Joint Programming Initiative

More Years, Better Lives

The Potential and Challenges of Demographic Change

European Union Statistics on Income and Living Conditions (EU-SILC)
European Union Statistics on Income and Living Conditions (EU-SILC)

Topic
Health and Performance
Social Systems and Welfare
Work and Productivity
Housing, Urban Development and Mobility
Wellbeing
Relevance for this Topic
Country Europe
More Topics

Governance

Contact information

Ms. Karien Reinig
European Commission, Eurostat – Unit B1
5, rue Alphonse Weicker
2721 Luxembourg
Luxembourg

Timeliness, transparency

Longitudinal and cross-sectional SILC microdata are released 2 years after collection. The extreme deadline currently is October N+1 for most countries for cross-sectional data, with N being the fieldwork year and not the (income) reference year. Following the Framework regulation, Member States shall transmit to Eurostat the cross-sectional data of Year N by 30 November (N+1) and the longitudinal data of Year N by 31 March (N+2). New users’ databases are released in March and August of each year. Most recent longitudinal UDB SILC microdata containing all the trajectories with the most recent wave: Longitudinal UDB SILC 2010 released in August 2012. Annual ad-hoc modules transmitted with and included in the cross-sectional files: Extreme deadline for transmission of micro-data to Eurostat is 30 November (N+1) for Member States where data are collected at the end of year N or through a continuous survey or through registers, and 1 October (N+1) for other Member States. The dissemination by Eurostat of national microdata must be accepted by each national authority.

Type of data


Registry + Survey

Type of Study

Longitude survey: long-term study of the same sample

Cross-section, regular


The longitudinal component of EU-SILC gathers individual changes over time observed over, typically, a four-year period.

Data gathering method

Telephone interview (CATI)

Face-to-face interview (CAPI, PAPI)

Registries

Self-administered questionnaire


The mode of collection varies depending on the country. Information can be collected either from registers or from interviews. For the interview, there are four different ways to collect the data: Paper-Assisted Personal Interview (PAPI), Computer-Assisted Personal Interview (CAPI) which is the most used, Computer-Assisted Telephone Interview (CATI) mainly used in countries where income data are extracted from registers, and Self-administrated questionnaire. All collected data is confidential. In register countries (Denmark, Finland, Iceland, the Netherlands, Norway, Sweden and Slovenia), most income components and some demographic information are retrieved from administrative registers. Other personal variables are collected through interviews. The rest of the countries (except Ireland) obtain all of the information by means of a survey of households and interviews with household members. In Ireland, upon the explicit agreement of the household collected, the information is obtained from administrative records. The survey design is flexible to allow countries to anchor EU-SILC within their national statistical systems. For instance, the cross-sectional and longitudinal components may come from separate sources, i.e. the longitudinal dataset does not have to be ‘linkable’ with the cross-sectional dataset.

Type of data


Registry + Survey

Type of Study

Longitude survey: long-term study of the same sample

Cross-section, regular


The longitudinal component of EU-SILC gathers individual changes over time observed over, typically, a four-year period.

Data gathering method

Telephone interview (CATI)

Face-to-face interview (CAPI, PAPI)

Registries

Self-administered questionnaire


The mode of collection varies depending on the country. Information can be collected either from registers or from interviews. For the interview, there are four different ways to collect the data: Paper-Assisted Personal Interview (PAPI), Computer-Assisted Personal Interview (CAPI) which is the most used, Computer-Assisted Telephone Interview (CATI) mainly used in countries where income data are extracted from registers, and Self-administrated questionnaire. All collected data is confidential. In register countries (Denmark, Finland, Iceland, the Netherlands, Norway, Sweden and Slovenia), most income components and some demographic information are retrieved from administrative registers. Other personal variables are collected through interviews. The rest of the countries (except Ireland) obtain all of the information by means of a survey of households and interviews with household members. In Ireland, upon the explicit agreement of the household collected, the information is obtained from administrative records. The survey design is flexible to allow countries to anchor EU-SILC within their national statistical systems. For instance, the cross-sectional and longitudinal components may come from separate sources, i.e. the longitudinal dataset does not have to be ‘linkable’ with the cross-sectional dataset.

Type of data


Registry + Survey

Type of Study

Longitude survey: long-term study of the same sample

Cross-section, regular


The longitudinal component of EU-SILC gathers individual changes over time observed over, typically, a four-year period.

Data gathering method

Telephone interview (CATI)

Face-to-face interview (CAPI, PAPI)

Registries

Self-administered questionnaire


The mode of collection varies depending on the country. Information can be collected either from registers or from interviews. For the interview, there are four different ways to collect the data: Paper-Assisted Personal Interview (PAPI), Computer-Assisted Personal Interview (CAPI) which is the most used, Computer-Assisted Telephone Interview (CATI) mainly used in countries where income data are extracted from registers, and Self-administrated questionnaire. All collected data is confidential. In register countries (Denmark, Finland, Iceland, the Netherlands, Norway, Sweden and Slovenia), most income components and some demographic information are retrieved from administrative registers. Other personal variables are collected through interviews. The rest of the countries (except Ireland) obtain all of the information by means of a survey of households and interviews with household members. In Ireland, upon the explicit agreement of the household collected, the information is obtained from administrative records. The survey design is flexible to allow countries to anchor EU-SILC within their national statistical systems. For instance, the cross-sectional and longitudinal components may come from separate sources, i.e. the longitudinal dataset does not have to be ‘linkable’ with the cross-sectional dataset.

Type of data


Registry + Survey

Type of Study

Longitude survey: long-term study of the same sample

Cross-section, regular


The longitudinal component of EU-SILC gathers individual changes over time observed over, typically, a four-year period.

Data gathering method

Telephone interview (CATI)

Face-to-face interview (CAPI, PAPI)

Registries

Self-administered questionnaire


The mode of collection varies depending on the country. Information can be collected either from registers or from interviews. For the interview, there are four different ways to collect the data: Paper-Assisted Personal Interview (PAPI), Computer-Assisted Personal Interview (CAPI) which is the most used, Computer-Assisted Telephone Interview (CATI) mainly used in countries where income data are extracted from registers, and Self-administrated questionnaire. All collected data is confidential. In register countries (Denmark, Finland, Iceland, the Netherlands, Norway, Sweden and Slovenia), most income components and some demographic information are retrieved from administrative registers. Other personal variables are collected through interviews. The rest of the countries (except Ireland) obtain all of the information by means of a survey of households and interviews with household members. In Ireland, upon the explicit agreement of the household collected, the information is obtained from administrative records. The survey design is flexible to allow countries to anchor EU-SILC within their national statistical systems. For instance, the cross-sectional and longitudinal components may come from separate sources, i.e. the longitudinal dataset does not have to be ‘linkable’ with the cross-sectional dataset.

Type of data


Registry + Survey

Type of Study

Longitude survey: long-term study of the same sample

Cross-section, regular


The longitudinal component of EU-SILC gathers individual changes over time observed over, typically, a four-year period.

Data gathering method

Telephone interview (CATI)

Face-to-face interview (CAPI, PAPI)

Registries

Self-administered questionnaire


The mode of collection varies depending on the country. Information can be collected either from registers or from interviews. For the interview, there are four different ways to collect the data: Paper-Assisted Personal Interview (PAPI), Computer-Assisted Personal Interview (CAPI) which is the most used, Computer-Assisted Telephone Interview (CATI) mainly used in countries where income data are extracted from registers, and Self-administrated questionnaire. All collected data is confidential. In register countries (Denmark, Finland, Iceland, the Netherlands, Norway, Sweden and Slovenia), most income components and some demographic information are retrieved from administrative registers. Other personal variables are collected through interviews. The rest of the countries (except Ireland) obtain all of the information by means of a survey of households and interviews with household members. In Ireland, upon the explicit agreement of the household collected, the information is obtained from administrative records. The survey design is flexible to allow countries to anchor EU-SILC within their national statistical systems. For instance, the cross-sectional and longitudinal components may come from separate sources, i.e. the longitudinal dataset does not have to be ‘linkable’ with the cross-sectional dataset.


Access to data


EU-SILC micro-data are available to researchers carrying out statistical analyses for scientific purposes. Multi-dimensional datasets and policy indicators are updated on the Eurostat website as soon as the new data become available. Basic indicators are downloadable free of charge as excel files.

Conditions of access


Direct access to the anonymised microdata is only provided by means of research contracts. Access is in principle restricted to universities, research institutes, national statistical institutes, central banks inside the EU and EEA countries, as well as to the European Central Bank. Individuals cannot be granted direct access. Beginning in July 2013, all entities requesting access to microdata will have to be first recognised as eligible for access. This will be required only once for all future access requests. The previous research contract will be replaced by a licence (confidentiality undertaking). For other kind of organisations inside the EU/EEA countries and any requests from any organisations outside the EU/EEA, approval for access needs to be requested first from the European Statistical System Committee by written procedure, which takes about 6 months. Access to microdata is provided free of charge.


It takes an average of ten weeks from the time when the request is received until the CD-Rom of the data is sent.


Anonymised microdata, basic indicators


Excel


Data and documentation are available in English.

Access to data


EU-SILC micro-data are available to researchers carrying out statistical analyses for scientific purposes. Multi-dimensional datasets and policy indicators are updated on the Eurostat website as soon as the new data become available. Basic indicators are downloadable free of charge as excel files.

Conditions of access


Direct access to the anonymised microdata is only provided by means of research contracts. Access is in principle restricted to universities, research institutes, national statistical institutes, central banks inside the EU and EEA countries, as well as to the European Central Bank. Individuals cannot be granted direct access. Beginning in July 2013, all entities requesting access to microdata will have to be first recognised as eligible for access. This will be required only once for all future access requests. The previous research contract will be replaced by a licence (confidentiality undertaking). For other kind of organisations inside the EU/EEA countries and any requests from any organisations outside the EU/EEA, approval for access needs to be requested first from the European Statistical System Committee by written procedure, which takes about 6 months. Access to microdata is provided free of charge.


It takes an average of ten weeks from the time when the request is received until the CD-Rom of the data is sent.


Anonymised microdata, basic indicators


Excel


Data and documentation are available in English.

Access to data


EU-SILC micro-data are available to researchers carrying out statistical analyses for scientific purposes. Multi-dimensional datasets and policy indicators are updated on the Eurostat website as soon as the new data become available. Basic indicators are downloadable free of charge as excel files.

Conditions of access


Direct access to the anonymised microdata is only provided by means of research contracts. Access is in principle restricted to universities, research institutes, national statistical institutes, central banks inside the EU and EEA countries, as well as to the European Central Bank. Individuals cannot be granted direct access. Beginning in July 2013, all entities requesting access to microdata will have to be first recognised as eligible for access. This will be required only once for all future access requests. The previous research contract will be replaced by a licence (confidentiality undertaking). For other kind of organisations inside the EU/EEA countries and any requests from any organisations outside the EU/EEA, approval for access needs to be requested first from the European Statistical System Committee by written procedure, which takes about 6 months. Access to microdata is provided free of charge.


It takes an average of ten weeks from the time when the request is received until the CD-Rom of the data is sent.


Anonymised microdata, basic indicators


Excel


Data and documentation are available in English.

Access to data


EU-SILC micro-data are available to researchers carrying out statistical analyses for scientific purposes. Multi-dimensional datasets and policy indicators are updated on the Eurostat website as soon as the new data become available. Basic indicators are downloadable free of charge as excel files.

Conditions of access


Direct access to the anonymised microdata is only provided by means of research contracts. Access is in principle restricted to universities, research institutes, national statistical institutes, central banks inside the EU and EEA countries, as well as to the European Central Bank. Individuals cannot be granted direct access. Beginning in July 2013, all entities requesting access to microdata will have to be first recognised as eligible for access. This will be required only once for all future access requests. The previous research contract will be replaced by a licence (confidentiality undertaking). For other kind of organisations inside the EU/EEA countries and any requests from any organisations outside the EU/EEA, approval for access needs to be requested first from the European Statistical System Committee by written procedure, which takes about 6 months. Access to microdata is provided free of charge.


It takes an average of ten weeks from the time when the request is received until the CD-Rom of the data is sent.


Anonymised microdata, basic indicators


Excel


Data and documentation are available in English.

Access to data


EU-SILC micro-data are available to researchers carrying out statistical analyses for scientific purposes. Multi-dimensional datasets and policy indicators are updated on the Eurostat website as soon as the new data become available. Basic indicators are downloadable free of charge as excel files.

Conditions of access


Direct access to the anonymised microdata is only provided by means of research contracts. Access is in principle restricted to universities, research institutes, national statistical institutes, central banks inside the EU and EEA countries, as well as to the European Central Bank. Individuals cannot be granted direct access. Beginning in July 2013, all entities requesting access to microdata will have to be first recognised as eligible for access. This will be required only once for all future access requests. The previous research contract will be replaced by a licence (confidentiality undertaking). For other kind of organisations inside the EU/EEA countries and any requests from any organisations outside the EU/EEA, approval for access needs to be requested first from the European Statistical System Committee by written procedure, which takes about 6 months. Access to microdata is provided free of charge.


It takes an average of ten weeks from the time when the request is received until the CD-Rom of the data is sent.


Anonymised microdata, basic indicators


Excel


Data and documentation are available in English.


Coverage


Minimum effective sample sizes 1. Cross-sectional data operation (EU members + Iceland and Norway): about 131,000 households and 273,000 individuals aged 16 or over to be interviewed. 2. Longitudinal data operation (EU members + Iceland and Norway): about 98,000 households and 204,000 individuals aged 16 or over to be interviewed. In most cases participant countries launch EU-SILC from scratch with integrated cross-sectional and longitudinal elements (this is the Eurostat recommendation). Other countries use a combination of registers and interviews. Others seek to adapt existing national sources. Precision requirements are set via the prescription of minimum effective sample sizes, which are specified in the EU-SILC framework regulation 1177/2003. They should be carefully designed to ensure representativeness - and are to be increased by participant countries to the extent that their national sample is not determined on a simple random basis, or to reflect likely levels of non-response, or to reflect any specific national requirements. Separate values are specified for the cross-sectional and longitudinal elements.


2004 (for some countries, EU-SILC was launched in 2003)


Most countries apply stratification on at least one stage, but no stratum indicator is available as part of the EU-SILC dataset. It does provide information on clustering (primary sampling units)


Varies depending on country. In EU-SILC two main groups can be defined in terms of the sampling source used: population registers and census (for address selection). A systematic source of coverage problems is the time lag between the reference date for the selection of the sample and the fieldwork period, which should be made the shortest. In addition, some countries carried out EU-SILC as a sub-sample of the units (addresses) that successfully participated in cooperated for other existing surveys. Assuming selective non-response in these surveys, this may entail selection bias (under-coverage).


Given its stepwise implementation, EU-SILC data has been released as following: 2004: Austria, Belgium, Denmark, Estonia, Finland, France, Greece, Iceland, Ireland, Italy, Luxembourg, Norway, Portugal, Spain, and Sweden. 2005: + Cyprus, Czech Republic, Germany, Hungary, Latvia, Lithuania, the Netherlands, Poland, Slovakia, Slovenia, and the United Kingdom. From 2007 onwards: all 27 Member States in addition to Iceland, Norway, Switzerland, and Turkey. For the anonymised microdata, region changed from NUTS2 to NUTS1.


The EU-SILC target population is 16+ and living in private households. Persons living in collective households and in institutions are generally excluded from the target population.


The EU-SILC collects harmonised information on income distribution, living conditions, and social exclusion. The survey also provides the demographic and educational characteristics of the population. Even when it captures the household characteristics, the information regarding living arrangements and household compositions might be limited. In the temporal dimension, cross-sectional data pertains to a given time or a certain time period with variables on income, poverty, social exclusion and other living conditions. Longitudinal data pertains to individual-level changes over time, observed periodically over, typically, a four year period. The EU-SILC collects information on the general health, chronic conditions, and limitation activities, which are due to health problems. These variables are collected in both the longitudinal and the cross-section component of the database. Since 2004, there have been at least three variables on self-perceived health.


• Atkinson, A. B., & Marlier, E. “Income poverty and income inequality”. Publications Office of the European Union, Luxembourg, 2010. • Baert, K., & De Norre, B. “Perception of Health and Access to Health Care in the EU-25 in 2007”. Eurostat Statistics in Focus 24 (2009). • Ekholm, O., & Brønnum-Hansen, H. "Cross-national comparisons of non-harmonised indicators may lead to more confusion than clarification." Scandinavian journal of public health (2009). DOI: 10.1177/1403494809341098. • Goedemé, T. “How much Confidence can we have in EU-SILC? Complex Sample Designs and the Standard Error of the Europe 2020 Poverty Indicators”. Social Indicators Research 110(1) (2013): 89-110. • Goedemé, T. “The EU-SILC sample design variables: critical review and recommendations”. Centre for Social Policy Working Paper 13/02, University of Antwerp (2013). • Guio, A.C., Fusco, A., & Marlier, E. "A European Union approach to material deprivation using EU-SILC and Eurobarometer data." Integrated Research Infrastructure in the Socio-economic Sciences (IRISS) Working Paper Series 19 (2009). • Hernández-Quevedo, C., Masseria, C., & Mossialos, E. "Methodological issues in the analysis of the socioeconomic determinants of health using EU-SILC data". Eurostat methodologies and working papers, European Commission (2010). • Iacovou, M., Kaminska, O., & H. Levy. “Using EU-SILC data for cross-national analysis: strengths, problems and recommendations”. ISER Working Paper Series 3 (2012). • Lelkes, O., & Zólyomi, E. "Housing Quality Deficiencies and the Link to Income in the EU". European Centre Policy Brief Series March (2010). • Lelkes, O., & Zólyomi, E. "Poverty Across Europe: The Latest Evidence Using the EU-SILC Survey". European Centre Policy Brief (2008): 1-15. • Nusselder, W. J., et al. "Gender differences in health of EU10 and EU15 populations: the double burden of EU10 men". European journal of ageing 7(4) (2010): 219-227. • Van Kerm, P. "Extreme incomes and the estimation of poverty and inequality indicators from EU-SILC". IRISS Working Paper Series 01, CEPS/INSTEAD (2007). • Van Oyen, H., et al. "Gender gaps in life expectancy and expected years with activity limitations at age 50 in the European Union: associations with macro-level structural indicators". European Journal of Ageing 7(4) (2010): 229-237. • Whelan, C.T., & Maître, B. "Welfare regime and social class variation in poverty and economic vulnerability in Europe: an analysis of EU-SILC". Journal of European Social Policy 20(4) (2010): 316-332.

Coverage


Minimum effective sample sizes 1. Cross-sectional data operation (EU members + Iceland and Norway): about 131,000 households and 273,000 individuals aged 16 or over to be interviewed . 2. Longitudinal data operation (EU members + Iceland and Norway): about 98,000 households and 204,000 individuals aged 16 or over to be interviewed. In most cases participant countries launch EU-SILC from scratch with integrated cross-sectional and longitudinal elements (this is the Eurostat recommendation). Other countries use a combination of registers and interviews. Others seek to adapt existing national sources. Precision requirements are set via the prescription of minimum effective sample sizes, which are specified in the EU-SILC framework regulation 1177/2003. They should be carefully designed to ensure representativeness - and are to be increased by participant countries to the extent that their national sample is not determined on a simple random basis, or to reflect likely levels of non-response, or to reflect any specific national requirements. Separate values are specified for the cross-sectional and longitudinal elements.


2004 (for some countries, EU-SILC was launched in 2003)


Most countries apply stratification on at least one stage, but no stratum indicator is available as part of the EU-SILC dataset. It does provide information on clustering (primary sampling units)


Varies depending on country. In EU-SILC two main groups can be defined in terms of the sampling source used: population registers and census (for address selection). A systematic source of coverage problems is the time lag between the reference date for the selection of the sample and the fieldwork period, which should be made the shortest. In addition, some countries carried out EU-SILC as a sub-sample of the units (addresses) that successfully participated in cooperated for other existing surveys. Assuming selective non-response in these surveys, this may entail selection bias (under-coverage).


Given its stepwise implementation, EU-SILC data has been released as following: 2004: Austria, Belgium, Denmark, Estonia, Finland, France, Greece, Iceland, Ireland, Italy, Luxembourg, Norway, Portugal, Spain, and Sweden. 2005: + Cyprus, Czech Republic, Germany, Hungary, Latvia, Lithuania, the Netherlands, Poland, Slovakia, Slovenia, and the United Kingdom. From 2007 onwards: all 27 Member States in addition to Iceland, Norway, Switzerland, and Turkey. For the anonymised microdata, region changed from NUTS2 to NUTS1.


The EU-SILC target population is 16+ and living in private households. Persons living in collective households and in institutions are generally excluded from the target population.


The EU-SILC collects harmonised information on income distribution, living conditions, and social exclusion. The survey also provides the demographic and educational characteristics of the population. Even when it captures the household characteristics, the information regarding living arrangements and household compositions might be limited. In the temporal dimension, cross-sectional data pertains to a given time or a certain time period with variables on income, poverty, social exclusion and other living conditions. Longitudinal data pertains to individual-level changes over time, observed periodically over, typically, a four year period. The database gathers information on the unmet needs for medical and dental examination and treatment, as well as the reasons for this. Regarding pensions and benefits, EU-SILC collects information on the payment of pension, unemployment, survivor, sickness and disability benefits, as well as education-related allowances.


• Atkinson, A. B., & Marlier, E. “Income poverty and income inequality”. Publications Office of the European Union, Luxembourg, 2010. • Baert, K., & De Norre, B. “Perception of Health and Access to Health Care in the EU-25 in 2007”. Eurostat Statistics in Focus 24 (2009). • Ekholm, O., & Brønnum-Hansen, H. "Cross-national comparisons of non-harmonised indicators may lead to more confusion than clarification". Scandinavian journal of public health (2009). DOI: 10.1177/1403494809341098. • Goedemé, T. “How much Confidence can we have in EU-SILC? Complex Sample Designs and the Standard Error of the Europe 2020 Poverty Indicators”. Social Indicators Research 110(1) (2013): 89-110. • Goedemé, T. “The EU-SILC sample design variables: critical review and recommendations”. Centre for Social Policy Working Paper 13/02, University of Antwerp (2013). • Guio, A.C., Fusco, A., & Marlier, E. "A European Union approach to material deprivation using EU-SILC and Eurobarometer data". Integrated Research Infrastructure in the Socio-economic Sciences (IRISS) Working Paper Series 19 (2009). • Hernández-Quevedo, C., Masseria, C., & Mossialos, E. "Methodological issues in the analysis of the socioeconomic determinants of health using EU-SILC data". Eurostat methodologies and working papers, European Commission (2010). • Iacovou, M., Kaminska, O., & H. Levy. “Using EU-SILC data for cross-national analysis: strengths, problems and recommendations”. ISER Working Paper Series 3 (2012). • Lelkes, O., & Zólyomi, E. "Housing Quality Deficiencies and the Link to Income in the EU". European Centre Policy Brief Series March (2010). • Lelkes, O., & Zólyomi, E. "Poverty Across Europe: The Latest Evidence Using the EU-SILC Survey". European Centre Policy Brief (2008): 1-15. • Nusselder, W. J., et al. "Gender differences in health of EU10 and EU15 populations: the double burden of EU10 men." European journal of ageing 7(4) (2010): 219-227. • Van Kerm, P. "Extreme incomes and the estimation of poverty and inequality indicators from EU-SILC". IRISS Working Paper Series 01, CEPS/INSTEAD (2007). • Van Oyen, H., et al. "Gender gaps in life expectancy and expected years with activity limitations at age 50 in the European Union: associations with macro-level structural indicators". European Journal of Ageing 7(4) (2010): 229-237. • Whelan, C.T., & Maître, B. "Welfare regime and social class variation in poverty and economic vulnerability in Europe: an analysis of EU-SILC". Journal of European Social Policy 20(4) (2010): 316-332.

Coverage


Minimum effective sample sizes 1. Cross-sectional data operation (EU members + Iceland and Norway): about 131,000 households and 273,000 individuals aged 16 or over to be interviewed . 2. Longitudinal data operation (EU members + Iceland and Norway): about 98,000 households and 204,000 individuals aged 16 or over to be interviewed. In most cases participant countries launch EU-SILC from scratch with integrated cross-sectional and longitudinal elements (this is the Eurostat recommendation). Other countries use a combination of registers and interviews. Others seek to adapt existing national sources. Precision requirements are set via the prescription of minimum effective sample sizes, which are specified in the EU-SILC framework regulation 1177/2003. They should be carefully designed to ensure representativeness - and are to be increased by participant countries to the extent that their national sample is not determined on a simple random basis, or to reflect likely levels of non-response, or to reflect any specific national requirements. Separate values are specified for the cross-sectional and longitudinal elements.


2004 (for some countries, EU-SILC was launched in 2003)


Most countries apply stratification on at least one stage, but no stratum indicator is available as part of the EU-SILC dataset. It does provide information on clustering (primary sampling units)


Varies depending on country. In EU-SILC two main groups can be defined in terms of the sampling source used: population registers and census (for address selection). A systematic source of coverage problems is the time lag between the reference date for the selection of the sample and the fieldwork period, which should be made the shortest. In addition, some countries carried out EU-SILC as a sub-sample of the units (addresses) that successfully participated in cooperated for other existing surveys. Assuming selective non-response in these surveys, this may entail selection bias (under-coverage).


Given its stepwise implementation, EU-SILC data has been released as following: 2004: Austria, Belgium, Denmark, Estonia, Finland, France, Greece, Iceland, Ireland, Italy, Luxembourg, Norway, Portugal, Spain, and Sweden. 2005: + Cyprus, Czech Republic, Germany, Hungary, Latvia, Lithuania, the Netherlands, Poland, Slovakia, Slovenia, and the United Kingdom. From 2007 onwards: all 27 Member States in addition to Iceland, Norway, Switzerland, and Turkey. For the anonymised microdata, region changed from NUTS2 to NUTS1.


The EU-SILC target population is 16+ and living in private households. Persons living in collective households and in institutions are generally excluded from the target population.


The EU-SILC collects harmonised information on income distribution, living conditions, and social exclusion. The survey also provides the demographic and educational characteristics of the population. Even when it captures the household characteristics, the information regarding living arrangements and household compositions might be limited. In the temporal dimension, cross-sectional data pertains to a given time or a certain time period with variables on income, poverty, social exclusion and other living conditions. Longitudinal data pertains to individual-level changes over time, observed periodically over, typically, a four year period.


• Atkinson, A. B., & Marlier, E. “Income poverty and income inequality”. Publications Office of the European Union, Luxembourg, 2010. • Baert, K., & De Norre, B. “Perception of Health and Access to Health Care in the EU-25 in 2007”. Eurostat Statistics in Focus 24 (2009). • Ekholm, O., & Brønnum-Hansen, H. "Cross-national comparisons of non-harmonised indicators may lead to more confusion than clarification". Scandinavian journal of public health (2009). DOI: 10.1177/1403494809341098. • Goedemé, T. “How much Confidence can we have in EU-SILC? Complex Sample Designs and the Standard Error of the Europe 2020 Poverty Indicators”. Social Indicators Research 110(1) (2013): 89-110. • Goedemé, T. “The EU-SILC sample design variables: critical review and recommendations”. Centre for Social Policy Working Paper 13/02, University of Antwerp (2013). • Guio, A.C., Fusco, A., & Marlier, E. "A European Union approach to material deprivation using EU-SILC and Eurobarometer data". Integrated Research Infrastructure in the Socio-economic Sciences (IRISS) Working Paper Series 19 (2009). • Hernández-Quevedo, C., Masseria, C., & Mossialos, E. "Methodological issues in the analysis of the socioeconomic determinants of health using EU-SILC data". Eurostat methodologies and working papers, European Commission (2010). • Iacovou, M., Kaminska, O., & H. Levy. “Using EU-SILC data for cross-national analysis: strengths, problems and recommendations”. ISER Working Paper Series 3 (2012). • Lelkes, O., & Zólyomi, E. "Housing Quality Deficiencies and the Link to Income in the EU". European Centre Policy Brief Series March (2010). • Lelkes, O., & Zólyomi, E. "Poverty Across Europe: The Latest Evidence Using the EU-SILC Survey". European Centre Policy Brief (2008): 1-15. • Nusselder, W. J., et al. "Gender differences in health of EU10 and EU15 populations: the double burden of EU10 men". European journal of ageing 7(4) (2010): 219-227. • Van Kerm, P. "Extreme incomes and the estimation of poverty and inequality indicators from EU-SILC". IRISS Working Paper Series 01, CEPS/INSTEAD (2007). • Van Oyen, H., et al. "Gender gaps in life expectancy and expected years with activity limitations at age 50 in the European Union: associations with macro-level structural indicators." European Journal of Ageing 7(4) (2010): 229-237. • Whelan, C.T., & Maître, B. "Welfare regime and social class variation in poverty and economic vulnerability in Europe: an analysis of EU-SILC." Journal of European Social Policy 20(4) (2010): 316-332.

Coverage


Minimum effective sample sizes 1. Cross-sectional data operation (EU members + Iceland and Norway): about 131,000 households and 273,000 individuals aged 16 or over to be interviewed . 2. Longitudinal data operation (EU members + Iceland and Norway): about 98,000 households and 204,000 individuals aged 16 or over to be interviewed. In most cases participant countries launch EU-SILC from scratch with integrated cross-sectional and longitudinal elements (this is the Eurostat recommendation). Other countries use a combination of registers and interviews. Others seek to adapt existing national sources. Precision requirements are set via the prescription of minimum effective sample sizes, which are specified in the EU-SILC framework regulation 1177/2003. They should be carefully designed to ensure representativeness - and are to be increased by participant countries to the extent that their national sample is not determined on a simple random basis, or to reflect likely levels of non-response, or to reflect any specific national requirements. Separate values are specified for the cross-sectional and longitudinal elements.


2004 (for some countries, EU-SILC was launched in 2003)


Most countries apply stratification on at least one stage, but no stratum indicator is available as part of the EU-SILC dataset. It does provide information on clustering (primary sampling units)


Varies depending on country. In EU-SILC two main groups can be defined in terms of the sampling source used: population registers and census (for address selection). A systematic source of coverage problems is the time lag between the reference date for the selection of the sample and the fieldwork period, which should be made the shortest. In addition, some countries carried out EU-SILC as a sub-sample of the units (addresses) that successfully participated in cooperated for other existing surveys. Assuming selective non-response in these surveys, this may entail selection bias (under-coverage).


Given its stepwise implementation, EU-SILC data has been released as following: 2004: Austria, Belgium, Denmark, Estonia, Finland, France, Greece, Iceland, Ireland, Italy, Luxembourg, Norway, Portugal, Spain, and Sweden. 2005: + Cyprus, Czech Republic, Germany, Hungary, Latvia, Lithuania, the Netherlands, Poland, Slovakia, Slovenia, and the United Kingdom. From 2007 onwards: all 27 Member States in addition to Iceland, Norway, Switzerland, and Turkey. For the anonymised microdata, region changed from NUTS2 to NUTS1.


The EU-SILC target population is 16+ and living in private households. Persons living in collective households and in institutions are generally excluded from the target population.


The EU-SILC collects harmonised information on income distribution, living conditions, and social exclusion. The survey also provides the demographic and educational characteristics of the population. Even when it captures the household characteristics, the information regarding living arrangements and household compositions might be limited. In the temporal dimension, cross-sectional data pertains to a given time or a certain time period with variables on income, poverty, social exclusion and other living conditions. Longitudinal data pertains to individual-level changes over time, observed periodically over, typically, a four year period. The EU-SILC allows researchers to retrieve the basic characteristics of the household (dwelling type, tenure status, damages, costs and arrears, heating, sanitary). Module 2007 on housing conditions provides additional variables collected on characteristics of dwellings, accessibility of services (health, postal, public transport), and reasons for change of dwelling. Social exclusion and housing condition information is collected at household level, while labour, education and health information is obtained for persons aged 16 and over.


• Atkinson, A. B., & Marlier, E. “Income poverty and income inequality”. Luxembourg, Publications Office of the European Union (2010). • Baert, K., & De Norre, B. “Perception of Health and Access to Health Care in the EU-25 in 2007”. Eurostat Statistics in Focus 24 (2009). • Ekholm, O., & Brønnum-Hansen, H. "Cross-national comparisons of non-harmonised indicators may lead to more confusion than clarification." Scandinavian journal of public health (2009). DOI: 10.1177/1403494809341098. • Goedemé, T. “How much Confidence can we have in EU-SILC? Complex Sample Designs and the Standard Error of the Europe 2020 Poverty Indicators”. Social Indicators Research 110 (1) (2013): 89-110. • Goedemé, T. “The EU-SILC sample design variables: critical review and recommendations”. Centre for Social Policy Working Paper 13/02.University of Antwerp: Antwerp (2013). • Guio, A.C., Fusco, A., & Marlier, E. "A European Union approach to material deprivation using EU-SILC and Eurobarometer data." Integrated Research Infrastructure in the Socio-economic Sciences (IRISS) Working Paper Series 19 (2009). • Hernández-Quevedo, C., Masseria, C., & Mossialos, E. "Methodological issues in the analysis of the socioeconomic determinants of health using EU-SILC data". Eurostat methodologies and working papers. Luxembourg, European Commission (2010). • Iacovou, M., Kaminska, O., & H. Levy. “Using EU-SILC data for cross-national analysis: strengths, problems and recommendations”. ISER Working Paper Series 3 (2012). • Lelkes, O., & Zólyomi, E. "Housing Quality Deficiencies and the Link to Income in the EU." European Centre Policy Brief Series March (2010). • Lelkes, O., & Zólyomi, E. "Poverty Across Europe: The Latest Evidence Using the EU-SILC Survey." European Centre Policy Brief (2008): 1-15. • Nusselder, W. J., et al. "Gender differences in health of EU10 and EU15 populations: the double burden of EU10 men." European journal of ageing 7 (4) (2010): 219-227. • Van Kerm, P. "Extreme incomes and the estimation of poverty and inequality indicators from EU-SILC. IRISS Working Paper Series 01, CEPS/INSTEAD (2007). • Van Oyen, H., et al. "Gender gaps in life expectancy and expected years with activity limitations at age 50 in the European Union: associations with macro-level structural indicators." European Journal of Ageing 7 (4) (2010): 229-237. • Whelan, C.T., & Maître, B. "Welfare regime and social class variation in poverty and economic vulnerability in Europe: an analysis of EU-SILC." Journal of European Social Policy 20 (4) (2010): 316-332.

Coverage


Minimum effective sample sizes 1. Cross-sectional data operation (EU members + Iceland and Norway): about 131,000 households and 273,000 individuals aged 16 or over to be interviewed . 2. Longitudinal data operation (EU members + Iceland and Norway): about 98,000 households and 204,000 individuals aged 16 or over to be interviewed. In most cases participant countries launch EU-SILC from scratch with integrated cross-sectional and longitudinal elements (this is the Eurostat recommendation). Other countries use a combination of registers and interviews. Others seek to adapt existing national sources. Precision requirements are set via the prescription of minimum effective sample sizes, which are specified in the EU-SILC framework regulation 1177/2003. They should be carefully designed to ensure representativeness - and are to be increased by participant countries to the extent that their national sample is not determined on a simple random basis, or to reflect likely levels of non-response, or to reflect any specific national requirements. Separate values are specified for the cross-sectional and longitudinal elements.


2004 (for some countries, EU-SILC was launched in 2003)


Most countries apply stratification on at least one stage, but no stratum indicator is available as part of the EU-SILC dataset. It does provide information on clustering (primary sampling units)


Varies depending on country. In EU-SILC two main groups can be defined in terms of the sampling source used: population registers and census (for address selection). A systematic source of coverage problems is the time lag between the reference date for the selection of the sample and the fieldwork period, which should be made the shortest. In addition, some countries carried out EU-SILC as a sub-sample of the units (addresses) that successfully participated in cooperated for other existing surveys. Assuming selective non-response in these surveys, this may entail selection bias (under-coverage).


Given its stepwise implementation, EU-SILC data has been released as following: 2004: Austria, Belgium, Denmark, Estonia, Finland, France, Greece, Iceland, Ireland, Italy, Luxembourg, Norway, Portugal, Spain, and Sweden. 2005: + Cyprus, Czech Republic, Germany, Hungary, Latvia, Lithuania, the Netherlands, Poland, Slovakia, Slovenia, and the United Kingdom. From 2007 onwards: all 27 Member States in addition to Iceland, Norway, Switzerland, and Turkey. For the anonymised microdata, region changed from NUTS2 to NUTS1.


The EU-SILC target population is 16+ and living in private households. Persons living in collective households and in institutions are generally excluded from the target population.


The EU-SILC collects harmonised information on income distribution, living conditions, and social exclusion. The survey also provides the demographic and educational characteristics of the population. Even when it captures the household characteristics, the information regarding living arrangements and household compositions might be limited. In the temporal dimension, cross-sectional data pertains to a given time or a certain time period with variables on income, poverty, social exclusion and other living conditions. Longitudinal data pertains to individual-level changes over time, observed periodically over, typically, a four year period. In regard to wellbeing, the EU-SILC allows for the study of material deprivation, as well as the financial characteristics of the individual and the household. Variables include the capacity to afford meals, holidays, unexpected financial expenses, as well as financial burdens. Therefore, EU-SILC could be used as a source for objective (or monetary) measures of wellbeing based on financial and material security. The 2013 special module will be devoted to Wellbeing.


• Atkinson, A. B., & Marlier, E. “Income poverty and income inequality”. Publications Office of the European Union, Luxembourg, 2010. • Baert, K., & De Norre, B. “Perception of Health and Access to Health Care in the EU-25 in 2007”. Eurostat Statistics in Focus 24 (2009). • Ekholm, O., & Brønnum-Hansen, H. "Cross-national comparisons of non-harmonised indicators may lead to more confusion than clarification". Scandinavian journal of public health (2009). DOI: 10.1177/1403494809341098. • Goedemé, T. “How much Confidence can we have in EU-SILC? Complex Sample Designs and the Standard Error of the Europe 2020 Poverty Indicators”. Social Indicators Research 110(1) (2013): 89-110. • Goedemé, T. “The EU-SILC sample design variables: critical review and recommendations”. Centre for Social Policy Working Paper 13/02.University of Antwerp: Antwerp (2013). • Guio, A.C., Fusco, A., & Marlier, E. "A European Union approach to material deprivation using EU-SILC and Eurobarometer data." Integrated Research Infrastructure in the Socio-economic Sciences (IRISS) Working Paper Series 19 (2009). • Hernández-Quevedo, C., Masseria, C., & Mossialos, E. "Methodological issues in the analysis of the socioeconomic determinants of health using EU-SILC data". Eurostat methodologies and working papers, European Commission (2010). • Iacovou, M., Kaminska, O., & H. Levy. “Using EU-SILC data for cross-national analysis: strengths, problems and recommendations”. ISER Working Paper Series 3 (2012). • Lelkes, O., & Zólyomi, E. "Housing Quality Deficiencies and the Link to Income in the EU." European Centre Policy Brief Series March (2010). • Lelkes, O., & Zólyomi, E. "Poverty Across Europe: The Latest Evidence Using the EU-SILC Survey". European Centre Policy Brief (2008): 1-15. • Nusselder, W. J., et al. "Gender differences in health of EU10 and EU15 populations: the double burden of EU10 men". European journal of ageing 7 (4) (2010): 219-227. • Van Kerm, P. "Extreme incomes and the estimation of poverty and inequality indicators from EU-SILC". IRISS Working Paper Series 01, CEPS/INSTEAD (2007). • Van Oyen, H., et al. "Gender gaps in life expectancy and expected years with activity limitations at age 50 in the European Union: associations with macro-level structural indicators". European Journal of Ageing 7(4) (2010): 229-237. • Whelan, C.T., & Maître, B. "Welfare regime and social class variation in poverty and economic vulnerability in Europe: an analysis of EU-SILC". Journal of European Social Policy 20(4) (2010): 316-332.


Linkage


– NACE for economic activity – ISCO 88(Com) for occupation – ISCED for education For the anonymised microdata: Most variables related to survey design have been withdrawn or randomised. Time of interview, age, dwelling type, origin, and NACE classification has been recoded. Additionally, there are some country-specific provisions to ensure anonymisation.


EU-SILC microdata do not contain any administrative information such as names or addresses that would allow direct identification.

Linkage


NACE for economic activity – ISCO 88(Com) for occupation – ISCED for education For the anonymised microdata: Most variables related to survey design have been withdrawn or randomised. Time of interview, age, dwelling type, origin, and NACE classification has been recoded. Additionally, there are some country-specific provisions to ensure anonymisation.


EU-SILC microdata do not contain any administrative information such as names or addresses that would allow direct identification.

Linkage


– NACE for economic activity – ISCO 88(Com) for occupation – ISCED for education For the anonymised microdata: Most variables related to survey design have been withdrawn or randomised. Time of interview, age, dwelling type, origin, and NACE classification has been recoded. Additionally, there are some country-specific provisions to ensure anonymisation.


EU-SILC microdata do not contain any administrative information such as names or addresses that would allow direct identification.

Linkage


– NACE for economic activity – ISCO 88(Com) for occupation – ISCED for education For the anonymised microdata: Most variables related to survey design have been withdrawn or randomised. Time of interview, age, dwelling type, origin, and NACE classification has been recoded. Additionally, there are some country-specific provisions to ensure anonymisation.


EU-SILC microdata do not contain any administrative information such as names or addresses that would allow direct identification.

Linkage


– NACE for economic activity – ISCO 88(Com) for occupation – ISCED for education For the anonymised microdata: Most variables related to survey design have been withdrawn or randomised. Time of interview, age, dwelling type, origin, and NACE classification has been recoded. Additionally, there are some country-specific provisions to ensure anonymisation.


EU-SILC microdata do not contain any administrative information such as names or addresses that would allow direct identification.


Data quality


Some labour variables collected on all adult household members (16+) through the interviews might suffer of proxy answers. The responses given by one member of the household on behalf of the others might also be a source of measurement error.


During the transition between the end of ECHP and start of EU-SILC (up to 2007 in some countries), data was provided by NSI's from national sources (with some breaks in series due to lack of information, transition from national data source to EU-SILC, etc.). Regarding sampling methods: Germany, which had previously used quota sampling methods, was granted a transition period until 2008 when it was required to introduce fully representative probability sampling. In most countries, the sample takes the form of a rotational panel by dividing the sample into sub-panels retained for a maximum of four years. Each year, one sub-panel is refreshed by a new replication. Even when the standard number of rotational groups is four, the exceptions are: France (nine-year panel), Norway (eight-year panel), and Luxembourg (pure panel). Member states are allowed to use different survey instruments to collect cross-sectional and longitudinal data. Also, linkages between these datasets are not required.

Data quality


Some labour variables collected on all adult household members (16+) through the interviews might suffer of proxy answers. The responses given by one member of the household on behalf of the others might also be a source of measurement error.


During the transition between the end of ECHP and start of EU-SILC (up to 2007 in some countries), data was provided by NSI's from national sources (with some breaks in series due to lack of information, transition from national data source to EU-SILC, etc.). Regarding sampling methods: Germany, which had previously used quota sampling methods, was granted a transition period until 2008 when it was required to introduce fully representative probability sampling. In most countries, the sample takes the form of a rotational panel by dividing the sample into sub-panels retained for a maximum of four years. Each year, one sub-panel is refreshed by a new replication. Even when the standard number of rotational groups is four, the exceptions are: France (nine-year panel), Norway (eight-year panel), and Luxembourg (pure panel). Member states are allowed to use different survey instruments to collect cross-sectional and longitudinal data. Also, linkages between these datasets are not required.

Data quality


Some labour variables collected on all adult household members (16+) through the interviews might suffer of proxy answers. The responses given by one member of the household on behalf of the others might also be a source of measurement error.


During the transition between the end of ECHP and start of EU-SILC (up to 2007 in some countries), data was provided by NSI's from national sources (with some breaks in series due to lack of information, transition from national data source to EU-SILC, etc.). Regarding sampling methods: Germany, which had previously used quota sampling methods, was granted a transition period until 2008 when it was required to introduce fully representative probability sampling. In most countries, the sample takes the form of a rotational panel by dividing the sample into sub-panels retained for a maximum of four years. Each year, one sub-panel is refreshed by a new replication. Even when the standard number of rotational groups is four, the exceptions are: France (nine-year panel), Norway (eight-year panel), and Luxembourg (pure panel). Member states are allowed to use different survey instruments to collect cross-sectional and longitudinal data. Also, linkages between these datasets are not required.

Data quality


Some labour variables collected on all adult household members (16+) through the interviews might suffer of proxy answers. The responses given by one member of the household on behalf of the others might also be a source of measurement error.


During the transition between the end of ECHP and start of EU-SILC (up to 2007 in some countries), data was provided by NSI's from national sources (with some breaks in series due to lack of information, transition from national data source to EU-SILC, etc.). Regarding sampling methods: Germany, which had previously used quota sampling methods, was granted a transition period until 2008 when it was required to introduce fully representative probability sampling. In most countries, the sample takes the form of a rotational panel by dividing the sample into sub-panels retained for a maximum of four years. Each year, one sub-panel is refreshed by a new replication. Even when the standard number of rotational groups is four, the exceptions are: France (nine-year panel), Norway (eight-year panel), and Luxembourg (pure panel). Member states are allowed to use different survey instruments to collect cross-sectional and longitudinal data. Also, linkages between these datasets are not required.

Data quality


Some labour variables collected on all adult household members (16+) through the interviews might suffer of proxy answers. The responses given by one member of the household on behalf of the others might also be a source of measurement error.


During the transition between the end of ECHP and start of EU-SILC (up to 2007 in some countries), data was provided by NSI's from national sources (with some breaks in series due to lack of information, transition from national data source to EU-SILC, etc.). Regarding sampling methods: Germany, which had previously used quota sampling methods, was granted a transition period until 2008 when it was required to introduce fully representative probability sampling. In most countries, the sample takes the form of a rotational panel by dividing the sample into sub-panels retained for a maximum of four years. Each year, one sub-panel is refreshed by a new replication. Even when the standard number of rotational groups is four, the exceptions are: France (nine-year panel), Norway (eight-year panel), and Luxembourg (pure panel). Member states are allowed to use different survey instruments to collect cross-sectional and longitudinal data. Also, linkages between these datasets are not required.


Applicability


EU-SILC is the reference source for the study of income distribution, living conditions and social exclusion at European level. The design of EU-SILC involves a common ex ante framework defining the harmonised target variables to be collected or produced by national statistical institutions. As a result, there is a large degree of flexibility in the underlying sources of EU-SILC, and some flexibility in the concepts and definitions used. The flexible survey design also allows embedding EU-SILC into the national systems of social surveys. Therefore, emphasis on output harmonisation, rather than input harmonisation, might lead to diminish the international comparability of the data. Social exclusion and housing condition information is collected at household level (core data, income and tax, housing, material deprivation), while basic demographic data, income, labour, education and health information is obtained at individual level for persons aged 16 and over. Access to personal income data is available only on the subsample of 'selected adult respondents 16+'. The selected respondents are to be analysed at the level of persons only, using special selected weights, without aggregation to household level. The absence of stratum indicators in the dataset should be considered in order to avoid underestimated standard errors, thus leading to erroneous conclusions. In regard of the household and familiar structure, one of the limitations of EU-SILC is that it does not provide a household grid, thus leaving aside the proper identification of the nature of living arrangements. Regarding income variables, some of the aggregations on income or social benefits might result in limitations for the analysis. The 4-year rotational format of the EU-SILC implies that the information on individuals’ history is reduced to four years. The time framework differs across member states, since only 13 countries launched the SILC in 2004. Health and Performance The EU-SILC allows the cross-country analysis on access to health care through its core and longitudinal components. The dataset provides subjective measures, such as general health status, chronic illnesses, or limitations in daily life because of health problems, as well as unmet needs for medical and dental examination or treatment in the last 12 months. Thus, the EU-SILC contributes to the cross-national study of the association of income and health. One of the potential limitations of the variables included is the reporting bias of subjective health measures, as well as the dimensions covered by the health variables depending on the national context.

Applicability


EU-SILC is the reference source for the study of income distribution, living conditions and social exclusion at European level. The design of EU-SILC involves a common ex ante framework defining the harmonised target variables to be collected or produced by national statistical institutions. As a result, there is a large degree of flexibility in the underlying sources of EU-SILC, and some flexibility in the concepts and definitions used. The flexible survey design also allows embedding EU-SILC into the national systems of social surveys. Therefore, emphasis on output harmonisation, rather than input harmonisation, might lead to diminish the international comparability of the data. Social exclusion and housing condition information is collected at household level (core data, income and tax, housing, material deprivation), while basic demographic data, income, labour, education and health information is obtained at individual level for persons aged 16 and over. Access to personal income data is available only on the subsample of 'selected adult respondents 16+'. The selected respondents are to be analysed at the level of persons only, using special selected weights, without aggregation to household level. The absence of stratum indicators in the dataset should be considered in order to avoid underestimated standard errors, thus leading to erroneous conclusions. In regard of the household and familiar structure, one of the limitations of EU-SILC is that it does not provide a household grid, thus leaving aside the proper identification of the nature of living arrangements. Regarding income variables, some of the aggregations on income or social benefits might result in limitations for the analysis. The 4-year rotational format of the EU-SILC implies that the information on individuals’ history is reduced to four years. The time framework differs across member states, since only 13 countries launched the SILC in 2004. The analysis of data collected by EU-SILC allows for the study of the fulfilled basic needs and access to services of the population. The data collected contributes to the identification of the population in risk of exclusion, as well as the factors behind unmet needs and material deprivation. Moreover, the database allows for the analysis of the income from work, investment, and social benefits. Based on the harmonised panel design of EU-SILC, the dataset could be considered as a good instrument for the analysis of long-term poverty persistence.

Applicability


EU-SILC is the reference source for the study of income distribution, living conditions and social exclusion at European level. The design of EU-SILC involves a common ex ante framework defining the harmonised target variables to be collected or produced by national statistical institutions. As a result, there is a large degree of flexibility in the underlying sources of EU-SILC, and some flexibility in the concepts and definitions used. The flexible survey design also allows embedding EU-SILC into the national systems of social surveys. Therefore, emphasis on output harmonisation, rather than input harmonisation, might lead to diminish the international comparability of the data. Social exclusion and housing condition information is collected at household level (core data, income and tax, housing, material deprivation), while basic demographic data, income, labour, education and health information is obtained at individual level for persons aged 16 and over. Access to personal income data is available only on the subsample of 'selected adult respondents 16+'. The selected respondents are to be analysed at the level of persons only, using special selected weights, without aggregation to household level. The absence of stratum indicators in the dataset should be considered in order to avoid underestimated standard errors, thus leading to erroneous conclusions. In regard of the household and familiar structure, one of the limitations of EU-SILC is that it does not provide a household grid, thus leaving aside the proper identification of the nature of living arrangements. Regarding income variables, some of the aggregations on income or social benefits might result in limitations for the analysis. The 4-year rotational format of the EU-SILC implies that the information on individuals’ history is reduced to four years. The time framework differs across member states, since only 13 countries launched the SILC in 2004. One of the main advantages of the study of EU-SILC is the combined analysis of the living and working conditions. This allows for approaching the working poor phenomenon based on the working status and income of employed individuals, and the extent to which they have a poverty-level of income within the household. This is particularly useful for the population 50+ as the participation in the labour market at older ages could contribute to decreasing the risk of poverty and social exclusion of the elderly. Thus, representing an additional incentive to remain active longer.

Applicability


EU-SILC is the reference source for the study of income distribution, living conditions and social exclusion at European level. The design of EU-SILC involves a common ex ante framework defining the harmonised target variables to be collected or produced by national statistical institutions. As a result, there is a large degree of flexibility in the underlying sources of EU-SILC, and some flexibility in the concepts and definitions used. The flexible survey design also allows embedding EU-SILC into the national systems of social surveys. Therefore, emphasis on output harmonisation, rather than input harmonisation, might lead to diminish the international comparability of the data. Social exclusion and housing condition information is collected at household level (core data, income and tax, housing, material deprivation), while basic demographic data, income, labour, education and health information is obtained at individual level for persons aged 16 and over. Access to personal income data is available only on the subsample of 'selected adult respondents 16+'. The selected respondents are to be analysed at the level of persons only, using special selected weights, without aggregation to household level. The absence of stratum indicators in the dataset should be considered in order to avoid underestimated standard errors, thus leading to erroneous conclusions. In regard of the household and familiar structure, one of the limitations of EU-SILC is that it does not provide a household grid, thus leaving aside the proper identification of the nature of living arrangements. Regarding income variables, some of the aggregations on income or social benefits might result in limitations for the analysis. The 4-year rotational format of the EU-SILC implies that the information on individuals’ history is reduced to four years. The time framework differs across member states, since only 13 countries launched the SILC in 2004. EU-SILC provides variables on dwelling types, tenure status, housing conditions, costs, as well as appliances. The collection allows for the study of material deprivation and living conditions of the households. The dataset allows for the retrieval of the basic information on the living arrangements and the type of households in which people live, even when there is no complete information regarding families or linkage among household members.

Applicability


EU-SILC is the reference source for the study of income distribution, living conditions and social exclusion at European level. The design of EU-SILC involves a common ex ante framework defining the harmonised target variables to be collected or produced by national statistical institutions. As a result, there is a large degree of flexibility in the underlying sources of EU-SILC, and some flexibility in the concepts and definitions used. The flexible survey design also allows embedding EU-SILC into the national systems of social surveys. Therefore, emphasis on output harmonisation, rather than input harmonisation, might lead to diminish the international comparability of the data. Social exclusion and housing condition information is collected at household level (core data, income and tax, housing, material deprivation), while basic demographic data, income, labour, education and health information is obtained at individual level for persons aged 16 and over. Access to personal income data is available only on the subsample of 'selected adult respondents 16+'. The selected respondents are to be analysed at the level of persons only, using special selected weights, without aggregation to household level. The absence of stratum indicators in the dataset should be considered in order to avoid underestimated standard errors, thus leading to erroneous conclusions. In regard of the household and familiar structure, one of the limitations of EU-SILC is that it does not provide a household grid, thus leaving aside the proper identification of the nature of living arrangements. Regarding income variables, some of the aggregations on income or social benefits might result in limitations for the analysis. The 4-year rotational format of the EU-SILC implies that the information on individuals’ history is reduced to four years. The time framework differs across member states, since only 13 countries launched the SILC in 2004. The study of EU-SILC data allows for the study of wellbeing by considering objective or monetary measures of quality of life. That is, the analysis based on living conditions, education, work and economic security among others. Moreover, the dataset allows for the comparative analysis of the household, as well as the risks of material deprivation and poverty. This approach will be complemented in the future by the inclusion of subjective measures of quality of life. The 2013 special module on Wellbeing will contribute to the quality of life coverage of EU-SILC by including variables such as life satisfaction, meaning of life, satisfaction with time use and financial situation, happiness, and trust in the institutions, among others.


  • The information about this dataset was compiled by the author:
  • Diana Lopez-Falcon
  • (see Partners)