Statistisches Bundesamt (Destatis)

Methodology of European Household Survey

Zweigstelle Bonn

Graurheindorfer Strasse 198

53117 Bonn

3 April 2025

3 April 2025

3 April 2025

The European Union Statistics on Income and Living Conditions (EU-SILC) is a survey-based instrument aiming at collecting timely and comparable cross-sectional and longitudinal multidimensional microdata on income, poverty, social exclusion and living conditions. In addition, it collects module variables every three years, six years or ad-hoc new policy needs modules.

The EU-SILC instrument provides two types of data:

1. Cross-sectional data pertaining to a given time or a certain time period with variables on income, poverty, social exclusion and other living conditions;
2. Longitudinal data pertaining to individual-level changes over time, observed periodically over four‐or more year rotation scheme (Annex III (2) of 2019/1700). Social exclusion and housing condition information is collected mainly at household level while labour, education and health information is obtained for persons aged 16 and over. The core of the instrument is income information at very detailed component level and mainly collected at personal level.

• International Standard Classification of Education (ISCED'2011);
• International Standard Classification of Occupations (ISCO-08);
• Classification of Economic Activities (NACE Rev.2-2008);
• Common classification of territorial units for statistics (NUTS 2); 
• SCL - geographical code list;
• The recommendations made by the United Nations in the Canberra Group Handbook on Household Income Statistics should also be taken into account.

For more details on the classification used please, see EU Vocabularies, Eurostat's metadata server or CIRCABC .

Data refers to all private households and individuals living in the private households in the national territory at the time of data collection.

The EU-SILC survey is a key instrument for the European Semester and the European Pillar of Social Rights, providing information on income distribution, poverty and social exclusion, as well as various related living conditions and poverty EU policies, such as on child poverty, access to health care and other services, housing, over indebtedness and quality of life. It is also the main source of data for microsimulation purposes and flash estimates of income distribution and poverty rates.

Statistical concepts and definitions for EU-SILC are specified in Regulation (EU) 2019/1700, Commission Implementing Regulation (EU) 2019/2181, and Commission Implementing Regulation (EU) 2019/2242. Additional information is available in the EU statistics on income and living conditions (EU-SILC) methodology and in the methodological guidelines and description of EU-SILC target variables (see CIRCABC).

Further details are provided in items 5, 15.1.1.1, 15.2.2 and 18.3.

Statistical units are private households and all persons living in these households who have usual residence in the Member State. Annex II of the Commission implementing regulation (EU) 2019/2242 defines specific statistical units per variable and specifies the, content of the quality reports on the organization of a sample survey in the income and living conditions domain pursuant to Regulation (EU) 2019/1700 of the European Parliament and of the Council.

The target population is private households and all persons composing these households having their usual residence in the Member State. Private household means a person living alone or a group of persons who live together, providing oneself or themselves with the essentials of living.

Definitions of reference population, household and household membership

The sub-populations that are not covered by the data collection includes: those who moved out of the country’s territory; or those with no usual residence; or those living in institutions or who have moved to an institution compared to the previous year.

The geographical area to which the statistical phenomenon measured relates is Germany: there is no regions that are excluded.
All 16 Federal States are covered: Schleswig-Holstein, Hamburg, Niedersachsen, Bremen, Nordrhein-Westfalen, Hessen, Rheinland-Pfalz, Baden-Württemberg, Bayern, Saarland, Berlin, Brandenburg, Mecklenburg-Vorpommern, Sachsen, Sachsen-Anhalt, Thüringen.

The cross-sectional data 2024 refer to a given time or a certain time period with variables on income, poverty, social exclusion and other living conditions. The longitudinal data refer to individual/household changes over time, observed periodically over a four-year period (or more years if a longer duration panel is used).

EU-SILC survey has been implemented in Germany since 2005. The 2020 was the first year where the integration into the German Microcensus was conducted. For this reason the "old" sample was discontinued.  The first four-year longitudinal data for Germany are available from the survey year 2023.

Not applicable.

The data involves several units of measure depending upon the variables. Income variables are transmitted to Eurostat in national currency. For more information, see methodological guidelines and description of EU-SILC target variables available on CIRCABC

Description of reference period used for incomes

Regulation (EU) 2019/1700 was publish in OJ on 10 October 2019, establishing a common framework for European statistics relating to persons and households, based on data at individual level collected from samples (IESS). The Annex to the Commission implementing regulation (EU) 2019/2180 of 16 December 2019 specifies the detailed arrangements and content for the quality reports pursuant to Regulation (EU) 2019/1700 of the European Parliament and of the Council and Regulation (EU) 2019/2242.

Confidential microdata are not disclosed by Eurostat. Access to confidential microdata for scientific purposes may be granted on the  basis of Commission Regulation 557/2013 and Regulation 223/2009 of  the European Parliament and the Council on European statistics.

The individual data collected are always kept confidential in accordance with Section 16 of the Federal Statistics Act (BStatG).

Individual data may be passed on only in exceptional cases explicitly regulated by law. Individual data may always be transmitted to:

• public agencies and institutions within the official statistics network which are entrusted with the production of federal or European statistics (e.g. the statistical offices of the Länder, the Deutsche Bundesbank, the Statistical Office of the European Union [Eurostat]),
• service providers with whom a contractual relationship exists (e.g. Federal Information Technology Centre (ITZBund), computer centres of the Länder).

Pursuant to Section 16 (6) of the Federal Statistics Act, institutions of higher education or other institutions tasked with independent scientific research may, for the purpose of carrying out scientific projects, be provided

1. with individual data if attributing the anonymised individual data to the relevant respondents or persons concerned requires unreasonable effort in terms of time, cost and manpower (de facto anonymised individual data),
2. with access to individual data not including name and address (formally anonymised individual data) within specially protected areas of the Federal Statistical Office and the statistical offices of the Länder, if effective measures are in place to safeguard confidentiality.

Persons receiving individual data are also obliged to maintain confidentiality.

The first names and surnames of the household members, the contact details of the household members, residential address, location of the dwelling in the building, first name and surname of the main tenant/owner-occupier of the dwelling, name and address of the household members‘ places of work, and the building age group are auxiliary variables which will only be used for the technical conduct of the survey. As soon as the survey and auxiliary variables have been checked for conclusiveness and completeness, the auxiliary variables will be separated from the information on the survey variables and will be kept separately or stored separately

• Pursuant to Section 14 (5), first sentence, of the Microcensus Act, the first names and surnames and the municipality, street, house number and contact details of the persons surveyed may also be used with regard to household relationships to conduct follow-up surveys in accordance with Section 5 (1) of the Microcensus Act.
• Pursuant to Section 14 (5), second sentence, of the Microcensus Act, the information on the variables pursuant to Section 14 (5), first sentence, of the Microcensus Act may also be used as a basis for recruiting suitable persons and households to conduct household budget surveys and other voluntary surveys.

Information on the survey variables is processed and stored for as long as necessary to comply with the legal obligations. All survey documents as well as the auxiliary variables and the reference numbers originally allocated will be destroyed or deleted after the processing of the last follow-up survey has been finished.

An EU micro data file (EU scientific use file Germany) is made available by Eurostat, after approval and permission provided by the German FSO.

Publications of the data at national level:

Annually: Statistical report (in German language; until EU-SILC 2021 "Fachserie 15 Reihe 3"), quality report, database GENESIS-Online, social reporting in official statistics

Irregular: News releases. Short-term release calendar ("Wochenvorschau"; in German language) 

Every 2-3 years: social report ("Sozialbericht"; in German language)

Publications of the data at national level:

Annually: Statistical report (in German language; until EU-SILC 2021 "Fachserie 15 Reihe 3"), quality report, database GENESIS-Online, social reporting in official statistics

Irregular: News releases. Short-term release calendar ("Wochenvorschau"; in German language) 

Every 2-3 years: social report ("Sozialbericht"; in German language)

Please refer to the Release calendar - Eurostat (europa.eu) publicly available on the Eurostat’s website.

In line with the Community legal framework and the European Statistics Code of Practice, Eurostat disseminates European statistics on Eurostat's website (see section 10 - 'Accessibility and clarity'), respecting professional independence and in an objective, professional and transparent manner in which all users are treated equitably. The detailed arrangements are governed by the Eurostat protocol on impartial access to Eurostat data for users. 

Additional information about microdata access is available in EU statistics on income and living conditions - Microdata - Eurostat.

Annual

More information can be found:

• Press releases can be accessed at destatis.de under the topic Income, living conditions, risk of poverty;
• Overview of all news releases at national level.

Publications at national level:

• Statistical report (in German language; until EU-SILC 2021 "Fachserie 15 Reihe 3") 
• Quality report (in German language) 
• Social reporting in official statistics 
• Social report ("Sozialbericht") 

All results are available in the Eurostat database :

Database - Income and living conditions – Eurostat.

National results are available in the GENESIS-Online database.

Not available.

Access to the anonymised EU-SILC microdata is provided by means of research contracts. Access is in principle restricted to universities, research institutes, national statistical institutes, central banks inside the EU, and to the European Central Bank. Individuals cannot be granted direct access. Contact point: estat-microdataaccess@ec.europa.eu.

More information can be found on the Eurostat website.

Not available.

The quality of the EU-SILC has to be ensured as required  in the regulations. Detailed guidelines of EU-SILC (DocSILC 065 methodological guidelines and description of EU-SILC target variables) can be found on CIRCABC. In addition Germany is continuously working to improve the quality of the survey, carrying out several methodological studies on relevant topics.

All the required concepts of the SIMS are provided.

All national quality reports are available under: Quality report (in German language).

The quality manual of the Federal and State Statistical Offices describes the framework for ensuring data quality in German official statistics. It informs users of statistical data (e.g. from the ministries, associations, science or the public) about management to ensure the quality of statistical results. In addition, it serves the employees of the Statistical Offices of the Federal and State governments as well as other bodies in Germany, which compile official statistics, as a guid. There are several training courses for interviewers as for internal staff responsible for the operation, both in the FSO and in the Federal Statistical State Offices. The entire production process of the German official microcensus including the different subsamples like the EU-SILC survey is constantly monitored and subject to close-knit business controlling in the FSO. Meeting EU deadlines for data delivery and for reporting are also subject to the FSO's internal controlling. Developing optimization measures are a constant part of the ongoing work. 

EU-SILC has been integrated as a module in the German microcensus since 2020. The entire microcensus system continues to be continually developed and evaluated. 

The main users of EU-SILC statistical data are policy makers, research institutes, media, and students.

There are regular consultations and information exchange in Germany with relevant Federal Ministries and other bodies of experts of the German governmental administration. User interests are taken into account in many different ways. The federal and state ministries can directly influence the survey program via the legislative procedure for the microcensus. Furthermore, the data requirement, for example from science or city statisticians, can be found in the Statistical Advisory Board, at microcensus user conferences and specialist committee meetings.

Eurostat carried out an online general User Satisfaction Survey (USS) in the period between April and July 2019 to obtain a better knowledge about users, considering their needs and satisfaction with the services provided by Eurostat. The survey has shown that EU-SILC is of very high relevance for users. For the majority, both aggregates and micro-data were important or essential in their work irrespective of the purpose of their use. The use of the ad-hoc modules was less widespread than the use of the nucleus variables. Nevertheless, there was high interest to repeat these modules in order to have the possibility of comparing data over time. Users emphasized their strong need for more detailed micro-data, which is currently not possible. Under the new legal framework implemented from 2021, the NUTS 2 division will be available for the main indicators. Finally, users were satisfied with overall quality of the service delivered by Eurostat, which encompasses data quality and the supporting service provided to them.

For more information, please consult the User Satisfaction Survey.

All variables according to the Regulation are beeing transmitted.

Not collected variables:

HY145: Repayments/receipts for tax adjustments

The income at component level is reported gross and some net of tax, adjustments will be recorded in the variable HY140G.

- OPTIONAL VARIABLES

HI130G: Interest expenses [not including interest expenses for purchasing the main dwelling] 

HI140G: HOUSEHOLD DEBTS

RL080: Remote education

Not available.

According to Reg. (EU) 2019/1700 Annex II, precision requirements for all data sets are expressed in standard errors and are defined as continuous functions of the actual estimates and of the size of the statistical population in a country or in a NUTS 2 region. For the income and living conditions domain, the estimated standard errors of the following indicators are examined according to certain parameters set:

• · Ratio at‐risk‐of‐poverty or social exclusion to population;
• · Ratio of at‐persistent‐risk‐of‐poverty over four years to population;
• · Ratio at‐risk‐of‐poverty or social exclusion to population in each NUTS 2 region.

Further information is provided in section 13.2 Sampling error.

EU-SILC is a complex survey involving different sampling designs in different countries. In order to harmonize and make sampling errors comparable among countries, Eurostat (with the substantial methodological support of Net-SILC2) has chosen to apply the "linearization" technique coupled with the “ultimate cluster” approach for variance estimation.

Linearization is a technique based on the use of linear approximation to reduce non-linear statistics to a linear form, justified by asymptotic properties of the estimator. This technique can encompass a wide variety of indicators, including EU-SILC indicators. The "ultimate cluster" approach is a simplification consisting in calculating the variance taking into account only variation among Primary Sampling Unit (PSU) totals. This method requires first stage sampling fractions to be small which is nearly always the case. This method allows a great flexibility and simplifies the calculations of variances. It can also be generalized to calculate variance of the differences of one year to another.

The main hypothesis on which the calculations are based is that the "at risk of poverty" threshold is fixed. According to the characteristics and availability of data for different countries, we have used different variables to specify strata and cluster information. 

DE sampling design corresponds to a on stage stratified cluster sampling. DB050 (primary strata) can be used for strata specification and DB060 (Primary Sampling Unit) for cluster specification.

The concept of accuracy refers to the precision of estimates computed from a sample rather than from the entire population. Accuracy depends on sample size, sampling design effects and structure of the population under study. In addition to that, sampling errors and non-sampling errors need to be taken into account. Sampling error refers to the variability that occurs at random because of the use of a sample rather than a census and non-sampling errors are errors that occur in all phases of the data collection and production process.

Variance estimation for cross-sectional indicators is performed in Germany using Statistics Swedens' SAS estimation software ETOS 2. The estimation approach takes both the sampling design (stratification and clustering of households in PSUs) and the employed model-assisted estimation approach of the Generalized regression (GREG) estimator into account. Taylor linearization is used to obtain variance estimators.

Non-sampling errors are basically of 4 types:

• Coverage errors: errors due to divergences existing between the target population and the sampling frame.
• Measurement errors: errors that occur at the time of data collection. There are a number of sources for these errors such as the survey instrument, the information system, the interviewer and the mode of collection.
• Processing errors: errors in post-data-collection processes such as data entry, keying, editing and weighting.
• Non-response errors: errors due to an unsuccessful attempt to obtain the desired information from an eligible unit. Two main types of non-response errors are considered:
  • Unit non-response: refers to absence of information of the whole units (households and/or persons) selected into the sample.
  • Item non-response: refers to the situation where a sample unit has been successfully enumerated, but not all required information has been obtained.

Coverage errors include over-coverage, under-coverage and misclassification:

• Over-coverage: relates either to wrongly classified units that are in fact out of scope, or to units that do not exist in practice.
• Under-coverage: refers to units not included in the sampling frame.
• Misclassification: refers to incorrect classification of units that belong to the target population

Not applicable. Sampling is done at the level of areas, not at the level of households/persons.

Not requested by Reg. 2019/2180

Measurement error for cross-sectional data

Non-response errors are errors due to an unsuccessful attempt to obtain the desired information from an eligible unit. Two main types of non-response errors are considered:

1) Unit non-response which refers to the absence of information of the whole units (households and/or persons) selected into the sample. According to Annex VI of the Reg.(EU) 2019/2242

• Household non-response rates (NRh) is computed as follows:

NRh=(1-(Ra * Rh)) * 100

Where Ra is the address contact rate defined as:

Ra= Number of address/selected person (including phone, mail if applicable) successfully contacted/Number of valid addresses/selected person (including phone, mail if applicable) selected

and Rh is the proportion of complete household interviews accepted for the database

Rh=Number of household interviews completed and accepted for database/Number of eligible households at contacted addresses (including phone, mail if applicable)

• Individual non-response rates (NRp) is computed as follows:

NRp=(1-(Rp)) * 100

Where Rp is the proportion of complete personal interviews within the households accepted for the database

Rp= Number of personal interview completed/Number of eligible individuals in the households whose interviews were completed and accepted for the database

• Overall individual non-response rates (*NRp) is computed as follows:

*NRp=(1-(Ra * Rh * Rp)) * 100

For those Members States where a sample of persons rather than a sample of households (addresses, phones, mails etc.) was selected, the individual non-response rates will be calculated for ‘the selected respondent.

2) Item non-response which refers to the situation where a sample unit has been successfully enumerated, but not all the required information has been obtained.

Unit non-response rate for cross-sectional

where

A=total (cross-sectional) sample,

B =New sub-sample (new rotational group) introduced for first time in the survey this year,

C= Sub-sample (rotational group) surveyed for last time in the survey this year.

The computation of item non-response is essential to fulfil the precision requirements. Item non-response rate is provided for the main income variables both at household and personal level.

Item non-response which refers to the situation where a sample unit has been successfully enumerated, but not all the required information has been obtained.

See Annex 2 - Item non response

 Description of data entry, coding controls and the editing system

Not applicable.

Date of the first full delivery of data to the Commission (Eurostat): 17 December 2024.

Date of dissemination of national results: 29 January 2025.

National publication date of first results are available in the DESTATIS website.

National publication date of finale results: not available.

Target delivery data: 31 December 2024.

First delivery data: 17 December 2024.

National publication: 29 January 2025.

Not available

Not applicable.

Information about breaks in series (Annex 8)

Not available

Comparability and deviation from definition for each income variable

F= Fully comparable; L= Largely comparable; P= Partly comparable and NC= Not collected.

The coherence of two or more statistical outputs refers to the degree to which the statistical processes, by which they were generated, used the same concepts and harmonised methods. A comparison with external sources for all income target variables and the number of persons who receive income from each ‘income component’ will be provided, where the Member States concerned consider such external data to be sufficiently reliable.

The HBS takes place every five years, the last one was in 2023. The data is currently being processed.

Not applicable.

EU-SILC and National Accounts (NA) are two separate statistical sources of data available to users and policymakers. Both surveys are based on different concepts for household income. More information can be found on the comparison document.

Not applicable.

Mean (average) interview duration per household =  63  minutes.

Mean (average) interview duration per person =27 minutes.

No revisions.

No revisions.

No revisions.

Detailed information concerning sampling frame, sampling design, sampling units, sampling size, weightings and mode of data collection can be found in this section (please see below). Such information is mainly used for the computation of the accuracy measures.

EU-SILC is integrated as a module into german microcensus, a highly reliable random sample covering one percent of the German population and carried out with legal obligation to respond, sampling units are areas (clusters). Every household in the sampled area is mandatory to participate in the survey. The sampling population for the EU-SILC sample comprises private households in their main residences. All sample persons of a household has to be followed-up over time. The advantage of the new design is a better coverage of the population structure in Germany.

Type of sampling design:

The German EU-SILC sample corresponds to a one stage cluster sample consisting of four rotation groups with one quarter leaving the sample every year and another one joining.

Sample frame is the German Census of 2011. The addresses were stratified by a combination of technical (building size classes) and regional (districts/counties or summaries of districts) information as described below. Based on the stratification, addresses were clustered into artificially delimited areas (selection areas; in German called "Auswahlbezirke") that consist of around 9 dwellings or respectively 15 persons.

Based on the specifications of the census, like clustering, the selected units were sampled and all sampling information, including rotation group allocation, was determined. Due to legislation only 20% of the sampling frame was stored. Based on the determined sampling variables, the sample districts were assigned to Microcensus and SILC- subsample. These variables are assigned as random numbers by permutation and are used to delimit the annual sample of the Microcensus, the rotation groups as well as the disjoint subsamples like SILC.

Stratification and sub-stratification criteria:

a) Stratification variables referring to building size classes:

The first stratum includes smaller buildings with 1 to 4 apartments. They are grouped into sample districts with a guideline value of 12 apartments, in the order of the house numbers within the street, if necessary also across streets.

The second stratum includes medium-sized buildings with 5 to 10 apartments. These buildings each form their own selection districts.

The third stratum includes buildings with 11 or more apartments. These are divided into sections with a guide size of 6 apartments.

There are two more strata: Stratum N°4 includes the population in communal accommodation, which is divided into selected units with a reference size of 15 people. Another stratum N°5

serves for updating the basic selection. This annual update of the selection takes place via the reports on the construction activity statistics (socalled

building permits). The new buildings registered there are divided into the three initial size classes mentioned.

b) Regional stratification variables:

There are 243 regional strata (districts or groups of districts), which as a rule should have at least 200.000 inhabitants. The technique of

selection, i.e. the sorting, zone formation and selection per zone ensures a stratification-like effect for these regions.

Sample size and allocation criteria:

Sampling is done on the level of areas not on households, therefore the sample size can only be estimated and corresponds to around 40000 households. The sample size was determined in order to meet precision requirements of council EU regulation 2019/1700 and takes into account the estimated design effect of German EU-SILC sample.

The sampling units are clusters ("Auswahlbezirke"), i.e. areas comprising several dwellings or, in the case of larger buildings, several apartments. On average, each cluster contains 9 apartments. All eligible private households within the sampled areas are included in the sample.

Concerning the SILC instrument, three different sample size definitions can be applied:

• the actual sample size which is the number of sampling units selected in the sample
• the achieved sample size which is the number of observed sampling units (household or individual) with an accepted interview
• the effective sample size which is defined as the achieved sample size divided by the design effect with regards to the at-risk-of poverty rate indicator

Given that the effective sample size has been already treated in the section dealing with sampling errors, in this section the attention focuses mainly on the achieved sample size.

Sample size and allocation criteria:

Sampling is done on the level of areas not on households, therefore the sample size can only be estimated and corresponds to around 40000 households. The sample size was determined in order to meet precision requirements of council regulation No 2019/1700  and takes into account the estimated design effect of German EU-SILC sample.

EU-SILC 2024 cross-sectional: Achieved sample size (total)

EU-SILC longitudinal: Sample size, adresses and household interviews 

The EU-SILC survey 2024 was carried out in Germany from February to August 2024.

 Sample distribution over time:

For the EU-SILC 2024 survey was a full multi-mode-design implemented.

Mode of data collection (Personal interview)

Number and percentage of Proxy interview

Mode of data collection by rotation group (Households interview)

Description of collecting income variables

Germany use many procedures for checking and validating the data from EU-SILC:

• checking the content of the variables in the raw data;
• checking household composition and respondent status;
• checking demografical information in the household;
• checking relationships between household members/ checking the age difference between household members, particularly between children and parents/ identifying the partner;
• checking the income data: lower and upper bounds, mean and median over time, distribution (interquartile range), changing by the same kind of income over the time;
• checking the incosistences between household income and household compositions;
• analysis of the missing values.

Additional check-ups are made with SAS checking programmes from Eurostat.

Description of the data compilation process.

Imputation is the process used to assign replacement values for missing, invalid or inconsistent data that have failed edits. This includes automatic and manual imputations; it excludes follow-up with respondents and the corresponding corrections (if applicable). The unweighted imputation rate for a variable is the ratio of the number of imputed values to the total number of values requested for the variable.

Weightings:

It is to outline that both phases, described under “Adjustment to external data” are part of the calibration itself. Additional steps for the calculation of response probabilities are done in advance. The input weights for calibration are calculated as the inverse of the product of inclusion and nonresponse probability. 

Imputation procedure used for income variables:

A. Deductive Methods:

• deductive Imputation by using regulations for social transfers (applicable for social transfers like family or housing allowances) and the available information. When no deductive imputation was possible, hot deck procedures or statistical imputation were used

B. Statistical Methods:

• predictive mean matching (PMM) based on all complete observations, a regression model is created where the dependent variable is the variable with the missing values we want to impute. For every observation with missing values, the predicted value is calculated based on the regression model. This predicted value is then compared with all predicted values from the complete observations. Depending on the specific model, it will be the real (observed) value from the complete case used, whose predicted value is closest to the predicted value of the incomplete case (with respect to some metric), or randomly choose from the real value from one of the complete cases, whose predicted values are closest to the predicted value of the incomplete case. This procedure can either be used as hot deck imputation, if the percentage of missing values is not too high (to prevent using the same value for imputation too often), or as cold deck imputation, if another data source is available with the same variables and the same distributions.

Imputation procedure used for non monetary variables:

Imputation methods used for non monetary variables and imputation rates are described in Annex 6.

Not applicable.

Not applicable.

No comment.