German Microdata Lab

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

About the EU-SILC

Population The reference population of EU-SILC is defined as all private households and all persons aged 16 and over within the household residing in the territory of the Member States at the time of data collection. Persons living in collective households and in institutions are generally excluded from the target population. For practical reasons, small parts of the national territory may also not be covered in the survey (e.g. the French Overseas Departments and territories; Scotland north of the Caledonian Canal and the Scilly Islands)
Survey PeriodEU-SILC is an annual statistic and was launched in 2004 in 13 Member States (BE, DK, EE, EL, ES, FR, IE, IT, LU, AT, PT, FI and SE) and in NO and IS. From 2005 onwards the data are available for all EU25 Member States and IS and NO. TR, RO, BG and CH have launched EU-SILC in 2006.
Survey MethodEU-SILC data are collected by National Statistical Institutes and could come from different sources. In some participant countries a new survey was launched with cross-sectional and longitudinal elements. In other countries a combination of registers and surveys is used, that is the data for the same respondents are collected partly by interview and partly from register.
TopicsEU-SILC provides cross-sectional and longitudinal microdata on income, poverty, social exclusion and living conditions. Topics covered by EU-SILC at the household level are basic information, income, social exclusion and housing. At the individual level the topics are basic demographic information, education, labour information, health and income. The income data is typically collected for the income reference year preceding the date of the survey and follows international standards.
Data Access

The current legal framework enables access to anonymised microdata available at Eurostat only for scientific purposes (Commission Regulations (EC) No 831/2002; (EC) No 1104/2006; (EC) No 1000/2007; Council Regulation 322/97), however the access is restricted to universities, research institutes, national statistical institutes, central banks inside the EU, as well as to the European Central Bank. Individuals cannot be granted direct data access. For detailled information concerning data access, costs and how to submit an access request see:

EU-SILC: Papers of Interest

Atkinson, Anthony B.; Guio, Anne-Catherine; Marlier, Eric (Eds.) 2017: Monitoring Social Inclusion in Europe. [link]

Atkinson, Anthony B.; Marlier, Eric (Eds.) 2010: Income and living conditions in Europe. [link]

Eurostat methodologies and working papers [link]

Equalsoc – Economic Change, Quality of Life and Social Cohesion (2009): Data Quality Issues in the EU-SILC Intergenerational Modules. [link]

Frick, Joachim R., Krell, Kristina (2010): Measuring Income in Household Panel Surveys for Germany: A Comparison of EU-SILC and SOEP. SOEPpapers No. 265. Berlin: DIW. [link]

Goedemé, T., (2010): The standard error of estimates based on EU-SILC. An exploration through the Europe 2020 poverty indicators, CSB Working Paper Series, WP  10/09, Antwerp, Herman Deleeck Centre for Social Policy, University of Antwerp. [link]

Hauser, Richard (2007): Probleme des deutschen Beitrags zu EU-SILC aus der Sicht der Wissenschaft – Ein Vergleich von EU-SILC, Mikrozensus und SOEP. SOEPpapers. No. 69. Berlin: DIW.[link]

Lohmann, Henning (2011): Comparability of EU-SILC survey and register data: The relationship among employment, earnings and poverty. Journal of European Social Policy February 2011 21: 37-54 [link]

Mysíková, M., (2011): Income Inequalities within Couples in the Czech Republic and European Countries. IES Working Paper 4/2011. IES FSV. Charles University. [link]

Schneider, Silke; Müller, Walter (2009): Measurement of Education in EU-SILC – Preliminary Evaluation of Measurement Quality. Equalsoc Working Papers.[link]



Variables by Year Matrix 2004-2017

Variables-by-Year-Matrix-2004-2017 (439 kB) [.xlsx-File]
(incl. variables, type of data, reference period, unit of observation, mode of collection)

Cumulation of country separated csv-files


EU-SILC covers a wide array of variables collected from households by the Member States. Among others, EU-SILC contains panel data that follows a rotational design. Each year, Eurostat publishes a series of separate datasets covering only up to 4 years, even though it has been collecting data since 2003. “eusilcpanel” is a script written by Marwin Borst in the form of a Stata package (eusilcpanel.ado; eusilcpanel.sthlp; totalpopulation.dta), that is able to merge these chunks of data into one cumulative dataset (separately for the D-,H-,R- and P-data). The script makes the EU-SILC panel more accessible to researchers in the vast majority of cases, but it can’t deal with data from all countries. Please acknowledge the work of Marwin Borst by citing both the program and the paper.                      

The steps necessary to cumulate the longitudinal data as well as a description of the Stata script are documented in:

Borst, Marwin (2018): EU-SILC Tools: eusilcpanel. First computational steps towards a cumulative sample based on the EU-SILC longitudinal datasets. GESIS Papers 2018/11, Mannheim. [link] (779 kB) 

Borst, Marwin (2018):eusilcpanel stata package (12.47 kB)

Contact: Marwin Borst

The eusilcpanel stata package was developed to operate on EU-SILC csv-files that have been delivered by Eurostat in a combined format with all countries in one user data base. Since October 2017, Eurostat delivers EU-SILC csv-files separated by country. In order to be able to apply the eusilcpanel stata package on the new format, you can use the following do-files to transform separated country csv-files into one csv-file for every release. (17.80 kB)

These do-files have been prepared by Valentina Ponomarenko

Contact: Valentina Ponomarenko


Syntax to generate ESeC and ESeG using EU-SILC-SPSS or Stata Systemfiles

For details on ESeC please visit the homepage of Eric Harrison
For details on ESeG please see Meron, Monique et al (2014)

These syntax files were prepared by Anika Herter and Heike Wirth

Descriptive information on ESeC (52 kB)
Descriptive information on ESeG (40 kB)

Contact: Heike Wirth

 European Socioeconomic ClassificationEuropean Socioeconomic Groups
Year of
based on ISCO88 (2004-2010) and ISCO08 (2011ff)based on ISCO08 (2011ff)
- only cross-sectional -- cross-sectional and longitudinal -
20142014_esec08.sps (9.09 kB)[*.do] (9.45 kB)2014_eseg08.sps (15.93 kB)[*.do] (15.63 kB)  
20132013_esec08.sps (8.87 kB)[*.do] (9.24 kB)2013_eseg08.sps (15.37 kB)[*.do] (15.47 kB)[*_lg.sps] (15.43 kB)[*.do] (15.49 kB)
20122012_esec08.sps (8.58 kB)[*.do] (9.27 kB)2012_eseg08.sps (15.38 kB)[*.do] (15.47 kB)[*_lg.sps] (15.43 kB)[*.do] (15.49 kB)
20112011_esec08.sps (12.75 kB)[*.do] (12.51 kB)2011_eseg08.sps (15.53 kB)[*.do] (15.47 kB)[*_lg.sps] (15.59 kB)[*.do] (15.49 kB)
20102010_esec88.sps (8.54 kB)[*.do] (9.03 kB)    
20092009_esec88.sps (8.57 kB)[*.do] (9.05 kB)    
20082008_esec88.sps (8.17 kB)[*.do] (8.17 kB)    
20072007_esec88.sps (8.17 kB)[*.do] (8.18 kB)    
20062006_esec88.sps (8.16 kB)[*.do] (8.18 kB)    
20052005_esec88.sps (8.06 kB)[*.do] (8.18 kB)    
20042004_esec88.sps (8.16 kB)[*.do] (8.18 kB)    

Standard Error Estimates

  • Stata do-filesthat prepare the sample design variables for the EU-SILC cross-sectional data (2005-2014). [by Tim Goedemé].
  • SPSS (27 kB) that prepare the sample design variables for the EU-SILC cross-sectional data (2010-2014) [by Anika Herter & Heike Wirth].

The do-file is discussed in the following article and working papers.

  • Herter, A. and Wirth, H. (2018): EU-SILC Tools: Calculating Standard Errors for EU-SILC using SPSS. GESIS Papers 2018|16.
  • Di Meglio, E., Osier, G., Goedemé, T. , Berger, Y. G. and Di Falco, E. (2013): Standard Error Estimation in EU-SILC – First Results of the Net-SILC2 Project, Proceedings of the Conference on New Techniques and Technologies for Statistics, March 5–7, 2013, Brussels.
  • Goedemé, T. (2013): 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): 89-110, doi:10.1007/s11205-011-9918-2.
  • Goedemé, T. (2013): The EU-SILC sample design variables: critical review and recommendations, CSB Working Paper Series, WP 13/02, Antwerp, Herman Deleeck Centre for Social Policy, University of Antwerp (PDF).
  • Osier, G., Berger, Y.G., and Goedemé. T. (2013): Standard Error Estimation for the EU-SILC Indicators of Poverty and Social Exclusion, Eurostat Methodologies and Working Papers, Publications Office of the European Union, Luxembourg (PDF).
  • Goedemé, T. (2010): The standard error of estimates based on EU-SILC. An exploration through the Europe 2020 poverty indicators, CSB Working Paper Series, WP 10/09, Antwerp, Herman Deleeck Centre for Social Policy, University of Antwerp (PDF).
  • Verma, V. and Betti, G. (2010): Data accuracy in EU-SILC in Marlier E., and A. B. Atkinson (Eds.) Income and living conditions in Europe, Publications Office of the European Union, Luxembourg.
  • Verma, V., Betti, G. and Gagliardi, F. (2010): An assessment of survey errors in EU-SILC, Eurostat Methodologies and Working Papers, Publications Office of the European Union, Luxembourg (PDF).