1.1. Contact organisation
[CH1] Office Federal de la Statistique (Swiss Federal Statistical Office)
1.2. Contact organisation unit
Population and education
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
Espace de l'Europe 10, 2010 Neuchâtel
1.6. Contact email address
Confidential because of GDPR
1.7. Contact phone number
Confidential because of GDPR
1.8. Contact fax number
Confidential because of GDPR
2.1. Metadata last certified
4 September 2024
2.2. Metadata last posted
27 January 2026
2.3. Metadata last update
3 December 2024
3.1. Data description
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.
3.2. Classification system
- 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.
3.3. Coverage - sector
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.
3.4. Statistical concepts and definitions
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.
3.5. Statistical unit
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.
3.6. Statistical population
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.
3.6.1. Reference population
Definitions of reference population, household and household membership
| Reference population |
Private household definition |
Household membership |
|---|---|---|
| The reference population is people living in private households (i.e. not in institutions) where at least one of the residents lives permanently
|
A private household is a person or group of people who live in the same unit of accommodation and who pool expenditure for necessities.
|
Making up a same household are:
|
3.6.2. Population not covered by the data collection
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.
3.7. Reference area
The entire national territory is covered.
3.8. Coverage - Time
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.
3.9. Base period
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
| Period for taxes on income and social insurance contributions |
Income reference periods used |
Reference period for taxes on wealth |
Lag between the income ref period and current variables |
|---|---|---|---|
| Social insurance contributions are calculated on the basis of income. Correspondingly, the reference period will be the same as for income, 2023 |
Reference period for income variables is 2023 |
Amounts relating to (income and wealth) taxation are from the 2023 calendar year |
As interviews took place between January and June 2024, the time lag between 2023 data and those corresponding to the time of the interview is 6 months at the most. |
6.1. Institutional Mandate - legal acts and other agreements
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.
6.2. Institutional Mandate - data sharing
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.
7.1. Confidentiality - policy
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.
7.2. Confidentiality - data treatment
Anonymisation rules are the same for national microdata as for the EU-SILC microdata.
8.1. Release calendar
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
8.2. Release calendar access
Please refer to the Release calendar - Eurostat (europa.eu) publicly available on the Eurostat’s website.
8.3. Release policy - user access
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
10.1. Dissemination format - News release
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
10.2. Dissemination format - Publications
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.
10.3. Dissemination format - online database
No database is available online.
10.3.1. Data tables - consultations
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.
10.4. Dissemination format - microdata access
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.
10.5. Dissemination format - other
No other format is used.
10.5.1. Metadata - consultations
Not available.
10.6. Documentation on methodology
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.
10.6.1. Metadata completeness - rate
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.
10.7. Quality management - documentation
Not applicable
11.1. Quality assurance
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.
11.2. Quality management - assessment
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.
12.1. Relevance - User Needs
The main users of EU-SILC statistical data are policy makers, research institutes, media, and students.
12.2. Relevance - User Satisfaction
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.
12.3. Completeness
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.
12.3.1. Data completeness - rate
Not available.
13.1. Accuracy - overall
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.
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:
- 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;
- 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;
- MT, SE, IS, NO, whose sampling design could be assimilated to a simple random sampling, we used DB030 for cluster specification and no strata.
13.2.1. Sampling error - indicators
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
13.3. Non-sampling error
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.
13.3.1. Coverage error
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
13.3.1.1. Over-coverage - rate
Coverage error
| Main problems |
Population (sub-population) |
Size of error |
Comments |
|---|---|---|---|
| Over-coverage |
|
0.02% |
|
| Under-coverage |
|
0.045% |
|
| Misclassification |
|
Not available |
|
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.
13.3.1.2. Common units - proportion
Not available.
13.3.2. Measurement error
Measurement error for cross-sectional data
| Cross-sectional data |
|||
|---|---|---|---|
| Source of measurement errors |
Building process of questionnaire |
Interview training |
Quality control |
| Measurement errors in the SILC survey may arise from the following sources:
While such errors are inevitable, the following processes have been implemented to keep such errors to a minimm.
|
The SILC survey is comprised of five questionnaires:
The 3-years module on children was implemented at the end of the household questionnaire and the 6-years module on access to services as well in the household questionnaire as in the individual gestionnaire for the questions on discrimination. Switzerland added questions on the discrimination at work or when searching for work. These various questionnaires were drawn up under Eurostat regulations. Income components were collected in detail, wherever possible from the individual who was directly concerned, or otherwise through the proxy (in which case total income and source of income are noted). Questions concerning income focus on income sub-components so that the respondent does not have to add up amounts, and to minimise the risk of item non-response. Likewise, to keep errors of estimation, memorisation or comprehension to a minimum, respondents have the option of stating either annual or monthly amounts for all types of income. For income stemming from employment or self-employment, respondents can provide gross or net figures. Where these alternatives are not helpful enough to respondents, it is then possible to provide an annual estimate or choose ranges of answers (ordinal categorical). These ranges are used as imputation boundaries. However, this rarely occurs as most income amounts regarding employment are filled with register data. |
The interviewers are trained each year on how to use the CATI system, on how to maximise response rates and on the specific issues covered. These training courses also aim to ensure the greatest possible neutrality and consistency in the way the questionnaires are read. The FSO staff were able to listen in on interviews and interviewers whose performance was insufficient were retrained and removed from the SILC survey if problems persisted. FSO members of staff were included in the sample as test households. On the request of the FSO, the DemoSCOPE institute organised intermediate training sessions for interviewers on specific SILC topics. The institute trained special groups of interviews to contact certain households, for example those who had already refused to take part in the survey.
|
To limit data-collection errors, filters and input controls (plausi-online) were inserted into CATI. These plausibility checks can be used to detect incoherent responses in relation to other variables or unusual answers (e.g. amounts which are too low or too high) as well as input errors by the interviewer (e.g. an extra zero added to an amount). A wide selection of baseline questionnaire variables were evaluated using cognitive interviews aimed at pinpointing comprehension problems. As the Swiss SILC questionnaire is drawn up in the three official languages (German, French and Italian), consistency analysis is conducted between the three versions. As SILC questionnaires are relatively long and complex, it is particularly important to check that the CATI/CAWI program corresponds precisely to the questionnaire's specifications. Two types of control are carried out: -Qualitative controls of the CATI and CAWI systems, in comparison with the questionnaire's specifications (existence and order of questions, repeat of questions and arrangements in the three languages, question readability and presentation, and workings of filters and plausi-online). -Quantitative controls, with approximately 15 predefined response scenarios input into the CATI/CAWI system. These data are then exported and compared with the expected response codes. |
Annexes:
Coordination of questionnaire
13.3.3. Non response error
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.
Annexes:
Minimizing non response
13.3.3.1. Unit non-response - rate
Unit non-response rate for cross-sectional
| Address (including phone, mail if applicable) contact rate |
Complete household interviews |
Complete personal interviews |
Household Non-response rate |
Individual non-response rate |
Overall individual non-response rate |
||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| (Ra) |
(Rh) |
(Rp) |
(NRh) |
(NRp) |
(NRp)* |
||||||||||||
| A |
B |
C |
A |
B |
C |
A |
B |
C |
A |
B |
C |
A |
B |
C |
A |
B |
C |
| 77.31 | 61.77 | 84.23 | 73.75 | 69.96 | 75.98 | 100.0 | 100.0 | 100.0 | 42.99 | 56.79 | 36.00 | 0.00 | 0.00 | 0.00 | 42.99 | 56.79 | 36.00 |
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.
Annexes:
Annex Non-response rate
13.3.3.2. Item non-response - rate
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.
13.3.3.2.1. Item non-response rate by indicator
See annex 2 : Item Non-response rate
13.3.4. Processing error
Description of data entry, coding controls and the editing system
| Data entry and coding (if any used) |
Editing controls |
|---|---|
| The data-preparation process is long and complex. The various stages of the process are used to improve the quality of the collected data. Basic data processing is conducted as follows:
Controls are implemented in each of these stages to limit the occurrence of processing errors. To maximise the scope for detecting programming errors, a dual control is put in place for important program along with regular rotation of stage responsible persons. During consolidation stages, Excel tables are used to document rules of consolidation. |
Stages of consolidation process sub-components separately but with no tests for quality. As such, they do not identify errors arising from confusion between various income sources, which may lead to the inputting of duplicate entries. The occupational pension plan system in Switzerland is relatively complex as it is comprised of three "pillars": the compulsory state pension, occupational pension and voluntary private contributions. Some people, especially the elderly, sometimes have trouble correctly identifying their sources of income (1st pillar - 2nd or 3rd pillar; income from employment - self-employment, etc.). The vast majority of interviews are conducted by telephone and respondents have to rely solely on their own recollections in answering the questionnaires. The quality-control stage, designed to keep this kind of error to a minimum, is comprised of various tests on income variables, such as detection of duplicate entries (identical sum but located under another income variable, same amount but assigned to a different member of the household, etc.), identification of excessive values and possible inconsistency between various sources of income. Quality control combines automatic and manual processing. Regarding manual processes, documentation setting out the main processing rules has been introduced, with a dual check used for doubtful cases. Nevertheless, manual processes hinge heavily on the subjectivity of the person carrying them out and are problematic in terms of reproducibility and process duration. When working with SAS data, the logging of changes is also problematic. A fluent organisation is required to avoid losing traceability of changes and to retain the possibility of backtracking should an error be identified at a later stage. As such, for each sub-stage, an input file and an output file (corresponding to the file after revisions) are both created, making it possible to detect what has been modified and to retrieve variables' initial values.
|
13.3.5. Model assumption error
No model is used.
14.1. Timeliness
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
14.1.1. Time lag - first result
No results were published on SILC24 yet.
14.1.2. Time lag - final result
National publication date (planned) : 16 February 2026 - 13 months after the end of the reference period
14.2. Punctuality
See below
14.2.1. Punctuality - delivery and publication
- 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
15.1. Comparability - geographical
Not available.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not applicable.
15.2. Comparability - over time
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.
15.2.1. Length of comparable time series
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).
15.2.2. Comparability and deviation from definition for each income variable
Comparability and deviation from definition for each income variable
| Income |
Identifier |
Comparability |
Deviation from definition if any |
|---|---|---|---|
| Total hh gross income |
(HY010) |
F |
|
| Total disposable hh income |
(HY020) |
F |
In contrast to Eurostat directives, the variable Non-cash employee income (PY020G) is part of total gross household income because this component is not distinct from employee income within the CCO register (see PY010G above). Conversely, the variable Company car (PY021G) is not included as this is not computed.
|
| Total disposable hh income before social transfers other than old-age and survivors' benefits |
(HY022) |
F |
|
| Total disposable hh income before all social transfers |
(HY023) |
F |
|
| Income from rental of property or land |
(HY040) |
F |
|
| Family/ Children related allowances |
(HY050) |
F |
|
| Social exclusion payments not elsewhere classified |
(HY060) |
F |
|
| Housing allowances |
(HY070) |
F |
|
| Regular inter-hh cash transfers received |
(HY080) |
F |
|
| Alimonies received |
(HY081) |
F |
|
| Interest, dividends, profit from capital investments in incorporated businesses |
(HY090) |
F |
|
| Interest paid on mortgage |
(HY100) |
F |
|
| Income received by people aged under 16 |
(HY110) |
F |
|
| Regular taxes on wealth |
(HY120) |
NC |
included in HY140G. Wealth tax is not distinguished from income tax. Both types of taxation feature under Tax on income and social contributions (HY140G). |
| Taxes paid on ownership of household main dwelling |
(HY121) |
NC |
see just above |
| Regular inter-hh transfers paid |
(HY130) |
F |
|
| Alimonies paid |
(HY131) |
F |
|
| Tax on income and social contributions |
(HY140) |
L |
It includes Taxes on wealth, that cannot be collected separately. It also includes mandatory health-insurance premiums (LAMal). |
| Repayments/receipts for tax adjustment |
(HY145) |
NC |
This variable is not collected, due to problems of timeliness. Furthermore, the complexity and diversity (26 cantons) of the swiss tax system make it problematic to collect. |
| Value of goods produced for own consumption |
(HY170) |
NC |
This variable is not collected as the value of goods produced for own consumption is not a material income component in Switzerland. According to the FSO Household Budget Survey, this variable represented in 2018 an average of less than 0.1% of gross income. |
| Cash or near-cash employee income |
(PY010) |
L |
Data is taken from registers (CCO) and includes Benefits in kind (PY020G), which cannot be distinguished from Employee cash or near-cash income (PY010G). |
| Other non-cash employee income |
(PY020) |
NC |
included in PY010 (see above) |
| Income from private use of company car |
(PY021) |
NC |
|
| Employers social insurance contributions |
(PY030) |
F |
|
| Contributions to individual private pension plans |
(PY035) |
F |
|
| Cash profits or losses from self-employment |
(PY050) |
F |
|
| Pension from individual private plans |
(PY080) |
F |
|
| Unemployment benefits |
(PY090) |
F |
|
| Old-age benefits |
(PY100) |
F |
|
| Survivors benefits |
(PY110) |
F |
|
| Sickness benefits |
(PY120) |
F |
|
| Disability benefits |
(PY130) |
F |
|
| Education-related allowances |
(PY140) |
F |
|
15.3. Coherence - cross domain
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.
15.3.1. Coherence - sub annual and annual statistics
Not applicable.
15.3.2. Coherence - National Accounts
See appendix 7 Coherence National accounts
15.4. Coherence - internal
See appendix Coherence Internal
Mean (average) interview duration per household = 59.1 minutes.
Mean (average) interview duration per person = 22.5 minutes.
17.1. Data revision - policy
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.
17.2. Data revision - practice
A review of the income imputation procedure is currently underway. This imputation procedure will be implemented from SILC25.
17.2.1. Data revision - average size
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.
18.1. Source data
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.
18.1.1. Sampling Design
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.
18.1.2. Sampling unit
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.
18.1.3. Sampling frame
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.
18.2. Frequency of data collection
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.
18.3. Data collection
Mode of data collection from the individual questionnaire
| 1-PAPI | 2-CAPI | 3-CATI | 4-CAWI | 5-PAPI proxy | 6-CAPI-proxy | 7-CATI-proxy | 8-CAWI proxy | 9-other | |
|---|---|---|---|---|---|---|---|---|---|
| % of total | 0 | 0 | 55.5% | 41.2% | 0 | 0 | 3.4% | 0 | 0 |
Description of collecting income variables
| The source or procedure used for the collection of income variables |
The form (gross, net) in which income variables at component level have been obtained |
The method used for obtaining target variables in the required form |
|---|---|---|
| In Switzerland, compensation offices collect social security contributions while calculating and paying out allowances and benefits. The Central compensation Office (CCO), which centralises data, is able to provide information on income arising from paid employment and self-employment, on income received by people under the age of 16, on 1st pillar old-age, survivor or disablity pensions, and on unemployment benefits. Information contained in the register of the Central compensation Office is used to fill in item non-response and validate or amend responses given by telephone. Most income variables are collected solely through the questionnaires. However, in regard to some income subcomponents, this information was reconciled with data from the Central Compensation Office register to improve reliability. This relates to the following income sub-components: Cash profits or losses from self-employment (PY050G) and income received by people aged under 16 (HY110G). From SILC2017 on, survivor and disability pensions (PY110G and PY130G), First-pillar old-age pensions (PY100G), Unemployment benefits (PY090G) and loss of earnings allowances (sub-components of HY050G and HY060G) are not collected anymore through the questionnaire, but only filled in with registers. Employee cash or near-cash income (PY010G) is only surveyed through questionnaires in certain particular cases, but for most people the question is not asked. The Swiss Social Assistance Statistics (SHS) register enables the HY060G to be filled. |
Respondents are asked to provide gross amounts for all income variables except cash or near-cash employee income (PY010G) and cash profits or losses from self-employment (PY050G). In this instance, the respondent may give gross or net income. Income taken from the CCO register corresponds to gross amounts. |
Employee cash or near-cash income (PY010G) Net income from employment is gross income minus social insurance contributions. These contributions, comprise various insurances: state pension funds (first pillar) and occupational pensions (second pillar). Contribution rates for the first pillar are fixed, whereas those relating to the second pillar vary by age and gender, pension plan and sector of employment. Contributions may even vary between companies. Premium rates for accident insurance depend on employer and wage level. Rates vary greatly from one pension plan to another. Data from the FSO Swiss Earnings Structure Survey can be used to calculate average contribution rates by industry (NOGA). As such, gross-net conversion rates by sector of employment, age bracket and gender were used for calculating gross income. Cash profits or losses from self-employment (PY050G) Self-employed workers pay first-pillar social-insurance contributions on their income. Membership of an occupational pension plan is optional. Self-employed workers' rates are obtained from a sliding scale. Net income can be determined by using the appropriate rate.
|
18.4. Data validation
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.
18.5. Data compilation
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.
18.5.1. Imputation - rate
See Annex 2 Item non-response and Annex 6 Estimation and Imputation
18.5.2. Calculation of weighting factors and weight adjustments
see Annex 5 Weighting
18.5.3. Estimation and imputation
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 .
18.6. Adjustment
Not applicable.
18.6.1. Seasonal adjustment
Not applicable.
No comments.
CH_2024_Annex 6 Estimation and Imputation
CH_2024_Annex 2 Item non-response
CH_2024_questionnaire EN
CH_2024_questionnaire IT
CH_2024_questionnaire DE
CH_2024_questionnaire FR
CH_2024_Annex A
CH_2024_Annex 3 Sampling error
CH_2024_Annex 4 Data collection
CH_2024_Annex 5 Weighting procedure
CH_2024_Annex 7 Coherence National Accounts
CH_2024_Annex 7_1 Coherence-annual
CH_2024_Annex 7_2 Coherence cross domain
CH_2024_Annex 8 Break in series
CH_2024_Annex 13 Non-response
CH-2024_Annex 14 Longitudinal erosion
CH-2024_Annex 15 Minimizing non response errors
CH_2024_Annex 9 Rolling module
CH_2024_Annex_11_Coordination_questionaire
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.
3 December 2024
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.
The entire national territory is covered.
Description of reference period used for incomes
| Period for taxes on income and social insurance contributions |
Income reference periods used |
Reference period for taxes on wealth |
Lag between the income ref period and current variables |
|---|---|---|---|
| Social insurance contributions are calculated on the basis of income. Correspondingly, the reference period will be the same as for income, 2023 |
Reference period for income variables is 2023 |
Amounts relating to (income and wealth) taxation are from the 2023 calendar year |
As interviews took place between January and June 2024, the time lag between 2023 data and those corresponding to the time of the interview is 6 months at the most. |
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.
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.
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.
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.
Annual
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
Not available.
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.
