - surveys (complete enumerations or sample surveys). Other methods (other mixed census or micro-census) can be used as well. Details for the method employed by each country are provided in "2001 Census Method"in the Annex at the bottom of the page. In the same table you can find the dates on which the census was carried out in each country.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 26 十月, 2019
      选择数据集
      Percentage of self-employed without employees as a share of all persons in employment.
    • 十一月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 06 十一月, 2019
      选择数据集
      The section 'LFS series - detailed quarterly survey results' reports detailed quarterly results going beyond the EU-LFS main aggregates, which have a separate data domain and some methodological differences. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 26 十月, 2019
      选择数据集
      The section 'LFS series - detailed quarterly survey results' reports detailed quarterly results going beyond the EU-LFS main aggregates, which have a separate data domain and some methodological differences. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 二月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 18 二月, 2019
      选择数据集
      The main aim of 2017 ad-hoc module is to provide information on the self-employed and on persons in an ambivalent professional status (at the border between employment and self-employment). The module includes 11 variables, split in 3 sub-modules. Sub-module 1: Economically dependent self-employed The first sub-module aims to measure the degree of economic/organisational dependency of the self-employed, in terms of the number of clients and the percentage of income coming from a client as well as in terms of control over working hours. This sub-module includes 2 variables: MAINCLNT: Economic dependencyWORKORG: Organisational dependencySub-module 2: Working conditions for self-employed The aim of the second sub-module is to investigate the working conditions of the self-employed, like working with partners or using employees. It also collects factors that motivated or forced a person to become self-employed, as well as the main difficulty they face working as self-employed. This sub-module includes 5 variables: REASSE: Main reason for becoming self-employed               SEDIFFIC: Main difficulty as self-employed                         REASNOEM: Main reason for not having employees                        BPARTNER:  Working with business partners                                    PLANEMPL:  Planning hiring of employees or subcontracting           Sub-module 3: Comparing employees and self-employed The third sub-module targets the comparison between self-employed, employees and family workers in terms of job satisfaction and autonomy. It also gathers information on the preferred professional status. This sub-module includes 4 variables: JBSATISFQ:  Job satisfaction                                                AUTONOMY: Job autonomy                                                PREFSTAP: Preferred professional status in the main job      OBSTACSE: Main reason for not becoming self-employed  Detailed information on the relevant methodology for the ad-hoc module (including the Commission regulation and explanatory notes) as well as documentation from each participating country (national questionnaires and interviewers instructions) can be found on EU-LFS (Statistics Explained) - Ad-hoc modules.
    • 二月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 18 二月, 2019
      选择数据集
      The main aim of 2017 ad-hoc module is to provide information on the self-employed and on persons in an ambivalent professional status (at the border between employment and self-employment). The module includes 11 variables, split in 3 sub-modules. Sub-module 1: Economically dependent self-employed The first sub-module aims to measure the degree of economic/organisational dependency of the self-employed, in terms of the number of clients and the percentage of income coming from a client as well as in terms of control over working hours. This sub-module includes 2 variables: MAINCLNT: Economic dependencyWORKORG: Organisational dependencySub-module 2: Working conditions for self-employed The aim of the second sub-module is to investigate the working conditions of the self-employed, like working with partners or using employees. It also collects factors that motivated or forced a person to become self-employed, as well as the main difficulty they face working as self-employed. This sub-module includes 5 variables: REASSE: Main reason for becoming self-employed               SEDIFFIC: Main difficulty as self-employed                         REASNOEM: Main reason for not having employees                        BPARTNER:  Working with business partners                                    PLANEMPL:  Planning hiring of employees or subcontracting           Sub-module 3: Comparing employees and self-employed The third sub-module targets the comparison between self-employed, employees and family workers in terms of job satisfaction and autonomy. It also gathers information on the preferred professional status. This sub-module includes 4 variables: JBSATISFQ:  Job satisfaction                                                AUTONOMY: Job autonomy                                                PREFSTAP: Preferred professional status in the main job      OBSTACSE: Main reason for not becoming self-employed  Detailed information on the relevant methodology for the ad-hoc module (including the Commission regulation and explanatory notes) as well as documentation from each participating country (national questionnaires and interviewers instructions) can be found on EU-LFS (Statistics Explained) - Ad-hoc modules.
    • 十一月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 06 十一月, 2019
      选择数据集
      The section 'LFS series - detailed quarterly survey results' reports detailed quarterly results going beyond the EU-LFS main aggregates, which have a separate data domain and some methodological differences. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 26 十月, 2019
      选择数据集
      The section 'LFS series - detailed quarterly survey results' reports detailed quarterly results going beyond the EU-LFS main aggregates, which have a separate data domain and some methodological differences. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 十一月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 06 十一月, 2019
      选择数据集
      The section 'LFS series - detailed quarterly survey results' reports detailed quarterly results going beyond the EU-LFS main aggregates, which have a separate data domain and some methodological differences. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 二月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 18 二月, 2019
      选择数据集
      The main aim of 2017 ad-hoc module is to provide information on the self-employed and on persons in an ambivalent professional status (at the border between employment and self-employment). The module includes 11 variables, split in 3 sub-modules. Sub-module 1: Economically dependent self-employed The first sub-module aims to measure the degree of economic/organisational dependency of the self-employed, in terms of the number of clients and the percentage of income coming from a client as well as in terms of control over working hours. This sub-module includes 2 variables: MAINCLNT: Economic dependencyWORKORG: Organisational dependencySub-module 2: Working conditions for self-employed The aim of the second sub-module is to investigate the working conditions of the self-employed, like working with partners or using employees. It also collects factors that motivated or forced a person to become self-employed, as well as the main difficulty they face working as self-employed. This sub-module includes 5 variables: REASSE: Main reason for becoming self-employed               SEDIFFIC: Main difficulty as self-employed                         REASNOEM: Main reason for not having employees                        BPARTNER:  Working with business partners                                    PLANEMPL:  Planning hiring of employees or subcontracting           Sub-module 3: Comparing employees and self-employed The third sub-module targets the comparison between self-employed, employees and family workers in terms of job satisfaction and autonomy. It also gathers information on the preferred professional status. This sub-module includes 4 variables: JBSATISFQ:  Job satisfaction                                                AUTONOMY: Job autonomy                                                PREFSTAP: Preferred professional status in the main job      OBSTACSE: Main reason for not becoming self-employed  Detailed information on the relevant methodology for the ad-hoc module (including the Commission regulation and explanatory notes) as well as documentation from each participating country (national questionnaires and interviewers instructions) can be found on EU-LFS (Statistics Explained) - Ad-hoc modules.
    • 二月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 18 二月, 2019
      选择数据集
      The main aim of 2017 ad-hoc module is to provide information on the self-employed and on persons in an ambivalent professional status (at the border between employment and self-employment). The module includes 11 variables, split in 3 sub-modules. Sub-module 1: Economically dependent self-employed The first sub-module aims to measure the degree of economic/organisational dependency of the self-employed, in terms of the number of clients and the percentage of income coming from a client as well as in terms of control over working hours. This sub-module includes 2 variables: MAINCLNT: Economic dependencyWORKORG: Organisational dependencySub-module 2: Working conditions for self-employed The aim of the second sub-module is to investigate the working conditions of the self-employed, like working with partners or using employees. It also collects factors that motivated or forced a person to become self-employed, as well as the main difficulty they face working as self-employed. This sub-module includes 5 variables: REASSE: Main reason for becoming self-employed               SEDIFFIC: Main difficulty as self-employed                         REASNOEM: Main reason for not having employees                        BPARTNER:  Working with business partners                                    PLANEMPL:  Planning hiring of employees or subcontracting           Sub-module 3: Comparing employees and self-employed The third sub-module targets the comparison between self-employed, employees and family workers in terms of job satisfaction and autonomy. It also gathers information on the preferred professional status. This sub-module includes 4 variables: JBSATISFQ:  Job satisfaction                                                AUTONOMY: Job autonomy                                                PREFSTAP: Preferred professional status in the main job      OBSTACSE: Main reason for not becoming self-employed  Detailed information on the relevant methodology for the ad-hoc module (including the Commission regulation and explanatory notes) as well as documentation from each participating country (national questionnaires and interviewers instructions) can be found on EU-LFS (Statistics Explained) - Ad-hoc modules.
    • 二月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 18 二月, 2019
      选择数据集
      The main aim of 2017 ad-hoc module is to provide information on the self-employed and on persons in an ambivalent professional status (at the border between employment and self-employment). The module includes 11 variables, split in 3 sub-modules. Sub-module 1: Economically dependent self-employed The first sub-module aims to measure the degree of economic/organisational dependency of the self-employed, in terms of the number of clients and the percentage of income coming from a client as well as in terms of control over working hours. This sub-module includes 2 variables: MAINCLNT: Economic dependencyWORKORG: Organisational dependencySub-module 2: Working conditions for self-employed The aim of the second sub-module is to investigate the working conditions of the self-employed, like working with partners or using employees. It also collects factors that motivated or forced a person to become self-employed, as well as the main difficulty they face working as self-employed. This sub-module includes 5 variables: REASSE: Main reason for becoming self-employed               SEDIFFIC: Main difficulty as self-employed                         REASNOEM: Main reason for not having employees                        BPARTNER:  Working with business partners                                    PLANEMPL:  Planning hiring of employees or subcontracting           Sub-module 3: Comparing employees and self-employed The third sub-module targets the comparison between self-employed, employees and family workers in terms of job satisfaction and autonomy. It also gathers information on the preferred professional status. This sub-module includes 4 variables: JBSATISFQ:  Job satisfaction                                                AUTONOMY: Job autonomy                                                PREFSTAP: Preferred professional status in the main job      OBSTACSE: Main reason for not becoming self-employed  Detailed information on the relevant methodology for the ad-hoc module (including the Commission regulation and explanatory notes) as well as documentation from each participating country (national questionnaires and interviewers instructions) can be found on EU-LFS (Statistics Explained) - Ad-hoc modules.
    • 二月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 18 二月, 2019
      选择数据集
      The main aim of 2017 ad-hoc module is to provide information on the self-employed and on persons in an ambivalent professional status (at the border between employment and self-employment). The module includes 11 variables, split in 3 sub-modules. Sub-module 1: Economically dependent self-employed The first sub-module aims to measure the degree of economic/organisational dependency of the self-employed, in terms of the number of clients and the percentage of income coming from a client as well as in terms of control over working hours. This sub-module includes 2 variables: MAINCLNT: Economic dependencyWORKORG: Organisational dependencySub-module 2: Working conditions for self-employed The aim of the second sub-module is to investigate the working conditions of the self-employed, like working with partners or using employees. It also collects factors that motivated or forced a person to become self-employed, as well as the main difficulty they face working as self-employed. This sub-module includes 5 variables: REASSE: Main reason for becoming self-employed               SEDIFFIC: Main difficulty as self-employed                         REASNOEM: Main reason for not having employees                        BPARTNER:  Working with business partners                                    PLANEMPL:  Planning hiring of employees or subcontracting           Sub-module 3: Comparing employees and self-employed The third sub-module targets the comparison between self-employed, employees and family workers in terms of job satisfaction and autonomy. It also gathers information on the preferred professional status. This sub-module includes 4 variables: JBSATISFQ:  Job satisfaction                                                AUTONOMY: Job autonomy                                                PREFSTAP: Preferred professional status in the main job      OBSTACSE: Main reason for not becoming self-employed  Detailed information on the relevant methodology for the ad-hoc module (including the Commission regulation and explanatory notes) as well as documentation from each participating country (national questionnaires and interviewers instructions) can be found on EU-LFS (Statistics Explained) - Ad-hoc modules.
    • 二月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 18 二月, 2019
      选择数据集
      The main aim of 2017 ad-hoc module is to provide information on the self-employed and on persons in an ambivalent professional status (at the border between employment and self-employment). The module includes 11 variables, split in 3 sub-modules. Sub-module 1: Economically dependent self-employed The first sub-module aims to measure the degree of economic/organisational dependency of the self-employed, in terms of the number of clients and the percentage of income coming from a client as well as in terms of control over working hours. This sub-module includes 2 variables: MAINCLNT: Economic dependencyWORKORG: Organisational dependencySub-module 2: Working conditions for self-employed The aim of the second sub-module is to investigate the working conditions of the self-employed, like working with partners or using employees. It also collects factors that motivated or forced a person to become self-employed, as well as the main difficulty they face working as self-employed. This sub-module includes 5 variables: REASSE: Main reason for becoming self-employed               SEDIFFIC: Main difficulty as self-employed                         REASNOEM: Main reason for not having employees                        BPARTNER:  Working with business partners                                    PLANEMPL:  Planning hiring of employees or subcontracting           Sub-module 3: Comparing employees and self-employed The third sub-module targets the comparison between self-employed, employees and family workers in terms of job satisfaction and autonomy. It also gathers information on the preferred professional status. This sub-module includes 4 variables: JBSATISFQ:  Job satisfaction                                                AUTONOMY: Job autonomy                                                PREFSTAP: Preferred professional status in the main job      OBSTACSE: Main reason for not becoming self-employed  Detailed information on the relevant methodology for the ad-hoc module (including the Commission regulation and explanatory notes) as well as documentation from each participating country (national questionnaires and interviewers instructions) can be found on EU-LFS (Statistics Explained) - Ad-hoc modules.
    • 二月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 18 二月, 2019
      选择数据集
      The main aim of 2017 ad-hoc module is to provide information on the self-employed and on persons in an ambivalent professional status (at the border between employment and self-employment). The module includes 11 variables, split in 3 sub-modules. Sub-module 1: Economically dependent self-employed The first sub-module aims to measure the degree of economic/organisational dependency of the self-employed, in terms of the number of clients and the percentage of income coming from a client as well as in terms of control over working hours. This sub-module includes 2 variables: MAINCLNT: Economic dependencyWORKORG: Organisational dependencySub-module 2: Working conditions for self-employed The aim of the second sub-module is to investigate the working conditions of the self-employed, like working with partners or using employees. It also collects factors that motivated or forced a person to become self-employed, as well as the main difficulty they face working as self-employed. This sub-module includes 5 variables: REASSE: Main reason for becoming self-employed               SEDIFFIC: Main difficulty as self-employed                         REASNOEM: Main reason for not having employees                        BPARTNER:  Working with business partners                                    PLANEMPL:  Planning hiring of employees or subcontracting           Sub-module 3: Comparing employees and self-employed The third sub-module targets the comparison between self-employed, employees and family workers in terms of job satisfaction and autonomy. It also gathers information on the preferred professional status. This sub-module includes 4 variables: JBSATISFQ:  Job satisfaction                                                AUTONOMY: Job autonomy                                                PREFSTAP: Preferred professional status in the main job      OBSTACSE: Main reason for not becoming self-employed  Detailed information on the relevant methodology for the ad-hoc module (including the Commission regulation and explanatory notes) as well as documentation from each participating country (national questionnaires and interviewers instructions) can be found on EU-LFS (Statistics Explained) - Ad-hoc modules.
    • 二月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 18 二月, 2019
      选择数据集
      The main aim of 2017 ad-hoc module is to provide information on the self-employed and on persons in an ambivalent professional status (at the border between employment and self-employment). The module includes 11 variables, split in 3 sub-modules. Sub-module 1: Economically dependent self-employed The first sub-module aims to measure the degree of economic/organisational dependency of the self-employed, in terms of the number of clients and the percentage of income coming from a client as well as in terms of control over working hours. This sub-module includes 2 variables: MAINCLNT: Economic dependencyWORKORG: Organisational dependencySub-module 2: Working conditions for self-employed The aim of the second sub-module is to investigate the working conditions of the self-employed, like working with partners or using employees. It also collects factors that motivated or forced a person to become self-employed, as well as the main difficulty they face working as self-employed. This sub-module includes 5 variables: REASSE: Main reason for becoming self-employed               SEDIFFIC: Main difficulty as self-employed                         REASNOEM: Main reason for not having employees                        BPARTNER:  Working with business partners                                    PLANEMPL:  Planning hiring of employees or subcontracting           Sub-module 3: Comparing employees and self-employed The third sub-module targets the comparison between self-employed, employees and family workers in terms of job satisfaction and autonomy. It also gathers information on the preferred professional status. This sub-module includes 4 variables: JBSATISFQ:  Job satisfaction                                                AUTONOMY: Job autonomy                                                PREFSTAP: Preferred professional status in the main job      OBSTACSE: Main reason for not becoming self-employed  Detailed information on the relevant methodology for the ad-hoc module (including the Commission regulation and explanatory notes) as well as documentation from each participating country (national questionnaires and interviewers instructions) can be found on EU-LFS (Statistics Explained) - Ad-hoc modules.
    • 二月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 18 二月, 2019
      选择数据集
      The main aim of 2017 ad-hoc module is to provide information on the self-employed and on persons in an ambivalent professional status (at the border between employment and self-employment). The module includes 11 variables, split in 3 sub-modules. Sub-module 1: Economically dependent self-employed The first sub-module aims to measure the degree of economic/organisational dependency of the self-employed, in terms of the number of clients and the percentage of income coming from a client as well as in terms of control over working hours. This sub-module includes 2 variables: MAINCLNT: Economic dependencyWORKORG: Organisational dependencySub-module 2: Working conditions for self-employed The aim of the second sub-module is to investigate the working conditions of the self-employed, like working with partners or using employees. It also collects factors that motivated or forced a person to become self-employed, as well as the main difficulty they face working as self-employed. This sub-module includes 5 variables: REASSE: Main reason for becoming self-employed               SEDIFFIC: Main difficulty as self-employed                         REASNOEM: Main reason for not having employees                        BPARTNER:  Working with business partners                                    PLANEMPL:  Planning hiring of employees or subcontracting           Sub-module 3: Comparing employees and self-employed The third sub-module targets the comparison between self-employed, employees and family workers in terms of job satisfaction and autonomy. It also gathers information on the preferred professional status. This sub-module includes 4 variables: JBSATISFQ:  Job satisfaction                                                AUTONOMY: Job autonomy                                                PREFSTAP: Preferred professional status in the main job      OBSTACSE: Main reason for not becoming self-employed  Detailed information on the relevant methodology for the ad-hoc module (including the Commission regulation and explanatory notes) as well as documentation from each participating country (national questionnaires and interviewers instructions) can be found on EU-LFS (Statistics Explained) - Ad-hoc modules.
    • 二月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 18 二月, 2019
      选择数据集
      The main aim of 2017 ad-hoc module is to provide information on the self-employed and on persons in an ambivalent professional status (at the border between employment and self-employment). The module includes 11 variables, split in 3 sub-modules. Sub-module 1: Economically dependent self-employed The first sub-module aims to measure the degree of economic/organisational dependency of the self-employed, in terms of the number of clients and the percentage of income coming from a client as well as in terms of control over working hours. This sub-module includes 2 variables: MAINCLNT: Economic dependencyWORKORG: Organisational dependencySub-module 2: Working conditions for self-employed The aim of the second sub-module is to investigate the working conditions of the self-employed, like working with partners or using employees. It also collects factors that motivated or forced a person to become self-employed, as well as the main difficulty they face working as self-employed. This sub-module includes 5 variables: REASSE: Main reason for becoming self-employed               SEDIFFIC: Main difficulty as self-employed                         REASNOEM: Main reason for not having employees                        BPARTNER:  Working with business partners                                    PLANEMPL:  Planning hiring of employees or subcontracting           Sub-module 3: Comparing employees and self-employed The third sub-module targets the comparison between self-employed, employees and family workers in terms of job satisfaction and autonomy. It also gathers information on the preferred professional status. This sub-module includes 4 variables: JBSATISFQ:  Job satisfaction                                                AUTONOMY: Job autonomy                                                PREFSTAP: Preferred professional status in the main job      OBSTACSE: Main reason for not becoming self-employed  Detailed information on the relevant methodology for the ad-hoc module (including the Commission regulation and explanatory notes) as well as documentation from each participating country (national questionnaires and interviewers instructions) can be found on EU-LFS (Statistics Explained) - Ad-hoc modules.
    • 二月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 18 二月, 2019
      选择数据集
      The main aim of 2017 ad-hoc module is to provide information on the self-employed and on persons in an ambivalent professional status (at the border between employment and self-employment). The module includes 11 variables, split in 3 sub-modules. Sub-module 1: Economically dependent self-employed The first sub-module aims to measure the degree of economic/organisational dependency of the self-employed, in terms of the number of clients and the percentage of income coming from a client as well as in terms of control over working hours. This sub-module includes 2 variables: MAINCLNT: Economic dependencyWORKORG: Organisational dependencySub-module 2: Working conditions for self-employed The aim of the second sub-module is to investigate the working conditions of the self-employed, like working with partners or using employees. It also collects factors that motivated or forced a person to become self-employed, as well as the main difficulty they face working as self-employed. This sub-module includes 5 variables: REASSE: Main reason for becoming self-employed               SEDIFFIC: Main difficulty as self-employed                         REASNOEM: Main reason for not having employees                        BPARTNER:  Working with business partners                                    PLANEMPL:  Planning hiring of employees or subcontracting           Sub-module 3: Comparing employees and self-employed The third sub-module targets the comparison between self-employed, employees and family workers in terms of job satisfaction and autonomy. It also gathers information on the preferred professional status. This sub-module includes 4 variables: JBSATISFQ:  Job satisfaction                                                AUTONOMY: Job autonomy                                                PREFSTAP: Preferred professional status in the main job      OBSTACSE: Main reason for not becoming self-employed  Detailed information on the relevant methodology for the ad-hoc module (including the Commission regulation and explanatory notes) as well as documentation from each participating country (national questionnaires and interviewers instructions) can be found on EU-LFS (Statistics Explained) - Ad-hoc modules.
    • 二月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 18 二月, 2019
      选择数据集
      The main aim of 2017 ad-hoc module is to provide information on the self-employed and on persons in an ambivalent professional status (at the border between employment and self-employment). The module includes 11 variables, split in 3 sub-modules. Sub-module 1: Economically dependent self-employed The first sub-module aims to measure the degree of economic/organisational dependency of the self-employed, in terms of the number of clients and the percentage of income coming from a client as well as in terms of control over working hours. This sub-module includes 2 variables: MAINCLNT: Economic dependencyWORKORG: Organisational dependencySub-module 2: Working conditions for self-employed The aim of the second sub-module is to investigate the working conditions of the self-employed, like working with partners or using employees. It also collects factors that motivated or forced a person to become self-employed, as well as the main difficulty they face working as self-employed. This sub-module includes 5 variables: REASSE: Main reason for becoming self-employed               SEDIFFIC: Main difficulty as self-employed                         REASNOEM: Main reason for not having employees                        BPARTNER:  Working with business partners                                    PLANEMPL:  Planning hiring of employees or subcontracting           Sub-module 3: Comparing employees and self-employed The third sub-module targets the comparison between self-employed, employees and family workers in terms of job satisfaction and autonomy. It also gathers information on the preferred professional status. This sub-module includes 4 variables: JBSATISFQ:  Job satisfaction                                                AUTONOMY: Job autonomy                                                PREFSTAP: Preferred professional status in the main job      OBSTACSE: Main reason for not becoming self-employed  Detailed information on the relevant methodology for the ad-hoc module (including the Commission regulation and explanatory notes) as well as documentation from each participating country (national questionnaires and interviewers instructions) can be found on EU-LFS (Statistics Explained) - Ad-hoc modules.
    • 二月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 18 二月, 2019
      选择数据集
      The main aim of 2017 ad-hoc module is to provide information on the self-employed and on persons in an ambivalent professional status (at the border between employment and self-employment). The module includes 11 variables, split in 3 sub-modules. Sub-module 1: Economically dependent self-employed The first sub-module aims to measure the degree of economic/organisational dependency of the self-employed, in terms of the number of clients and the percentage of income coming from a client as well as in terms of control over working hours. This sub-module includes 2 variables: MAINCLNT: Economic dependencyWORKORG: Organisational dependencySub-module 2: Working conditions for self-employed The aim of the second sub-module is to investigate the working conditions of the self-employed, like working with partners or using employees. It also collects factors that motivated or forced a person to become self-employed, as well as the main difficulty they face working as self-employed. This sub-module includes 5 variables: REASSE: Main reason for becoming self-employed               SEDIFFIC: Main difficulty as self-employed                         REASNOEM: Main reason for not having employees                        BPARTNER:  Working with business partners                                    PLANEMPL:  Planning hiring of employees or subcontracting           Sub-module 3: Comparing employees and self-employed The third sub-module targets the comparison between self-employed, employees and family workers in terms of job satisfaction and autonomy. It also gathers information on the preferred professional status. This sub-module includes 4 variables: JBSATISFQ:  Job satisfaction                                                AUTONOMY: Job autonomy                                                PREFSTAP: Preferred professional status in the main job      OBSTACSE: Main reason for not becoming self-employed  Detailed information on the relevant methodology for the ad-hoc module (including the Commission regulation and explanatory notes) as well as documentation from each participating country (national questionnaires and interviewers instructions) can be found on EU-LFS (Statistics Explained) - Ad-hoc modules.
    • 四月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 08 五月, 2019
      选择数据集
      The ad-hoc module "young people on the labour market" provides supplementary information on the correlation between work-based learning and labour market outcomes.
    • 四月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 10 五月, 2019
      选择数据集
      The ad-hoc module "young people on the labour market" provides supplementary information on the correlation between work-based learning and labour market outcomes.
    • 四月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 07 五月, 2019
      选择数据集
      The ad-hoc module "young people on the labour market" provides supplementary information on the correlation between work-based learning and labour market outcomes.
    • 四月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 08 五月, 2019
      选择数据集
      The ad-hoc module "young people on the labour market" provides supplementary information on the correlation between work-based learning and labour market outcomes.
    • 二月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 18 二月, 2019
      选择数据集
      The main aim of 2017 ad-hoc module is to provide information on the self-employed and on persons in an ambivalent professional status (at the border between employment and self-employment). The module includes 11 variables, split in 3 sub-modules. Sub-module 1: Economically dependent self-employed The first sub-module aims to measure the degree of economic/organisational dependency of the self-employed, in terms of the number of clients and the percentage of income coming from a client as well as in terms of control over working hours. This sub-module includes 2 variables: MAINCLNT: Economic dependencyWORKORG: Organisational dependencySub-module 2: Working conditions for self-employed The aim of the second sub-module is to investigate the working conditions of the self-employed, like working with partners or using employees. It also collects factors that motivated or forced a person to become self-employed, as well as the main difficulty they face working as self-employed. This sub-module includes 5 variables: REASSE: Main reason for becoming self-employed               SEDIFFIC: Main difficulty as self-employed                         REASNOEM: Main reason for not having employees                        BPARTNER:  Working with business partners                                    PLANEMPL:  Planning hiring of employees or subcontracting           Sub-module 3: Comparing employees and self-employed The third sub-module targets the comparison between self-employed, employees and family workers in terms of job satisfaction and autonomy. It also gathers information on the preferred professional status. This sub-module includes 4 variables: JBSATISFQ:  Job satisfaction                                                AUTONOMY: Job autonomy                                                PREFSTAP: Preferred professional status in the main job      OBSTACSE: Main reason for not becoming self-employed  Detailed information on the relevant methodology for the ad-hoc module (including the Commission regulation and explanatory notes) as well as documentation from each participating country (national questionnaires and interviewers instructions) can be found on EU-LFS (Statistics Explained) - Ad-hoc modules.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 26 十月, 2019
      选择数据集
    • 十一月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 11 十一月, 2019
      选择数据集
      The section 'LFS series - detailed quarterly survey results' reports detailed quarterly results going beyond the EU-LFS main aggregates, which have a separate data domain and some methodological differences. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 31 十月, 2019
      选择数据集
      The section 'LFS series - detailed annual survey results' reports annual results from the EU-LFS. While LFS is a quarterly survey, it is also possible to produce annual results. There are several ways of doing it, see section '18.5 Data compilation' below for details. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self-employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, population in employment working during unsocial hours, working time, total unemployment, inactivity and quality of employment. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metadata. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 十一月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 11 十一月, 2019
      选择数据集
      The section 'LFS series - detailed quarterly survey results' reports detailed quarterly results going beyond the EU-LFS main aggregates, which have a separate data domain and some methodological differences. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 31 十月, 2019
      选择数据集
      The section 'LFS series - detailed annual survey results' reports annual results from the EU-LFS. While LFS is a quarterly survey, it is also possible to produce annual results. There are several ways of doing it, see section '18.5 Data compilation' below for details. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self-employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, population in employment working during unsocial hours, working time, total unemployment, inactivity and quality of employment. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metadata. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 26 十月, 2019
      选择数据集
      Percentage of persons with more than one job as a share of all persons in employment. The indicator refers to persons who had more than one job or business during the reference week, not due to change of job or business (persons having changed job or business during the reference week are not considered as having more than one job).
    • 二月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 18 二月, 2019
      选择数据集
      The main aim of 2017 ad-hoc module is to provide information on the self-employed and on persons in an ambivalent professional status (at the border between employment and self-employment). The module includes 11 variables, split in 3 sub-modules. Sub-module 1: Economically dependent self-employed The first sub-module aims to measure the degree of economic/organisational dependency of the self-employed, in terms of the number of clients and the percentage of income coming from a client as well as in terms of control over working hours. This sub-module includes 2 variables: MAINCLNT: Economic dependencyWORKORG: Organisational dependencySub-module 2: Working conditions for self-employed The aim of the second sub-module is to investigate the working conditions of the self-employed, like working with partners or using employees. It also collects factors that motivated or forced a person to become self-employed, as well as the main difficulty they face working as self-employed. This sub-module includes 5 variables: REASSE: Main reason for becoming self-employed               SEDIFFIC: Main difficulty as self-employed                         REASNOEM: Main reason for not having employees                        BPARTNER:  Working with business partners                                    PLANEMPL:  Planning hiring of employees or subcontracting           Sub-module 3: Comparing employees and self-employed The third sub-module targets the comparison between self-employed, employees and family workers in terms of job satisfaction and autonomy. It also gathers information on the preferred professional status. This sub-module includes 4 variables: JBSATISFQ:  Job satisfaction                                                AUTONOMY: Job autonomy                                                PREFSTAP: Preferred professional status in the main job      OBSTACSE: Main reason for not becoming self-employed  Detailed information on the relevant methodology for the ad-hoc module (including the Commission regulation and explanatory notes) as well as documentation from each participating country (national questionnaires and interviewers instructions) can be found on EU-LFS (Statistics Explained) - Ad-hoc modules.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 26 十月, 2019
      选择数据集
      The indicator, 'employed persons with a second job' refers only to persons with more than one job at the same time. Consequently, persons having changed job during the reference week are not covered.
    • 九月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 22 九月, 2019
      选择数据集
      The ICT education statistics make part of the domain ICT training, which in its term is one of the domains in the wider concept of Digital skills. ICT education indicators are constructed using the secondary statistical approach. This approach has a virtue of ensuring cost-efficient and high-quality data production. At the same time, this approach has limited options for designing new indicators, as well as for control over data quality and over data release timing. ICT education indicators are based on the microdata from the EU Labour Force Survey (EU-LFS). For this reason, the EU-LFS reference metadata need to be consulted for all questions related to the underlying primary source data. Following the underlying EU-LFS microdata, the ICT education indicators set the lower bound on the age at 15 years. The upper age bound is set at 74 years to align these data with other indicator on digital skills derived from the Community Survey on ICT Usage in Households and by Individuals. ICT education indicators are presented in four tables:Employed and unemployed persons with ICT education (isoc_ski_itemp)Employed persons with ICT education by sex (isoc_ski_itsex)Employed persons with ICT education by educational attainment level (isoc_ski_itedu)Employed persons with ICT education by age (isoc_ski_itage) The first table (isoc_ski_itemp) describes persons with ICT education in labour force by their employment status. The rest of tables (isoc_ski_itsex, isoc_ski_itedu and isoc_ski_itage) present different breakdowns of the persons with ICT education in employment. Each indicator is presented in the country/year dimensions and is measured in absolute (in 1000s) and relative (in %) terms. Data cover all years starting from 2004 until the latest year available. Following the release practice of the EU-LFS, the publication year is calculated as (Y+1), with Y being the reference year. Yearly data release depends on the EU-LFS release practice and normally takes place in April-May. Data for all indicators are regularly updated and revised to incorporate the latest revisions made in the source data, usually once a week.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 26 十月, 2019
      选择数据集
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 26 十月, 2019
      选择数据集
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 31 十月, 2019
      选择数据集
      The section 'LFS series - detailed annual survey results' reports annual results from the EU-LFS. While LFS is a quarterly survey, it is also possible to produce annual results. There are several ways of doing it, see section '20.5 Data compilation' below for details. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 三月 2014
      来源: Eurostat
      上传者: Knoema
      访问日期: 28 十一月, 2015
      选择数据集
      Results from the 2006 LFS (Labour Force Survey) ad hoc module on 'transition form work into retirement'. The aim of the ad hoc module was to know how the transition at the end of the career towards full retirement is expected to take place, takes place or took place: • plans for transitions/past transitions towards full retirement • plans for exit from work Another aim was to know which factors would be/were at play in determining the exit from work, and which factors could make/could have made persons postpone the exit from work: • working conditions factors (health and safety at the workplace, flexible working time arrangements …) • other factors linked to work (training/obsolescence of skills …) • financial factors (financial incentives to remain at work or to exit) • personal factors (health, family reasons …).
    • 四月 2014
      来源: Eurostat
      上传者: Knoema
      访问日期: 28 十一月, 2015
      选择数据集
      Introduction Key available data are presented on population and housing based on the decennial census rounds 1981-2011. Separate tables cover: - Population by sex and major age group - Population by educational attainment - Population by activity status - Population by citizenship - Households by household size - Occupied conventional dwellings by number of rooms Data availability varies between census rounds. The countries covered by the data vary between different census rounds. There are also differences in definitions and disaggregations between countries and between census rounds.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 22 十月, 2019
      选择数据集
      The section 'LFS series - detailed annual survey results' reports annual results from the EU-LFS. While LFS is a quarterly survey, it is also possible to produce annual results. There are several ways of doing it, see section '18.5 Data compilation' below for details. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metadata. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 22 十月, 2019
      选择数据集
      The section 'LFS series - detailed annual survey results' reports annual results from the EU-LFS. While LFS is a quarterly survey, it is also possible to produce annual results. There are several ways of doing it, see section '18.5 Data compilation' below for details. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metadata. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 26 十月, 2019
      选择数据集
      Percentage of women in the occupational group of managerial positions as a share of all women in employment. The occupational group of managerial positions is defined as the ISCO major group 1.
    • 五月 2016
      来源: Eurostat
      上传者: Knoema
      访问日期: 20 五月, 2016
      选择数据集
      Data given in this domain are collected on a yearly basis by the National Statistical Institutes or Ministries and are based on the annual Eurostat Model Questionnaires on ICT (Information and Communication Technologies) usage and e-commerce in enterprises. Large part of the data collected are used in the context of the 2011 - 2015 benchmarking framework (endorsed by i2010 High Level Group in November 2009) for the Digital Agenda Scoreboard, Europe's strategy for a flourishing digital economy by 2020. This conceptual framework follows the i2010 Benchmarking Framework which itself followed-up the eEurope 2005 Action Plan. The aim of the European ICT usage surveys is to collect and disseminate harmonised and comparable information on the use of Information and Communication Technologies in enterprises and e-commerce at European level. Coverage: The characteristics to be provided are drawn from the following list of subjects: - ICT systems and their usage in enterprises, - use of the Internet and other electronic networks by enterprises, - e-commerce, - e-business processes and organisational aspects, - use of ICT by enterprises to exchange information and services with governments and public administrations (e-government), - ICT competence in the enterprise and the need for ICT skills, - barriers to the use of ICT, the Internet and other electronic networks, e-commerce and e-business processes, - ICT expenditure and investment, - ICT security and trust, - use of ICT and its impact on the environment (Green ICT), - access to and use of the Internet and other network technologies for connecting objects and devices (Internet of Things), - access to and use of technologies providing the ability to connect to the Internet or other networks from anywhere at any time (ubiquitous connectivity). Breakdowns: - by size class, - by NACE categories, - by region (until 2010)
    • 六月 2014
      来源: Eurostat
      上传者: Knoema
      访问日期: 27 十一月, 2015
      选择数据集
      Data given in this domain are collected on a yearly basis by the National Statistical Institutes or Ministries and are based on the annual Eurostat Model Questionnaires on ICT (Information and Communication Technologies) usage and e-commerce in enterprises. Large part of the data collected are used in the context of the 2011 - 2015 benchmarking framework (endorsed by i2010 High Level Group in November 2009) for the Digital Agenda Scoreboard, Europe's strategy for a flourishing digital economy by 2020. This conceptual framework follows the i2010 Benchmarking Framework which itself followed-up the eEurope 2005 Action Plan. The aim of the European ICT usage surveys is to collect and disseminate harmonised and comparable information on the use of Information and Communication Technologies in enterprises and e-commerce at European level. Coverage: The characteristics to be provided are drawn from the following list of subjects: - ICT systems and their usage in enterprises, - use of the Internet and other electronic networks by enterprises, - e-commerce, - e-business processes and organisational aspects, - use of ICT by enterprises to exchange information and services with governments and public administrations (e-government), - ICT competence in the enterprise and the need for ICT skills, - barriers to the use of ICT, the Internet and other electronic networks, e-commerce and e-business processes, - ICT expenditure and investment, - ICT security and trust, - use of ICT and its impact on the environment (Green ICT), - access to and use of the Internet and other network technologies for connecting objects and devices (Internet of Things), - access to and use of technologies providing the ability to connect to the Internet or other networks from anywhere at any time (ubiquitous connectivity). Breakdowns: - by size class, - by NACE categories, - by region (until 2010)
    • 五月 2016
      来源: Eurostat
      上传者: Knoema
      访问日期: 20 五月, 2016
      选择数据集
      Data given in this domain are collected on a yearly basis by the National Statistical Institutes or Ministries and are based on the annual Eurostat Model Questionnaires on ICT (Information and Communication Technologies) usage and e-commerce in enterprises. Large part of the data collected are used in the context of the 2011 - 2015 benchmarking framework (endorsed by i2010 High Level Group in November 2009) for the Digital Agenda Scoreboard, Europe's strategy for a flourishing digital economy by 2020. This conceptual framework follows the i2010 Benchmarking Framework which itself followed-up the eEurope 2005 Action Plan. The aim of the European ICT usage surveys is to collect and disseminate harmonised and comparable information on the use of Information and Communication Technologies in enterprises and e-commerce at European level. Coverage: The characteristics to be provided are drawn from the following list of subjects: - ICT systems and their usage in enterprises, - use of the Internet and other electronic networks by enterprises, - e-commerce, - e-business processes and organisational aspects, - use of ICT by enterprises to exchange information and services with governments and public administrations (e-government), - ICT competence in the enterprise and the need for ICT skills, - barriers to the use of ICT, the Internet and other electronic networks, e-commerce and e-business processes, - ICT expenditure and investment, - ICT security and trust, - use of ICT and its impact on the environment (Green ICT), - access to and use of the Internet and other network technologies for connecting objects and devices (Internet of Things), - access to and use of technologies providing the ability to connect to the Internet or other networks from anywhere at any time (ubiquitous connectivity). Breakdowns: - by size class, - by NACE categories, - by region (until 2010)
    • 二月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 18 二月, 2019
      选择数据集
      The main aim of 2017 ad-hoc module is to provide information on the self-employed and on persons in an ambivalent professional status (at the border between employment and self-employment). The module includes 11 variables, split in 3 sub-modules. Sub-module 1: Economically dependent self-employed The first sub-module aims to measure the degree of economic/organisational dependency of the self-employed, in terms of the number of clients and the percentage of income coming from a client as well as in terms of control over working hours. This sub-module includes 2 variables: MAINCLNT: Economic dependencyWORKORG: Organisational dependencySub-module 2: Working conditions for self-employed The aim of the second sub-module is to investigate the working conditions of the self-employed, like working with partners or using employees. It also collects factors that motivated or forced a person to become self-employed, as well as the main difficulty they face working as self-employed. This sub-module includes 5 variables: REASSE: Main reason for becoming self-employed               SEDIFFIC: Main difficulty as self-employed                         REASNOEM: Main reason for not having employees                        BPARTNER:  Working with business partners                                    PLANEMPL:  Planning hiring of employees or subcontracting           Sub-module 3: Comparing employees and self-employed The third sub-module targets the comparison between self-employed, employees and family workers in terms of job satisfaction and autonomy. It also gathers information on the preferred professional status. This sub-module includes 4 variables: JBSATISFQ:  Job satisfaction                                                AUTONOMY: Job autonomy                                                PREFSTAP: Preferred professional status in the main job      OBSTACSE: Main reason for not becoming self-employed  Detailed information on the relevant methodology for the ad-hoc module (including the Commission regulation and explanatory notes) as well as documentation from each participating country (national questionnaires and interviewers instructions) can be found on EU-LFS (Statistics Explained) - Ad-hoc modules.
    • 七月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 01 八月, 2019
      选择数据集
      Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. The series is part of the ILO estimates and is harmonized to account for differences in national data and scope of coverage, collection and tabulation methodologies as well as for other country-specific factors. For more information, refer to the ILO estimates and projections methodological note.
    • 十一月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 12 十一月, 2019
      选择数据集
      Employees are all those workers who hold paid employment jobs, which are those where the incumbents hold employment contracts which give them a basic remuneration not directly dependent upon the revenue of the unit for which they work. Data are disaggregated by economic activity and occupation, according to the latest versions of the International Standard Industrial Classification of All Economic Activities (ISIC) and International Standard Classification of Occupations (ISCO), respectively. Economic activity refers to the main activity of the establishment in which a person worked during the reference period and does not depend on the specific duties or functions of the person's job, but on the characteristics of the economic unit in which this person works. Information on occupation provides a description of the set of tasks and duties which are carried out by, or can be assigned to, one person.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 16 十月, 2019
      选择数据集
      The folder 'population by educational attainment level (edat1)' presents data on the highest level of education successfully completed by the individuals of a given population. The folder 'transition from education to work (edatt)' covers data on young people neither in employment nor in education and training – NEET, early leavers from education and training and the labour status of young people by years since completion of highest level of education. The data shown are calculated as annual averages of quarterly EU Labour Force Survey data (EU-LFS). Up to the reference year 2008, the data source (EU-LFS) is, where necessary, adjusted and enriched in various ways, in accordance with the specificities of an indicator, including the following: correction of the main breaks in the LFS series,estimation of the missing values, i.e. in case of missing quarters, annual results and EU aggregates are estimated using adjusted quarterly national labour force survey data or interpolations of the EU-LFS data with reference to the available quarter(s). Details on the adjustments are available in CIRCABC. The adjustments are applied in the following online tables: Population by educational attainment level (edat1) - Population by educational attainment level, sex and age (%) - main indicators (edat_lfse_03) - Population aged 25-64 by educational attainment level, sex and NUTS 2 regions (%) (edat_lfse_04) - Population aged 30-34 by educational attainment level, sex and NUTS 2 regions (%) (edat_lfse_12) (Other tables shown in the folder 'population by educational attainment level (edat1)' are not adjusted and therefore the results in these tables might differ).Young people by educational and labour status (incl. neither in employment nor in education and training - NEET) (edatt0) – all tablesEarly leavers from education and training (edatt1) – all tablesLabour status of young people by years since completion of highest level of education (edatt2) – all tables LFS ad-hoc module data available in the folder 'transition from education to work (edatt)' are not adjusted. The folder 'transition from education to work (edatt)' also presents one table with quarterly NEET data for the age group 15-24 (lfsi_neet_q). Deviating from the NEET indicator calculation as provided in 3.4, the denominator in this table is the total population of the same age group and sex which explains differences in results. For further information, see the ESMS on "Unemployment - LFS adjusted series".
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 16 十月, 2019
      选择数据集
      The folder 'population by educational attainment level (edat1)' presents data on the highest level of education successfully completed by the individuals of a given population. The folder 'transition from education to work (edatt)' covers data on young people neither in employment nor in education and training – NEET, early leavers from education and training and the labour status of young people by years since completion of highest level of education. The data shown are calculated as annual averages of quarterly EU Labour Force Survey data (EU-LFS). Up to the reference year 2008, the data source (EU-LFS) is, where necessary, adjusted and enriched in various ways, in accordance with the specificities of an indicator, including the following:correction of the main breaks in the LFS series,estimation of the missing values, i.e. in case of missing quarters, annual results and EU aggregates are estimated using adjusted quarterly national labour force survey data or interpolations of the EU-LFS data with reference to the available quarter(s). Details on the adjustments are available in CIRCABC. The adjustments are applied in the following online tables:Population by educational attainment level (edat1)   - Population by educational attainment level, sex and age (%) - main indicators (edat_lfse_03) - Population aged 25-64 by educational attainment level, sex and NUTS 2 regions (%) (edat_lfse_04) - Population aged 30-34 by educational attainment level, sex and NUTS 2 regions (%) (edat_lfse_12) (Other tables shown in the folder 'population by educational attainment level (edat1)' are not adjusted and therefore the results in these tables might differ).Young people by educational and labour status (incl. neither in employment nor in education and training - NEET) (edatt0) – all tablesEarly leavers from education and training (edatt1) – all tablesLabour status of young people by years since completion of highest level of education (edatt2) – all tables  LFS ad-hoc module data available in the folder 'transition from education to work (edatt)' are not adjusted.
    • 四月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 14 五月, 2019
      选择数据集
      Population by educational attainment level presents data on the highest level of education successfully completed by the individuals of a given population. Transition from education to work covers data on young people neither in employment nor in education and training – NEET, early leavers from education and training and the labour status of young people by years since completion of highest level of education. The data shown are calculated as annual averages of quarterly EU Labour Force Survey data (EU-LFS). Up to the reference year 2008, the data source (EU-LFS) is, where necessary, adjusted and enriched in various ways, in accordance with the specificities of an indicator, including the following: correction of the main breaks in the LFS series,estimation of the missing values, i.e. in case of missing quarters, annual results and EU aggregates are estimated using adjusted quarterly national labour force survey data or interpolations of the EU-LFS data with reference to the available quarter(s). Details on the adjustments are available in CIRCABC. The adjustments are applied in the following online tables Population by educational attainment level (edat1)- Population with lower secondary education attainment by sex and age (edat_lfse_05) - Population with upper secondary education attainment by sex and age (edat_lfse_06) - Population with tertiary education attainment by sex and age (edat_lfse_07) - Population with upper secondary or tertiary education attainment by sex and age (edat_lfse_08) - Population aged 25-64 with lower secondary education attainment by sex and NUTS 2 regions (edat_lfse_09) - Population aged 25-64 with upper secondary education attainment by sex and NUTS 2 regions (edat_lfse_10) - Population aged 25-64 with tertiary education attainment by sex and NUTS 2 regions (edat_lfse_11) - Population aged 30-34 with tertiary education attainment by sex and NUTS 2 regions (edat_lfse_12) - Population aged 25-64 with upper secondary or tertiary education attainment by sex and NUTS 2 regions (edat_lfse_13) (Other tables shown in the folder 'population by educational attainment level (edat1)' are not adjusted and therefore the results in these tables might differ).Young people by educational and labour status (incl. neither in employment nor in education and training - NEET) (edatt0) – all tablesEarly leavers from education and training (edatt1) – all tablesLabour status of young people by years since completion of highest level of education (edatt2) – all tables  LFS ad-hoc module data available in the folder 'transition from education to work (edatt)' are not adjusted.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 16 十月, 2019
      选择数据集
      The folder 'population by educational attainment level (edat1)' presents data on the highest level of education successfully completed by the individuals of a given population. The folder 'transition from education to work (edatt)' covers data on young people neither in employment nor in education and training – NEET, early leavers from education and training and the labour status of young people by years since completion of highest level of education. The data shown are calculated as annual averages of quarterly EU Labour Force Survey data (EU-LFS). Up to the reference year 2008, the data source (EU-LFS) is, where necessary, adjusted and enriched in various ways, in accordance with the specificities of an indicator, including the following:correction of the main breaks in the LFS series,estimation of the missing values, i.e. in case of missing quarters, annual results and EU aggregates are estimated using adjusted quarterly national labour force survey data or interpolations of the EU-LFS data with reference to the available quarter(s). Details on the adjustments are available in CIRCABC. The adjustments are applied in the following online tables:Population by educational attainment level (edat1)   - Population by educational attainment level, sex and age (%) - main indicators (edat_lfse_03) - Population aged 25-64 by educational attainment level, sex and NUTS 2 regions (%) (edat_lfse_04) - Population aged 30-34 by educational attainment level, sex and NUTS 2 regions (%) (edat_lfse_12) (Other tables shown in the folder 'population by educational attainment level (edat1)' are not adjusted and therefore the results in these tables might differ).Young people by educational and labour status (incl. neither in employment nor in education and training - NEET) (edatt0) – all tablesEarly leavers from education and training (edatt1) – all tablesLabour status of young people by years since completion of highest level of education (edatt2) – all tables  LFS ad-hoc module data available in the folder 'transition from education to work (edatt)' are not adjusted.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 16 十月, 2019
      选择数据集
      The folder 'population by educational attainment level (edat1)' presents data on the highest level of education successfully completed by the individuals of a given population. The folder 'transition from education to work (edatt)' covers data on young people neither in employment nor in education and training – NEET, early leavers from education and training and the labour status of young people by years since completion of highest level of education. The data shown are calculated as annual averages of quarterly EU Labour Force Survey data (EU-LFS). Up to the reference year 2008, the data source (EU-LFS) is, where necessary, adjusted and enriched in various ways, in accordance with the specificities of an indicator, including the following:correction of the main breaks in the LFS series,estimation of the missing values, i.e. in case of missing quarters, annual results and EU aggregates are estimated using adjusted quarterly national labour force survey data or interpolations of the EU-LFS data with reference to the available quarter(s). Details on the adjustments are available in CIRCABC. The adjustments are applied in the following online tables:Population by educational attainment level (edat1) - Population by educational attainment level, sex and age (%) - main indicators (edat_lfse_03) - Population aged 25-64 by educational attainment level, sex and NUTS 2 regions (%) (edat_lfse_04) - Population aged 30-34 by educational attainment level, sex and NUTS 2 regions (%) (edat_lfse_12) (Other tables shown in the folder 'population by educational attainment level (edat1)' are not adjusted and therefore the results in these tables might differ).Young people by educational and labour status (incl. neither in employment nor in education and training - NEET) (edatt0) – all tablesEarly leavers from education and training (edatt1) – all tablesLabour status of young people by years since completion of highest level of education (edatt2) – all tables LFS ad-hoc module data available in the folder 'transition from education to work (edatt)' are not adjusted.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 16 十月, 2019
      选择数据集
      The folder 'population by educational attainment level (edat1)' presents data on the highest level of education successfully completed by the individuals of a given population. The folder 'transition from education to work (edatt)' covers data on young people neither in employment nor in education and training – NEET, early leavers from education and training and the labour status of young people by years since completion of highest level of education. The data shown are calculated as annual averages of quarterly EU Labour Force Survey data (EU-LFS). Up to the reference year 2008, the data source (EU-LFS) is, where necessary, adjusted and enriched in various ways, in accordance with the specificities of an indicator, including the following:correction of the main breaks in the LFS series,estimation of the missing values, i.e. in case of missing quarters, annual results and EU aggregates are estimated using adjusted quarterly national labour force survey data or interpolations of the EU-LFS data with reference to the available quarter(s). Details on the adjustments are available in CIRCABC. The adjustments are applied in the following online tables:Population by educational attainment level (edat1)   - Population by educational attainment level, sex and age (%) - main indicators (edat_lfse_03) - Population aged 25-64 by educational attainment level, sex and NUTS 2 regions (%) (edat_lfse_04) - Population aged 30-34 by educational attainment level, sex and NUTS 2 regions (%) (edat_lfse_12) (Other tables shown in the folder 'population by educational attainment level (edat1)' are not adjusted and therefore the results in these tables might differ).Young people by educational and labour status (incl. neither in employment nor in education and training - NEET) (edatt0) – all tablesEarly leavers from education and training (edatt1) – all tablesLabour status of young people by years since completion of highest level of education (edatt2) – all tables  LFS ad-hoc module data available in the folder 'transition from education to work (edatt)' are not adjusted.
    • 二月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 18 二月, 2019
      选择数据集
      Results from the 2010 LFS (Labour Force Survey) ad hoc module on the reconciliation between work and family life. The aims of the module is to establish how far persons participate in the labour force as they wish and if not, whether the reasons are connected with a lack of suitable care services for children and dependant persons: 1. identification of care responsibilities (children and dependants) 2. analysis of the consequences on labour market participation taking into account the options and constraints given 3. in case of constraints, identification of those linked with the lack or unsuitability of care services A further aim is to analyse the degree of flexibility offered at work in terms of reconciliation with family life as well as to estimate how often career breaks occur and how far leave of absence is taken.
    • 二月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 18 二月, 2019
      选择数据集
      Results from the 2010 LFS (Labour Force Survey) ad hoc module on the reconciliation between work and family life. The aims of the module is to establish how far persons participate in the labour force as they wish and if not, whether the reasons are connected with a lack of suitable care services for children and dependant persons: 1. identification of care responsibilities (children and dependants) 2. analysis of the consequences on labour market participation taking into account the options and constraints given 3. in case of constraints, identification of those linked with the lack or unsuitability of care services A further aim is to analyse the degree of flexibility offered at work in terms of reconciliation with family life as well as to estimate how often career breaks occur and how far leave of absence is taken.
    • 十一月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 12 十一月, 2019
      选择数据集
      Employees are all those workers who hold paid employment jobs, which are those where the incumbents hold employment contracts which give them a basic remuneration not directly dependent upon the revenue of the unit for which they work. Data are disaggregated by economic activity according to the latest version of the International Standard Industrial Classification of All Economic Activities (ISIC) available for that year. Economic activity refers to the main activity of the establishment in which a person worked during the reference period and does not depend on the specific duties or functions of the person's job, but on the characteristics of the economic unit in which this person works.
    • 十一月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 12 十一月, 2019
      选择数据集
      Employees are all those workers who hold paid employment jobs, which are those where the incumbents hold employment contracts which give them a basic remuneration not directly dependent upon the revenue of the unit for which they work. Data are disaggregated by economic activity according to the latest version of the International Standard Industrial Classification of All Economic Activities (ISIC) available for that year, and presented for categories at the 2-digit level of the classification. Economic activity refers to the main activity of the establishment in which a person worked during the reference period and does not depend on the specific duties or functions of the person's job, but on the characteristics of the economic unit in which this person works.
    • 八月 2018
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 03 九月, 2018
      选择数据集
      Employees are all those workers who hold paid employment jobs, which are those where the incumbents hold employment contracts which give them a basic remuneration not directly dependent upon the revenue of the unit for which they work. Data are disaggregated by economic activity according to the latest version of the International Standard Industrial Classification of All Economic Activities (ISIC) available for that year, and presented for a selection of categories at the 2-digit level of the classification. Economic activity refers to the main activity of the establishment in which a person worked during the reference period and does not depend on the specific duties or functions of the person's job, but on the characteristics of the economic unit in which this person works.
    • 十一月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 12 十一月, 2019
      选择数据集
      Employees are all those workers who hold paid employment jobs, which are those where the incumbents hold employment contracts which give them a basic remuneration not directly dependent upon the revenue of the unit for which they work. Data are disaggregated by occupation according to the latest version of the International Standard Classification of Occupations (ISCO) available for that year and presented for categories at the 2-digit level of the classification. Information on occupation provides a description of the set of tasks and duties which are carried out by, or can be assigned to, one person.
    • 八月 2018
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 03 九月, 2018
      选择数据集
      Employees are all those workers who hold paid employment jobs, which are those where the incumbents hold employment contracts which give them a basic remuneration not directly dependent upon the revenue of the unit for which they work. Data are disaggregated by occupation according to the latest version of the International Standard Classification of Occupations (ISCO) available for that year and presented for a selection of categories at the 2-digit level of the classification. Information on occupation provides a description of the set of tasks and duties which are carried out by, or can be assigned to, one person.
    • 十一月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 12 十一月, 2019
      选择数据集
      Employees are all those workers who hold paid employment jobs, which are those where the incumbents hold employment contracts which give them a basic remuneration not directly dependent upon the revenue of the unit for which they work. Data are disaggregated by occupation according to the latest version of the International Standard Classification of Occupations (ISCO) available for that year. Information on occupation provides a description of the set of tasks and duties which are carried out by, or can be assigned to, one person.
    • 十一月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 12 十一月, 2019
      选择数据集
      Employees are all those workers who hold paid employment jobs, which are those where the incumbents hold employment contracts which give them a basic remuneration not directly dependent upon the revenue of the unit for which they work. Data are disaggregated by weekly hours actually worked, on the basis of the mean number of hours of work per week, and with reference to hours worked in all jobs of employed persons and in all types of working time arrangements (e.g. full-time and part-time). Hours actually worked include (a) direct hours or the time spent carrying out the tasks and duties of a job, (b) related hours, or the time spent maintaining, facilitating or enhancing productive activities (c) down time, or time when a person in a job cannot work due to machinery or process breakdown, accident, lack of supplies or power or Internet access and (d) resting time, or time spent in short periods of rest, relief or refreshment, including tea, coffee or prayer breaks, generally practised by custom or contract according to established norms and/or national circumstances. Hours actually worked excludes time not worked during activities such as: (a) Annual leave, public holidays, sick leave, parental leave or maternity/paternity leave, other leave for personal or family reasons or civic duty, (b) Commuting time between work and home when no productive activity for the job is performed; for paid employment, even when paid by the employer; (c) Time spent in certain educational activities; for paid employment, even when authorized, paid or provided by the employer; (d) Longer breaks distinguished from short resting time when no productive activity is performed (such as meal breaks or natural repose during long trips); for paid employment, even when paid by the employer.
    • 八月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 10 八月, 2019
      选择数据集
      The section 'LFS series - detailed quarterly survey results' reports detailed quarterly results going beyond the EU-LFS main aggregates, which have a separate data domain and some methodological differences. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 31 十月, 2019
      选择数据集
      The section 'LFS series - detailed annual survey results' reports annual results from the EU-LFS. While LFS is a quarterly survey, it is also possible to produce annual results. There are several ways of doing it, see section '20.5 Data compilation' below for details. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 十一月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 11 十一月, 2019
      选择数据集
      The section 'LFS series - detailed quarterly survey results' reports detailed quarterly results going beyond the EU-LFS main aggregates, which have a separate data domain and some methodological differences. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 十一月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 11 十一月, 2019
      选择数据集
      The section 'LFS series - detailed quarterly survey results' reports detailed quarterly results going beyond the EU-LFS main aggregates, which have a separate data domain and some methodological differences. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 31 十月, 2019
      选择数据集
      The section 'LFS series - detailed annual survey results' reports annual results from the EU-LFS. While LFS is a quarterly survey, it is also possible to produce annual results. There are several ways of doing it, see section '20.5 Data compilation' below for details. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 十一月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 12 十一月, 2019
      选择数据集
    • 十一月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 11 十一月, 2019
      选择数据集
      The section 'LFS series - detailed quarterly survey results' reports detailed quarterly results going beyond the EU-LFS main aggregates, which have a separate data domain and some methodological differences. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 31 十月, 2019
      选择数据集
      The section 'LFS series - detailed annual survey results' reports annual results from the EU-LFS. While LFS is a quarterly survey, it is also possible to produce annual results. There are several ways of doing it, see section '20.5 Data compilation' below for details. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 四月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 09 五月, 2019
      选择数据集
      The ad-hoc module "young people on the labour market" provides supplementary information on the correlation between work-based learning and labour market outcomes.
    • 二月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 18 二月, 2019
      选择数据集
      Results from the 2010 LFS (Labour Force Survey) ad hoc module on the reconciliation between work and family life. The aims of the module is to establish how far persons participate in the labour force as they wish and if not, whether the reasons are connected with a lack of suitable care services for children and dependant persons: 1. identification of care responsibilities (children and dependants) 2. analysis of the consequences on labour market participation taking into account the options and constraints given 3. in case of constraints, identification of those linked with the lack or unsuitability of care services A further aim is to analyse the degree of flexibility offered at work in terms of reconciliation with family life as well as to estimate how often career breaks occur and how far leave of absence is taken.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 31 十月, 2019
      选择数据集
      The section 'LFS series - detailed annual survey results' reports annual results from the EU-LFS. While LFS is a quarterly survey, it is also possible to produce annual results. There are several ways of doing it, see section '18.5 Data compilation' below for details. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metadata. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 二月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 18 二月, 2019
      选择数据集
      Results from the 2010 LFS (Labour Force Survey) ad hoc module on the reconciliation between work and family life. The aims of the module is to establish how far persons participate in the labour force as they wish and if not, whether the reasons are connected with a lack of suitable care services for children and dependant persons: 1. identification of care responsibilities (children and dependants) 2. analysis of the consequences on labour market participation taking into account the options and constraints given 3. in case of constraints, identification of those linked with the lack or unsuitability of care services A further aim is to analyse the degree of flexibility offered at work in terms of reconciliation with family life as well as to estimate how often career breaks occur and how far leave of absence is taken.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 10 十月, 2019
      选择数据集
      The 'LFS main indicators' section presents a selection of the main statistics on the labour market. They encompass indicators of activity employment and unemployment. Those indicators are based on the results of the European Labour Force Survey (EU-LFS), in few cases integrated with data sources like national accounts employment or registered unemployment. As a result of the application of adjustments, corrections and reconciliation of EU Labour Force Survey (EU-LFS), 'LFS main indicators' is the most complete and reliable collection of employment and unemployment data available in the sub-domain ' Employment and unemployment'. The EU-LFS data used for 'LFS main indicators' are, where necessary, adjusted and enriched in various ways, in accordance with the specificities of an indicator.  The most common adjustments cover: - correction of the main breaks in the LFS series - estimation of the missing values, (i.e. in case of missing quarters, annual results and EU aggregates are estimated using adjusted quarterly national labour force survey data or interpolations of the EU Labour Force Survey data with reference to the available quarter(s)) - reconciliations of the LFS data with other sources, mainly National Accounts (for Employment growth and activity branches) and national statistics on monthly unemployment (for Harmonised unemployment series). - for a number of indicators (employment, activity, unemployment, supplementary indicators) seasonally adjusted data are available Those adjustments may produce some differences between data published under 'LFS main indicators' and 'LFS series - Detailed survey results', particularly for back data. For the most recent years these two series converge, due to the implementation of a continuous quarterly survey and the improved quality of the data. This page focuses on the particularities of 'LFS main indicators' in general. There are special pages for indicators 'employment growth', 'population in jobless households', 'average exit age of labour market' and 'education indicators: life-long learning, early school leavers and youth education attainment level. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 五月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 05 五月, 2019
      选择数据集
      Percentage of employees who have flexible work schedule as a share of all employees. Flexible means that employees can decide on their work schedule, at least to a certain extent, like start and end of working day.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 01 十一月, 2019
      选择数据集
      In the MIP context the indicators Employment and Employees are used for the calculation of the Unit labour cost index. Both Employment and Employees source from the National accounts domain. National accounts are a coherent and consistent set of macroeconomic indicators, which provide an overall picture of the economic situation and are widely used for economic analysis and forecasting, policy design and policy making. Eurostat publishes annual and quarterly national accounts data. Annual national accounts are compiled in accordance with the European System of Accounts - ESA 2010 as defined in Annex B of the Council Regulation (EU) No 549/2013 of the European Parliament and of the Council of 21 May 2013. The revisions are reflected in an updated Regulation of the European Parliament and of the Council on the European system of national and regional accounts in the European Union of 2010 (ESA 2010). The associated transmission programme is also updated and data transmissions in accordance with ESA 2010 are compulsory from September 2014 onwards.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 21 十月, 2019
      选择数据集
      The annual Business demography data collection covers variables which explain the characteristics and demography of the business population. The methodology allows for the production of data on enterprise births (and deaths), that is, enterprise creations (cessations) that amount to the creation (dissolution) of a combination of production factors and where no other enterprises are involved. In other words, enterprises created or closed solely as a result of e.g. restructuring, merger or break-up are not considered. The data are drawn from business registers, although some countries improve the availability of data on employment and turnover by integrating other sources. Until 2010 reference year the harmonised data collection is carried out to satisfy the requirements for the Structural Indicators, used for monitoring progress of the Lisbon process, regarding business births, deaths and survival. Currently, business demography delivers key information for policy decision-making and for the indicators to support the Europe 2020 strategy. It also provides key data for the joint OECD-Eurostat "Entrepreneurship Indicators Programme". In summary, the collected indicators are as follows:Population of active enterprisesNumber of enterprise birthsNumber of enterprise survivals up to five yearsNumber of enterprise deathsRelated variables on employmentDerived indicators such as birth rates, death rates, survival rates and employment sharesAn additional set of indicators on high-growth enterprises and 'gazelles' (high-growth enterprises that are up to five years old) The complete list of the basic variables, delivered from the data providers (National Statistical Institutes) and the derived indicators, calculated by Eurostat, is attached in the Annexes of this document (see Business demography indicators).  Geographically EU Member States and EFTA countries are covered. In practice not all Member States have participated in the first harmonised data collection exercises. The methodology laid down in the Eurostat-OECD Manual on Business Demography Statistics  is followed closely by most of the countries (see Country specific notes in the Annexes).
    • 八月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 21 八月, 2019
      选择数据集
    • 七月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 01 八月, 2019
      选择数据集
      Imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. The series is part of the ILO estimates and is harmonized to account for differences in national data and scope of coverage, collection and tabulation methodologies as well as for other country-specific factors. For more information, refer to the ILO estimates and projections methodological note.
    • 十月 2019
      来源: Organisation for Economic Co-operation and Development
      上传者: Knoema
      访问日期: 28 十月, 2019
      选择数据集
      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.
    • 八月 2014
      来源: Eurostat
      上传者: Knoema
      访问日期: 28 十一月, 2015
      选择数据集
      The data in this domain is collected by Eurostat in close cooperation with DG MARKT in the context of the annual "EU Postal Survey" (voluntary data collection). The partners in the data collection are the National Regulatory Authorities (NRAs) in the participating countries. The list of indicators/questionnaires and the definitions (Glossary) were agreed in cooperation with the European Postal Regulators in the project group "Assistance and development of EU statistics" of the European Committee for Postal Regulation (CERP). The data presented cover the companies operating under the Universal Service obligation (Universal Service Providers - USP). For countries where a USP no longer exists, the company which was the USP prior to liberalisation is referred to. "Universal service" refers here to the set of general interest demands to which services such as the mail should be subject throughout the Community.  The collection of 'Postal Services' includes data on employment, turnover, access points, traffic, prices and quality of service.
    • 九月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 22 九月, 2019
      选择数据集
      Regional accounts are a regional specification of the national accounts and therefore based on the same concepts and definitions as national accounts (see domain nama10). The main specific regional issues are addressed in chapter 13 of ESA2010, but not practically specified. For practical rules and recommendations on sources and methods see the publication "Manual on regional accounts methods": http://ec.europa.eu/eurostat/en/web/products-manuals-and-guidelines/-/KS-GQ-13-001 . Gross domestic product (GDP) at market prices is the final result of the production activity of resident producer units. It can be defined in three ways: 1. Output approach GDP is the sum of gross value added of the various institutional sectors or the various industries plus taxes and less subsidies on products (which are not allocated to sectors and industries). It is also the balancing item in the total economy production account. 2. Expenditure approach GDP is the sum of final uses of goods and services by resident institutional units (final consumption expenditure and gross capital formation), plus exports and minus imports of goods and services. At regional level the expenditure approach cannot be used in the EU, because there is no data on regional exports and imports.  3. Income approach GDP is the sum of uses in the total economy generation of income account: compensation of employees plus gross operating surplus and mixed income plus taxes on products less subsidies plus consumption of fixed capital. The different measures for the regional GDP are absolute figures in € and Purchasing Power Standards (PPS), figures per inhabitant and relative data compared to the EU28 average.
    • 八月 2017
      来源: Eurostat
      上传者: Knoema
      访问日期: 23 八月, 2017
      选择数据集
      The source for regional typology statistics are regional indicators at NUTS level 3 published on the Eurostat website or existing in the Eurostat production database. The structure of this domain is as follows: - Metropolitan regions (met)    For details see http://ec.europa.eu/eurostat/web/metropolitan-regions/overview - Maritime policy indicators (mare)    For details see http://ec.europa.eu/eurostat/web/maritime-policy-indicators/overview - Urban-rural typology (urt)    For details see http://ec.europa.eu/eurostat/web/rural-development/overview
    • 九月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 09 九月, 2019
      选择数据集
      Regional accounts are a regional specification of the national accounts and therefore based on the same concepts and definitions as national accounts (see domain nama10). The main specific regional issues are addressed in chapter 13 of ESA2010, but not practically specified. For practical rules and recommendations on sources and methods see the publication "Manual on regional accounts methods": http://ec.europa.eu/eurostat/en/web/products-manuals-and-guidelines/-/KS-GQ-13-001 . Gross domestic product (GDP) at market prices is the final result of the production activity of resident producer units. It can be defined in three ways: 1. Output approach GDP is the sum of gross value added of the various institutional sectors or the various industries plus taxes and less subsidies on products (which are not allocated to sectors and industries). It is also the balancing item in the total economy production account. 2. Expenditure approach GDP is the sum of final uses of goods and services by resident institutional units (final consumption expenditure and gross capital formation), plus exports and minus imports of goods and services. At regional level the expenditure approach cannot be used in the EU, because there is no data on regional exports and imports.  3. Income approach GDP is the sum of uses in the total economy generation of income account: compensation of employees plus gross operating surplus and mixed income plus taxes on products less subsidies plus consumption of fixed capital. The different measures for the regional GDP are absolute figures in € and Purchasing Power Standards (PPS), figures per inhabitant and relative data compared to the EU28 average.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 26 十月, 2019
      选择数据集
      The source for the regional labour market information down to NUTS level 2 is the EU Labour Force Survey (EU-LFS). This is a quarterly household sample survey conducted in all Member States of the EU and in EFTA and Candidate countries.  The EU-LFS survey follows the definitions and recommendations of the International Labour Organisation (ILO). To achieve further harmonisation, the Member States also adhere to common principles when formulating questionnaires. The LFS' target population is made up of all persons in private households aged 15 and over. For more information see the EU Labour Force Survey (lfsi_esms, see paragraph 21.1.).  The EU-LFS is designed to give accurate quarterly information at national level as well as annual information at NUTS 2 regional level and the compilation of these figures is well specified in the regulation. Microdata including the NUTS 2 level codes are provided by all the participating countries with a good degree of geographical comparability, which allows the production and dissemination of a complete set of comparable indicators for this territorial level. At present the transmission of the regional labour market data at NUTS 3 level has no legal basis. However many countries transmit NUTS 3 figures to Eurostat on a voluntary basis, under the understanding that they are not for publication with such detail, but for aggregation in few categories per country, i.e., metropolitan regions and urban-rural typology. Most of the NUTS 3 data are based on the LFS while some countries transmit data based on registers, administrative data, small area estimation and other reliable sources.
    • 十一月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 06 十一月, 2019
      选择数据集
      National accounts are a coherent set of macroeconomic indicators, which provide an overall picture of the economic situation and are widely used for economic analysis and forecasting, policy design and policy making. The data presented in this collection are the results of a pilot exercise on the sharing selected main GDP aggregates, population and employment data collected by different international organisations. It wasconducted by the Task Force in International Data Collection (TFIDC) which was established by the  Inter-Agency Group on Economic and Financial Statistics (IAG).  The goal of this pilot is to develop a set of commonly shared principles and working arrangements for data cooperation that could be implemented by the international agencies. The data sets are an experimental exercise to present national accounts data form various countries across the globe in one coherent folder, but users should be aware that these data are collected and validated by different organisations and not fully harmonised from a methodological point of view.  The domain consists of the following collections:
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 01 十一月, 2019
      选择数据集
      The section 'LFS series - detailed annual survey results' reports annual results from the EU-LFS. While LFS is a quarterly survey, it is also possible to produce annual results. There are several ways of doing it, see section '18.5 Data compilation' below for details. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metadata. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 16 十月, 2019
      选择数据集
      National accounts are a coherent and consistent set of macroeconomic indicators, which provide an overall picture of the economic situation and are widely used for economic analysis and forecasting, policy design and policy making. Eurostat publishes annual and quarterly national accounts, annual and quarterly sector accounts as well as supply, use and input-output tables, which are each presented with associated metadata. Even though consistency checks are a major aspect of data validation, temporary (usually limited) inconsistencies between datasets may occur, mainly due to vintage effects. Annual national accounts are compiled in accordance with the European System of Accounts - ESA 2010 as defined in Annex B of the Council Regulation (EU) No 549/2013 of the European Parliament and of the Council of 21 May 2013.   The previous European System of Accounts, ESA95, was reviewed to bring national accounts in the European Union, in line with new economic environment, advances in methodological research and needs of users and the updated national accounts framework at the international level, the SNA 2008. The revisions are reflected in an updated Regulation of the European Parliament and of the Council on the European system of national and regional accounts in the European Union of 2010 (ESA 2010). The associated transmission programme is also updated and data transmissions in accordance with ESA 2010 are compulsory from September 2014 onwards. Further information (including actual communications) is presented on the Eurostat website. The domain consists of the following collections:   1. Main GDP aggregates: main components from the output, expenditure and income side, expenditure breakdowns by durability and exports and imports by origin.
    • 十月 2019
      来源: Organisation for Economic Co-operation and Development
      上传者: Knoema
      访问日期: 15 十月, 2019
      选择数据集
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 31 十月, 2019
      选择数据集
      The section 'LFS series - detailed annual survey results' reports annual results from the EU-LFS. While LFS is a quarterly survey, it is also possible to produce annual results. There are several ways of doing it, see section '18.5 Data compilation' below for details. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 八月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 13 八月, 2019
      选择数据集
      The source for the regional labour market information down to NUTS level 2 is the EU Labour Force Survey (EU-LFS). This is a quarterly household sample survey conducted in all Member States of the EU and in EFTA and Candidate countries.  The EU-LFS survey follows the definitions and recommendations of the International Labour Organisation (ILO). To achieve further harmonisation, the Member States also adhere to common principles when formulating questionnaires. The LFS' target population is made up of all persons in private households aged 15 and over. For more information see the EU Labour Force Survey (lfsi_esms, see paragraph 21.1.).  The EU-LFS is designed to give accurate quarterly information at national level as well as annual information at NUTS 2 regional level and the compilation of these figures is well specified in the regulation. Microdata including the NUTS 2 level codes are provided by all the participating countries with a good degree of geographical comparability, which allows the production and dissemination of a complete set of comparable indicators for this territorial level. At present the transmission of the regional labour market data at NUTS 3 level has no legal basis. However many countries transmit NUTS 3 figures to Eurostat on a voluntary basis, under the understanding that they are not for publication with such detail, but for aggregation in few categories per country, i.e., metropolitan regions and urban-rural typology. Most of the NUTS 3 data are based on the LFS while some countries transmit data based on registers, administrative data, small area estimation and other reliable sources.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 30 十月, 2019
      选择数据集
      The source for the regional labour market information down to NUTS level 2 is the EU Labour Force Survey (EU-LFS). This is a quarterly household sample survey conducted in all Member States of the EU and in EFTA and Candidate countries.  The EU-LFS survey follows the definitions and recommendations of the International Labour Organisation (ILO). To achieve further harmonisation, the Member States also adhere to common principles when formulating questionnaires. The LFS' target population is made up of all persons in private households aged 15 and over. For more information see the EU Labour Force Survey (lfsi_esms, see paragraph 21.1.).  The EU-LFS is designed to give accurate quarterly information at national level as well as annual information at NUTS 2 regional level and the compilation of these figures is well specified in the regulation. Microdata including the NUTS 2 level codes are provided by all the participating countries with a good degree of geographical comparability, which allows the production and dissemination of a complete set of comparable indicators for this territorial level. At present the transmission of the regional labour market data at NUTS 3 level has no legal basis. However many countries transmit NUTS 3 figures to Eurostat on a voluntary basis, under the understanding that they are not for publication with such detail, but for aggregation in few categories per country, i.e., metropolitan regions and urban-rural typology. Most of the NUTS 3 data are based on the LFS while some countries transmit data based on registers, administrative data, small area estimation and other reliable sources.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 30 十月, 2019
      选择数据集
      The source for the regional labour market information down to NUTS level 2 is the EU Labour Force Survey (EU-LFS). This is a quarterly household sample survey conducted in all Member States of the EU and in EFTA and Candidate countries.  The EU-LFS survey follows the definitions and recommendations of the International Labour Organisation (ILO). To achieve further harmonisation, the Member States also adhere to common principles when formulating questionnaires. The LFS' target population is made up of all persons in private households aged 15 and over. For more information see the EU Labour Force Survey (lfsi_esms, see paragraph 21.1.).  The EU-LFS is designed to give accurate quarterly information at national level as well as annual information at NUTS 2 regional level and the compilation of these figures is well specified in the regulation. Microdata including the NUTS 2 level codes are provided by all the participating countries with a good degree of geographical comparability, which allows the production and dissemination of a complete set of comparable indicators for this territorial level. At present the transmission of the regional labour market data at NUTS 3 level has no legal basis. However many countries transmit NUTS 3 figures to Eurostat on a voluntary basis, under the understanding that they are not for publication with such detail, but for aggregation in few categories per country, i.e., metropolitan regions and urban-rural typology. Most of the NUTS 3 data are based on the LFS while some countries transmit data based on registers, administrative data, small area estimation and other reliable sources.
    • 九月 2019
      来源: United Nations Economic Commission for Europe
      上传者: Knoema
      访问日期: 26 九月, 2019
      选择数据集
      .. - data not available Source: UNECE Statistical Database, compiled from national and international (EUROSTAT, OECD, CIS) official sources. Definition:Employment, as referred to the System of National Accounts 1993, covers all persons - both employees and self-employed - engaged in a productive activity that falls within the production boundary of the system. It includes both the residents and the non-residents who work for resident producer units. In case of deviation, the actual definition is provided in the country footnote. Employment data provided in this table generally differ from employment data provided in Gender Statistics, which cover only residents. General note: The UNECE secretariat presents time series ready for immediate analysis. When appropriate, source segments with methodological differences have been linked or rescaled to build long consistent time series. As a result, absolute figures presented in this table may differ from those published by National Statistical Offices and should be taken with caution. However, the derived growth rates correspond to the originally reported series. Regional aggregates are computed by UNECE secretariat. For more details see the composition of regions note. Country: Albania Employment: end of period. Country: Armenia Employment: LFS - based. Country: Azerbaijan Geographical coverage: excludes Nagorno-Karabakh. Population: Number of population for the latest year refers to the beginning of the year, not to an annual average as usually. Employment: LFS - based. Country: Bosnia and Herzegovina Employment:LFS - based. Country: Croatia Employment: LFS-based. Country: France Geographical Coverage: Data for France include the overseas departments (DOM). Country: Georgia Geographical Coverage: from 1993 excludes Abkhazia and South Ossetia (Tshinvali). Population: Number of population for the latest year refers to the beginning of the year, not to an annual average as usually. Employment: Register-based. Country: Israel Employment: LFS-based. Designation and data provided by Israel. The position of the United Nations on the question of Jerusalem is contained in General Assembly resolution 181 (II) and subsequent resolutions of the General Assembly and the Security Council concerning this question. Data include East Jerusalem. Country: Kazakhstan Employment: LFS-based. Country: Lithuania Employment: LFS-based. Country: Moldova, Republic of Geographical Coverage: from 1993 excludes Transnistria. Population: Number of population for the latest year refers to the beginning of the year, not to an annual average as usually. Employment: LFS-based. Country: Romania Employment: LFS-based. For the years 1990-2001 UNECE estimates. Country: Russian Federation Population: Number of population for the latest year refers to the beginning of the year, not to an annual average as usually. Employment: LFS-based. Data for Russian Federation was updated only until the end of 2013. Country: Serbia Geographical Coverage: from 1999, excludes Kosovo and Metohija. Employment: LFS - based. Country: The former Yugoslav Republic of Macedonia Employment: LFS-based. Country: Turkey Employment: Annual breakdowns by activity and quarterly data are LFS-based. Country: Ukraine Employment: LFS-based. Geographical coverage: from 2014, does not includes all territory of Ukraine.
    • 十一月 2019
      来源: United Nations Economic Commission for Europe
      上传者: Knoema
      访问日期: 07 十一月, 2019
      选择数据集
      .. - data not available Source: UNECE Statistical Database, compiled from national and international (EUROSTAT, OECD, CIS) official sources. Definition:Employment, as referred to the System of National Accounts 1993, covers all persons - both employees and self-employed - engaged in a productive activity that falls within the production boundary of the system. It includes both the residents and the non-residents who work for resident producer units. In case of deviation, the actual definition is provided in the country footnote. Employment data provided in this table generally differ from employment data provided in Gender Statistics, which cover only residents. General note: The UNECE secretariat presents time series ready for immediate analysis. When appropriate, source segments with methodological differences have been linked or rescaled to build long consistent time series. As a result, absolute figures presented in this table may differ from those published by National Statistical Offices and should be taken with caution. However, the derived growth rates correspond to the originally reported series. Regional aggregates are computed by UNECE secretariat. For more details see the composition of regions note. Country: Albania Employment: end of period. Country: Armenia Employment: LFS - based. Country: Azerbaijan Geographical coverage: excludes Nagorno-Karabakh. Population: Number of population for the latest year refers to the beginning of the year, not to an annual average as usually. Employment: LFS - based. Country: Bosnia and Herzegovina Employment:LFS - based. Country: Croatia Employment: LFS-based. Country: France Geographical Coverage: Data for France include the overseas departments (DOM). Country: Georgia Geographical Coverage: from 1993 excludes Abkhazia and South Ossetia (Tshinvali). Population: Number of population for the latest year refers to the beginning of the year, not to an annual average as usually. Employment: Register-based. Country: Iceland Employment: LFS - based. Country: Israel Employment: LFS-based. Designation and data provided by Israel. The position of the United Nations on the question of Jerusalem is contained in General Assembly resolution 181 (II) and subsequent resolutions of the General Assembly and the Security Council concerning this question. Data include East Jerusalem. Country: Kazakhstan Employment: LFS-based. Country: Kyrgyzstan Employment: LFS - based. Country: Lithuania Employment: LFS-based. Country: Moldova, Republic of Geographical Coverage: from 1993 excludes Transnistria. Population: Number of population for the latest year refers to the beginning of the year, not to an annual average as usually. Employment: LFS-based. Country: Romania Employment: LFS-based. For the years 1990-2001 UNECE estimates. Country: Russian Federation Population: Number of population for the latest year refers to the beginning of the year, not to an annual average as usually. Employment: LFS-based. Data for Russian Federation was updated only until the end of 2013. Country: Serbia Geographical Coverage: from 1999, excludes Kosovo and Metohija. Employment: LFS - based. Country: The former Yugoslav Republic of Macedonia Employment: LFS-based. Country: Turkey Employment: Annual breakdowns by activity and quarterly data are LFS-based. Country: Ukraine Employment: LFS-based. Geographical coverage: from 2014, does not includes all territory of Ukraine.
    • 十一月 2019
      来源: United Nations Economic Commission for Europe
      上传者: Knoema
      访问日期: 07 十一月, 2019
      选择数据集
      .. - data not available Source: UNECE Statistical Database, compiled from national and international (EUROSTAT, OECD, CIS) official sources. Definition:Employment, as referred to the System of National Accounts 1993, covers all persons - both employees and self-employed - engaged in a productive activity that falls within the production boundary of the system. It includes both the residents and the non-residents who work for resident producer units. In case of deviation, the actual definition is provided in the country footnote. Employment data provided in this table generally differ from employment data provided in Gender Statistics, which cover only residents. General note: The UNECE secretariat presents time series ready for immediate analysis. When appropriate, source segments with methodological differences have been linked or rescaled to build long consistent time series. As a result, absolute figures presented in this table may differ from those published by National Statistical Offices and should be taken with caution. However, the derived growth rates correspond to the originally reported series. Regional aggregates are computed by UNECE secretariat. For more details see the composition of regions note. Country: Albania Employment: end of period. Country: Armenia Employment: LFS - based. Country: Azerbaijan Geographical coverage: excludes Nagorno-Karabakh. Population: Number of population for the latest year refers to the beginning of the year, not to an annual average as usually. Employment: LFS - based. Country: Bosnia and Herzegovina Employment:LFS - based. Country: Croatia Employment: LFS-based. Country: France Geographical Coverage: Data for France include the overseas departments (DOM). Country: Georgia Geographical Coverage: from 1993 excludes Abkhazia and South Ossetia (Tshinvali). Population: Number of population for the latest year refers to the beginning of the year, not to an annual average as usually. Employment: Register-based. Country: Iceland Employment: LFS - based. Country: Israel Employment: LFS-based. Designation and data provided by Israel. The position of the United Nations on the question of Jerusalem is contained in General Assembly resolution 181 (II) and subsequent resolutions of the General Assembly and the Security Council concerning this question. Data include East Jerusalem. Country: Kazakhstan Employment: LFS-based. Country: Moldova, Republic of Geographical Coverage: from 1993 excludes Transnistria. Population: Number of population for the latest year refers to the beginning of the year, not to an annual average as usually. Employment: LFS-based. Country: Romania Employment: LFS-based. For the years 1990-2001 UNECE estimates. Country: Russian Federation Population: Number of population for the latest year refers to the beginning of the year, not to an annual average as usually. Employment: LFS-based. Data for Russian Federation was updated only until the end of 2013. Country: Serbia Geographical Coverage: from 1999, excludes Kosovo and Metohija. Employment: LFS - based. Country: The former Yugoslav Republic of Macedonia Employment: LFS-based. Country: Turkey Employment: Annual breakdowns by activity and quarterly data are LFS-based. Country: Ukraine Employment: LFS-based. Geographical coverage: from 2014, does not includes all territory of Ukraine.
    • 十一月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 12 十一月, 2019
      选择数据集
      The employed comprise all persons of working age who, during a specified brief period, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are disaggregated by economic activity and occupation, according to the latest versions of the International Standard Industrial Classification of All Economic Activities (ISIC) and International Standard Classification of Occupations (ISCO), respectively. Economic activity refers to the main activity of the establishment in which a person worked during the reference period and does not depend on the specific duties or functions of the person's job, but on the characteristics of the economic unit in which this person works. Information on occupation provides a description of the set of tasks and duties which are carried out by, or can be assigned to, one person.
    • 二月 2016
      来源: United Nations Economic Commission for Europe
      上传者: Knoema
      访问日期: 21 十一月, 2018
      选择数据集
      To view the original national data please open the questionnaires. Source: Joint Forest Europe / UNECE / FAO Questionnaire on Pan-European Indicators for Sustainable Forest Management. Country: Russian Federation The source of the data of Russian Federation is the National Report for the Joint Forest Europe / UNECE / FAO reporting on quantitative pan-European indicators 2011.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 15 十月, 2019
      选择数据集
      The 'LFS main indicators' section presents a selection of the main statistics on the labour market. They encompass indicators of activity employment and unemployment. Those indicators are based on the results of the European Labour Force Survey (EU-LFS), in few cases integrated with data sources like national accounts employment or registered unemployment. As a result of the application of adjustments, corrections and reconciliation of EU Labour Force Survey (EU-LFS), 'LFS main indicators' is the most complete and reliable collection of employment and unemployment data available in the sub-domain ' Employment and unemployment'. The EU-LFS data used for 'LFS main indicators' are, where necessary, adjusted and enriched in various ways, in accordance with the specificities of an indicator.  The most common adjustments cover: - correction of the main breaks in the LFS series - estimation of the missing values, (i.e. in case of missing quarters, annual results and EU aggregates are estimated using adjusted quarterly national labour force survey data or interpolations of the EU Labour Force Survey data with reference to the available quarter(s)) - reconciliations of the LFS data with other sources, mainly National Accounts (for Employment growth and activity branches) and national statistics on monthly unemployment (for Harmonised unemployment series). - for a number of indicators (employment, activity, unemployment, supplementary indicators) seasonally adjusted data are available Those adjustments may produce some differences between data published under 'LFS main indicators' and 'LFS series - Detailed survey results', particularly for back data. For the most recent years these two series converge, due to the implementation of a continuous quarterly survey and the improved quality of the data. This page focuses on the particularities of 'LFS main indicators' in general. There are special pages for indicators 'employment growth', 'population in jobless households', 'average exit age of labour market' and 'education indicators: life-long learning, early school leavers and youth education attainment level. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 28 十月, 2019
      选择数据集
      The 'LFS main indicators' section presents a selection of the main statistics on the labour market. They encompass indicators of activity employment and unemployment. Those indicators are based on the results of the European Labour Force Survey (EU-LFS), in few cases integrated with data sources like national accounts employment or registered unemployment. As a result of the application of adjustments, corrections and reconciliation of EU Labour Force Survey (EU-LFS), 'LFS main indicators' is the most complete and reliable collection of employment and unemployment data available in the sub-domain ' Employment and unemployment'. The EU-LFS data used for 'LFS main indicators' are, where necessary, adjusted and enriched in various ways, in accordance with the specificities of an indicator.  The most common adjustments cover: - correction of the main breaks in the LFS series - estimation of the missing values, (i.e. in case of missing quarters, annual results and EU aggregates are estimated using adjusted quarterly national labour force survey data or interpolations of the EU Labour Force Survey data with reference to the available quarter(s)) - reconciliations of the LFS data with other sources, mainly National Accounts (for Employment growth and activity branches) and national statistics on monthly unemployment (for Harmonised unemployment series). - for a number of indicators (employment, activity, unemployment, supplementary indicators) seasonally adjusted data are available Those adjustments may produce some differences between data published under 'LFS main indicators' and 'LFS series - Detailed survey results', particularly for back data. For the most recent years these two series converge, due to the implementation of a continuous quarterly survey and the improved quality of the data. This page focuses on the particularities of 'LFS main indicators' in general. There are special pages for indicators 'employment growth', 'population in jobless households', 'average exit age of labour market' and 'education indicators: life-long learning, early school leavers and youth education attainment level. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 31 十月, 2019
      选择数据集
      The section 'LFS series - detailed annual survey results' reports annual results from the EU-LFS. While LFS is a quarterly survey, it is also possible to produce annual results. There are several ways of doing it, see section '18.5 Data compilation' below for details. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metadata. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 四月 2019
      来源: United Nations Economic Commission for Europe
      上传者: Knoema
      访问日期: 22 四月, 2019
      选择数据集
      Source: UNECE Statistical Database, compiled from national and international (Eurostat) official sources. Definition: The employed are all the residents above a specified age who, during a specified brief period, either one week or one day, were in the following categories: (a) paid employment: (a1) at work: persons who, during the reference period, performed some work for wage or salary, in cash or in kind; (a2) with a job but not at work: persons who, having already worked in their present job, were temporarily not at work during the reference period and had a formal attachment to their job; (b) self-employment: (b1) at work: persons who, during the reference period, performed some work for profit or family gain, in cash or in kind; (b2) with an enterprise but not at work: persons with an enterprise, which may be a business enterprise, a farm or a service undertaking, who were temporarily not at work during the reference period for any specific reason. For additional information, see the International Conference of Labour Statisticians (ICLS). Part-time/full-time: A part-time worker is an employed person whose normal hours of work are less than those of comparable full-time workers. In most countries, the distinction between part-time and full-time work is based on self-declaration. In a few countries, work is defined as part-time when the hours usually worked are below a fixed threshold. Data for EU-27, Croatia, Iceland, Norway, the Former Yugoslav Republic of Macedonia and Turkey from the year 2008 corresponds to the NACE rev 2, before 2008 data is according to the NACE rev1.1. General note: Data come from the Labour Force Survey (LFS) unless otherwise specified. Data from the LFS and from population censuses normally comply with the definition above. .. - data not available Country: Albania 2007-2012: Part-time worker refers to an employed person whose usual hours of work are less than 35 hours/week. Country: Albania 2013-2015: Distinction between part-time and full-time workers is based on worker self-identification. Country: Armenia Break in methodlogy (2008): 2007 data refer to population aged 16-75. Since 2008 data refer to population aged 15-75. Break in methodlogy (2014): From 2007 to 2013 data are based on the Integrated Survey of the Household Living Standards. Since 2014 data are based on the Labour Force Survey. Country: Belarus 2014: changes in methodology Country: France Since 2014 data include also the French overseas departments (Guadeloupe, Martinique, Guyane, La Reunion) with the exception of Mayotte. Country: Georgia Territorial change (2002 onward): Data do not cover Abkhazia AR and Tskhinvali Region Country: Israel Break in methodlogy (2000): In 1998: 1) Changes in the weighting method; 2) Transition to the 1995 Population Census estimates; See explanations: http://www.cbs.gov.il/www/publications/saka_change/tch_e.pdf Country: Israel Break in methodlogy (2001): Changes in the weighting method. See explanations: http://www.cbs.gov.il/www/saka_y/e_intro_f1_comparison-mimi.f Country: Israel Break in methodlogy (2009): 1) Update of the definition of the civilian labour force characteristics; 2) Transition to the 2008 Population Census estimates. See explanations: http://www.cbs.gov.il/publications11/1460/pdf/intro05_e.pdf Country: Israel Break in methodlogy (2012): 1) Transitiom from a quarterly to a monthly LFS; 2) Changes in the definitions of labour force characteristics (including compulsory and permanent military service into labour force). See explanations: http://www.cbs.gov.il/publications/labour_survey04/labour_f--orce_survey/answer_question_e_2012.pdf Country: Israel Change in definition (1980): Data refers to population 14+. Country: Israel Change in definition (2005): 1) Update of the definitions of labour force characteristics; 2) Changes in the Standard Industrial Classification of Economic Activities; See explanations: http://www.cbs.gov.il/www/publications/saka_change/tch_e.pdf Country: Moldova, Republic of Data exclude the territory of the Transnistria and municipality of Bender Country: Russian Federation Change in definition (1990 - 2013): Data present the population aged 15-72 years. Underemployment - the person who work less than 30 hours in the surveyed week Country: Russian Federation Reference period (1990): Data refer to 1992 Country: Russian Federation Territorial change (1990 - 2006): Data do not include the Chechen Republic Country: Serbia Data do not cover Kosovo and Metohija. Country: Ukraine From 2014 data cover the territories under the government control. Country: Ukraine Data do not cover the persons who are still living in the area of Chernobyl contaminated with radioactive material. Data do not cover the persons who are living in institutions and those who are working in the army. Data refer to the population aged 15-70.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 30 十月, 2019
      选择数据集
      The source for the regional labour market information down to NUTS level 2 is the EU Labour Force Survey (EU-LFS). This is a quarterly household sample survey conducted in all Member States of the EU and in EFTA and Candidate countries.  The EU-LFS survey follows the definitions and recommendations of the International Labour Organisation (ILO). To achieve further harmonisation, the Member States also adhere to common principles when formulating questionnaires. The LFS' target population is made up of all persons in private households aged 15 and over. For more information see the EU Labour Force Survey (lfsi_esms, see paragraph 21.1.).  The EU-LFS is designed to give accurate quarterly information at national level as well as annual information at NUTS 2 regional level and the compilation of these figures is well specified in the regulation. Microdata including the NUTS 2 level codes are provided by all the participating countries with a good degree of geographical comparability, which allows the production and dissemination of a complete set of comparable indicators for this territorial level. At present the transmission of the regional labour market data at NUTS 3 level has no legal basis. However many countries transmit NUTS 3 figures to Eurostat on a voluntary basis, under the understanding that they are not for publication with such detail, but for aggregation in few categories per country, i.e., metropolitan regions and urban-rural typology. Most of the NUTS 3 data are based on the LFS while some countries transmit data based on registers, administrative data, small area estimation and other reliable sources.
    • 二月 2016
      来源: United Nations Economic Commission for Europe
      上传者: Knoema
      访问日期: 21 十一月, 2018
      选择数据集
      To view the original national data please open the questionnaires. Source: Joint Forest Europe / UNECE / FAO Questionnaire on Pan-European Indicators for Sustainable Forest Management. Country: Russian Federation The source of the data of Russian Federation is the National Report for the Joint Forest Europe / UNECE / FAO reporting on quantitative pan-European indicators 2011.
    • 七月 2019
      来源: Organisation for Economic Co-operation and Development
      上传者: Knoema
      访问日期: 02 七月, 2019
      选择数据集
      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.
    • 七月 2019
      来源: Organisation for Economic Co-operation and Development
      上传者: Sandeep Reddy
      访问日期: 18 七月, 2019
      选择数据集
      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.
    • 七月 2019
      来源: Organisation for Economic Co-operation and Development
      上传者: Knoema
      访问日期: 14 七月, 2019
      选择数据集
      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.
    • 四月 2019
      来源: United Nations Economic Commission for Europe
      上传者: Knoema
      访问日期: 07 五月, 2019
      选择数据集
      Source: UNECE Statistical Database, compiled from national and international (Eurostat and ILO) official sources. Definition: The employed are all the persons above a specified age who, during a specified brief period, either one week or one day, were in the following categories: (a) paid employment: (a1) at work: persons who, during the reference period, performed some work for wage or salary, in cash or in kind; (a2) with a job but not at work: persons who, having already worked in their present job, were temporarily not at work during the reference period and had a formal attachment to their job; (b) self-employment: (b1) at work: persons who, during the reference period, performed some work for profit or family gain, in cash or in kind; (b2) with an enterprise but not at work: persons with an enterprise, which may be a business enterprise, a farm or a service undertaking, who were temporarily not at work during the reference period for any specific reason. For additional information, see the International Conference of Labour Statisticians (ICLS). The occupation groups correspond to first-level categories in the 2008 version of the International Standard Classification of Occupations (ISCO-08). For the EU and EFTA member-states the year of transition to ISCO-08 is 2011, for other countries please see Country footnotes. The level of education is the highest level successfully completed in the educational system of the country where the education is received. The levels are defined with reference to the International Standard Classifications of Education ISCED 1997 and ISCED 2011. For the EU and EFTA member-states the levels of education are classified according to ISCED 2011 from 2014. For other countries please see Country footnotes. The transition from ISCO-88 to ISCO-08 and from ISCED 1997 to ISCED 2011 could entail a break in time series. General note: Data come from the Labour Force Survey (LFS) unless otherwise specified. Data from the LFS and from population censuses normally comply with the definition above. .. - data not available Country: Armenia Data for 2001 are from Population Census. Since 2014 data are based on the Labour Force Survey. Country: Azerbaijan Data compiled according to ISCO-08. Country: Belarus Break in methodlogy (2000): Data refer to 1999 Population Census. Measurement: Employment (thousands) , Country: Belarus Data compiled according to ISCO-88 Measurement: Percent of corresponding total of both sexes , Country: Belarus Data compiled according to ISCO-88 Measurement: Employment (thousands) , Country: Belarus Parts by education level may not add up due to the persons who did not indicate their levels of education Measurement: Percent of corresponding total of both sexes , Country: Belarus Parts by education level may not add up due to the persons who did not indicate their levels of education Country: Bosnia and Herzegovina From 2006 to 2014 data compiled using ISCED 97, from 2015 using ISCED 11. Country: Canada Change in definition (1990 onwards): Data are annual averages. Cells with 0 are estimates with less than 1,500 employed. Country: Canada Data do not cover the three northern territories (Yukon, Northwest and Nunavuk ) Country: Israel Break in methodlogy (2000): In 1998: 1) Changes in the weighting method; 2) Transition to the 1995 Population Census estimates; See explanations: http://www.cbs.gov.il/www/publications/saka_change/tch_e.pdf Country: Israel Break in methodlogy (2001): Changes in the weighting method. See explanations: http://www.cbs.gov.il/www/saka_y/e_intro_f1_comparison-mimi.f Country: Israel Break in methodlogy (2009): 1) Update of the definition of the civilian labour force characteristics; 2) Transition to the 2008 Population Census estimates. See explanations: http://www.cbs.gov.il/publications11/1460/pdf/intro05_e.pdf Country: Israel Break in methodlogy (2012):1) Transitiom from a quarterly to a monthly LFS; 2) Changes in the definitions of labour force characteristics (including compulsory and permanent military service into labour force). See explanations: http://www.cbs.gov.il/publications/labour_survey04/labour_f--orce_survey/answer_question_e_2012.pdf Country: Israel Change in definition (2000 - 2012): Changes in the questionnaire (Highest Diploma Received, Discouraged Workers, Employees hired through employment agencies or employment contractors); See explanations: http://www.cbs.gov.il/www/saka_y/e_intro_e_changes.pdf Country: Israel Change in definition (2013): Changes in the Standard Classification of Occupations based on ISCO-08; See explanations: http://www.cbs.gov.il/publications12/occupations_class11/pd--f/draft_h.pdf (draft, Hebrew only) Country: Moldova, Republic of Data exclude the territory of the Transnistria and municipality of Bender Country: Russian Federation Change in definition (2000 - 2013): Data present the population aged 15-72 years Country: Russian Federation Territorial change (2000 - 2006): Data do not include the Chechen Republic Country: Serbia Data do not cover Kosovo and Metohija. From 2013 data compiled according to ISCO-08. Country: Turkey Break in series (2014): Since 2014 series are not comparable with the previous years due to methodological changes in LFS. Country: Turkey Break in methodlogy (2004): Data are revised according to the 2008 population projections. Country: Turkey Until 2012, all occupations were coded according to ISCO-88. Since 2013, all occupations have been coded according to ISCO-08. Country: Ukraine Change in definition (2000 - 2012): Distribution by institutional sectors of the economy based on the assessment carried out in accordance with the National Classification of Occupations developed on the basis of ISCO 88. Country: Ukraine Territorial change (2000 - 2012): Data do not cover the area of radioactive contamination from the Chernobyl disaster. Country: United States Data for occupation refer to population 15+ and who have worked in the past 5 years. Data do not cover the armed forces. Occupation is classified according to the Standard Occupational Classification (SOC) 2000 manual (www.bls.gov/soc). For individuals with two or more jobs, data refer to the job having the greatest number of hours. For unemployed persons and persons who are not currently employed but report having a job within the last five years, data refer to their last job.
    • 六月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 19 六月, 2019
      选择数据集
      The source for regional typology statistics are regional indicators at NUTS level 3 published on the Eurostat website or existing in the Eurostat production database. The structure of this domain is as follows: - Metropolitan regions (met)    For details see http://ec.europa.eu/eurostat/web/metropolitan-regions/overview - Maritime policy indicators (mare)    For details see http://ec.europa.eu/eurostat/web/maritime-policy-indicators/overview - Urban-rural typology (urt)    For details see http://ec.europa.eu/eurostat/web/rural-development/overview
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 31 十月, 2019
      选择数据集
      The section 'LFS series - detailed annual survey results' reports annual results from the EU-LFS. While LFS is a quarterly survey, it is also possible to produce annual results. There are several ways of doing it, see section '18.5 Data compilation' below for details. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metadata. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 01 十一月, 2019
      选择数据集
      Employment consists of both employees and self-employed, who are engaged in some productive activity that falls within the production boundary of the system (ESA 2010, 11.11). Employment covers employees and self-employed working for production units resident on the economic territory (i.e. the domestic employment concept). Employment is measured in number of persons without distinction according to full-time or part-time work. Growth rates with respect to the previous quarter (Q/Q-1) are calculated from seasonally adjusted figures while growth rates with respect to the same quarter of the previous year (Q/Q-4) are calculated from non-seasonal adjusted data. The following countries provide employment data seasonally adjusted, without calendar adjustment: CZ, GR, FR, MT, PL, PT, SK and CH. The remaining countries provide employment data seasonally and calendar adjusted.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 01 十一月, 2019
      选择数据集
      Employment consists of both employees and self-employed, who are engaged in some productive activity that falls within the production boundary of the system (ESA 2010, 11.11). Employment covers employees and self-employed working for production units resident on the economic territory (i.e. the domestic employment concept). Employment is measured in number of persons without distinction according to full-time or part-time work. Growth rates with respect to the previous quarter (Q/Q-1) are calculated from seasonally and calendar adjusted figures while growth rates with respect to the same quarter of the previous year (Q/Q-4) are calculated from non-seasonal adjusted data.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 01 十一月, 2019
      选择数据集
      Employment consists of both employees and self-employed, who are engaged in some productive activity that falls within the production boundary of the system (ESA 2010, 11.11). Employment covers employees and self-employed working for production units resident on the economic territory (i.e. the domestic employment concept). Employment is measured in number of persons without distinction according to full-time or part-time work. Growth rates with respect to the previous quarter (Q/Q-1) are calculated from seasonally adjusted figures while growth rates with respect to the same quarter of the previous year (Q/Q-4) are calculated from non-seasonal adjusted data.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 01 十一月, 2019
      选择数据集
      Employment consists of both employees and self-employed, who are engaged in some productive activity that falls within the production boundary of the system (ESA 2010, 11.11). Employment covers employees and self-employed working for production units resident on the economic territory (i.e. the domestic employment concept). Employment is measured in number of persons without distinction according to full-time or part-time work. The following countries provide employment data seasonally adjusted, without calendar adjustment: CZ, GR, FR, MT, PL, PT, SK and CH. The remaining countries provide employment data seasonally and calendar adjusted.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 31 十月, 2019
      选择数据集
      The section 'LFS series - detailed annual survey results' reports annual results from the EU-LFS. While LFS is a quarterly survey, it is also possible to produce annual results. There are several ways of doing it, see section '20.5 Data compilation' below for details. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 四月 2019
      来源: United Nations Economic Commission for Europe
      上传者: Knoema
      访问日期: 22 四月, 2019
      选择数据集
      Source: UNECE Statistical Database, compiled from national and international (Eurostat and ILO) official sources. Definition: The employed are all the residents above a specified age who, during a specified brief period, either one week or one day, were in the following categories: (a) paid employment: (a1) at work: persons who, during the reference period, performed some work for wage or salary, in cash or in kind; (a2) with a job but not at work: persons who, having already worked in their present job, were temporarily not at work during the reference period and had a formal attachment to their job; (b) self-employment: (b1) at work: persons who, during the reference period, performed some work for profit or family gain, in cash or in kind; (b2) with an enterprise but not at work: persons with an enterprise, which may be a business enterprise, a farm or a service undertaking, who were temporarily not at work during the reference period for any specific reason. For additional information, see the International Conference of Labour Statisticians (ICLS). The occupation groups correspond to first-level categories in the 2008 version of the International Standard Classification of Occupations (ISCO-08). For the EU and EFTA member-states the year of transition from ISCO-88 to ISCO-08 is 2011. For other countries please see Country footnotes. The transition to ISCO-08 could entail a break in time series. General note: Data come from the Labour Force Survey (LFS) unless otherwise specified. Data from the LFS and from population censuses normally comply with the definition above. .. - data not available Country: Albania From 2010 occupational groups according to ISCO-08. Country: Armenia Break in methodlogy (2014): since 2014 data refer to the population aged 15-75 and are based on the Labour Force Survey.2001: data come from Population Census. Country: Azerbaijan Data compiled according to ISCO-08. Country: Azerbaijan Data are based on administrative registers. Country: Belarus Data compiled according to ISCO-88 Country: Belarus 2000 : data refer to 1999 and come from Population Census. Country: Belgium 1980 : data refer to 1983. Country: Bosnia and Herzegovina From year 2006 to 2010 data compiling using ISCO 88, from 2011 using ISCO 08. Country: Bulgaria 1995 : data refer to 1997. Country: Canada Change in definition (1990 onwards): Data are annual averages. Cells with 0 are estimates with less than 1,500 employed. Country: Canada Data do not cover the three northern territories (Yukon, Northwest and Nunavuk ) Country: Cyprus Data cover only the area controlled by the Republic of Cyprus. 1990 : data refer to 1992. Country: Estonia 1990 and 1995 : data refer to the population aged 15-69. From 2000 : data refer to the population aged 15-74. Country: Finland Data refer to the population aged 15-74. Country: France Since 2014, data include also the French overseas departments (Guadeloupe, Martinique, Guyane, La Reunion), with the exception of Mayotte. Country: Georgia Data do not cover Abkhazia and South Ossetia (Tshinvali). Country: Germany 1980 : data refer to 1983. Country: Iceland Data refer to the population aged 16-74. 1990 : data refer to 1991. Country: Israel Break in methodlogy (2000): In 1998: 1) Changes in the weighting method; 2) Transition to the 1995 Population Census estimates; See explanations: http://www.cbs.gov.il/www/publications/saka_change/tch_e.pdf Country: Israel Break in methodlogy (2001): Changes in the weighting method. See explanations: http://www.cbs.gov.il/www/saka_y/e_intro_f1_comparison-mimi.f Country: Israel Break in methodlogy (2009): 1) Update of the definition of the civilian labour force characteristics; 2) Transition to the 2008 Population Census estimates. See explanations: http://www.cbs.gov.il/publications11/1460/pdf/intro05_e.pdf Country: Israel Break in methodlogy (2012): 1) Transitiom from a quarterly to a monthly LFS; 2) Changes in the definitions of labour force characteristics (including compulsory and permanent military service into labour force). See explanations: http://www.cbs.gov.il/publications/labour_survey04/labour_f--orce_survey/answer_question_e_2012.pdf Country: Israel Change in definition (2000 - 2012): Changes in the questionnaire (Highest Diploma Received, Discouraged Workers, Employees hired through employment agencies or employment contractors); See explanations: http://www.cbs.gov.il/www/saka_y/e_intro_e_changes.pdf Country: Israel Change in definition (2013): Changes in the Standard Classification of Occupations based on ISCO-08; See explanations: http://www.cbs.gov.il/publications12/occupations_class11/pd--f/draft_h.pdf (draft, Hebrew only) Country: Kyrgyzstan Up to 2015 ISCO-88 has been used Country: Latvia 1995 : data refer to 1996. Country: Lithuania 1995 : data refer to 1997. Country: Moldova, Republic of Data exclude the territory of the Transnistria and municipality of Bender Country: Portugal 1990 : data refer to 1992. Country: Russian Federation Change in definition (2000 - 2013): Data present the population aged 15-72 years Country: Russian Federation Territorial change (1995 - 2006): Data do not include the Chechen Republic Country: Serbia Data do not cover Kosovo and Metohija. Starting in 2013 data compiled according ISCO-08. Country: Slovakia 1995 : the persons working in the armed forces are counted in the other groups. Country: Sweden Data refer to the population aged 16-64. Country: Switzerland 1990 : data refer to 1991. Country: Ukraine Change in definition (2000 - 2012): Distribution by institutional sectors of the economy based on the assessment carried out in accordance with the National Classification of Occupations developed on the basis of ISCO 88. Country: Ukraine Territorial change (2000 - 2012): Data do not cover the area of radioactive contamination from the Chernobyl disaster. Country: United Kingdom Data refer to the population aged 16+. Country: United States Data refer to the population aged 16+. Data do not cover the armed forces. Occupation groups : 'Professionals' includes 'Technicians and associate professionals'; 'Craft and related workers' includes 'Plant machine operators and assemblers'.
    • 十一月 2018
      来源: Statistics Finland
      上传者: Knoema
      访问日期: 29 十一月, 2018
      选择数据集
      Data cited at: Statistics Finland http://www.stat.fi/index_en.html Publication: 018 -- Employed persons by occupational group (Classification of Occupations 2010, levels 1 to 2), background country, sex and year 2010-2016 http://pxnet2.stat.fi/PXWeb/pxweb/en/StatFin/StatFin__vrm__tyokay/statfin_tyokay_pxt_018.px License: http://creativecommons.org/licenses/by/4.0/ Hint: Occupational data can be filtered to different hierarchy levels of the classification (Level 1, Level 2) by entering in the Search field, e.g. Level 2. The figures in the tables are final. Description of statistics Concepts and definitions Classifications .. = Data not available or too uncertain for presentation, or subject to secrecy. From 2005, the employment pension insurance includes those aged 18 to 68, while previously the obligation to take out pension insurance for employees already started from the age of 14. This is visible in the employment statistics from 2005 onwards as a fall in employment by young people and a rise in the number of students. Statistics cannot be compiled reliably on employment by under-age people on the basis of register data. Background countries are specified in the table if the number of employed persons in the background country exceeds 99. © Tilastokeskus - Statistics Finland
    • 七月 2019
      来源: Organisation for Economic Co-operation and Development
      上传者: Knoema
      访问日期: 02 七月, 2019
      选择数据集
      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.
    • 五月 2019
      来源: United Nations Economic Commission for Europe
      上传者: Knoema
      访问日期: 09 五月, 2019
      选择数据集
      Source: UNECE Statistical Database, compiled from national official sources. Definition: The employed are all the persons above a specified age who, during a specified brief period, either one week or one day, were in the following categories: (a) paid employment: (a1) at work: persons who, during the reference period, performed some work for wage or salary, in cash or in kind; (a2) with a job but not at work: persons who, having already worked in their present job, were temporarily not at work during the reference period and had a formal attachment to their job; (b) self-employment: (b1) at work: persons who, during the reference period, performed some work for profit or family gain, in cash or in kind; (b2) with an enterprise but not at work: persons with an enterprise, which may be a business enterprise, a farm or a service undertaking, who were temporarily not at work during the reference period for any specific reason. For additional information, see the International Conference of Labour Statisticians (ICLS). The private sector covers private corporations (including those in foreign control), households and Non-Profit Institutions Serving Households (NPISHs). The public sector covers all sub-sectors of general government (mainly central, state and local government units, together with social security funds imposed and controlled by those units) and public corporations, i.e. corporations which are subject to control by government units (usually defined by the government owning the majority of shares). General note: Data come from the Labour Force Survey (LFS) unless otherwise specified. Data from the LFS and from population censuses normally comply with the definition above. .. - data not available Country: Armenia 2007 data refer to population aged 16-75. Break in methodlogy (2008): since 2008 data refer to population aged 15-75. Break in methodlogy(2001, 2002): For the periods of 1980-2000 and 2002-2006 data on employment are based on integrated data received from various sources. For 2001 data are from Population Census. Break in methodlogy (2007): From 2007 to 2013 data are based on the Integrated Survey of the Household Living Standards. Break in methodlogy (2014): Since 2014 data are based on the Labour Force Survey. Country: Austria Break in methodlogy (2004): Break in series due to change in data collection procedure. Country: Azerbaijan Data are based on Population Census, establishment survey and registers Country: Belarus Data are based on administrative registers. Data for private sector include corporations with mixed ownership. 2010: changes in methodology Country: Bosnia and Herzegovina Additional information (1990 - 2008): Data are based on administrative records and related sources Country: Bulgaria Change in definition (2003 - 2012): Annual average data Country: Bulgaria Reference period (1990): Data refer to 1993 (September). Country: Bulgaria Reference period (1995 - 2002): Data refer to June of the corresponding year Country: Canada Data for not stated refers to self-employed. Country: Croatia Data given for 2009 onwards are calibrated according to the results of the Census 2011 and are not fully comparable with data given for previous years. Country: Cyprus Change in definition (1980 - 2008): Data refer to full-time equivalent (FTE) employment. Data are based on official estimates Country: Cyprus Reference period (1980): Data refer to 1981 Country: Cyprus Territorial change (1980 - 2008): Data cover the area controlled by the Republic of Cyprus Country: Czechia Break in methodlogy (1990 - 2008): Data are based on Labour Force Survey, enterprise survey and registers Country: Denmark Data are based on administrative records and related sources Country: France Reference area: Metropolitan France Country: France Data are based on Labour Force Survey, enterprise survey and registers Country: Georgia Data do not cover Abkhazia and South Ossetia (Tshinvali). Country: Germany Additional information (1995 - 2007): Data are based on Labour Force Survey, enterprise survey and registers Country: Greece Data refer to annual averages. Country: Hungary Data are based on Labour Force Survey, enterprise survey and registers. Private sector : data include corporations with mixed ownership. Country: Ireland Data are based on administrative registers. 2008 : break in series due to change in methodology. The series previously published up to 2008 was derived from the Quarterly Public Sector inquiry (QPI). The data from 2008,2009 and 2010 is now generated from the Earnings,Hours and Employment Cost Survey (EHECS)There are different methodologies used in both.They are as follows: The QPI was data generated from one reference period in the quarter.The EHECS survey is an average over the full quarter. The QPI had some whole time equivalents in the data ,EHECS uses a head count. The data from EHECS will therefore be higher Country: Israel Change in definition (2000 - 2008): Data on public sector refer to General Government only. Country: Italy Additional information (1990 - 2008): Data are based on Labour Force Survey, enterprise survey and registers Country: Kyrgyzstan Additional information (1995 - onwards): Data for private sector are obtained by subtracting the number of employed in public sector from the total number of employed. Country: Latvia Change in definition (1995 - 2001): Data refer to the population aged 15+. Country: Latvia Change in definition (2002 - 2012): Data refer to the population aged 15-74. Country: Latvia Reference period (1995): Data refer to 1996. Country: Luxembourg Change in definition (1990 - 2008): There is no sector variable in the LFS. The public sector is defined as the sum of the NACE rev1 sections L and M Country: Luxembourg Change in definition (2009 - 2012): There is no sector variable in the LFS. The public sector is defined as the sum of the NACE rev2 sections O and P Country: Luxembourg Reference period (1980): Data refer to 1983 Country: Poland Data are not fully comparable with the results of the surveys prior to 2010 as persons staying outside households for 12 months or longer are excluded from the survey (previously over 3 months). Country: Romania Mixed sector - included in ''private sector'' for years 2007 onward; for year 1995-2006 mixed sector is included in the ''sector not stated'' row. Break in series starting with year 2009. For years 2014 onward data were estimated using the resident population. For years 2009-2013 data were estimated based on revised population figures (resident population) in accordance to the 2011 Census results. Country: Serbia Data do not cover Kosovo and Metohija. Country: Slovakia Data are based on Labour Force Survey, enterprise survey and registers. Country: Slovenia Data come from the Slovenian Statistical Register of Employment and cover persons who hold paid employment, self-empoyed persons who have compulsory social insurance and trainees. Data do not cover persons working abroad. Country: Sweden Break in methodlogy (2004 - 2005): For "Employment Public/private sector not stated" persons working abroad are included in 2005 and forward but seen as outside the labor force in 2004 and before. Country: Switzerland Break in methodlogy (2010): Change to continuous survey. As of 2010: annual averages Country: Switzerland Change in definition (1980 - 1990): Sector not stated : data include trainees. Country: Switzerland Reference period (2000 - 2009): Data refer to 2nd quarter Country: Tajikistan Change in definition (2004): Data include working migrants Country: Turkey Break in methodlogy (2004): Data are revised according to the 2008 population projections. Country: Ukraine From 2014 data cover the territories under the government control. Country: Ukraine For 2000-2011 data compiled according ISIC 3 Rev.1, since 2012 ISIC 4 is in use Country: Ukraine Data do not cover the area of radioactive contamination from the Chernobyl disaster.
    • 四月 2019
      来源: United Nations Economic Commission for Europe
      上传者: Knoema
      访问日期: 22 四月, 2019
      选择数据集
      Source: UNECE Statistical Database, compiled from national and international (Eurostat and ILO) official sources. Definition: The employed are all the residents above a specified age who, during a specified brief period, either one week or one day, were in the following categories: (a) paid employment: (a1) at work: persons who, during the reference period, performed some work for wage or salary, in cash or in kind; (a2) with a job but not at work: persons who, having already worked in their present job, were temporarily not at work during the reference period and had a formal attachment to their job; (b) self-employment: (b1) at work: persons who, during the reference period, performed some work for profit or family gain, in cash or in kind; (b2) with an enterprise but not at work: persons with an enterprise, which may be a business enterprise, a farm or a service undertaking, who were temporarily not at work during the reference period for any specific reason. For additional information, see the International Conference of Labour Statisticians (ICLS). The breakdown by kind of economic activity is grouped into 3 categories. Agriculture includes agriculture, hunting, forestry and fishing (ISIC Rev.3.1 Sections A-B or ISIC Rev.4 Section A). Industry includes mining and quarrying, manufacturing, electricity, gas and water supply, and construction (ISIC Rev.3.1 Sections C-F or ISIC Rev.4 Sections B-F ). Services comprise all other economic activities (ISIC Rev.3.1 Sections G-Q or ISIC Rev.4 Sections G-U). Total employment provided in this table generally differ from total employment provided in Economic Statistics, which cover both residents and non-residents (according to the System of National Accounts 1993). General note: Data come from the Labour Force Survey (LFS) unless otherwise specified in country footnotes. Data from the LFS and from population censuses normally comply with the definition above. .. - data not available Country: Albania Break in methodology (1980): from 1990 to 2006, data are based on administrative registers with sector breakdown according of NACE rev 1.1 Country: Albania Break in methodology (2007): As of 2007 data are based on the Labour Force Survey. Sectors broken down according to NACE rev 1.1 (2007-2014) and NACE rev since 2015. Country: Armenia Break in methodlogy (2007, 2014): For the period of 1980-2000 and 2002-2006 data on employment are based on integrated data received from various sources. From 2007 to 2013 data are based on the Integrated Survey of the Household Living Standards. Since 2014 data are based on the Labour Force Survey. Country: Armenia Break in methodlogy (2008): Data for 2007 refer to the age group 16-75. Since 2008 data refer to the age group 15-75. Country: Austria 1980-1990 : data refer to national definition (Life Subsistence Concept). From 1995 : data comply with ILO definition. Country: Azerbaijan Official estimates. 1980 : data refer to 1983. Country: Belarus Data refer to the national classification. Services include construction. Country: Belgium 1980 : data refer to 1983. Country: Bosnia and Herzegovina From year 2006 to 2011, data compiled using ISIC Rev 3.1, from 2012 using ISIC Rev 4. Country: Bulgaria 1995 : data refer to 1997. Country: Canada Data do not cover the three northern territories (Yukon, Northwest and Nunavuk ). Country: Croatia 1995 : data refer to 1996. Country: Cyprus Data cover only the area controlled by the Republic of Cyprus. 1990 : data refer to 1992. Country: Denmark 1980 : data refer to 1982. Country: Estonia 1990-1995 : data refer to the population aged 15-69. From 2000 : data refer to the population aged 15-74. Country: Finland Data refer to the population aged 15-74. Country: France Data do not cover overseas departments (DOM). Country: Georgia Break in methodology (1980 - 1995): Data are based on administrative registers Country: Georgia Territorial change (1995 onward): Data do not cover Abkhazia AR and Tskhinvali Region Country: Germany 1980 : data refer to 1983. From 1991 : data cover former German Democratic Republic (East Germany). Country: Hungary 1990 : data refer to 1992. Country: Iceland 1980 : data refer to 1981 and are based on administrative registers. 1990 : data refer to 1991. 1980 : data refer to the population aged 15-74. From 1990 : data refer to the population aged 16-74. Country: Ireland 1980 : data refer to 1983. Country: Israel Break in methodlogy (2000): In 1998: 1) Changes in the weighting method; 2) Transition to the 1995 Population Census estimates; See explanations: http://www.cbs.gov.il/www/publications/saka_change/tch_e.pdf Country: Israel Break in methodlogy (2001): Changes in the weighting method. See explanations: http://www.cbs.gov.il/www/saka_y/e_intro_f1_comparison-mimi.f Country: Israel Break in methodlogy (2009): 1) Update of the definition of the civilian labour force characteristics; 2) Transition to the 2008 Population Census estimates. See explanations: http://www.cbs.gov.il/publications11/1460/pdf/intro05_e.pdf Country: Israel Break in methodlogy (2012): 1) Transitiom from a quarterly to a monthly LFS; 2) Changes in the definitions of labour force characteristics (including compulsory and permanent military service into labour force). See explanations: http://www.cbs.gov.il/publications/labour_survey04/labour_f--orce_survey/answer_question_e_2012.pdf Country: Israel Break in methodlogy (2013): Changes in the Standard Industrial Classification of Economic Activities based on ISIC Rev.4; See explanations: http://www.cbs.gov.il/publications12/economic_activities11/--pdf/e_print.pdf Country: Israel Change in definition (1995): 1) Update of the definitions of labour force characteristics; 2) Changes in the Standard Industrial Classification of Economic Activities; See explanations: http://www.cbs.gov.il/www/publications/saka_change/tch_e.pdf Country: Israel Change in definition (2003): Changes in the Standard Industrial Classification of Economic Activities 2003, which mainly involved expanding the classification of high-tech industries; See explanations: http://www.cbs.gov.il/www/saka_y/e_int_g.pdf Country: Italy 1980 : data refer to 1983. 1980-1990 : data refer to the economically active population aged 14+, which includes the persons who have been seeking employment in the last 6 months. From 1995 : data refer to the economically active population aged 15+, which includes the persons who have been seeking employment in the last 30 days. Country: Kyrgyzstan Reference period (1995): Data refer to 1996 Country: Latvia 1995 : data refer to 1996. Country: Lithuania 1995 : data refer to 1997. Country: Luxembourg 1980 : data refer to 1983. Country: Moldova, Republic of Data exclude the territory of the Transnistria and municipality of Bender Country: Netherlands 1980 : data refer to 1983. Country: Poland 1990 : official estimates based on administrative registers. Country: Romania 1995 : data refer to the population aged 14+. Country: Russian Federation Change in definition (2000 - 2013): Data present the population aged 15-72 years Country: Russian Federation Territorial change (1990 - 2006): Data do not include the Chechen Republic Country: Serbia Territorial change (2000 onward): Data do not cover Kosovo and Metohija. Country: Sweden Data refer to the population aged 16-64. Country: Turkey Break in series (2014): Since 2014 series are not comparable with the previous years due to methodological changes in LFS. Country: Turkey Break in methodlogy (2004): Data are revised according to the 2008 population projections. Country: Turkey Up to 2008, economic activities in labour force survey (LFS) were coded by NACE Rev 1. From 2009 onwards, NACE Rev 2 has been used. Country: Ukraine For 2000-2011 data compiled according ISIC 3 Rev.1, since 2012 ISIC 4 is in use Country: Ukraine Territorial change (2000 - 2012): Data do not cover the area of radioactive contamination from the Chernobyl disaster. Country: United Kingdom Data refer to the population aged 16+. Country: United States Data refer to the population aged 16+. Agriculture excludes forestry and fishing. Country: Uzbekistan Services include construction
    • 六月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 18 七月, 2019
      选择数据集
      The employed comprise all persons of working age who, during a specified brief period, were in one of the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). The series is part of the ILO estimates and is harmonized to account for differences in national data and scope of coverage, collection and tabulation methodologies as well as for other country-specific factors. Data for 1991-2016 are estimates while 2017-2021 data are projections. The dataset was updated as of November 2017. For more information, refer to the ILO estimates and projections methodological note.
    • 一月 2017
      来源: Eurostat
      上传者: Knoema
      访问日期: 21 一月, 2017
      选择数据集
      The source for regional typology statistics are regional indicators at NUTS level 3 published on the Eurostat website or existing in the Eurostat production database. The structure of this domain is as follows: - Metropolitan regions (met)    For details see http://ec.europa.eu/eurostat/web/metropolitan-regions/overview - Maritime policy indicators (mare)    For details see http://ec.europa.eu/eurostat/web/maritime-policy-indicators/overview - Urban-rural typology (urt)    For details see http://ec.europa.eu/eurostat/web/rural-development/overview
    • 十一月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 12 十一月, 2019
      选择数据集
      The employed comprise all persons of working age who, during a specified brief period, were in one of the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).
    • 十一月 2018
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 24 三月, 2019
      选择数据集
      The employed comprise all persons of working age who, during a specified brief period, were in one of the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).
    • 十一月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 12 十一月, 2019
      选择数据集
      The employed comprise all persons of working age who, during a specified brief period, were in one of the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).
    • 六月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 18 七月, 2019
      选择数据集
      The employed comprise all persons of working age who, during a specified brief period, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are disaggregated by economic activity, which refers to the main activity of the establishment in which a person worked during the reference period and does not depend on the specific duties or functions of the person's job, but on the characteristics of the economic unit in which this person works. The series is part of the ILO estimates and is harmonized to account for differences in national data and scope of coverage, collection and tabulation methodologies as well as for other country-specific factors. Data for 1991-2016 are estimates while 2017-2021 data are projections. The dataset was updated as of November 2017. For more information, refer to the indicator description and the ILO estimates and projections methodological note.
    • 十一月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 12 十一月, 2019
      选择数据集
      The employed comprise all persons of working age who, during a specified brief period, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are disaggregated by economic activity according to the latest version of the International Standard Industrial Classification of All Economic Activities (ISIC) available for that year, and presented for categories at the 2-digit level of the classification. Economic activity refers to the main activity of the establishment in which a person worked during the reference period and does not depend on the specific duties or functions of the person's job, but on the characteristics of the economic unit in which this person works.
    • 八月 2018
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 03 九月, 2018
      选择数据集
      The employed comprise all persons of working age who, during a specified brief period, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are disaggregated by economic activity according to the latest version of the International Standard Industrial Classification of All Economic Activities (ISIC) available for that year, and presented for a selection of categories at the 2-digit level of the classification. Economic activity refers to the main activity of the establishment in which a person worked during the reference period and does not depend on the specific duties or functions of the person's job, but on the characteristics of the economic unit in which this person works.
    • 十一月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 12 十一月, 2019
      选择数据集
      The employed comprise all persons of working age who, during a specified brief period, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are disaggregated by economic activity according to the latest version of the International Standard Industrial Classification of All Economic Activities (ISIC) available for that year. Economic activity refers to the main activity of the establishment in which a person worked during the reference period and does not depend on the specific duties or functions of the person's job, but on the characteristics of the economic unit in which this person works.
    • 十一月 2018
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 24 三月, 2019
      选择数据集
      The employed comprise all persons of working age who, during a specified brief period, were in one of the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).
    • 十一月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 12 十一月, 2019
      选择数据集
      The employed comprise all persons of working age who, during a specified brief period, were in one of the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are disaggregated by level of education, which refers to the highest level of education completed, classified according to the International Standard Classification of Education (ISCED).
    • 十一月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 12 十一月, 2019
      选择数据集
      The employed comprise all persons of working age who, during a specified brief period, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are provided by institutional sector, which refers to disaggregations by public and private sector employment. Public sector employment covers employment in the government sector plus employment in publicly-owned resident enterprises and companies, operating at central, state (or regional) and local levels of government. It covers all persons employed directly by those institutions, regardless of the particular type of employment contract. Private sector employment comprises employment in all resident units operated by private enterprises, that is, it excludes enterprises controlled or operated by the government sector.
    • 六月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 18 七月, 2019
      选择数据集
      The employed comprise all persons of working age who, during a specified brief period, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are disaggregated by occupation according to the latest version of the International Standard Classification of Occupations (ISCO). Information on occupation provides a description of the set of tasks and duties which are carried out by, or can be assigned to, one person. The series is part of the ILO estimates and is harmonized to account for differences in national data and scope of coverage, collection and tabulation methodologies as well as for other country-specific factors. Data for 1991-2016 are estimates while 2017-2021 data are projections. The dataset was updated as of November 2017. For more information, refer to the indicator description and the labour force estimates and projections methodological paper. 
    • 十一月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 12 十一月, 2019
      选择数据集
      The employed comprise all persons of working age who, during a specified brief period, were in one of the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are disaggregated by occupation according to the latest version of the International Standard Classification of Occupations (ISCO) available for that year and presented for a selection of categories at the 2-digit level of the classification. Information on occupation provides a description of the set of tasks and duties which are carried out by, or can be assigned to, one person.
    • 八月 2018
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 03 九月, 2018
      选择数据集
      The employed comprise all persons of working age who, during a specified brief period, were in one of the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are disaggregated by occupation according to the latest version of the International Standard Classification of Occupations (ISCO) available for that year and presented for a selection of categories at the 2-digit level of the classification. Information on occupation provides a description of the set of tasks and duties which are carried out by, or can be assigned to, one person.
    • 十一月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 12 十一月, 2019
      选择数据集
      The employed comprise all persons of working age who, during a specified brief period, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are disaggregated by occupation according to the latest version of the International Standard Classification of Occupations (ISCO) available for that year. Information on occupation provides a description of the set of tasks and duties which are carried out by, or can be assigned to, one person.
    • 六月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 18 七月, 2019
      选择数据集
      The employed comprise all persons of working age who, during a specified brief period, were in one of the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are disaggregated by status in employment according to the latest version of the International Standard Classification of Status in Employment (ICSE-93). Status in employment refers to the type of explicit or implicit contract of employment the person has with other persons or organizations. The basic criteria used to define the groups of the classification are the type of economic risk and the type of authority over establishments and other workers which the job incumbents have or will have. The series is part of the ILO estimates and is harmonized to account for differences in national data and scope of coverage, collection and tabulation methodologies as well as for other country-specific factors. Data for 1991-2016 are estimates while 2017-2021 data are projections. The dataset was updated as of November 2017. For more information, refer to the ILO estimates and projections methodological note.
    • 十一月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 12 十一月, 2019
      选择数据集
      The employed comprise all persons of working age who, during a specified brief period, were in one of the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are disaggregated by status in employment according to the latest version of the International Standard Classification of Status in Employment (ICSE-93). Status in employment refers to the type of explicit or implicit contract of employment the person has with other persons or organizations. The basic criteria used to define the groups of the classification are the type of economic risk and the type of authority over establishments and other workers which the job incumbents have or will have.
    • 十一月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 12 十一月, 2019
      选择数据集
      The employed comprise all persons of working age who, during a specified brief period, were in one of the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are disaggregated by weekly hours actually worked, on the basis of the mean number of hours of work per week, and with reference to hours worked in all jobs of employed persons and in all types of working time arrangements (e.g. full-time and part-time). Hours actually worked include (a) direct hours or the time spent carrying out the tasks and duties of a job, (b) related hours, or the time spent maintaining, facilitating or enhancing productive activities (c) down time, or time when a person in a job cannot work due to machinery or process breakdown, accident, lack of supplies or power or Internet access and (d) resting time, or time spent in short periods of rest, relief or refreshment, including tea, coffee or prayer breaks, generally practised by custom or contract according to established norms and/or national circumstances. Hours actually worked excludes time not worked during activities such as: (a) Annual leave, public holidays, sick leave, parental leave or maternity/paternity leave, other leave for personal or family reasons or civic duty, (b) Commuting time between work and home when no productive activity for the job is performed; for paid employment, even when paid by the employer; (c) Time spent in certain educational activities; for paid employment, even when authorized, paid or provided by the employer; (d) Longer breaks distinguished from short resting time when no productive activity is performed (such as meal breaks or natural repose during long trips); for paid employment, even when paid by the employer.
    • 十一月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 10 十一月, 2019
      选择数据集
      The section 'LFS series - detailed quarterly survey results' reports detailed quarterly results going beyond the EU-LFS main aggregates, which have a separate data domain and some methodological differences. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 31 十月, 2019
      选择数据集
      The section 'LFS series - detailed annual survey results' reports annual results from the EU-LFS. While LFS is a quarterly survey, it is also possible to produce annual results. There are several ways of doing it, see section '18.5 Data compilation' below for details. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metadata. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 十一月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 11 十一月, 2019
      选择数据集
    • 八月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 12 八月, 2019
      选择数据集
      The section 'LFS series - detailed annual survey results' reports annual results from the EU-LFS. While LFS is a quarterly survey, it is also possible to produce annual results. There are several ways of doing it, see section '20.5 Data compilation' below for details. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 八月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 09 八月, 2019
      选择数据集
      The section 'LFS series - detailed quarterly survey results' reports detailed quarterly results going beyond the EU-LFS main aggregates, which have a separate data domain and some methodological differences. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 十一月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 09 十一月, 2019
      选择数据集
      The section 'LFS series - detailed quarterly survey results' reports detailed quarterly results going beyond the EU-LFS main aggregates, which have a separate data domain and some methodological differences. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity.
    • 八月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 14 八月, 2019
      选择数据集
      The section 'LFS series - detailed annual survey results' reports annual results from the EU-LFS. While LFS is a quarterly survey, it is also possible to produce annual results. There are several ways of doing it, see section '20.5 Data compilation' below for details. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 八月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 09 八月, 2019
      选择数据集
      The section 'LFS series - detailed quarterly survey results' reports detailed quarterly results going beyond the EU-LFS main aggregates, which have a separate data domain and some methodological differences. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 十一月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 10 十一月, 2019
      选择数据集
      The section 'LFS series - detailed quarterly survey results' reports detailed quarterly results going beyond the EU-LFS main aggregates, which have a separate data domain and some methodological differences. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 31 十月, 2019
      选择数据集
      The section 'LFS series - detailed annual survey results' reports annual results from the EU-LFS. While LFS is a quarterly survey, it is also possible to produce annual results. There are several ways of doing it, see section '20.5 Data compilation' below for details. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 十一月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 12 十一月, 2019
      选择数据集
      The employed comprise all persons of working age who, during a specified brief period, were in one of the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are disaggregated by level of education, which refers to the highest levelof education completed, classified according to the International Standard Classification of Education (ISCE).
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 31 十月, 2019
      选择数据集
      The section 'LFS series - detailed annual survey results' reports annual results from the EU-LFS. While LFS is a quarterly survey, it is also possible to produce annual results. There are several ways of doing it, see section '20.5 Data compilation' below for details. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 十一月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 10 十一月, 2019
      选择数据集
      The section 'LFS series - detailed quarterly survey results' reports detailed quarterly results going beyond the EU-LFS main aggregates, which have a separate data domain and some methodological differences. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 31 十月, 2019
      选择数据集
      The section 'LFS series - detailed annual survey results' reports annual results from the EU-LFS. While LFS is a quarterly survey, it is also possible to produce annual results. There are several ways of doing it, see section '18.5 Data compilation' below for details. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metadata. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 31 十月, 2019
      选择数据集
      The section 'LFS series - detailed annual survey results' reports annual results from the EU-LFS. While LFS is a quarterly survey, it is also possible to produce annual results. There are several ways of doing it, see section '18.5 Data compilation' below for details. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metadata. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 30 十月, 2019
      选择数据集
      The source for the regional labour market information down to NUTS level 2 is the EU Labour Force Survey (EU-LFS). This is a quarterly household sample survey conducted in all Member States of the EU and in EFTA and Candidate countries.  The EU-LFS survey follows the definitions and recommendations of the International Labour Organisation (ILO). To achieve further harmonisation, the Member States also adhere to common principles when formulating questionnaires. The LFS' target population is made up of all persons in private households aged 15 and over. For more information see the EU Labour Force Survey (lfsi_esms, see paragraph 21.1.).  The EU-LFS is designed to give accurate quarterly information at national level as well as annual information at NUTS 2 regional level and the compilation of these figures is well specified in the regulation. Microdata including the NUTS 2 level codes are provided by all the participating countries with a good degree of geographical comparability, which allows the production and dissemination of a complete set of comparable indicators for this territorial level. At present the transmission of the regional labour market data at NUTS 3 level has no legal basis. However many countries transmit NUTS 3 figures to Eurostat on a voluntary basis, under the understanding that they are not for publication with such detail, but for aggregation in few categories per country, i.e., metropolitan regions and urban-rural typology. Most of the NUTS 3 data are based on the LFS while some countries transmit data based on registers, administrative data, small area estimation and other reliable sources.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 12 十月, 2019
      选择数据集
      The source for regional typology statistics are regional indicators at NUTS level 3 published on the Eurostat website or existing in the Eurostat production database. The structure of this domain is as follows: - Metropolitan regions (met)    For details see http://ec.europa.eu/eurostat/web/metropolitan-regions/overview - Maritime policy indicators (mare)    For details see http://ec.europa.eu/eurostat/web/maritime-policy-indicators/overview - Urban-rural typology (urt)    For details see http://ec.europa.eu/eurostat/web/rural-development/overview
    • 十一月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 11 十一月, 2019
      选择数据集
    • 十一月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 10 十一月, 2019
      选择数据集
      The section 'LFS series - detailed quarterly survey results' reports detailed quarterly results going beyond the EU-LFS main aggregates, which have a separate data domain and some methodological differences. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 31 十月, 2019
      选择数据集
      The section 'LFS series - detailed annual survey results' reports annual results from the EU-LFS. While LFS is a quarterly survey, it is also possible to produce annual results. There are several ways of doing it, see section '20.5 Data compilation' below for details. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 十一月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 12 十一月, 2019
      选择数据集
      The employed comprise all persons of working age who, during a specified brief period, were in one of the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are provided by age and geographical coverage, which entails a disaggregation by rural and urban areas.
    • 一月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 22 一月, 2019
      选择数据集
      The employed comprise all persons of working age who, during a specified brief period, were in one of the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are disaggregated by age and status in employment according to the latest version of the International Standard Classification of Status in Employment (ICSE-93). Status in employment refers to the type of explicit or implicit contract of employment the person has with other persons or organizations. The basic criteria used to define the groups of the classification are the type of economic risk and the type of authority over establishments and other workers which the job incumbents have or will have.
    • 十一月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 12 十一月, 2019
      选择数据集
      The employed comprise all persons of working age who, during a specified brief period, were in one of the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 30 十月, 2019
      选择数据集
      The source for the regional labour market information down to NUTS level 2 is the EU Labour Force Survey (EU-LFS). This is a quarterly household sample survey conducted in all Member States of the EU and in EFTA and Candidate countries.  The EU-LFS survey follows the definitions and recommendations of the International Labour Organisation (ILO). To achieve further harmonisation, the Member States also adhere to common principles when formulating questionnaires. The LFS' target population is made up of all persons in private households aged 15 and over. For more information see the EU Labour Force Survey (lfsi_esms, see paragraph 21.1.).  The EU-LFS is designed to give accurate quarterly information at national level as well as annual information at NUTS 2 regional level and the compilation of these figures is well specified in the regulation. Microdata including the NUTS 2 level codes are provided by all the participating countries with a good degree of geographical comparability, which allows the production and dissemination of a complete set of comparable indicators for this territorial level. At present the transmission of the regional labour market data at NUTS 3 level has no legal basis. However many countries transmit NUTS 3 figures to Eurostat on a voluntary basis, under the understanding that they are not for publication with such detail, but for aggregation in few categories per country, i.e., metropolitan regions and urban-rural typology. Most of the NUTS 3 data are based on the LFS while some countries transmit data based on registers, administrative data, small area estimation and other reliable sources.
    • 十一月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 10 十一月, 2019
      选择数据集
      The section 'LFS series - detailed quarterly survey results' reports detailed quarterly results going beyond the EU-LFS main aggregates, which have a separate data domain and some methodological differences. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 31 十月, 2019
      选择数据集
      The section 'LFS series - detailed annual survey results' reports annual results from the EU-LFS. While LFS is a quarterly survey, it is also possible to produce annual results. There are several ways of doing it, see section '20.5 Data compilation' below for details. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 十一月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 09 十一月, 2019
      选择数据集
      The section 'LFS series - detailed quarterly survey results' reports detailed quarterly results going beyond the EU-LFS main aggregates, which have a separate data domain and some methodological differences. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 31 十月, 2019
      选择数据集
      The section 'LFS series - detailed annual survey results' reports annual results from the EU-LFS. While LFS is a quarterly survey, it is also possible to produce annual results. There are several ways of doing it, see section '20.5 Data compilation' below for details. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 十一月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 10 十一月, 2019
      选择数据集
      The section 'LFS series - detailed quarterly survey results' reports detailed quarterly results going beyond the EU-LFS main aggregates, which have a separate data domain and some methodological differences. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 31 十月, 2019
      选择数据集
      The section 'LFS series - detailed annual survey results' reports annual results from the EU-LFS. While LFS is a quarterly survey, it is also possible to produce annual results. There are several ways of doing it, see section '20.5 Data compilation' below for details. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 十一月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 11 十一月, 2019
      选择数据集
    • 十一月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 11 十一月, 2019
      选择数据集
    • 十一月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 12 十一月, 2019
      选择数据集
    • 十一月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 12 十一月, 2019
      选择数据集
    • 十一月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 11 十一月, 2019
      选择数据集
    • 十一月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 11 十一月, 2019
      选择数据集
      The section 'LFS series - detailed quarterly survey results' reports detailed quarterly results going beyond the EU-LFS main aggregates, which have a separate data domain and some methodological differences. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 十一月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 11 十一月, 2019
      选择数据集
    • 十一月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 12 十一月, 2019
      选择数据集
    • 十一月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 12 十一月, 2019
      选择数据集
      The employed comprise all persons of working age who, during a specified brief period, were in one of the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are disaggregated by status in employment according to the latest version of the International Standard Classification of Status in Employment (ICSE-93). Status in employment refers to the type of explicit or implicit contract of employment the person has with other persons or organizations. The basic criteria used to define the groups of the classification are the type of economic risk and the type of authority over establishments and other workers which the job incumbents have or will have. Data are also disaggregated by economic activity according to the latest version of the International Standard Industrial Classification of All Economic Activities (ISIC) available for that year. Economic activity refers to the main activity of the establishment in which a person worked during the reference period and does not depend on the specific duties or functions of the person's job, but on the characteristics of the economic unit in which this person works.
    • 十一月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 12 十一月, 2019
      选择数据集
      The employed comprise all persons of working age who, during a specified brief period, were in one of the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are disaggregated by status in employment according to the latest version of the International Standard Classification of Status in Employment (ICSE-93). Status in employment refers to the type of explicit or implicit contract of employment the person has with other persons or organizations. The basic criteria used to define the groups of the classification are the type of economic risk and the type of authority over establishments and other workers which the job incumbents have or will have. Data are also disaggregated by occupation according to the latest version of the International Standard Classification of Occupations (ISCO) available for that year. Information on occupation provides a description of the set of tasks and duties which are carried out by, or can be assigned to, one person.
    • 十一月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 12 十一月, 2019
      选择数据集
    • 十一月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 12 十一月, 2019
      选择数据集
    • 四月 2019
      来源: United Nations Economic Commission for Europe
      上传者: Knoema
      访问日期: 22 四月, 2019
      选择数据集
      Source: UNECE Statistical Database, compiled from national and international (Eurostat and ILO) official sources. Definition: The status of employment is defined with reference to the distinction between 'paid employment' and 'self-employment' jobs. Workers holding paid-employment jobs have explicit (written or oral) or implicit employment contracts which give them a basic remuneration which is not directly dependent upon the revenue of the unit for which they work. Self-employment jobs are jobs where the remuneration is directly dependent upon the profits derived from the goods and services produced. Employees are all the workers who hold paid employment jobs. Employers are workers who hold self-employment jobs and have engaged, on a continuous basis, one or more persons to work for them in their business as employees. Own-account workers are workers who hold self-employment jobs and have not engaged, on a continuous basis, any employees to work for them during the reference period. Members of producers cooperatives are workers who hold self-employment jobs in a cooperative producing goods and services, in which each member takes part on an equal footing with other members in determining the organisation of production, sales and/or other work of the establishment, the investments and the distribution of the proceeds of the establishment amongst their members. Family workers are workers who hold self-employment jobs in a market-oriented establishment operated by a related person living in the same household. For additional information, see the International Classification of Status in Employment (ICSE-93). General note: Data come from the Labour Force Survey (LFS) unless otherwise specified. Data from the LFS and from population censuses normally comply with the definition above. .. - data not available Country: Austria 1980-1990 : data refer to national definition (Life Subsistence Concept). 1980 : data on employers include own-account workers and family workers. 1990 : data on employers include own-account workers. Country: Azerbaijan Data are based on Population Census and administrative registers. Country: Belarus Break in methodlogy (2000): Data refer to 1999 Population Census. Country: Belarus 2009: data are from the Population Census. Parts do not equal the totals due to employed persons not indicated their status in employment. Country: Belgium 1980 : data refer to 1983. Country: Bosnia and Herzegovina Estimates for family workers are less reliable in 2014-2015. Country: Bulgaria 1990 : data refer to 1993. Data on own-account workers include members of producers cooperatives. Country: Croatia 1995 : data refer to 1996. Country: Cyprus Data cover only the area controlled by the Republic of Cyprus. 1990 : data refer to 1992. Country: Czechia From 2002 : data on own-account workers include members of producers cooperatives. Country: Denmark 1980 : data refer to 1983; data on employers include own-account workers. Country: Estonia Data on employers and own-account workers include members of producers cooperatives. 1990-1995 : data refer to the population aged 15-69. From 2000 : data refer to the population aged 15-74. Country: Finland 1980-1995 : data on employers include own-account workers. Country: France Data do not cover overseas departments (DOM). 1980 : data refer to 1983. Country: Germany 1980 : data refer to 1983. Country: Greece 1980 : data refer to 1983. Country: Iceland 1990 : data refer to 1991. Country: Ireland 1980 : data refer to 1983. Country: Israel 1990: data refer to 1992. 1998, 2001: methodology revised, data not strictly comparable. Country: Latvia 1995 : data refer to 1996. Country: Lithuania 1995 : data refer to 1997. Data on employers include own-account workers. Country: Netherlands 1980 : data refer to 1983. 1980-2001 : data on employers include own-account workers and members of producers cooperatives. Country: Norway 1980-2001 : data on employers include own-account workers and members of producers cooperatives. Country: Poland 1990 : data refer to 1992. Country: Romania 1995: data refer to population aged 14+. Country: Russian Federation Data refer to population aged 15-72. Country: Serbia Data do not cover Kosovo and Metohija. Country: Spain Data refer to population aged 16+. 2005: methodology revised, data not strictly comparable. Country: Switzerland 1990 : data refer to 1991. Country: Turkey 2000: data revision based on Population Census 2000 Country: Ukraine Data do not cover the persons who are still living in the area of Chernobyl contaminated with radioactive material. Data do not cover the persons who are living in institutions and those who are working in the army. Data refer to the population aged 15-70. Country: United Kingdom 1980 : data refer to 1983. Country: United States Data on employers include own-account workers. Data refer to population aged 16+. 1994: methodology revised, data not strictly comparable
    • 六月 2014
      来源: Eurostat
      上传者: Knoema
      访问日期: 28 十一月, 2015
      选择数据集
      In 2011, the European Union Labour Force Survey (EU-LFS) included an ad hoc module (AHM) on employment of disabled people. The module consisted of 11 variables dealing with: Health problems and difficulties in basic activities;Limitations in work caused by health problems/difficulties in basic activities;Special assistance needed or used by people with health problems/difficulties in basic activities;Limitation in work because of other reasons. On the basis of how the module was operationalised, the following two main definitions for disability were considered for presenting the results: Disabled persons = People having a basic activity difficulty (such as seeing, hearing, walking, communicating);Disabled persons = People having a work limitation caused by a longstanding health condition and/or a basic activity difficulty. 32 countries have implemented this module: the EU 28 Member States plus Turkey, Iceland, Norway and Switzerland. The Norwegian data are not disseminated because the AHM questionnaire in Norway only partly complies with the Commission Regulation (EU) No 317/2010 and consequently, the data are incomplete and partly comparable. Missing values, don't know and refusal answers are not considered in the calculations. It means the indicators have been worked out on the respondents and validated answers only.
    • 六月 2014
      来源: Eurostat
      上传者: Knoema
      访问日期: 28 十一月, 2015
      选择数据集
      In 2011, the European Union Labour Force Survey (EU-LFS) included an ad hoc module (AHM) on employment of disabled people. The module consisted of 11 variables dealing with: Health problems and difficulties in basic activities;Limitations in work caused by health problems/difficulties in basic activities;Special assistance needed or used by people with health problems/difficulties in basic activities;Limitation in work because of other reasons. On the basis of how the module was operationalised, the following two main definitions for disability were considered for presenting the results: Disabled persons = People having a basic activity difficulty (such as seeing, hearing, walking, communicating);Disabled persons = People having a work limitation caused by a longstanding health condition and/or a basic activity difficulty. 32 countries have implemented this module: the EU 28 Member States plus Turkey, Iceland, Norway and Switzerland. The Norwegian data are not disseminated because the AHM questionnaire in Norway only partly complies with the Commission Regulation (EU) No 317/2010 and consequently, the data are incomplete and partly comparable. Missing values, don't know and refusal answers are not considered in the calculations. It means the indicators have been worked out on the respondents and validated answers only.
    • 六月 2014
      来源: Eurostat
      上传者: Knoema
      访问日期: 28 十一月, 2015
      选择数据集
      In 2011, the European Union Labour Force Survey (EU-LFS) included an ad hoc module (AHM) on employment of disabled people. The module consisted of 11 variables dealing with: Health problems and difficulties in basic activities;Limitations in work caused by health problems/difficulties in basic activities;Special assistance needed or used by people with health problems/difficulties in basic activities;Limitation in work because of other reasons. On the basis of how the module was operationalised, the following two main definitions for disability were considered for presenting the results: Disabled persons = People having a basic activity difficulty (such as seeing, hearing, walking, communicating);Disabled persons = People having a work limitation caused by a longstanding health condition and/or a basic activity difficulty. 32 countries have implemented this module: the EU 28 Member States plus Turkey, Iceland, Norway and Switzerland. The Norwegian data are not disseminated because the AHM questionnaire in Norway only partly complies with the Commission Regulation (EU) No 317/2010 and consequently, the data are incomplete and partly comparable. Missing values, don't know and refusal answers are not considered in the calculations. It means the indicators have been worked out on the respondents and validated answers only.
    • 六月 2014
      来源: Eurostat
      上传者: Knoema
      访问日期: 28 十一月, 2015
      选择数据集
      In 2011, the European Union Labour Force Survey (EU-LFS) included an ad hoc module (AHM) on employment of disabled people. The module consisted of 11 variables dealing with: Health problems and difficulties in basic activities;Limitations in work caused by health problems/difficulties in basic activities;Special assistance needed or used by people with health problems/difficulties in basic activities;Limitation in work because of other reasons. On the basis of how the module was operationalised, the following two main definitions for disability were considered for presenting the results: Disabled persons = People having a basic activity difficulty (such as seeing, hearing, walking, communicating);Disabled persons = People having a work limitation caused by a longstanding health condition and/or a basic activity difficulty. 32 countries have implemented this module: the EU 28 Member States plus Turkey, Iceland, Norway and Switzerland. The Norwegian data are not disseminated because the AHM questionnaire in Norway only partly complies with the Commission Regulation (EU) No 317/2010 and consequently, the data are incomplete and partly comparable. Missing values, don't know and refusal answers are not considered in the calculations. It means the indicators have been worked out on the respondents and validated answers only.
    • 六月 2014
      来源: Eurostat
      上传者: Knoema
      访问日期: 25 十一月, 2015
      选择数据集
      In 2011, the European Union Labour Force Survey (EU-LFS) included an ad hoc module (AHM) on employment of disabled people. The module consisted of 11 variables dealing with:Health problems and difficulties in basic activities;Limitations in work caused by health problems/difficulties in basic activities;Special assistance needed or used by people with health problems/difficulties in basic activities;Limitation in work because of other reasons. On the basis of how the module was operationalised, the following two main definitions for disability were considered for presenting the results:Disabled persons = People having a basic activity difficulty (such as seeing, hearing, walking, communicating);Disabled persons = People having a work limitation caused by a longstanding health condition and/or a basic activity difficulty. 32 countries have implemented this module: the EU 28 Member States plus Turkey, Iceland, Norway and Switzerland. The Norwegian data are not disseminated because the AHM questionnaire in Norway only partly complies with the Commission Regulation (EU) No 317/2010 and consequently, the data are incomplete and partly comparable. Missing values, don't know and refusal answers are not considered in the calculations. It means the indicators have been worked out on the respondents and validated answers only.
    • 六月 2014
      来源: Eurostat
      上传者: Knoema
      访问日期: 28 十一月, 2015
      选择数据集
      In 2011, the European Union Labour Force Survey (EU-LFS) included an ad hoc module (AHM) on employment of disabled people. The module consisted of 11 variables dealing with: Health problems and difficulties in basic activities;Limitations in work caused by health problems/difficulties in basic activities;Special assistance needed or used by people with health problems/difficulties in basic activities;Limitation in work because of other reasons. On the basis of how the module was operationalised, the following two main definitions for disability were considered for presenting the results: Disabled persons = People having a basic activity difficulty (such as seeing, hearing, walking, communicating);Disabled persons = People having a work limitation caused by a longstanding health condition and/or a basic activity difficulty. 32 countries have implemented this module: the EU 28 Member States plus Turkey, Iceland, Norway and Switzerland. The Norwegian data are not disseminated because the AHM questionnaire in Norway only partly complies with the Commission Regulation (EU) No 317/2010 and consequently, the data are incomplete and partly comparable. Missing values, don't know and refusal answers are not considered in the calculations. It means the indicators have been worked out on the respondents and validated answers only.
    • 六月 2014
      来源: Eurostat
      上传者: Knoema
      访问日期: 28 十一月, 2015
      选择数据集
      In 2011, the European Union Labour Force Survey (EU-LFS) included an ad hoc module (AHM) on employment of disabled people. The module consisted of 11 variables dealing with: Health problems and difficulties in basic activities;Limitations in work caused by health problems/difficulties in basic activities;Special assistance needed or used by people with health problems/difficulties in basic activities;Limitation in work because of other reasons. On the basis of how the module was operationalised, the following two main definitions for disability were considered for presenting the results: Disabled persons = People having a basic activity difficulty (such as seeing, hearing, walking, communicating);Disabled persons = People having a work limitation caused by a longstanding health condition and/or a basic activity difficulty. 32 countries have implemented this module: the EU 28 Member States plus Turkey, Iceland, Norway and Switzerland. The Norwegian data are not disseminated because the AHM questionnaire in Norway only partly complies with the Commission Regulation (EU) No 317/2010 and consequently, the data are incomplete and partly comparable. Missing values, don't know and refusal answers are not considered in the calculations. It means the indicators have been worked out on the respondents and validated answers only.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 31 十月, 2019
      选择数据集
      The section 'LFS series - detailed annual survey results' reports annual results from the EU-LFS. While LFS is a quarterly survey, it is also possible to produce annual results. There are several ways of doing it, see section '18.5 Data compilation' below for details. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metadata. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 二月 2018
      来源: Eurostat
      上传者: Knoema
      访问日期: 17 二月, 2018
      选择数据集
    • 四月 2015
      来源: Eurostat
      上传者: Knoema
      访问日期: 18 十二月, 2015
      选择数据集
    • 十一月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 11 十一月, 2019
      选择数据集
      The employed comprise all persons of working age who, during a specified brief period, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are disaggregated by economic activity, which refers to the main activity of the establishment in which a person worked during the reference period and does not depend on the specific duties or functions of the person's job, but on the characteristics of the economic unit in which this person works.
    • 十一月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 11 十一月, 2019
      选择数据集
      The employed comprise all persons of working age who, during a specified brief period, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are disaggregated by economic activity, which refers to the main activity of the establishment in which a person worked during the reference period and does not depend on the specific duties or functions of the person's job, but on the characteristics of the economic unit in which this person works.
    • 六月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 18 七月, 2019
      选择数据集
      The employed comprise all persons of working age who, during a specified brief period, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are disaggregated by economic activity, which refers to the main activity of the establishment in which a person worked during the reference period and does not depend on the specific duties or functions of the person's job, but on the characteristics of the economic unit in which this person works. The series is part of the ILO estimates and is harmonized to account for differences in national data and scope of coverage, collection and tabulation methodologies as well as for other country-specific factors. Data for 1991-2016 are estimates while 2017-2021 data are projections. The dataset was updated as of November 2017. For more information, refer to the indicator description and the ILO estimates and projections methodological note.
    • 十一月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 12 十一月, 2019
      选择数据集
      The employed comprise all persons of working age who, during a specified brief period, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are disaggregated by economic activity, which refers to the main activity of the establishment in which a person worked during the reference period and does not depend on the specific duties or functions of the person's job, but on the characteristics of the economic unit in which this person works.
    • 十一月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 12 十一月, 2019
      选择数据集
      The employed comprise all persons of working age who, during a specified brief period, were in one of the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are disaggregated by level of education, which refers to the highest levelof education completed, classified according to the International Standard Classification of Education (ISCE).
    • 十一月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 11 十一月, 2019
      选择数据集
      The employed comprise all persons of working age who, during a specified brief period, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are disaggregated by economic activity, which refers to the main activity of the establishment in which a person worked during the reference period and does not depend on the specific duties or functions of the person's job, but on the characteristics of the economic unit in which this person works.
    • 十一月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 11 十一月, 2019
      选择数据集
      The employed comprise all persons of working age who, during a specified brief period, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are disaggregated by economic activity, which refers to the main activity of the establishment in which a person worked during the reference period and does not depend on the specific duties or functions of the person's job, but on the characteristics of the economic unit in which this person works.
    • 六月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 18 七月, 2019
      选择数据集
      The employed comprise all persons of working age who, during a specified brief period, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are disaggregated by occupation according to the latest version of the International Standard Classification of Occupations (ISCO). Information on occupation provides a description of the set of tasks and duties which are carried out by, or can be assigned to, one person. The series is part of the ILO estimates and is harmonized to account for differences in national data and scope of coverage, collection and tabulation methodologies as well as for other country-specific factors. Data for 1991-2016 are estimates while 2017-2021 data are projections. The dataset was updated as of November 2017. For more information, refer to the indicator description and the labour force estimates and projections methodological paper. 
    • 十一月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 12 十一月, 2019
      选择数据集
      The employed comprise all persons of working age who, during a specified brief period, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are disaggregated by occupation according to the latest version of the International Standard Classification of Occupations (ISCO). Information on occupation provides a description of the set of tasks and duties which are carried out by, or can be assigned to, one person.
    • 六月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 18 七月, 2019
      选择数据集
      The employed comprise all persons of working age who, during a specified brief period, were in one of the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are disaggregated by status in employment according to the latest version of the International Standard Classification of Status in Employment (ICSE-93). Status in employment refers to the type of explicit or implicit contract of employment the person has with other persons or organizations. The basic criteria used to define the groups of the classification are the type of economic risk and the type of authority over establishments and other workers which the job incumbents have or will have. The series is part of the ILO estimates and is harmonized to account for differences in national data and scope of coverage, collection and tabulation methodologies as well as for other country-specific factors. Data for 1991-2016 are estimates while 2017-2021 data are projections. The dataset was updated as of November 2017. For more information, refer to the ILO estimates and projections methodological note.
    • 十一月 2019
      来源: International Labour Organization
      上传者: Knoema
      访问日期: 12 十一月, 2019
      选择数据集
      The employed comprise all persons of working age who, during a specified brief period, were in one of the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data are disaggregated by status in employment according to the latest version of the International Standard Classification of Status in Employment (ICSE-93). Status in employment refers to the type of explicit or implicit contract of employment the person has with other persons or organizations. The basic criteria used to define the groups of the classification are the type of economic risk and the type of authority over establishments and other workers which the job incumbents have or will have.
    • 十月 2018
      来源: Eurostat
      上传者: Knoema
      访问日期: 03 十一月, 2018
      选择数据集
      The 'LFS main indicators' section presents a selection of the main statistics on the labour market. They encompass indicators of activity employment and unemployment. Those indicators are based on the results of the European Labour Force Survey (EU-LFS), in few cases integrated with data sources like national accounts employment or registered unemployment. As a result of the application of adjustments, corrections and reconciliation of EU Labour Force Survey (EU-LFS), 'LFS main indicators' is the most complete and reliable collection of employment and unemployment data available in the sub-domain ' Employment and unemployment'. The EU-LFS data used for 'LFS main indicators' are, where necessary, adjusted and enriched in various ways, in accordance with the specificities of an indicator.  The most common adjustments cover: - correction of the main breaks in the LFS series - estimation of the missing values, (i.e. in case of missing quarters, annual results and EU aggregates are estimated using adjusted quarterly national labour force survey data or interpolations of the EU Labour Force Survey data with reference to the available quarter(s)) - reconciliations of the LFS data with other sources, mainly National Accounts (for Employment growth and activity branches) and national statistics on monthly unemployment (for Harmonised unemployment series). - for a number of indicators (employment, activity, unemployment, supplementary indicators) seasonally adjusted data are available Those adjustments may produce some differences between data published under 'LFS main indicators' and 'LFS series - Detailed survey results', particularly for back data. For the most recent years these two series converge, due to the implementation of a continuous quarterly survey and the improved quality of the data. This page focuses on the particularities of 'LFS main indicators' in general. There are special pages for indicators 'employment growth', 'population in jobless households', 'average exit age of labour market' and 'education indicators: life-long learning, early school leavers and youth education attainment level. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 26 十月, 2019
      选择数据集
      The indicator shows the percentage distribution of persons in employment aged 20-64 by job duration, i.e. for how many months they have been in their current job. Persons in employment are those who, during the reference week, performed work, even for just one hour a week, for pay, profit or family gain or who were not at work but had a job or business from which they were temporarily absent because of something like illness, holiday, industrial dispute or education and training. The indicator is based on the EU Labour Force Survey.
    • 十月 2019
      来源: Organisation for Economic Co-operation and Development
      上传者: Knoema
      访问日期: 08 十月, 2019
      选择数据集
      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.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 26 十月, 2019
      选择数据集
      Eurostat's database covers: 1) Production and trade in roundwood and wood products, including primary and secondary products; 2) Economic data on forestry and logging, including employment data; 3) Sustainable forest management, comprising forest resources (assets) and environmental data. The main types of primary forest products included in (1) are: roundwood, sawnwood, wood-based panels, pulp, and paper and paperboard. Secondary products include further processed wood and paper products. These products are presented in greater detail; definitions are available. All of the data are compiled from the Joint Forest Sector Questionnaire (JFSQ), except for table (e), which is directly extracted from Eurostat's international trade database COMEXT (HS/CN Chapter 44). The tables in (1) cover details of the following topics: - Roundwood removals and production by type of wood and assortment (a) - Roundwood production by type of ownership (b) - Production and trade in roundwood, fuelwood and other basic products (c) - Trade in industrial roundwood by assortment and species (d) - Tropical wood imports to the EU from Chapter 44 of the Harmonised System (e) - Production and trade in sawnwood, panels and other primary products (f) - Sawnwood trade by species (g) - Production and trade in pulp and paper & paperboard (h) - Trade in secondary wood and paper products (i) Data in (2) include the output, intermediate consumption, gross value added, fixed capital consumption, gross fixed capital formation and different measures of income of forestry and logging. The data are in current basic prices and are compatible with National Accounts. They are collected as part of Intergrated environmental and economic accounting for forests (IEEAF), which also covers labour input in annual work units (AWU). Under (2), two separate tables cover the number of employees of forestry and logging, the manufacture of wood and products of wood and cork, and the manufacture of paper and paper products, as estimated from the Labour Force Survey results. There are two separate tables because of the change in the EU's classification of economic activities from NACE Rev. 1.1 to NACE Rev. 2 in 2008. More detailed information on wood products and accounting, including definitions and questionnaires, can be found on our open-access communication platform under the interest group 'Forestry statistics and accounts'. Data in (3) are not collected by Eurostat, but by the FAO, UNECE, Forest Euope, the European Commission's departments for Environment and the Joint Research Centre. They include forest area, wood volume, defoliation on sample plots, fires and areas with protective functions.
    • 八月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 09 八月, 2019
      选择数据集
      Eurostat's database covers 1) Production and trade in roundwood and wood products, including primary and secondary products 2) Economic data on forestry and logging, including employment data 3) Sustainable forest management, comprising forest resources (assets) and environmental data. The main types of primary forest products included in (1) are: roundwood, sawnwood, wood-based panels, pulp, and paper and paperboard. Secondary products include further processed wood and paper products. These products are presented in greater detail; definitions are available. All of the data are compiled from the Joint Forest Sector Questionnaire (JFSQ), except for table (e), which is directly extracted from Eurostat's international trade database COMEXT (HS/CN Chapter 44). The tables in (1) cover details of the following topics: - Roundwood removals and production by type of wood and assortment (a) - Roundwood production by type of ownership (b) - Production and trade in roundwood, fuelwood and other basic products (c) - Trade in industrial roundwood by assortment and species (d) - Tropical wood imports to the EU from Chapter 44 of the Harmonised System (e) - Production and trade in sawnwood, panels and other primary products (f) - Sawnwood trade by species (g) - Production and trade in pulp and paper & paperboard (h) - Trade in secondary wood and paper products (i) Data in (2) include the output, intermediate consumption, gross value added, fixed capital consumption, gross fixed capital formation and different measures of income of forestry and logging.  The data are in current basic prices and are compatible with National Accounts. They are collected as part of Intergrated environmental and economic accounting for forests (IEEAF), which also covers labour input in annual work units (AWU).  Under (2), two separate tables cover the number of employees of forestry and logging, the manufacture of wood and products of wood and cork, and the manufacture of paper and paper products, as estimated from the Labour Force Survey results. There are two separate tables because of the change in the EU's classification of economic activities from NACE Rev. 1.1 to NACE Rev. 2 in 2008. More detailed information on wood products and accounting, including definitions and questionnaires, can be found on our open-access communication platform under the interest group 'Forestry statistics and accounts'.  Data in (3) are not collected by Eurostat, but by the FAO, UNECE, Forest Euope, the European Commission's departments for Environment and the Joint Research Centre. They include forest area, wood volume, defoliation on sample plots, fires and areas with protective functions.
    • 三月 2015
      来源: Eurostat
      上传者: Knoema
      访问日期: 22 十一月, 2015
      选择数据集
      The data in this dataset comes from the Common Questionnaire for Transport Statistics, developed and surveyed in co-operation between the United Nations Economic Commission for Europe (UNECE), the International Transport Forum (ITF) and Eurostat. The Common Questionnaire is not supported by a legal act, but is based on a gentlemen's agreement with the participating countries; the completeness varies from country to country. Eurostat’s datasets based on the Common Questionnaire cover annual data for the EU Member States, EFTA states and Candidate countries to the EU. Data for other participating countries are available through the ITF and the UNECE. In total, comparable transport data collected through the Common Questionnaire is available for close to 60 countries worldwide. The Common Questionnaire collects aggregated annual data on:Railway transportRoad transportInland waterways transportOil pipelines transportGas pipelines transport For each mode of transport, the Common Questionnaire cover some or all of the following sub-modules (the number of questions/variables within each sub-module varies between the different modes of transport):Infrastructure (All modes)Transport equipment (RAIL, ROAD and INLAND WATERWAYS)Enterprises, economic performance and employment (All modes)Traffic (RAIL, ROAD and INLAND WATERWAYS)Transport measurement (All modes)Accidents (ROAD only) The Common Questionnaire is completed by the competent national authorities. The responsibility for completing specific modules (e.g. Transport by Rail) or part of modules (e.g. Road Infrastructure) may be delegated to other national authorities in charge of specific fields.
    • 三月 2015
      来源: Eurostat
      上传者: Knoema
      访问日期: 22 十一月, 2015
      选择数据集
      The data in this dataset comes from the Common Questionnaire for Transport Statistics, developed and surveyed in co-operation between the United Nations Economic Commission for Europe (UNECE), the International Transport Forum (ITF) and Eurostat. The Common Questionnaire is not supported by a legal act, but is based on a gentlemen's agreement with the participating countries; the completeness varies from country to country. Eurostat’s datasets based on the Common Questionnaire cover annual data for the EU Member States, EFTA states and Candidate countries to the EU. Data for other participating countries are available through the ITF and the UNECE. In total, comparable transport data collected through the Common Questionnaire is available for close to 60 countries worldwide. The Common Questionnaire collects aggregated annual data on:Railway transportRoad transportInland waterways transportOil pipelines transportGas pipelines transport For each mode of transport, the Common Questionnaire cover some or all of the following sub-modules (the number of questions/variables within each sub-module varies between the different modes of transport):Infrastructure (All modes)Transport equipment (RAIL, ROAD and INLAND WATERWAYS)Enterprises, economic performance and employment (All modes)Traffic (RAIL, ROAD and INLAND WATERWAYS)Transport measurement (All modes)Accidents (ROAD only) The Common Questionnaire is completed by the competent national authorities. The responsibility for completing specific modules (e.g. Transport by Rail) or part of modules (e.g. Road Infrastructure) may be delegated to other national authorities in charge of specific fields.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 26 十月, 2019
      选择数据集
      'Statistics on high-tech industry and knowledge-intensive services' (sometimes referred to as simply 'high-tech statistics') comprise economic, employment and science, technology and innovation (STI) data describing manufacturing and services industries or products traded broken down by technological intensity. The domain uses various other domains and sources of  Eurostat's official statistics (CIS, COMEXT, HRST, LFS, PATENT, R&D and SBS) and its coverage is therefore dependent on these other primary sources. Two main approaches are used in the domain to identify technology-intensity: the sectoral approach and the product approach. A third approach is used for data on high-tech and biotechnology patents aggregated on the basis of the International Patent Classification (IPC) 8th edition (see summary table in Annex 1 for which approach is used by each type of data). The sectoral approach: The sectoral approach is an aggregation of the manufacturing industries according to technological intensity (R&D expenditure/value added) and based on the Statistical classification of economic activities in the European Community (NACE)  at 2-digit level. The level of R&D intensity served as a criterion of classification of economic sectors into high-technology, medium high-technology, medium low-technology and low-technology industries. Services are mainly aggregated into knowledge-intensive services (KIS) and less knowledge-intensive services (LKIS) based on the share of tertiary educated persons at NACE 2-digit level. The sectoral approach is used for all indicators except data on high-tech trade and patents. Note that due to the revision of the NACE from NACE Rev. 1.1 to NACE Rev. 2 the definition of high-technology industries and knowledge-intensive services has changed in 2008. For high-tech statistics it means that two different definitions (one according NACE Rev. 1.1 and one according NACE Rev. 2) are used in parallel and the data according to both NACE versions are presented in separated tables depending on the data availability. For example as the LFS provides the results both by NACE Rev. 1.1 and NACE Rev. 2, all the table using this source have been duplicated to present the results by NACE Rev. 2 from 2008. For more details, see both definitions of high-tech sectors in Annex 2 and 3. Within the sectoral approach, a second classification was created , named Knowledge Intensive Activities KIA) and based on the share of tertiary educated people in each sectors of industries and services according to NACE at 2-digit level and for all EU28 Member States. A threshold was applied to judge sectors as knowledge intensive. In contrast to first sectoral approach mixing two methodologies, one for manufacturing industries and one for services, the KIA classification is based on one methodology for all the sectors of industries and services covering even public sector activities. The aggregations in use are Total Knowledge Intensive Activities (KIA) and Knowledge Intensive Activities in Business Industries (KIABI). Both classifications are made according to NACE Rev. 1.1 and NACE Rev. 2 at 2- digit level. Note that due to revision of the NACE Rev.1.1 to NACE Rev. 2 the list of Knowledge Intensive Activities has changed as well, the two definitions are used in parallel and the data are shown in two separate tables. NACE Rev.2 collection includes data starting from 2008 reference year. For more details please see the definitions in Annex 7 and 8. The product approach: The product approach was created to complement the sectoral approach and it is used for data on high-tech trade. The product list is based on the calculations of R&D intensity by groups of products (R&D expenditure/total sales). The groups classified as high-technology products are aggregated on the basis of the Standard International Trade Classification (SITC). The initial definition was built based on SITC Rev.3 and served to compile the high-tech product aggregates until 2007. With the implementation in 2007 of the new version of SITC Rev.4, the definition of high-tech groups was revised and adapted according to new classification. Starting from 2007 the Eurostat presents the trade data for high-tech groups aggregated based on the SITC Rev.4. . For more details, see definition of high-tech products in Annex 4 and 5. High-tech patents: High-tech patentsare defined according to another approach. The groups classified as high-tech patents are aggregated on the basis of the International Patent Classification (IPC 8th edition). Biotechnology patents are also aggregated on the basis of the IPC 8th edition. For more details, see the aggregation list of high-tech and biotechnology patents in Annex 6. The high-tech domain also comprises the sub-domain Venture Capital Investments: data are provided by INVEST Europe (formerly named the European Private Equity and Venture Capital Association EVCA). More details are available in the Eurostat metadata under Venture capital investments. Please note that for paragraphs where no metadata for regional data has been specified, the regional metadata is identical to the metadata provided for the national data.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 26 十月, 2019
      选择数据集
      The data shows the employment in high- and medium-high technology manufacturing sectors (code C_HTC_MH) and in knowledge-intensive service sectors (code KIS) as a share of total employment. Data source is the European Labour force survey (LFS). The definition of high- and medium-high technology manufacturing sectors and of knowledge-intensive services is based on a selection of relevant items of NACE Rev. 2 on 2-digit level and is oriented on the ratio of highly qualified working in these areas.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 26 十月, 2019
      选择数据集
      The data shows the employment in high-tech sectors (code HTC) as a percentage of total employment. The data are aggregated according to the sectoral approach at NACE Rev.2 on 2-digit level and is oriented on the ratio of highly qualified working in these areas.
    • 六月 2014
      来源: Eurostat
      上传者: Knoema
      访问日期: 22 十一月, 2015
      选择数据集
      The data in this dataset comes from the Common Questionnaire for Transport Statistics, developed and surveyed in co-operation between the United Nations Economic Commission for Europe (UNECE), the International Transport Forum (ITF) and Eurostat. The Common Questionnaire is not supported by a legal act, but is based on a gentlemen's agreement with the participating countries; the completeness varies from country to country. Eurostat’s datasets based on the Common Questionnaire cover annual data for the EU Member States, EFTA states and Candidate countries to the EU. Data for other participating countries are available through the ITFand the UNECE. In total, comparable transport data collected through the Common Questionnaire is available for close to 60 countries worldwide. The Common Questionnaire collects aggregated annual data on:Railway transportRoad transportInland waterways transportOil pipelines transportGas pipelines transport For each mode of transport, the Common Questionnaire cover some or all of the following sub-modules (the number of questions/variables within each sub-module varies between the different modes of transport):Infrastructure (All modes)Transport equipment (RAIL, ROAD and INLAND WATERWAYS)Enterprises, economic performance and employment (All modes)Traffic (RAIL, ROAD and INLAND WATERWAYS)Transport measurement (All modes) Accidents (ROAD only) The Common Questionnaire is completed by the competent national authorities. The responsibility for completing specific modules (e.g. Transport by Rail) or part of modules (e.g. Road Infrastructure) may be delegated to other national authorities in charge of specific fields.
    • 四月 2019
      来源: United Nations Economic Commission for Europe
      上传者: Knoema
      访问日期: 30 四月, 2019
      选择数据集
      .. - data not available Source: UNECE Transport Division Database. Please note that country footnotes are not always in alphabetical order.
    • 七月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 02 七月, 2019
      选择数据集
      Sport employment statistics are derived from data on employment based on the results of the European Labour Force Survey (EU-LFS). Employment in sport statistics aim at investigating on the dimension of the contribution of sport employment to the overall employment. The EU-LFS is the main source of information about the situation and trends on the labour market in the European Union . The methodology for the design and development of sport employment statistics is based on the one proposed by the final report of the European Statistical System Network on Culture (ESS-Net Culture Report 2012) which takes into account two reference classifications: the NACE classification (‘Nomenclature générale des Activités économiques dans les Communautés Européennes’) which classifies the main economic  activitiesthe ISCO classification (‘International Standard Classification of Occupations’) which classifies occupations. Results from the EU-LFS allow characterizing employment in sport by different variables such as gender, age, educational attainment by cross-tabulating ISCO and NACE selected sport codes.
    • 七月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 02 七月, 2019
      选择数据集
      Sport employment statistics are derived from data on employment based on the results of the European Labour Force Survey (EU-LFS). Employment in sport statistics aim at investigating on the dimension of the contribution of sport employment to the overall employment. The EU-LFS is the main source of information about the situation and trends on the labour market in the European Union . The methodology for the design and development of sport employment statistics is based on the one proposed by the final report of the European Statistical System Network on Culture (ESS-Net Culture Report 2012) which takes into account two reference classifications: the NACE classification (‘Nomenclature générale des Activités économiques dans les Communautés Européennes’) which classifies the main economic  activitiesthe ISCO classification (‘International Standard Classification of Occupations’) which classifies occupations. Results from the EU-LFS allow characterizing employment in sport by different variables such as gender, age, educational attainment by cross-tabulating ISCO and NACE selected sport codes.
    • 七月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 02 七月, 2019
      选择数据集
      Sport employment statistics are derived from data on employment based on the results of the European Labour Force Survey (EU-LFS). Employment in sport statistics aim at investigating on the dimension of the contribution of sport employment to the overall employment. The EU-LFS is the main source of information about the situation and trends on the labour market in the European Union . The methodology for the design and development of sport employment statistics is based on the one proposed by the final report of the European Statistical System Network on Culture (ESS-Net Culture Report 2012) which takes into account two reference classifications: the NACE classification (‘Nomenclature générale des Activités économiques dans les Communautés Européennes’) which classifies the main economic  activitiesthe ISCO classification (‘International Standard Classification of Occupations’) which classifies occupations. Results from the EU-LFS allow characterizing employment in sport by different variables such as gender, age, educational attainment by cross-tabulating ISCO and NACE selected sport codes.
    • 八月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 15 八月, 2019
      选择数据集
      'Statistics on high-tech industry and knowledge-intensive services' (sometimes referred to as simply 'high-tech statistics') comprise economic, employment and science, technology and innovation (STI) data describing manufacturing and services industries or products traded broken down by technological intensity. The domain uses various other domains and sources of  Eurostat's official statistics (CIS, COMEXT, HRST, LFS, PATENT, R&D and SBS) and its coverage is therefore dependent on these other primary sources. Two main approaches are used in the domain to identify technology-intensity: the sectoral approach and the product approach. A third approach is used for data on high-tech and biotechnology patents aggregated on the basis of the International Patent Classification (IPC) 8th edition (see summary table in Annex 1 for which approach is used by each type of data). The sectoral approach: The sectoral approach is an aggregation of the manufacturing industries according to technological intensity (R&D expenditure/value added) and based on the Statistical classification of economic activities in the European Community (NACE)  at 2-digit level. The level of R&D intensity served as a criterion of classification of economic sectors into high-technology, medium high-technology, medium low-technology and low-technology industries. Services are mainly aggregated into knowledge-intensive services (KIS) and less knowledge-intensive services (LKIS) based on the share of tertiary educated persons at NACE 2-digit level. The sectoral approach is used for all indicators except data on high-tech trade and patents. Note that due to the revision of the NACE from NACE Rev. 1.1 to NACE Rev. 2 the definition of high-technology industries and knowledge-intensive services has changed in 2008. For high-tech statistics it means that two different definitions (one according NACE Rev. 1.1 and one according NACE Rev. 2) are used in parallel and the data according to both NACE versions are presented in separated tables depending on the data availability. For example as the LFS provides the results both by NACE Rev. 1.1 and NACE Rev. 2, all the table using this source have been duplicated to present the results by NACE Rev. 2 from 2008. For more details, see both definitions of high-tech sectors under Annexes section. Within the sectoral approach, a second classification was created , named Knowledge Intensive Activities KIA) and based on the share of tertiary educated people in each sectors of industries and services according to NACE at 2-digit level and for all EU28 Member States. A threshold was applied to judge sectors as knowledge intensive. In contrast to first sectoral approach mixing two methodologies, one for manufacturing industries and one for services, the KIA classification is based on one methodology for all the sectors of industries and services covering even public sector activities. The aggregations in use are Total Knowledge Intensive Activities (KIA) and Knowledge Intensive Activities in Business Industries (KIABI). Both classifications are made according to NACE Rev. 1.1 and NACE Rev. 2 at 2- digit level. Note that due to revision of the NACE Rev.1.1 to NACE Rev. 2 the list of Knowledge Intensive Activities has changed as well, the two definitions are used in parallel and the data are shown in two separate tables. NACE Rev.2 collection includes data starting from 2008 reference year. For more details please see the definitions under Annexes section. The product approach: The product approach was created to complement the sectoral approach and it is used for data on high-tech trade. The product list is based on the calculations of R&D intensity by groups of products (R&D expenditure/total sales). The groups classified as high-technology products are aggregated on the basis of the Standard International Trade Classification (SITC). The initial definition was built based on SITC Rev.3 and served to compile the high-tech product aggregates until 2007. With the implementation in 2007 of the new version of SITC Rev.4, the definition of high-tech groups was revised and adapted according to new classification. Starting from 2007 the Eurostat presents the trade data for high-tech groups aggregated based on the SITC Rev.4. . For more details, see definition of high-tech products under Annexes section. High-tech patents: High-tech patents are defined according to another approach. The groups classified as high-tech patents are aggregated on the basis of the International Patent Classification (IPC 8th edition). Biotechnology patents are also aggregated on the basis of the IPC 8th edition. For more details, see the aggregation list of high-tech and biotechnology patents under Annexes section. The high-tech domain also comprises the sub-domain Venture Capital Investments: data are provided by INVEST Europe (formerly named the European Private Equity and Venture Capital Association EVCA). More details are available in the Eurostat metadata under Venture capital investments. Please note that for paragraphs where no metadata for regional data has been specified, the regional metadata is identical to the metadata provided for the national data.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 01 十一月, 2019
      选择数据集
      'Statistics on high-tech industry and knowledge-intensive services' (sometimes referred to as simply 'high-tech statistics') comprise economic, employment and science, technology and innovation (STI) data describing manufacturing and services industries or products traded broken down by technological intensity. The domain uses various other domains and sources of  Eurostat's official statistics (CIS, COMEXT, HRST, LFS, PATENT, R&D and SBS) and its coverage is therefore dependent on these other primary sources. Two main approaches are used in the domain to identify technology-intensity: the sectoral approach and the product approach. A third approach is used for data on high-tech and biotechnology patents aggregated on the basis of the International Patent Classification (IPC) 8th edition (see summary table in Annex 1 for which approach is used by each type of data). The sectoral approach: The sectoral approach is an aggregation of the manufacturing industries according to technological intensity (R&D expenditure/value added) and based on the Statistical classification of economic activities in the European Community (NACE) at 2-digit level. The level of R&D intensity served as a criterion of classification of economic sectors into high-technology, medium high-technology, medium low-technology and low-technology industries. Services are mainly aggregated into knowledge-intensive services (KIS) and less knowledge-intensive services (LKIS) based on the share of tertiary educated persons at NACE 2-digit level. The sectoral approach is used for all indicators except data on high-tech trade and patents. Note that due to the revision of the NACE from NACE Rev. 1.1 to NACE Rev. 2 the definition of high-technology industries and knowledge-intensive services has changed in 2008. For high-tech statistics it means that two different definitions (one according NACE Rev. 1.1 and one according NACE Rev. 2) are used in parallel and the data according to both NACE versions are presented in separated tables depending on the data availability. For example as the LFS provides the results both by NACE Rev. 1.1 and NACE Rev. 2, all the table using this source have been duplicated to present the results by NACE Rev. 2 from 2008. For more details, see both definitions of high-tech sectors in Annex 2 and 3. Within the sectoral approach, a second classification was created, named Knowledge Intensive Activities KIA) and based on the share of tertiary educated people in each sectors of industries and services according to NACE at 2-digit level and for all EU28 Member States. A threshold was applied to judge sectors as knowledge intensive. In contrast to first sectoral approach mixing two methodologies, one for manufacturing industries and one for services, the KIA classification is based on one methodology for all the sectors of industries and services covering even public sector activities. The aggregations in use are Total Knowledge Intensive Activities (KIA) and Knowledge Intensive Activities in Business Industries (KIABI). Both classifications are made according to NACE Rev. 1.1 and NACE Rev. 2 at 2- digit level. Note that due to revision of the NACE Rev.1.1 to NACE Rev. 2 the list of Knowledge Intensive Activities has changed as well, the two definitions are used in parallel and the data are shown in two separate tables. NACE Rev.2 collection includes data starting from 2008 reference year. For more details please see the definitions in Annex 7 and 8. The product approach: The product approach was created to complement the sectoral approach and it is used for data on high-tech trade. The product list is based on the calculations of R&D intensity by groups of products (R&D expenditure/total sales). The groups classified as high-technology products are aggregated on the basis of the Standard International Trade Classification (SITC). The initial definition was built based on SITC Rev.3 and served to compile the high-tech product aggregates until 2007. With the implementation in 2007 of the new version of SITC Rev.4, the definition of high-tech groups was revised and adapted according to new classification. Starting from 2007 the Eurostat presents the trade data for high-tech groups aggregated based on the SITC Rev.4. For more details, see definition of high-tech products in Annex 4 and 5. High-tech patents: High-tech patentsare defined according to another approach. The groups classified as high-tech patents are aggregated on the basis of the International Patent Classification (IPC 8th edition). Biotechnology patents are also aggregated on the basis of the IPC 8th edition. For more details, see the aggregation list of high-tech and biotechnology patents in Annex 6. The high-tech domain also comprises the sub-domain Venture Capital Investments: data are provided by INVEST Europe (formerly named the European Private Equity and Venture Capital Association EVCA). More details are available in the Eurostat metadata under Venture capital investments. Please note that for paragraphs where no metadata for regional data has been specified, the regional metadata is identical to the metadata provided for the national data.
    • 八月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 15 八月, 2019
      选择数据集
      'Statistics on high-tech industry and knowledge-intensive services' (sometimes referred to as simply 'high-tech statistics') comprise economic, employment and science, technology and innovation (STI) data describing manufacturing and services industries or products traded broken down by technological intensity. The domain uses various other domains and sources of  Eurostat's official statistics (CIS, COMEXT, HRST, LFS, PATENT, R&D and SBS) and its coverage is therefore dependent on these other primary sources. Two main approaches are used in the domain to identify technology-intensity: the sectoral approach and the product approach. A third approach is used for data on high-tech and biotechnology patents aggregated on the basis of the International Patent Classification (IPC) 8th edition (see summary table in Annex 1 for which approach is used by each type of data). The sectoral approach: The sectoral approach is an aggregation of the manufacturing industries according to technological intensity (R&D expenditure/value added) and based on the Statistical classification of economic activities in the European Community (NACE)  at 2-digit level. The level of R&D intensity served as a criterion of classification of economic sectors into high-technology, medium high-technology, medium low-technology and low-technology industries. Services are mainly aggregated into knowledge-intensive services (KIS) and less knowledge-intensive services (LKIS) based on the share of tertiary educated persons at NACE 2-digit level. The sectoral approach is used for all indicators except data on high-tech trade and patents. Note that due to the revision of the NACE from NACE Rev. 1.1 to NACE Rev. 2 the definition of high-technology industries and knowledge-intensive services has changed in 2008. For high-tech statistics it means that two different definitions (one according NACE Rev. 1.1 and one according NACE Rev. 2) are used in parallel and the data according to both NACE versions are presented in separated tables depending on the data availability. For example as the LFS provides the results both by NACE Rev. 1.1 and NACE Rev. 2, all the table using this source have been duplicated to present the results by NACE Rev. 2 from 2008. For more details, see both definitions of high-tech sectors under Annexes section. Within the sectoral approach, a second classification was created , named Knowledge Intensive Activities KIA) and based on the share of tertiary educated people in each sectors of industries and services according to NACE at 2-digit level and for all EU28 Member States. A threshold was applied to judge sectors as knowledge intensive. In contrast to first sectoral approach mixing two methodologies, one for manufacturing industries and one for services, the KIA classification is based on one methodology for all the sectors of industries and services covering even public sector activities. The aggregations in use are Total Knowledge Intensive Activities (KIA) and Knowledge Intensive Activities in Business Industries (KIABI). Both classifications are made according to NACE Rev. 1.1 and NACE Rev. 2 at 2- digit level. Note that due to revision of the NACE Rev.1.1 to NACE Rev. 2 the list of Knowledge Intensive Activities has changed as well, the two definitions are used in parallel and the data are shown in two separate tables. NACE Rev.2 collection includes data starting from 2008 reference year. For more details please see the definitions under Annexes section. The product approach: The product approach was created to complement the sectoral approach and it is used for data on high-tech trade. The product list is based on the calculations of R&D intensity by groups of products (R&D expenditure/total sales). The groups classified as high-technology products are aggregated on the basis of the Standard International Trade Classification (SITC). The initial definition was built based on SITC Rev.3 and served to compile the high-tech product aggregates until 2007. With the implementation in 2007 of the new version of SITC Rev.4, the definition of high-tech groups was revised and adapted according to new classification. Starting from 2007 the Eurostat presents the trade data for high-tech groups aggregated based on the SITC Rev.4. . For more details, see definition of high-tech products under Annexes section. High-tech patents: High-tech patents are defined according to another approach. The groups classified as high-tech patents are aggregated on the basis of the International Patent Classification (IPC 8th edition). Biotechnology patents are also aggregated on the basis of the IPC 8th edition. For more details, see the aggregation list of high-tech and biotechnology patents under Annexes section. The high-tech domain also comprises the sub-domain Venture Capital Investments: data are provided by INVEST Europe (formerly named the European Private Equity and Venture Capital Association EVCA). More details are available in the Eurostat metadata under Venture capital investments. Please note that for paragraphs where no metadata for regional data has been specified, the regional metadata is identical to the metadata provided for the national data.
    • 八月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 15 八月, 2019
      选择数据集
      'Statistics on high-tech industry and knowledge-intensive services' (sometimes referred to as simply 'high-tech statistics') comprise economic, employment and science, technology and innovation (STI) data describing manufacturing and services industries or products traded broken down by technological intensity. The domain uses various other domains and sources of  Eurostat's official statistics (CIS, COMEXT, HRST, LFS, PATENT, R&D and SBS) and its coverage is therefore dependent on these other primary sources. Two main approaches are used in the domain to identify technology-intensity: the sectoral approach and the product approach. A third approach is used for data on high-tech and biotechnology patents aggregated on the basis of the International Patent Classification (IPC) 8th edition (see summary table in Annex 1 for which approach is used by each type of data). The sectoral approach: The sectoral approach is an aggregation of the manufacturing industries according to technological intensity (R&D expenditure/value added) and based on the Statistical classification of economic activities in the European Community (NACE)  at 2-digit level. The level of R&D intensity served as a criterion of classification of economic sectors into high-technology, medium high-technology, medium low-technology and low-technology industries. Services are mainly aggregated into knowledge-intensive services (KIS) and less knowledge-intensive services (LKIS) based on the share of tertiary educated persons at NACE 2-digit level. The sectoral approach is used for all indicators except data on high-tech trade and patents. Note that due to the revision of the NACE from NACE Rev. 1.1 to NACE Rev. 2 the definition of high-technology industries and knowledge-intensive services has changed in 2008. For high-tech statistics it means that two different definitions (one according NACE Rev. 1.1 and one according NACE Rev. 2) are used in parallel and the data according to both NACE versions are presented in separated tables depending on the data availability. For example as the LFS provides the results both by NACE Rev. 1.1 and NACE Rev. 2, all the table using this source have been duplicated to present the results by NACE Rev. 2 from 2008. For more details, see both definitions of high-tech sectors under Annexes section. Within the sectoral approach, a second classification was created , named Knowledge Intensive Activities KIA) and based on the share of tertiary educated people in each sectors of industries and services according to NACE at 2-digit level and for all EU28 Member States. A threshold was applied to judge sectors as knowledge intensive. In contrast to first sectoral approach mixing two methodologies, one for manufacturing industries and one for services, the KIA classification is based on one methodology for all the sectors of industries and services covering even public sector activities. The aggregations in use are Total Knowledge Intensive Activities (KIA) and Knowledge Intensive Activities in Business Industries (KIABI). Both classifications are made according to NACE Rev. 1.1 and NACE Rev. 2 at 2- digit level. Note that due to revision of the NACE Rev.1.1 to NACE Rev. 2 the list of Knowledge Intensive Activities has changed as well, the two definitions are used in parallel and the data are shown in two separate tables. NACE Rev.2 collection includes data starting from 2008 reference year. For more details please see the definitions under Annexes section. The product approach: The product approach was created to complement the sectoral approach and it is used for data on high-tech trade. The product list is based on the calculations of R&D intensity by groups of products (R&D expenditure/total sales). The groups classified as high-technology products are aggregated on the basis of the Standard International Trade Classification (SITC). The initial definition was built based on SITC Rev.3 and served to compile the high-tech product aggregates until 2007. With the implementation in 2007 of the new version of SITC Rev.4, the definition of high-tech groups was revised and adapted according to new classification. Starting from 2007 the Eurostat presents the trade data for high-tech groups aggregated based on the SITC Rev.4. . For more details, see definition of high-tech products under Annexes section. High-tech patents: High-tech patentsare defined according to another approach. The groups classified as high-tech patents are aggregated on the basis of the International Patent Classification (IPC 8th edition). Biotechnology patents are also aggregated on the basis of the IPC 8th edition. For more details, see the aggregation list of high-tech and biotechnology patents under Annexes section. The high-tech domain also comprises the sub-domain Venture Capital Investments: data are provided by INVEST Europe (formerly named the European Private Equity and Venture Capital Association EVCA). More details are available in the Eurostat metadata under Venture capital investments. Please note that for paragraphs where no metadata for regional data has been specified, the regional metadata is identical to the metadata provided for the national data.
    • 八月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 13 八月, 2019
      选择数据集
      'Statistics on high-tech industry and knowledge-intensive services' (sometimes referred to as simply 'high-tech statistics') comprise economic, employment and science, technology and innovation (STI) data describing manufacturing and services industries or products traded broken down by technological intensity. The domain uses various other domains and sources of  Eurostat's official statistics (CIS, COMEXT, HRST, LFS, PATENT, R&D and SBS) and its coverage is therefore dependent on these other primary sources. Two main approaches are used in the domain to identify technology-intensity: the sectoral approach and the product approach. A third approach is used for data on high-tech and biotechnology patents aggregated on the basis of the International Patent Classification (IPC) 8th edition (see summary table in Annex 1 for which approach is used by each type of data). The sectoral approach: The sectoral approach is an aggregation of the manufacturing industries according to technological intensity (R&D expenditure/value added) and based on the Statistical classification of economic activities in the European Community (NACE) at 2-digit level. The level of R&D intensity served as a criterion of classification of economic sectors into high-technology, medium high-technology, medium low-technology and low-technology industries. Services are mainly aggregated into knowledge-intensive services (KIS) and less knowledge-intensive services (LKIS) based on the share of tertiary educated persons at NACE 2-digit level. The sectoral approach is used for all indicators except data on high-tech trade and patents. Note that due to the revision of the NACE from NACE Rev. 1.1 to NACE Rev. 2 the definition of high-technology industries and knowledge-intensive services has changed in 2008. For high-tech statistics it means that two different definitions (one according NACE Rev. 1.1 and one according NACE Rev. 2) are used in parallel and the data according to both NACE versions are presented in separated tables depending on the data availability. For example as the LFS provides the results both by NACE Rev. 1.1 and NACE Rev. 2, all the table using this source have been duplicated to present the results by NACE Rev. 2 from 2008. For more details, see both definitions of high-tech sectors in Annex 2 and 3. Within the sectoral approach, a second classification was created, named Knowledge Intensive Activities KIA) and based on the share of tertiary educated people in each sectors of industries and services according to NACE at 2-digit level and for all EU28 Member States. A threshold was applied to judge sectors as knowledge intensive. In contrast to first sectoral approach mixing two methodologies, one for manufacturing industries and one for services, the KIA classification is based on one methodology for all the sectors of industries and services covering even public sector activities. The aggregations in use are Total Knowledge Intensive Activities (KIA) and Knowledge Intensive Activities in Business Industries (KIABI). Both classifications are made according to NACE Rev. 1.1 and NACE Rev. 2 at 2- digit level. Note that due to revision of the NACE Rev.1.1 to NACE Rev. 2 the list of Knowledge Intensive Activities has changed as well, the two definitions are used in parallel and the data are shown in two separate tables. NACE Rev.2 collection includes data starting from 2008 reference year. For more details please see the definitions in Annex 7 and 8. The product approach: The product approach was created to complement the sectoral approach and it is used for data on high-tech trade. The product list is based on the calculations of R&D intensity by groups of products (R&D expenditure/total sales). The groups classified as high-technology products are aggregated on the basis of the Standard International Trade Classification (SITC). The initial definition was built based on SITC Rev.3 and served to compile the high-tech product aggregates until 2007. With the implementation in 2007 of the new version of SITC Rev.4, the definition of high-tech groups was revised and adapted according to new classification. Starting from 2007 the Eurostat presents the trade data for high-tech groups aggregated based on the SITC Rev.4. For more details, see definition of high-tech products in Annex 4 and 5. High-tech patents: High-tech patentsare defined according to another approach. The groups classified as high-tech patents are aggregated on the basis of the International Patent Classification (IPC 8th edition). Biotechnology patents are also aggregated on the basis of the IPC 8th edition. For more details, see the aggregation list of high-tech and biotechnology patents in Annex 6. The high-tech domain also comprises the sub-domain Venture Capital Investments: data are provided by INVEST Europe (formerly named the European Private Equity and Venture Capital Association EVCA). More details are available in the Eurostat metadata under Venture capital investments. Please note that for paragraphs where no metadata for regional data has been specified, the regional metadata is identical to the metadata provided for the national data.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 30 十月, 2019
      选择数据集
      'Statistics on high-tech industry and knowledge-intensive services' (sometimes referred to as simply 'high-tech statistics') comprise economic, employment and science, technology and innovation (STI) data describing manufacturing and services industries or products traded broken down by technological intensity. The domain uses various other domains and sources of  Eurostat's official statistics (CIS, COMEXT, HRST, LFS, PATENT, R&D and SBS) and its coverage is therefore dependent on these other primary sources. Two main approaches are used in the domain to identify technology-intensity: the sectoral approach and the product approach. A third approach is used for data on high-tech and biotechnology patents aggregated on the basis of the International Patent Classification (IPC) 8th edition (see summary table in Annex 1 for which approach is used by each type of data). The sectoral approach: The sectoral approach is an aggregation of the manufacturing industries according to technological intensity (R&D expenditure/value added) and based on the Statistical classification of economic activities in the European Community (NACE) at 2-digit level. The level of R&D intensity served as a criterion of classification of economic sectors into high-technology, medium high-technology, medium low-technology and low-technology industries. Services are mainly aggregated into knowledge-intensive services (KIS) and less knowledge-intensive services (LKIS) based on the share of tertiary educated persons at NACE 2-digit level. The sectoral approach is used for all indicators except data on high-tech trade and patents. Note that due to the revision of the NACE from NACE Rev. 1.1 to NACE Rev. 2 the definition of high-technology industries and knowledge-intensive services has changed in 2008. For high-tech statistics it means that two different definitions (one according NACE Rev. 1.1 and one according NACE Rev. 2) are used in parallel and the data according to both NACE versions are presented in separated tables depending on the data availability. For example as the LFS provides the results both by NACE Rev. 1.1 and NACE Rev. 2, all the table using this source have been duplicated to present the results by NACE Rev. 2 from 2008. For more details, see both definitions of high-tech sectors in Annex 2 and 3. Within the sectoral approach, a second classification was created, named Knowledge Intensive Activities KIA) and based on the share of tertiary educated people in each sectors of industries and services according to NACE at 2-digit level and for all EU28 Member States. A threshold was applied to judge sectors as knowledge intensive. In contrast to first sectoral approach mixing two methodologies, one for manufacturing industries and one for services, the KIA classification is based on one methodology for all the sectors of industries and services covering even public sector activities. The aggregations in use are Total Knowledge Intensive Activities (KIA) and Knowledge Intensive Activities in Business Industries (KIABI). Both classifications are made according to NACE Rev. 1.1 and NACE Rev. 2 at 2- digit level. Note that due to revision of the NACE Rev.1.1 to NACE Rev. 2 the list of Knowledge Intensive Activities has changed as well, the two definitions are used in parallel and the data are shown in two separate tables. NACE Rev.2 collection includes data starting from 2008 reference year. For more details please see the definitions in Annex 7 and 8. The product approach: The product approach was created to complement the sectoral approach and it is used for data on high-tech trade. The product list is based on the calculations of R&D intensity by groups of products (R&D expenditure/total sales). The groups classified as high-technology products are aggregated on the basis of the Standard International Trade Classification (SITC). The initial definition was built based on SITC Rev.3 and served to compile the high-tech product aggregates until 2007. With the implementation in 2007 of the new version of SITC Rev.4, the definition of high-tech groups was revised and adapted according to new classification. Starting from 2007 the Eurostat presents the trade data for high-tech groups aggregated based on the SITC Rev.4. For more details, see definition of high-tech products in Annex 4 and 5. High-tech patents: High-tech patentsare defined according to another approach. The groups classified as high-tech patents are aggregated on the basis of the International Patent Classification (IPC 8th edition). Biotechnology patents are also aggregated on the basis of the IPC 8th edition. For more details, see the aggregation list of high-tech and biotechnology patents in Annex 6. The high-tech domain also comprises the sub-domain Venture Capital Investments: data are provided by INVEST Europe (formerly named the European Private Equity and Venture Capital Association EVCA). More details are available in the Eurostat metadata under Venture capital investments. Please note that for paragraphs where no metadata for regional data has been specified, the regional metadata is identical to the metadata provided for the national data.
    • 八月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 13 八月, 2019
      选择数据集
      'Statistics on high-tech industry and knowledge-intensive services' (sometimes referred to as simply 'high-tech statistics') comprise economic, employment and science, technology and innovation (STI) data describing manufacturing and services industries or products traded broken down by technological intensity. The domain uses various other domains and sources of  Eurostat's official statistics (CIS, COMEXT, HRST, LFS, PATENT, R&D and SBS) and its coverage is therefore dependent on these other primary sources. Two main approaches are used in the domain to identify technology-intensity: the sectoral approach and the product approach. A third approach is used for data on high-tech and biotechnology patents aggregated on the basis of the International Patent Classification (IPC) 8th edition (see summary table in Annex 1 for which approach is used by each type of data). The sectoral approach: The sectoral approach is an aggregation of the manufacturing industries according to technological intensity (R&D expenditure/value added) and based on the Statistical classification of economic activities in the European Community (NACE) at 2-digit level. The level of R&D intensity served as a criterion of classification of economic sectors into high-technology, medium high-technology, medium low-technology and low-technology industries. Services are mainly aggregated into knowledge-intensive services (KIS) and less knowledge-intensive services (LKIS) based on the share of tertiary educated persons at NACE 2-digit level. The sectoral approach is used for all indicators except data on high-tech trade and patents. Note that due to the revision of the NACE from NACE Rev. 1.1 to NACE Rev. 2 the definition of high-technology industries and knowledge-intensive services has changed in 2008. For high-tech statistics it means that two different definitions (one according NACE Rev. 1.1 and one according NACE Rev. 2) are used in parallel and the data according to both NACE versions are presented in separated tables depending on the data availability. For example as the LFS provides the results both by NACE Rev. 1.1 and NACE Rev. 2, all the table using this source have been duplicated to present the results by NACE Rev. 2 from 2008. For more details, see both definitions of high-tech sectors in Annex 2 and 3. Within the sectoral approach, a second classification was created, named Knowledge Intensive Activities KIA) and based on the share of tertiary educated people in each sectors of industries and services according to NACE at 2-digit level and for all EU28 Member States. A threshold was applied to judge sectors as knowledge intensive. In contrast to first sectoral approach mixing two methodologies, one for manufacturing industries and one for services, the KIA classification is based on one methodology for all the sectors of industries and services covering even public sector activities. The aggregations in use are Total Knowledge Intensive Activities (KIA) and Knowledge Intensive Activities in Business Industries (KIABI). Both classifications are made according to NACE Rev. 1.1 and NACE Rev. 2 at 2- digit level. Note that due to revision of the NACE Rev.1.1 to NACE Rev. 2 the list of Knowledge Intensive Activities has changed as well, the two definitions are used in parallel and the data are shown in two separate tables. NACE Rev.2 collection includes data starting from 2008 reference year. For more details please see the definitions in Annex 7 and 8. The product approach: The product approach was created to complement the sectoral approach and it is used for data on high-tech trade. The product list is based on the calculations of R&D intensity by groups of products (R&D expenditure/total sales). The groups classified as high-technology products are aggregated on the basis of the Standard International Trade Classification (SITC). The initial definition was built based on SITC Rev.3 and served to compile the high-tech product aggregates until 2007. With the implementation in 2007 of the new version of SITC Rev.4, the definition of high-tech groups was revised and adapted according to new classification. Starting from 2007 the Eurostat presents the trade data for high-tech groups aggregated based on the SITC Rev.4. For more details, see definition of high-tech products in Annex 4 and 5. High-tech patents: High-tech patentsare defined according to another approach. The groups classified as high-tech patents are aggregated on the basis of the International Patent Classification (IPC 8th edition). Biotechnology patents are also aggregated on the basis of the IPC 8th edition. For more details, see the aggregation list of high-tech and biotechnology patents in Annex 6. The high-tech domain also comprises the sub-domain Venture Capital Investments: data are provided by INVEST Europe (formerly named the European Private Equity and Venture Capital Association EVCA). More details are available in the Eurostat metadata under Venture capital investments. Please note that for paragraphs where no metadata for regional data has been specified, the regional metadata is identical to the metadata provided for the national data.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 30 十月, 2019
      选择数据集
      'Statistics on high-tech industry and knowledge-intensive services' (sometimes referred to as simply 'high-tech statistics') comprise economic, employment and science, technology and innovation (STI) data describing manufacturing and services industries or products traded broken down by technological intensity. The domain uses various other domains and sources of  Eurostat's official statistics (CIS, COMEXT, HRST, LFS, PATENT, R&D and SBS) and its coverage is therefore dependent on these other primary sources. Two main approaches are used in the domain to identify technology-intensity: the sectoral approach and the product approach. A third approach is used for data on high-tech and biotechnology patents aggregated on the basis of the International Patent Classification (IPC) 8th edition (see summary table in Annex 1 for which approach is used by each type of data). The sectoral approach: The sectoral approach is an aggregation of the manufacturing industries according to technological intensity (R&D expenditure/value added) and based on the Statistical classification of economic activities in the European Community (NACE) at 2-digit level. The level of R&D intensity served as a criterion of classification of economic sectors into high-technology, medium high-technology, medium low-technology and low-technology industries. Services are mainly aggregated into knowledge-intensive services (KIS) and less knowledge-intensive services (LKIS) based on the share of tertiary educated persons at NACE 2-digit level. The sectoral approach is used for all indicators except data on high-tech trade and patents. Note that due to the revision of the NACE from NACE Rev. 1.1 to NACE Rev. 2 the definition of high-technology industries and knowledge-intensive services has changed in 2008. For high-tech statistics it means that two different definitions (one according NACE Rev. 1.1 and one according NACE Rev. 2) are used in parallel and the data according to both NACE versions are presented in separated tables depending on the data availability. For example as the LFS provides the results both by NACE Rev. 1.1 and NACE Rev. 2, all the table using this source have been duplicated to present the results by NACE Rev. 2 from 2008. For more details, see both definitions of high-tech sectors in Annex 2 and 3. Within the sectoral approach, a second classification was created, named Knowledge Intensive Activities KIA) and based on the share of tertiary educated people in each sectors of industries and services according to NACE at 2-digit level and for all EU28 Member States. A threshold was applied to judge sectors as knowledge intensive. In contrast to first sectoral approach mixing two methodologies, one for manufacturing industries and one for services, the KIA classification is based on one methodology for all the sectors of industries and services covering even public sector activities. The aggregations in use are Total Knowledge Intensive Activities (KIA) and Knowledge Intensive Activities in Business Industries (KIABI). Both classifications are made according to NACE Rev. 1.1 and NACE Rev. 2 at 2- digit level. Note that due to revision of the NACE Rev.1.1 to NACE Rev. 2 the list of Knowledge Intensive Activities has changed as well, the two definitions are used in parallel and the data are shown in two separate tables. NACE Rev.2 collection includes data starting from 2008 reference year. For more details please see the definitions in Annex 7 and 8. The product approach: The product approach was created to complement the sectoral approach and it is used for data on high-tech trade. The product list is based on the calculations of R&D intensity by groups of products (R&D expenditure/total sales). The groups classified as high-technology products are aggregated on the basis of the Standard International Trade Classification (SITC). The initial definition was built based on SITC Rev.3 and served to compile the high-tech product aggregates until 2007. With the implementation in 2007 of the new version of SITC Rev.4, the definition of high-tech groups was revised and adapted according to new classification. Starting from 2007 the Eurostat presents the trade data for high-tech groups aggregated based on the SITC Rev.4. For more details, see definition of high-tech products in Annex 4 and 5. High-tech patents: High-tech patentsare defined according to another approach. The groups classified as high-tech patents are aggregated on the basis of the International Patent Classification (IPC 8th edition). Biotechnology patents are also aggregated on the basis of the IPC 8th edition. For more details, see the aggregation list of high-tech and biotechnology patents in Annex 6. The high-tech domain also comprises the sub-domain Venture Capital Investments: data are provided by INVEST Europe (formerly named the European Private Equity and Venture Capital Association EVCA). More details are available in the Eurostat metadata under Venture capital investments. Please note that for paragraphs where no metadata for regional data has been specified, the regional metadata is identical to the metadata provided for the national data.
    • 七月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 19 七月, 2019
      选择数据集
      This dataset provides estimates of the production, value added, exports and employment of the environmental goods and services sector (EGSS). The EGSS is the part of the economy that generate environmental products, i.e. those produced for the purpose of environmental protection and resource management. Environmental protection includes all activities and actions which have as their main purpose the prevention, reduction and elimination of pollution and of any other degradation of the environment. Those activities and actions include all measures taken in order to restore the environment after it has been degraded. Resource management includes the preservation, maintenance and enhancement of the stock of natural resources and therefore the safeguarding of those resources against depletion. The EGSS accounts are produced in accordance with the statistical concepts and definitions set out in the system of environmental economic accounting 2012 – central framework (SEEA CF 2012, see annex). Datasets env_ac_egss1 and env_ac_egss2 consist of country data produced by the Member States, who transmit the data to Eurostat and further disseminates it. The EU estimates in datasets env_ac_egss1, env_ac_egss2 and env_ac_egss3 are produced by Eurostat not as a sum of available countries but using methods documented in the Eurostat EGSS practical guide (see methodology page) and data sources publicly available. In addition, Eurostat produces output and gross value added volume estimates, i.e. discounting changes in prices, for all countries published in dataset env_ac_egss2.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 10 十月, 2019
      选择数据集
      The indicator presents employment rates by age. The employment rate is calculated by dividing the number of persons in employment in a given age group by the total population of the same age group. The indicator is based on the EU Labour Force Survey.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 26 十月, 2019
      选择数据集
      The section 'LFS series - detailed annual survey results' reports annual results from the EU-LFS. While LFS is a quarterly survey, it is also possible to produce annual results. There are several ways of doing it, see section '18.5 Data compilation' below for details. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metadata. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 四月 2018
      来源: Eurostat
      上传者: Knoema
      访问日期: 11 四月, 2018
      选择数据集
      % of age group 20-64 yearsThe indicator is calculated by dividing the number of employed people within the age group 20-64 years having attained a specific level of education by the total population of the same age group. The educational attainment level is coded according to the International Standard Classification of Education (ISCED). Data until 2013 are classified according to ISCED 1997 and data as from 2014 according to ISCED 2011.- Less than primary, primary and lower secondary education (ISCED levels 0-2) -Upper secondary and post-secondary non-tertiary education (ISCED levels 3 and 4) -Tertiary education (ISCED levels 5-8) (ISCED 1997: levels 5 and 6) The indicator is based on the EU Labour Force Survey (LFS), covering the population living in private households. Employment rate (total, females, males): The number of persons (females, males) aged 20-64 in employment as a share of the total population (females, males) of the same age group.
    • 六月 2019
      来源: United Nations Economic Commission for Europe
      上传者: Knoema
      访问日期: 11 六月, 2019
      选择数据集
      Source: UNECE Statististical Database, compiled from national and international (Eurostat) official sources. Definition: The employment rate is the share of employed persons in the population of the corresponding sex and age group. Marital status is defined as the legal conjugal status of each individual in relation to the marriage laws or customs of the country. The following classification is used: - Never married (single), - Married, - Widowed (and not remarried), - Divorced (and not remarried). In some countries the legal status of separated also exists and persons of this group are included here in the group of married. General note: Data come from the Labour Force Survey (LFS) unless otherwise specified. .. - data not available Country: Armenia 2007 data refer to population aged 16-75. Break in methodlogy: since 2008 data refer to population aged 15-75.From 2007 to 2013 data are based on the Integrated Survey of the Household Living Standards.Break in methodlogy: since 2014 data are based on the Labour Force Survey. Country: Austria Break in methodology (2004): Break in series due to change in data collection procedure. Country: Bosnia and Herzegovina Estimates for the age group 65+ are less reliable for 2015. Country: Canada Data do not cover the three northern territories (Yukon, Northwest and Nunavuk ) Country: Georgia Change in definition (2008 onward): Unknown marital status refers to non-registered marriage Country: Georgia Territorial change (2000 onward): Data do not cover Abkhazia AR and Tskhinvali Region Country: Israel Break in methodlogy (2000): In 1998: 1) Changes in the weighting method; 2) Transition to the 1995 Population Census estimates; See explanations: http://www.cbs.gov.il/www/publications/saka_change/tch_e.pdf Country: Israel Break in methodlogy (2001): Changes in the weighting method. See explanations: http://www.cbs.gov.il/www/saka_y/e_intro_f1_comparison-mimi.f Country: Israel Break in methodlogy (2009): 1) Update of the definition of the civilian labour force characteristics; 2) Transition to the 2008 Population Census estimates. See explanations: http://www.cbs.gov.il/publications11/1460/pdf/intro05_e.pdf Country: Israel Break in methodlogy (2012): 1) Transitiom from a quarterly to a monthly LFS; 2) Changes in the definitions of labour force characteristics (including compulsory and permanent military service into labour force). See explanations: http://www.cbs.gov.il/publications/labour_survey04/labour_f--orce_survey/answer_question_e_2012.pdf Country: Israel Married persons include Married but living apart; From 2005, 1) Update of the definitions of labour force characteristics; 2) Changes in the Standard Industrial Classification of Economic Activities; See explanations: http://www.cbs.gov.il/www/publications/saka_change/tch_e.pdf Country: Moldova, Republic of Significance (2000 - 2012): Category "married" includes the persons who are not officially registered their marriage, but live together Country: Moldova, Republic of Data exclude the territory of the Transnistria and municipality of Bender Country: Russian Federation Change in definition (1990 - 2013): Data present the population aged 15-72 years Country: Russian Federation Reference period (1990): Data refer to 1992 Country: Russian Federation Territorial change (1990 - 2006): Data do not include the Chechen Republic Country: Serbia Data do not cover Kosovo and Metohija. Country: Turkey Break in methodlogy (2004): Data are revised according to the 2008 population projections. Country: Turkey Break in series (2014): Since 2014 series are not comparable with the previous years due to methodological changes in LFS. Country: Ukraine From 2014 data cover the territories under the government control. Country: Ukraine Change in definition (2000 - 2012): Determining the level of employment corresponds to the definition given above. Country: Ukraine Territorial change (2000 - 2012): Data do not cover the area of radioactive contamination from the Chernobyl disaster. Country: United States Age group 15+ refers to 16+; age group 15-24 refers to 16-24; age group 25-49 refers to 25-54 and age group 50-64 refers to 55-64.
    • 十二月 2016
      来源: Eurostat
      上传者: Knoema
      访问日期: 20 十二月, 2016
      选择数据集
      Employment rate (total, females, males): The number of persons (females, males) aged 20-64 in employment as a share of the total population (females, males) of the same age group.
    • 四月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 09 五月, 2019
      选择数据集
      The data collection 'LFS - specific topics, household statistics' covers a range of statistics on number, characteristics and typologies of households, based on the European Union Labour Force Survey (EU-LFS). The data collection also encompasses some labour market indicators broken down by household composition. Only annual data are available. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 四月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 07 五月, 2019
      选择数据集
      The ad-hoc module "young people on the labour market" provides supplementary information on the correlation between work-based learning and labour market outcomes.
    • 四月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 10 五月, 2019
      选择数据集
      The data collection 'LFS - specific topics, household statistics' covers a range of statistics on number, characteristics and typologies of households, based on the European Union Labour Force Survey (EU-LFS). The data collection also encompasses some labour market indicators broken down by household composition. Only annual data are available. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 26 十月, 2019
      选择数据集
      The employment rate of low skilled persons is calculated by dividing the number of persons in employment with at most lower secondary education (i.e. ISCED 0-2) and aged 20-64 by the total population in the same age and skill group. The indicator is based on the EU Labour Force Survey.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 26 十月, 2019
      选择数据集
      The employment rate of non-EU nationals is calculated by dividing the number of citizens of countries outside the EU-28 in employment and aged 20-64 by the total number of citizens of countries outside the EU-28 in the same age group. The indicator is based on the EU Labour Force Survey.
    • 四月 2018
      来源: Eurostat
      上传者: Knoema
      访问日期: 11 四月, 2018
      选择数据集
      The employment rate of older workers is calculated by dividing the number of persons in employment and aged 55 to 64 by the total population of the same age group. The indicator is based on the EU Labour Force Survey. The survey covers the entire population living in private households and excludes those in collective households such as boarding houses, halls of residence and hospitals. Employed population consists of those persons who during the reference week did any work for pay or profit for at least one hour, or were not working but had jobs from which they were temporarily absent.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 10 十月, 2019
      选择数据集
      The employment rate of older workers is calculated by dividing the number of persons in employment and aged 55 to 64 by the total population of the same age group. The indicator is based on the EU Labour Force Survey.
    • 七月 2014
      来源: Eurostat
      上传者: Knoema
      访问日期: 25 十一月, 2015
      选择数据集
      In 2011, the European Union Labour Force Survey (EU-LFS) included an ad hoc module (AHM) on employment of disabled people. The module consisted of 11 variables dealing with:Health problems and difficulties in basic activities;Limitations in work caused by health problems/difficulties in basic activities;Special assistance needed or used by people with health problems/difficulties in basic activities;Limitation in work because of other reasons. On the basis of how the module was operationalised, the following two main definitions for disability were considered for presenting the results:Disabled persons = People having a basic activity difficulty (such as seeing, hearing, walking, communicating);Disabled persons = People having a work limitation caused by a longstanding health condition and/or a basic activity difficulty. 32 countries have implemented this module: the EU 28 Member States plus Turkey, Iceland, Norway and Switzerland. The Norwegian data are not disseminated because the AHM questionnaire in Norway only partly complies with the Commission Regulation (EU) No 317/2010 and consequently, the data are incomplete and partly comparable. Missing values, don't know and refusal answers are not considered in the calculations. It means the indicators have been worked out on the respondents and validated answers only.
    • 五月 2019
      来源: United Nations Economic Commission for Europe
      上传者: Knoema
      访问日期: 21 五月, 2019
      选择数据集
      Source: UNECE Statistical Database, compiled from national official sources. Definition: The employment rate is the share of employed persons aged 25 to 49 in the population of the corresponding sex and age group. Data are reported according to the age of the youngest child living in the household. Children living outside the household are not considered. General note: Data come from the Labour Force Survey (LFS) unless otherwise specified. .. - data not available Country: Austria Break in methodlogy (2004): Break in series due to change in data collection procedure. Country: Austria Change in definition (1980): Data refer to the livelihood concept Country: Austria Reference period (1980): Data refer to 1984 Country: Belarus Data refer to age groups 0-2, 3-6, 7-14, 15-17. Country: Belgium Change in definition (2005 - 2015): A child is considered as a person below 17 who lives in the household whatever the relation to the reference person may be. Country: Croatia Data given for 2013 onwards are calibrated according to the results of the Census 2011 and are not fully comparable with data given for previous years. Country: Finland Only children under the age of 18 are considered. The age group 6-16 refers to 6-17, no child refers to no child under 18. Country: France Reference area: Metropolitan France Country: Germany Break in methodlogy (2005): Until 2004, data refer to one reporting week. From 2005 data are annual average figures. Country: Greece Data refer to annual averages. Country: Ireland Data refer to 2nd quarter of each year. Country: Israel Break in methodlogy (2000): In 1998: 1) Changes in the weighting method; 2) Transition to the 1995 Population Census estimates; See explanations: http://www.cbs.gov.il/www/publications/saka_change/tch_e.pdf Country: Israel Break in methodlogy (2001): Changes in the weighting method. See explanations: http://www.cbs.gov.il/www/saka_y/e_intro_f1_comparison-mimi.f Country: Israel Break in methodlogy (2009): 1) Update of the definition of the civilian labour force characteristics; 2) Transition to the 2008 Population Census estimates. See explanations: http://www.cbs.gov.il/publications11/1460/pdf/intro05_e.pdf Country: Israel Break in methodlogy (2012): 1) Transitiom from a quarterly to a monthly LFS; 2) Changes in the definitions of labour force characteristics (including compulsory and permanent military service into labour force). See explanations: http://www.cbs.gov.il/publications/labour_survey04/labour_f--orce_survey/answer_question_e_2012.pdf Country: Israel Change in definition (1980 - 2013): Data refer to age groups 0-1 instead of 0-2; 2-4 instead of 3-5 . Country: Israel Change in definition (2005): 1) Update of the definitions of labour force characteristics; 2) Changes in the Standard Industrial Classification of Economic Activities; See explanations: http://www.cbs.gov.il/www/publications/saka_change/tch_e.pdf Country: Italy Break in methodlogy (2004): From 2004, there is a break in series due to change in survey and data collection procedure (continuous survey). Country: Latvia Change in definition (2002 - 2012): Age 17+& 39; refer to the population aged 17-18. No child& 39; refer to data on no child or age of the youngest child 19+. Country: Luxembourg Reference period (1980): Reference year 1983 Country: Moldova, Republic of Data exclude the territory of the Transnistria and municipality of Bender Country: Poland Data are not fully comparable with the results of the surveys prior to 2010 as persons staying outside households for 12 months or longer are excluded from the survey (previously over 3 months). Country: Portugal Data from 2011 onwards are not directly comparable with data for the previous years due to new data collection methods used in the Portuguese Labour Force Survey series. Country: Romania Break in methodology (2002): Due to the revision of the definitions and the coverage, the data series of 2002-2012 are not perfectly comparable with data series of previous years. Break in series starting with year 2013. For years 2014 onward data were estimated using the resident population. For year 2013 data were estimated based on revised population figures (resident population) in accordance to the 2011 Census results. Country: Romania Reference period (1995): Data for 1995 refers to March 1995 Country: Serbia Data do not cover Kosovo and Metohija. Country: Switzerland Break in methodlogy (2010): Change to continuous survey. As of 2010: annual averages Country: Switzerland Reference period (1990): Data refer to 1991 Data refer to 2nd quarter Country: Switzerland Reference period (1995 - 2009): Data refer to 2nd quarter Country: Switzerland Territorial change (1980 - 1990): In 1980, Federal Population Census: resident population. From 1990 and onwards, Labour Force Survey: permanent resident population
    • 五月 2019
      来源: United Nations Economic Commission for Europe
      上传者: Knoema
      访问日期: 21 五月, 2019
      选择数据集
      .. - data not available Source: UNECE Statistical Database, compiled from national official sources. Definition: The employment rate is the share of employed persons aged 25-49 in the population of the corresponding sex and age group. Data are reported according to the number of children under the age of 17. Children living outside the household are not considered. General note: Data come from the Labour Force Survey (LFS) unless otherwise specified. Country: Austria Break in methodlogy (2004): Break in series due to change in data collection procedure. Country: Austria Change in definition (1980): Data refer to the livelihood concept Country: Austria Reference period (1980): Data refer to 1984 Country: Belarus The number of children under the age of 15 is considered. Country: Belgium Change in definition (2003 - 2015): A child is considered as a person below 17 who lives in the household whatever the relation to the reference person may be. Country: Croatia Data given for 2013 onwards are calibrated according to the results of the Census 2011 and are not fully comparable with data given for previous years. Country: Finland The number of children under the age of 18 is considered. Country: France Reference area: Metropolitan France Country: Germany Break in methodlogy (2005): Until 2004, data refer to one reporting week. From 2005 data are annual average figures. Country: Greece Data refer to annual averages. Country: Israel Break in methodlogy (2000): In 1998: 1) Changes in the weighting method; 2) Transition to the 1995 Population Census estimates; See explanations: http://www.cbs.gov.il/www/publications/saka_change/tch_e.pdf Country: Israel Break in methodlogy (2001): Changes in the weighting method. See explanations: http://www.cbs.gov.il/www/saka_y/e_intro_f1_comparison-mimi.f Country: Israel Break in methodlogy (2009): 1) Update of the definition of the civilian labour force characteristics; 2) Transition to the 2008 Population Census estimates. See explanations: http://www.cbs.gov.il/publications11/1460/pdf/intro05_e.pdf Country: Israel Break in methodlogy (2012): 1) Transitiom from a quarterly to a monthly LFS; 2) Changes in the definitions of labour force characteristics (including compulsory and permanent military service into labour force). See explanations: http://www.cbs.gov.il/publications/labour_survey04/labour_f--orce_survey/answer_question_e_2012.pdf Country: Israel Change in definition (2005): 1) Update of the definitions of labour force characteristics; 2) Changes in the Standard Industrial Classification of Economic Activities; See explanations: http://www.cbs.gov.il/www/publications/saka_change/tch_e.pdf Country: Italy Break in methodlogy (2004): From 2004, there is a break in series due to change in survey and data collection procedure (continuous survey). Country: Luxembourg Reference period (1980): Reference year 1983 Country: Moldova, Republic of Data exclude the territory of the Transnistria and municipality of Bender Country: Poland Data are not fully comparable with the results of the surveys prior to 2010 as persons staying outside households for 12 months or longer are excluded from the survey (previously over 3 months). Country: Portugal Data from 2011 onwards are not directly comparable with data for the previous years due to new data collection methods used in the Portuguese Labour Force Survey series. Country: Romania Break in methodology (2002): Due to the revision of the definitions and the coverage, the data series of 2002-2012 are not perfectly comparable with data series of previous years. Break in series starting with year 2013. For years 2014 onward data were estimated using the resident population. For year 2013 data were estimated based on revised population figures (resident population) in accordance to the 2011 Census results. Country: Romania Reference period (1995): Data for 1995 refers to March 1995 Country: Russian Federation Change in definition (2009 - 2013): Data present the population aged 15-72 years Country: Serbia Data do not cover Kosovo and Metohija. Country: Switzerland Break in methodlogy (2010): Change to continuous survey. As of 2010: annual averages Country: Switzerland Reference period (1990): Data refer to 1991 Data refer to 2nd quarter Country: Switzerland Reference period (1995 - 2009): Data refer to 2nd quarter Country: Switzerland Territorial change (1980 - 1990): In 1980, Federal Population Census: resident population. From 1990 and onwards, Labour Force Survey: permanent resident population
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 26 十月, 2019
      选择数据集
      The source for the regional labour market information down to NUTS level 2 is the EU Labour Force Survey (EU-LFS). This is a quarterly household sample survey conducted in all Member States of the EU and in EFTA and Candidate countries.  The EU-LFS survey follows the definitions and recommendations of the International Labour Organisation (ILO). To achieve further harmonisation, the Member States also adhere to common principles when formulating questionnaires. The LFS' target population is made up of all persons in private households aged 15 and over. For more information see the EU Labour Force Survey (lfsi_esms, see paragraph 21.1.).  The EU-LFS is designed to give accurate quarterly information at national level as well as annual information at NUTS 2 regional level and the compilation of these figures is well specified in the regulation. Microdata including the NUTS 2 level codes are provided by all the participating countries with a good degree of geographical comparability, which allows the production and dissemination of a complete set of comparable indicators for this territorial level. At present the transmission of the regional labour market data at NUTS 3 level has no legal basis. However many countries transmit NUTS 3 figures to Eurostat on a voluntary basis, under the understanding that they are not for publication with such detail, but for aggregation in few categories per country, i.e., metropolitan regions and urban-rural typology. Most of the NUTS 3 data are based on the LFS while some countries transmit data based on registers, administrative data, small area estimation and other reliable sources.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 26 十月, 2019
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      The source for the regional labour market information down to NUTS level 2 is the EU Labour Force Survey (EU-LFS). This is a quarterly household sample survey conducted in all Member States of the EU and in EFTA and Candidate countries.  The EU-LFS survey follows the definitions and recommendations of the International Labour Organisation (ILO). To achieve further harmonisation, the Member States also adhere to common principles when formulating questionnaires. The LFS' target population is made up of all persons in private households aged 15 and over. For more information see the EU Labour Force Survey (lfsi_esms, see paragraph 21.1.).  The EU-LFS is designed to give accurate quarterly information at national level as well as annual information at NUTS 2 regional level and the compilation of these figures is well specified in the regulation. Microdata including the NUTS 2 level codes are provided by all the participating countries with a good degree of geographical comparability, which allows the production and dissemination of a complete set of comparable indicators for this territorial level. At present the transmission of the regional labour market data at NUTS 3 level has no legal basis. However many countries transmit NUTS 3 figures to Eurostat on a voluntary basis, under the understanding that they are not for publication with such detail, but for aggregation in few categories per country, i.e., metropolitan regions and urban-rural typology. Most of the NUTS 3 data are based on the LFS while some countries transmit data based on registers, administrative data, small area estimation and other reliable sources.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 26 十月, 2019
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      The source for the regional labour market information down to NUTS level 2 is the EU Labour Force Survey (EU-LFS). This is a quarterly household sample survey conducted in all Member States of the EU and in EFTA and Candidate countries.  The EU-LFS survey follows the definitions and recommendations of the International Labour Organisation (ILO). To achieve further harmonisation, the Member States also adhere to common principles when formulating questionnaires. The LFS' target population is made up of all persons in private households aged 15 and over. For more information see the EU Labour Force Survey (lfsi_esms, see paragraph 21.1.).  The EU-LFS is designed to give accurate quarterly information at national level as well as annual information at NUTS 2 regional level and the compilation of these figures is well specified in the regulation. Microdata including the NUTS 2 level codes are provided by all the participating countries with a good degree of geographical comparability, which allows the production and dissemination of a complete set of comparable indicators for this territorial level. At present the transmission of the regional labour market data at NUTS 3 level has no legal basis. However many countries transmit NUTS 3 figures to Eurostat on a voluntary basis, under the understanding that they are not for publication with such detail, but for aggregation in few categories per country, i.e., metropolitan regions and urban-rural typology. Most of the NUTS 3 data are based on the LFS while some countries transmit data based on registers, administrative data, small area estimation and other reliable sources.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 10 十月, 2019
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      The employment rate of the total population is calculated by dividing the number of person aged 20 to 64 in employment by the total population of the same age group. The employment rate of men is calculated by dividing the number of men aged 20 to 64 in employment by the total male population of the same age group. The employment rate of women is calculated by dividing the number of women aged 20 to 64 in employment by the total female population of the same age group. The indicators are based on the EU Labour Force Survey.
    • 四月 2013
      来源: Eurostat
      上传者: Knoema
      访问日期: 12 十二月, 2015
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      The European Union Labour Force Survey (EU-LFS) provides population estimates for the main labour market characteristics, such as employment, unemployment, inactivity, hours of work, occupation, economic activity and much else, as well as important socio-demographic characteristics, such as sex, age, education, households and regions of residence. Since 1999 an inherent part of the European Union labour force survey (LFS) are the so called 'ad-hoc modules' (AHM). Council Regulation No 577/98 specifies that a further set of variables (the AHM) may be added to supplement the information obtained from the core questionnaire of the LFS. The topic covered by the ad hoc module change every year, although some of them have been repeated.
    • 一月 2017
      来源: Eurostat
      上传者: Knoema
      访问日期: 21 一月, 2017
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      The source for regional typology statistics are regional indicators at NUTS level 3 published on the Eurostat website or existing in the Eurostat production database. The structure of this domain is as follows: - Metropolitan regions (met)    For details see http://ec.europa.eu/eurostat/web/metropolitan-regions/overview - Maritime policy indicators (mare)    For details see http://ec.europa.eu/eurostat/web/maritime-policy-indicators/overview - Urban-rural typology (urt)    For details see http://ec.europa.eu/eurostat/web/rural-development/overview
    • 十一月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 11 十一月, 2019
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      The section 'LFS series - detailed quarterly survey results' reports detailed quarterly results going beyond the EU-LFS main aggregates, which have a separate data domain and some methodological differences. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metada. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 31 十月, 2019
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      The section 'LFS series - detailed annual survey results' reports annual results from the EU-LFS. While LFS is a quarterly survey, it is also possible to produce annual results. There are several ways of doing it, see section '18.5 Data compilation' below for details. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metadata. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 31 十月, 2019
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      The section 'LFS series - detailed annual survey results' reports annual results from the EU-LFS. While LFS is a quarterly survey, it is also possible to produce annual results. There are several ways of doing it, see section '18.5 Data compilation' below for details. This data collection covers all main labour market characteristics, i.e. the total population, activity and activity rates, employment, employment rates, self employed, employees, temporary employment, full-time and part-time employment, population in employment having a second job, working time, total unemployment and inactivity. General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metadata. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.
    • 十月 2019
      来源: Eurostat
      上传者: Knoema
      访问日期: 31 十月, 2019
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