[CH1] Office Federal de la Statistique (Swiss Federal Statistical Office)

Population and education

Espace de l'Europe 10, 2010 Neuchâtel

4 September 2024

27 January 2026

3 December 2024

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 on an annual basis. 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:

• Cross-sectional data pertaining to a given time or a certain time period;
• Longitudinal data pertaining to individual-level changes over time, observed periodically over four‐or more year rotation scheme (Annex III (2) of 2019/1700).

Information on housing conditions, part of income and material and social deprivation is collected at household level, while information on work, education, health and satisfaction in different areas of life is obtained for persons aged 16 and over. The core of the instrument consists of highly detailed income information, mainly collected at individual level, largely using registers.

• 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 the list of classification on the Eurostat webpage and statistics explained on classification.

Data refer 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.

In addition to the variables requested by Eurostat, Switzerland collects information on the following topics in the SILC survey:

• Policy, interest and orientation;
• Social participation and associations;
• Wealth (SILC15, SILC18, SILC20, SILC22);
• Indebtedness (SILC13, SILC17, SILC20, SILC22);
• Sense of security/insecurity;
• Capability - subjective poverty (SILC23).

as well as questions that complement the themes covered by Eurostat:

• Arrears;
• Satisfaction in various areas;
• Childcare;
• Working conditions.

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.

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 Switzerland.  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 permanent resident population living in private households (incl. non-permanent residents living in a household with at least one permanent resident). 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 non-permanent residents not living in a household with at least one permanent resident; or those living in institutions or who have moved to an institution compared to the previous year.

The entire national territory is covered. 

This report and the related data refer to 2024. EU- SILC has been implemented in Switzerland on the base of a four-year rotational panel since 2007. Data are available for the survey years 2007-2024.

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.

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.

No SILC result is published on the FSO web pages if the calculations are based on fewer than 200 observations, or with parenthesis if they are based on 100 to 200 observations. All results are published with a confidence interval.

Anonymisation rules are the same for national microdata as for the EU-SILC microdata. 

All planned publications are announced a few weeks in advance in the online diary of the Swiss Federal Statistical Office : Agenda | Federal Statistical Office (admin.ch) -> Theme 20 Economic and social situation of the population  

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

SILC24 results will be published on FSO website on 16 February 2026 with the three-yearly module on children.

The results of the SILC23 module on intergenerational reproduction of educational attainment and financial status in Switzerland and Europe were published on 23 October 2025 in German, French, Italian and English on this page: Social mobility

SILC results are published yearly on the fso website. All published information is linked on this page, available in English, French, German and Italian:  Statistics on Income and Living Conditions (SILC) | Federal Statistical Office - FSO.

No database is available online. 

About 20 tables (each in 3 languages) based on SILC results are published on the FSO website. Some tables are downloaded over 4000 times a year, e.g. table about Disposable income distribution. The corresponding pages (4 languages) have been consulted over 27'000 times.

SILC microdata are available to those who want them, under certain conditions. They have to sign a data protection agreement before receiving the data. More information is available on Statistics on Income and Living Conditions (SILC) | Federal Statistical Office (admin.ch)-> FAQ

Swiss SILC microdata contain all EU-SILC variables, plus national variables, including important income sub-components. 

No other format is used.

Not available.

All the available methodological documents can be found on the FSO silc web page Statistics on Income and Living Conditions (SILC) | Federal Statistical Office - FSO, at the bottom, on the "Methodologies" sheet. More documents are available on the French and German web pages. 

100%. All metadata, from the questionnaire to the final dataset, are documented on SAE-SMS Metadata Editor (V. 1.46) and Data Structure Definitions are created on each step of the data editing.

Not applicable

Detailed quality checks are carried out each year to verify that the CATI-CAWI system fully complies with the questionnaire specifications in SDMX.

As mentionned in 18.4 and 18.5, several controls are carried out at each step to ensure quality and comparability of the data. Metadata of each intermediate data are documented in SAE-SMS Metadata Editor, which then enable to match codes in the data with those theoretically present. At each important stage of the data preparation, frequencies / means / max /min / P5/ P95 /missing /n  values of each variable is compared to those of the previous year, and those of the previous stage of data preparation, in order to identify and correct any mistake. Logical consistency tests are also carried out, mainly on income sub-components, using automatic or manual processing.

As indicated in chapter 18, the data is checked at each of the main production stages. At the end of each stage, metadata is created and integrity checks are carried out to ensure that the data corresponds to the theoretical metadata (e.g. non-existent code). Furthermore, the distribution and frequency of each variable is examined and compared with that of the previous year. If there are significant differences, the content of the variable is examined to identify any error.

The income sub-components are analysed for consistency with the previous year and with the HBS. These are summarised in the appendices (coherence internal and cross-domain). Further consistency analyses are regularly carried out with other statistical data sources on overlapping domains.

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

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 User Satisfaction Survey 2022.

With the exception of the following variables (also mentioned in ch. 15.2.2. ), which are delivered but empty, all the variables requested have been delivered:

• PY020G is included in PY010;
• PY021G is not collected;
• HY120, HY121, HY145G are included in HY140;
• HY170G is not collected.

All the variables of the modules on children and on access to services were collected including the optional answer modalities on the reason for discrimination.

With regards of the optional variables, only HY030G: Inputed rent is completed.

• RL080: Remote education (Optional) not collecte;
• HI130G: Interest expenses [not including interest expenses for purchasing the main dwelling] (OPTIONAL) not collected;
• HI140G: Household debts (OPTIONAL) not collected.

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. 

In particular, countries have been split into 3 groups:

1. BE, BG, CZ, IE, EL, ES, FR, HR, IT, LV, HU, PL, PT, RO, SI, UK and AL, whose sampling design could be assimilated to a two-stage stratified type we used DB050 (primary strata) for strata specification and DB060 (Primary Sampling Unit) for cluster specification;
2. DK, DE, EE, CY, LT, LU, NL, AT, SK, FI, CH whose sampling design could be assimilated to a one stage stratified type we used DB050 for strata specification and DB030 (household ID) for cluster specification;
3. MT, SE, IS, NO, whose sampling design could be assimilated to a simple random sampling, we used DB030 for cluster specification and no strata.

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.

See Annex 3: Sampling errors

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

Coverage error

The SRPH register is used in the SILC survey since SILC14. A coverage estimation has been conducted with the introduction of this frame in the Federal Statistical Office in 2012  (BFS website , available only in French). No coverage estimation has been made recently. 

Not available.

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 rate

 Description of data entry, coding controls and the editing system

No model is used.

Due to late availability of register data, Switzerland is not subject to the same deadlines as the EU countries. First delivery is to be made by the end of September N+1, and final delivery by the end of November N+1. 

• National publication of the results : planned on 16 Febrary 2026
• End of field-work: 16 June 2024
• First delivery of the data: 21 August 2025
• Final delivery of the data: 24 October 2025
• Months between the end of reference year N (2024) and the first delivery: 8
• Months between the end of reference year N (2024) and the final delivery: 10

No results were published on SILC24 yet. 

National publication date (planned) : 16 February 2026  - 13 months after the end of the reference period

See below

• First delivery (due date 30 September 2024, delivery on 21 August 2025) -  0 days .
• Final delivery (due date 30 November 2024, delivery on 24 October 2025) - 0 days

Not available.

Not applicable.

A revision of the weightings occured in SILC14. Since then, the latest survey framework SRPH enabled more register data to be used. Longitudinal weightings could be revised from SILC17 on, when all waves had been drawn in the SRPH. These revision led to breaks in serie in SILC14 for the cross-sectional indicators, and a break in SILC17 for longitudinal indicators.

A new online survey method (CAWI) was introduced in 2023 for the SILC individual questionnaire, in parallel with the telephone survey method (CATI). The implementation of this survey method aims to increase response rates by offering online questionnaires to people who are more willing to respond via the Internet, as well as to people who no longer have a landline (ALTEL households, which have been increasing in our samples in recent years). It also reduces survey costs and increases flexibility for respondents.
The survey method can have an effect on the answer itself. In CATI, the respondent tends to adapt their answers so as to be perceived positively by the interviewer. Thus respondents may adjust their answers, unconsciously or not, to conform to social norms, the expec[1]tations of the interviewers and their own perception of social ideals. This phenomenon of social desirability, observed in telephone surveys, does not occur in CAWI. CAWI, on the other hand, can lead to a form of critical and negative venting. It is also less suitable for complex questions that can be explained to the person when they respond in CATI method. The effect of the survey method on the answer cannot be corrected by weighting.
In general, the main indicators showing a break in series following the introduction of the CAWI survey method are subjective evaluations such as satisfaction in different areas of life, trust in institutions and certain unmet needs for health care.

The length of comparable time series is then of 11 years (SILC14-SILC24) for the cross-sectional and 8 years for the longitudinal (SILC17-SILC24).

Comparability and deviation from definition for each income variable

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.

See annexes 7 Coherence National accounts, 7.1 Coherence – Annual, 7.2 Coherence – Cross domain.

Not applicable.

See appendix 7 Coherence National accounts

See appendix Coherence Internal

Mean (average) interview duration per household =  59.1 minutes.

Mean (average) interview duration per person = 22.5 minutes.

Important revisions occured in SILC14 (cross-sectionnal) and SILC17 (longitudinal) as explained in 15.2 Comparability. For the revision of the longitudinal weighting method, it was first developped on SILC18, and when finished applied back on SILC17 and SILC19 (already published). This led to revised versions of SILC17 to SILC19.

In 2023, a new online survey method (CAWI) was introduced for the SILC individual questionnaire, alongside the telephone survey method (CATI). CAWI will gradually become the main survey mode, with CAWI being rolled out for all questionnaires from 2026 onwards.

A summary of the revisions is available on this website.

A methodological report on the 2014 revision (change in the sampling frame and revision of cross-sectional weighting) is available on website.

A methodological report on the revision of longitudinal weights is available on BFS website.

A review of the income imputation procedure is currently underway. This imputation procedure will be implemented from SILC25.

Not available.

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.

Register data from administrative sources are used when reliable and available at the time of statistical processing. This is the case for income variables First-pillar old-age pensions (PY100G) and Income received by people aged under 16 (HY110G). Employee cash or near-cash income (PY010G) is only surveyed through CATI in certain particular cases, but for most people the question is not asked and registers are used. Cash benefits or losses from self-employment (PY050G) is coming from register in most cases. 

Other income variables include some sub-components coming from registers: Survivor and disability pensions (PY110G and PY130G), Unemployment benefits (PY090G), Family/Children related allowances (HY050G), Social exclusion not elsewhere classified (HY060G) and Tax on income and social contributions (HY140G).

Full record imputation is used for Imputed rent (HY030)-collected each year in Switzerland, and Health insurance premium, which are included in the HY140G (see annex Estimation and imputation).

For the housing module, GEWO (Building and Flats) Register was used to build some variables. 

All other variables are collected through CATI/CAWI.

SILC in Switzerland is a 4-year panel. W1 households are drawned from the SRPH survey frame. Households from wave 2 to 4 added to the new sub-sample. Contrary to the Eurostat monitoring rules, households complete in w1 are kept in the sample even if not complete for one of the following years. If not complete for a second year in a row, they are taken out of the sample. All individuals are kept in the sample, even if they have never answered the individual questionnaire, but their household is in the net sample.  It is for example possible that a household is complete because person 1 answered in w1 and w3, but person 2 (out of 2) only answered in w4.    

The sample of w1 is drawn in the survey framework according to a proportional, stratified design in the seven major geographical regions (NUTS2).  

Information from the previous years (w2-w4) is also sent to the survey institute, which only has to check that it is still valid (age, adress, nationality, educational level, etc).

Raw sample size per wave as well as achieved sample size is presented on the table annexed in 13.3.3.1. 

Sampling units (one-stage) are households made up of permanent residents in Switzerland in which, wherever possible, all individuals aged 16 or over are interviewed (two-stage). Non-permanent residents living in a household with at least one permanent resident are also included. 

The SRPH survey framework is based on the communal and cantonal population registers in which all persons resident in Switzerland have to be registered. The registers contain information such as the names of people living in a household, their age, sex, nationality AVS/AHV insurance number, etc. but not their telephone number. This valuable information can be used to simplify the questionnaire grid but also to better establish the profile of non-respondents (see Appendix Weightings ch. 4), or to link AVS/AHV numbers with other register data for the whole of the gross sample. The survey framework is updated every three months. 

Fieldwork for the SILC survey was carried out by a private research institute, Demoscope, between January and June. Addresses of the households in the sample were split into four distinct batches, independently from rotational groups. A few days before the activation date of each batch when interviewers started calling, survey introduction letters were sent out to the households concerned. By using time distribution, management of contacts and appointments could be optimised in line with the research institute's resources. Moreover, one of our targets for all households was to minimise the time between letter receipt and initial contact. As shown in annex 10 (Time distribution of interviews), most interviews occurred between January and April. 

Mode of data collection from the individual questionnaire

Description of collecting income variables

After the field work, data are exported form the survey institute. Several checks are conducted to verify that:

• all variables are present in the dataset, with the codes that are defined at that stage
• in cases of households being split, that presence /absence of individuals are coherent in the households, and with information from the population register
• households that are indicated as complete indeed meet the requirements to be complete
• the person who answers the household questionnaire is indeed living in the household
• grid variables enable some questions to be filtered. In cases when the grid variable was wrong, but only has been corrected further in the (individual) questionnaire, manual corrections are made to delete the information that should not have been asked, or to fill with appropriate flag/values the codes that should have been. 

And more generally, other checks are conducted to detect any inconsistency or to verify the plausibility of the data. An iterative process is carried out, with manual corrections until no check appears anymore. 

Furthermore, variables from the proxy interviews are transfered to individual variables.

Among the first stages, data are prepared to be used in the sample for next survey ( w1-3 ) in the "masterfile", with consolidated variables like age, sex, citizenship, marital status, highest educational level attained. Some rare missing values are imputed with a multiple imputation procedure. An arbitrary choice of the most plausible value is then made from the imputed values. This step is also essential for the following weightings and imputation procedures. AHV numbers are also searched for the new cohabitants, to enable a pairing with registers. 

Some individual information (consolidated) that does not change from year to year is recovered form previous years if the individual questionnaire has been filled in before. This is for example the case for variable height (PH110A, still asked yearly in the first individual interview), age at first job (PL190), year of immigration (RB031). Some household variables (HH010 , HH031) are also imported from the previous years if no change has been announced in the questionnaire. Furthermore, checks are conducted to verify, for example, that:

• occupational status is consistent with ag;
• family relationships are consistant with age and marital status (parents older than their children e.g.);
• educational level stay equal or increase in time, and is consistant with age;
• ISCO codes are consistant with NACE codes.

See Annex 2 Item non-response and Annex 6 Estimation and Imputation

see Annex 5 Weighting

see Annex 6 Estimation and imputation

Because of a high Item non-response rate for several Material and social deprivation items, imputations have been made on all missing values for the 13 items, as explained in the Annex Estimation and imputation .

Not applicable.

Not applicable.

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