Reference metadata describe statistical concepts and methodologies used for the collection and generation of data. They provide information on data quality and, since they are strongly content-oriented, assist users in interpreting the data. Reference metadata, unlike structural metadata, can be decoupled from the data.
Eurostat, the statistical office of the European Union
1.2. Contact organisation unit
Unit F4 - Income and living conditions; Quality of life
1.3. Contact name
Restricted from publication
1.4. Contact person function
Restricted from publication
1.5. Contact mail address
2920 Luxembourg LUXEMBOURG
1.6. Contact email address
Restricted from publication
1.7. Contact phone number
Restricted from publication
1.8. Contact fax number
Restricted from publication
2.1. Metadata last certified
7 January 2025
2.2. Metadata last posted
7 January 2025
2.3. Metadata last update
7 January 2025
3.1. Data description
The European Union Statistics on Income and Living Conditions (EU-SILC) collects timely and comparable multidimensional microdata on income, poverty, social exclusion and living conditions.
The EU-SILC collection is a key instrument for providing information required by the European Semester ([1]) and the European Pillar of Social Rights, and the main source of data for microsimulation purposes and flash estimates of income distribution and poverty rates.
The EU-SILC instrument provides two types of data:
Cross-sectional data pertaining to a given time or a certain time period with variables on income, poverty, social exclusion and other living conditions.
Longitudinal data pertaining to individual-level changes over time, observed periodically over four‐or more year rotation scheme (Annex III (2) of 2019/1700).
EU-SILC collects:
annual variables,
three-yearly modules,
six-yearly modules,
ad-hoc new policy needs modules,
optional variables.
The variables collected are grouped by topic and detailed topic and transmitted to Eurostat in four main files (D-File, H-File, R-File and P-file).
The domain ‘Income and Living Conditions’ covers the following topics: persons at risk of poverty or social exclusion, income inequality, income distribution and monetary poverty, living conditions, material deprivation, and EU-SILC ad-hoc modules, which are structured into collections of indicators on specific topics.
Data refers to all private households and individuals living in the private households in the national territory at the time of data collection.
3.4. Statistical concepts and definitions
Statistical concepts and definitions for EU-SILC are specified in the EU regulation 2019/1700, EU regulation 2019/2181, and EU regulation 2019/2242. The Regulation and its implementing and delegated acts provide for multiple changes to the EU-SILC data collection starting from 2021.
Countries shall follow the annex of EU regulation 2019/2242, where the list of variables is defined, including their modalities, flags, unit and reference period. A more detailed description of the list of variables as well as information on their implementation are available in the methodological guidelines.
The information collected in EU-SILC pertains to the following types of statistical units: private households and persons living in these households. Annex II of Commission EU regulation 2019/2242 defines the 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 EU regulation 2019/1700 of the European Parliament and of the Council.
3.6. Statistical population
The target population is the private households and all persons composing these households having their usual residence in the national territory. A 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
The reference population of EU-SILC is the private households and all persons composing these households having their usual residence in the national territory. A private household means a person living alone or a group of persons who live together, providing oneself or themselves with the essentials of living. All household members are surveyed, but only those aged 16 and older are interviewed. Persons living in collective households and in institutions are generally excluded from the target population.
Most countries follow the standard definitions for reference population (except for Estonia, and Malta) and for household membership (except for Belgium, and Portugal). There is a slight deviation in the definition of reference population applied in Denmark and Finland due to the use of registers on sample selection (please consult national quality reports for more information).
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, those with no usual residence, living in institutions or who have moved to an institution compared to the previous year. For more information, please see the national quality reports.
3.7. Reference area
The data refers to the Member States, Iceland, Norway, Switzerland, United Kingdom, Albania, Bosnia and Herzegovina, Montenegro, North Macedonia, Serbia, Türkiye and Kosovo([1]); national territory and NUTS II level.
EU-SILC may exclude small parts of the national territory amounting to no more than 2% of the national population and the national territories as defined in Article 6 of EU regulation 2019/2242.
Specific cases of coverage areas are for the following countries:
France (excluding Mayotte). Four overseas departments (Guadeloupe, Martinique, Guyane, la Réunion) have been included from 2022 data collection,
Ireland (Including the following offshore islands: Achill, Bull, Cruit, Gorumna, Inishnee, Lettermore, Lettermullan and Valentia),
Malta (Malta and Gozo),
Netherlands (Kingdom of the Netherlands excluding overseas territories),
Cyprus (Government-controlled areas of the Republic of Cyprus).
Portugal (The whole national territory, including the mainland and the two Autonomous Regions (Região Autónoma dos Açores and Região Autónoma da Madeira).
([1]) This designation is without prejudice to positions on status and is in line with UNSCR 1244/1999 and the ICJ Opinion on the Kosovo declaration of independence.
3.8. Coverage - Time
Annual data, data collection year 2023.
The EU-SILC project was launched in 2003 based on a "gentlemen's agreement" in six Member States (Belgium, Denmark, Greece, Ireland, Luxembourg, and Austria) and Norway.
The start of the EU-SILC instrument was in 2004 for the EU-15 (except Germany, the Netherlands, and the United Kingdom), Estonia, Norway, and Iceland.
A derogation was provided in the cases of Germany, the Netherlands and UK and nine of the ten new Member States (all except Estonia). It permitted them to begin in 2005, under the condition that they supply comparable data for the year 2004 for the common EU indicators that have been adopted by the Council in the context of the open method of coordination.
Bulgaria and Türkiye started the full implementation of the EU-SILC instrument in 2006, while Romania and Switzerland launched EU-SILC in 2007.
North Macedonia started in 2010, Croatia in 2011, Montenegro and Serbia in 2013, Albania in 2017, Kosovo ([1]) in 2018 and Bosnia and Herzegovina in 2022.
The United Kingdom left the EU on 31 January 2020. In the absence of an agreement regarding the transmission of statistical information, the country has ceased to transmit data for EU-SILC. The latest data available for the United Kingdom is EU-SILC 2018.
Annex 1- EU-SILC implementation by country provides an overview of the EU-SILC implementation across countries until 2023.
Figure 1: EU-SILC Implementation by countries
([1]) This designation is without prejudice to positions on status and is in line with UNSCR 1244/1999 and the ICJ Opinion on the Kosovo declaration of independence.
3.9. Base period
Not applicable.
The data involves several units of measure, depending on the variable. For more information, see the methodological guidelines and description of EU-SILC target variables available in CIRCABC. Most indicators are reported as shares. Some are reported in other units (e.g., percent, thousands of persons, monetary units, etc.). More information is available in Eurobase, living condition database section.
The reference period is the survey year. The nucleus or annual variables are collected yearly using the reference period as specified in Annex II, Article 7(1) of EU regulation 2019/2242 and as well as in the Methodological guidelines.
For all countries, the reference period for income variables in EU-SILC is the previous calendar year. Ireland, until 2019, collected income information for the 12-month period immediately preceding the sample household's interview date. For most of the countries, the fieldwork was carried out from January until July 2023. The lag between the income variables and the other variables varies across countries (Figure 2).
Figure 2: Lag between the income reference period and current variables by countries, 2023
Source: EU-SILC Micro-database 2023 (July 2024)
6.1. Institutional Mandate - legal acts and other agreements
EU regulation 2019/1700 establishing a common framework for European statistics relating to persons and households based on data at the individual level collected from samples (IESS) was published in the OJ on 10 October 2019. The Annex to the EU regulation 2019/2180 of 16 December 2019 specifies the detailed arrangements and content for the quality reports pursuant to EU regulation 2019/1700 of the European Parliament and of the Council and EU regulation 2019/2242.
More information on derogations to the IESS regulation, Commission implementing regulations, Commission delegated regulations, and past legislations can be found in EU SILC Legal Framework.
6.2. Institutional Mandate - data sharing
Confidential microdata are not disclosed by Eurostat. Access to anonymised microdata for scientific purposes may be granted on the basis of EU regulation 557/2013 and EU regulation 223/2009 of the European Parliament and the Council on European statistics.
EU regulation 223/2009 on European statistics (recital 24 and Article 20(4)) of 11 March 2009 (OJ L 87, p. 164) stipulates the need to establish common principles and guidelines. It ensures the confidentiality of the data used to produce European statistics and access to those confidential data, considering the technical developments and the requirements of users in a democratic society.
More information on data confidentiality can be found on the Eurostat website.
7.2. Confidentiality - data treatment
EU-SILC microdata do not contain any administrative information, such as names or addresses, that would allow direct identification. For more details, see access to microdata. To ensure disclosure control and confidentiality of EU SILC microdata when disseminating them to researchers via the User Database (UDB), some collected variables are removed or changed. On the other hand, in order to ease the use of the data, some derived variables are added. For more details, see User Database (UDB).
estimates should not be published if they are based on fewer than 20 sample observations or if the non-response for the item concerned exceeds 50%;
estimates should be published with a flag if they are based on 20 to 49 sample observations or if non-response for the item concerned exceeds 20% and is lower or equal to 50%;
estimates shall be published in the normal way when they are based on 50 or more sample observations and the item nonresponse rate for the variable(s) used does not exceed 20%.
The data are flagged when it is needed. The following flags are used:
(b) Break in series (i.e., change of source or change of methodology);
(c) Confidential;
(d) Definition differs, see metadata;
(e) Estimated;
(f) Forecast;
(n) Not significant;
(p) Provisional;
(r) Revised;
(s) Eurostat estimate;
(u) Low reliability (i.e., due to small sample size).
8.1. Release calendar
EU-SILC data are published annually. The main tables are available on the Eurostat website. In addition, statistical articles or working papers on specific topics using SILC data are published (Publications). Indicators based on national SILC data are published as soon as data are validated for each country. For more information, see the datasets availability table and release calendar, as well as the release calendar.
8.2. Release calendar access
Please refer to the release calendar publicly available on Eurostat’s website.
Please consult the database on Income and living conditions for more information.
10.3.1. Data tables - consultations
Eurostat monitors user behaviour by analysing database extractions for each domain and page views for each dedicated section by months and years. In this way, Eurostat keeps track of users and their needs by monitoring the website (Starting from 2022, the website of Eurostat was updated).
10.4. Dissemination format - microdata access
In accordance with EU regulation 557/2013, the Commission has released SILC anonymized micro-data in CIRCABC via encrypted zip file.
The results of the EU-SILC survey are useful for policymakers and users. For this purpose, Eurostat produces several requests in table format. In addition, as part of different workshops or conferences, EU-SILC data are useful for drafting papers for such purposes.
For more information, please consult Eurostat website.
10.5.1. Metadata - consultations
Eurostat consults Member States on metadata guidelines and the new metadata structures before implementation.
Starting from 2021, EU-SILC follows the IESS regulation: EU regulation 2019/1700, establishing a common framework for European statistics relating to persons and households, considering granted derogations (EU decision 2020/2050).
EU-SILC 2023 implementation follows the EU regulation on:
titles of the variables, for the income and living conditions domain, EU regulation 2020/258.
arrangements and content of the quality reports for the income and living conditions domain, EU regulation 2019/2242.
As well, the following regulations are common for all the surveys:
the technical characteristics of the statistical populations and observation units, descriptions of variables and of the statistical classifications, EU regulation 2019/2181.
the structure of quality reports related to datasets to be transmitted by EU Member States to Eurostat, EU regulation 2019/2180.
For more information, please see the Eurostat website on EU-SILC legislations.
Countries, based on EU-SILC data, provide the annex - metadata on benefits useful for Euromod. The annex on the metadata of the income benefits is collected for EUROMOD-purposes, and it is shared only with the UDB users (not published with the quality report). More information about EUROMOD can be found on the respective website: EUROMOD - Tax-benefit microsimulation model for the European Union.
10.6.1. Metadata completeness - rate
All the elements required in the SIMS are provided. Therefore, the metadata completeness rate is 100%.
10.7. Quality management - documentation
The document on the description of the variables to be implemented in the operation year, quality report guidelines or other supportive documents are stored in CIRCABC.
The quality report guidelines, which describe and support countries in filling in their national quality reports, is updated yearly.
EU-SILC is based on EU regulation 2019/2242 that defines the scope, definitions, time reference, characteristics of the data, data required, sampling, sample sizes, transmission of data, publication, access for scientific purposes, financing, reports and studies. In addition, Eurostat and Member States have developed the technical aspects of the instrument, in particular the Regulation on Quality Reports (EU regulation 2019/2180).
11.2. Quality management - assessment
Countries are obliged to report to Eurostat any deviation from the standards, which should be described in their national quality reports. Data are accompanied by quality reports analysing the accuracy, coherence, and comparability of the data. Output harmonisation is achieved by defining the format (list and content of target variables and data format) and the timetable of data transmission. This is complemented by Eurostat consistency and integrity checks on the microdata, so that a minimum output quality standard is reached.
12.1. Relevance - User Needs
The relevance of an instrument should be assessed considering the needs of its users. The list of variables collected as well as the indicators produced, follow the users’ needs. As for EU-SILC, the main users are the following:
Policymakers (Multiple Directorate General of the Commission and the Social Protection Committee, in charge of the monitoring of social protection and social inclusion, or other Commission services);
Statistical users in Eurostat or in Member States to feed sectorial or transversal publications;
Researchers having access to microdata;
Media;
Students, etc.
12.2. Relevance - User Satisfaction
Eurostat carried out a general User Satisfaction Survey (USS) in the period between June and July 2022 (User Satisfaction Survey by years) to obtain a better understanding of users’ needs and satisfaction with the services provided by Eurostat. The survey has shown that EU-SILC is of very high relevance to users. For the majority, both aggregates and microdata are 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 annual variables. Users emphasized their strong need for more detailed micro-data.
The results of the USS on living conditions and social protection statistics on overall quality are summarised in the figure below (% of respondents).
Figure 3: Overall quality on Living conditions and social protection statistics, 2022, 2019
Countries list and describe cases where some of the variables are not collected or deviate from the methodological guidelines of the operation year. For more information, please see ‘Annex 3 - Income variables: Deviations from EU-SILC definitions’, or consult national quality reports.
12.3.1. Data completeness - rate
Data should be transmitted according to the methodological guidelines of the operation year. When a variable is not collected or is missing, the respective flag must be used. For more information, please consult the national quality reports.
13.1. Accuracy - overall
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 the structure of the population under study. In addition to that, sampling errors and non-sampling errors need to be considered. A 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.
From 2021
EU regulation 2019/1700 foresees the requirements relating to geographical coverage, detailed sample characteristics, including subsampling, in accordance with Annex III, common data gathering periods, common standards for editing and imputation, weighting, estimation and variance estimation.
More specifically, Annex II – Precision requirements, foresees the following:
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.
The estimated standard error of a particular estimate must not be bigger than the following amount:
The function shall have the form of .
The following values for parameters shall be used:
Indicator
N
a
b
Ratio at‐risk‐of‐poverty or social exclusion to population
Number of private households in the country in millions, rounded to 3 decimal digits
900
2600
Ratio of at‐persistent‐risk‐of‐poverty over four years to population
Number of private households in the country in millions, rounded to 3 decimal digits
350
1000
Ratio at‐risk‐of‐poverty or social exclusion to population in each NUTS 2 region (see *)
Number of private households in the NUTS 2 region in millions, rounded to 3 decimal digits
600
0
When countries obtain negative f(N) values with the parameters expressed above, they shall be exempt from the corresponding requirement.
(*) For the estimated ratio at‐risk‐of‐poverty or social exclusion to population in each NUTS 2 region, those requirements are not compulsory for NUTS 2 regions with less than 0,500 million inhabitants, provided that the corresponding NUTS 1 region complies with this requirement. NUTS 1 regions with fewer than 100 000 inhabitants are exempt from the requirement.
Before 2021
According to EU regulation 1982/2003 on sampling and tracing rules, for all components of EU-SILC (whether survey or register-based), the cross-sectional and longitudinal (initial sample) data were to be based on a nationally representative probability sample of the population residing in private households within the country, irrespective of language, nationality or legal residence status. The sampling frame and methods of sample selection ensured that every individual and household in the target population was assigned a known and non-zero probability of selection.
EU regulation 1177/2003 defined the minimum effective sample sizes to be achieved, i.e., the actual sample sizes had to be larger to the extent that the design effect exceeds 1.0 and to compensate for all kinds of non-response. Furthermore, the sample size referred to the number of valid households which were households for which (and all respective household members), all or nearly all the required information had been obtained. The allocation of the effective sample size was done according to the size of the country and ensuring minimum precision criteria for the key indicator at national level (absolute precision of the at-risk-of-poverty rate of 1%).
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, a "linearization" technique coupled with the “ultimate cluster” approach for variance estimation is applied.
Linearization is a technique based on the use of linear approximation to reduce non-linear statistics to a linear form, justified by the 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 of calculating the variance by considering only variation among Primary Sampling Unit (PSU) totals. This method requires the first-stage sampling fractions to be small, which is nearly always the case. This method allows a great flexibility and simplifies the calculation of variances. It can also be generalized to calculate the variance of the differences from 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.
Countries have been split into three groups:
1) BE, BG, CZ, IE, EL, ES, FR, HR, IT, LV, HU, PL, PT, RO, SI and RS, 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, and 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 and NO, whose sampling designs could be assimilated to simple random sampling, we used DB030 for cluster specification and no strata.
Countries provide the sampling error for incomes and the standard error. Standard errors of key indicators are commonly used as a measure of the reliability of data collected through sample survey. EU-SILC was designed to provide measure of the at-risk-of-poverty rate with an absolute precision of about one point. The sample sizes were defined considering this accuracy requirement. Member States compute variance estimates for the main indicators; linearization, jackknife and bootstrap techniques are programmed. For further information, please consult the EU and national quality reports.
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 the structure of the population under study. In addition to that, sampling errors and non-sampling errors need to be considered. 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.
This section describes the closeness of computations or estimates to the exact or true values that the statistics were intended to measure (accuracy) and the closeness of the initial estimated value to the subsequent value (reliability).
Annex 4-Sampling errors shows the estimates for the leading indicator "People at-risk of poverty or social exclusion (AROPE)" and its components, namely: At-risk of poverty rate, persistent-risk-of-poverty, people living in households with very low work intensity and severe material deprivation rate.
Some of the countries use additional national data to get more precise estimates for the main indicators (Belgium, France, Italy, Latvia, Lithuania, Hungary, Malta, Netherlands, Austria, Romania, Finland and Sweden).
13.3. Non-sampling error
Non-sampling errors are basically of four 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 several 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 the absence of information for 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 the required information has been obtained.
Normally, these errors are faced at the national level. Specific cases are described in the national quality reports.
13.3.1. Coverage error
Coverage errors are caused by the imperfections of the sampling frame for the target population of the survey. Coverage errors include over-coverage (units wrongly classified while they are out of scope or do not exist), under-coverage (units not included in the sampling), and misclassification (incorrect classification of units belonging to the target population).
In EU-SILC, two main groups can be defined in terms of the sampling source used. Some countries have relied on household information from population registers. Other countries have used census databases to select addresses. In order to get the best coverage, both sources need to be updated.
A systematic source of coverage problems is the time lag between the reference date for the selection of the sample and the fieldwork period, which should be as short as possible. In addition, some countries carried out EU-SILC as a subsample of the units (addresses) that successfully answered other existing surveys. Assuming selective non-response in these surveys, this may entail selection bias (under-coverage).
13.3.1.1. Over-coverage - rate
Over-coverage: related either to wrongly classified units that are in fact out of scope, or to units that do not exist in practice.
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: unit non-response and item non-response.
13.3.3.1. Unit non-response - rate
Unit non-response relates to when a household refuses to cooperate or is away during the fieldwork period. Other reasons can explain unit non-response: the questionnaire is lost; the household is unable to respond because of incapacity or illness. It may also happen that a person in a household refuses to cooperate, although the household interview has been accepted ('individual' non-response). Countries have increased the sample in cases of high non-response for the first wave or in cases where there was an increase in the number of the rotations used.
Commission regulations define indicators aimed at measuring unit non-response in EU-SILC: address contact rate (Ra), household response rate (Rh), individual response rate (Rp). In addition, models must be used to correct non-responses. Most of the countries apply either a standard post-stratification based on homogeneous response groups or a more sophisticated logistic regression model. Individual non-response rate appears to be marginal and most of the countries impute missing individual questionnaires. More information about the contact rate and response rate is provided in the ‘Annex 5 - Sampling size, rotation and non-response rate’.
13.3.3.2. Item non-response - rate
Item non-response refers to the situation where a sample unit has been successfully enumerated, but not all the required information has been obtained.
Item non-response, which typically happens when the interviewee, does not answer a question because they consider it personal, too sensitive, or not easily understandable.
Item non-response is high for some income components, and it has been addressed with imputation. The technique aims at 'filling the holes' in a distribution, only unit non-response can be assumed. The computation of item non-response is essential to fulfilling the precision requirements. In national quality reports, item non-response rate is provided for the main income variables both at the household and personal level. In cases where a record is imputed for a specific variable, this will be reflected in the variable flags.
13.3.3.2.1. Item non-response rate by indicator
In the national quality reports, information on the imputation of households and personal incomes is provided. The imputation process is done at the national level and is described in the national quality reports.
13.3.4. Processing error
Processing error describes an error generated during data collection. The household and individual follow-up rules and the tracking rules should be applied in accordance with Article 8 of EU regulation 2019/2242. In the national quality reports, countries should describe the errors generated due data editing, data entry and imputation (Article 9, EU regulation 2019/2242). Some errors are detected by the data-checking process applied by Eurostat. These checks include structural, logical checks, year to year comparisons and descriptive statistics. It also detects easy errors related to wrong identifiers, modalities, flags, outliers, consistency, etc.
13.3.5. Model assumption error
Countries, in their national quality reports, describe the methods and models (when available) used to treat specific sources of errors and the way that they deal with these errors. Calibration is often used to treat such errors.
14.1. Timeliness
IESS (EU regulation 2019/1700) establishes the timeliness of data transmissions from the national statistical institutes. In the first years of implementing the IESS regulation, some of the countries were granted a new deadline for data submission as specified in the annex to EU regulation 2020/2050.
Pursuing Annex V of the IESS regulation, Member States shall submit for the Income and Living Conditions’ domain pre-checked microdata without direct identifiers, according to the following deadlines:
(a) Variables for the data collection of year N should be transmitted by the end of the year N (cross-sectional and longitudinal variables, cross-sectional weights), but in exceptional cases, provisional microdata concerning income may be transmitted by the end of year N and revised data by 28 February of the year N+1;
(b) Variables related to the observation covering the years of the rotation scheme ending in year N (longitudinal weights), should be transmitted by 31 October of the year N+1.
According to the regulation, the aggregated data will be published on the Eurostat website as soon as possible and within six months of the transmission deadline for annual and infra‐annual data collection, and within 12 months of the transmission deadline for other data collection, save in duly justified cases.
14.1.1. Time lag - first result
Indicators based on national SILC data are published as soon as Member States’ data are validated. For EU-SILC 2022 data collection, the following deadlines were applied:
Mid-June of the year N: Complete set of data relating to year N-1 (excl. longitudinal weights);
End of September of the year N: the user database (scientific use files released to researchers);
Mid-October of the year N: complete set of data relating to year N-1 (incl. longitudinal weights).
For more information, please consult the released calendar on the Eurostat website.
14.1.2. Time lag - final result
Considering the deadlines described in Concept 14.1, the final data are published after they are checked and validated by Eurostat.
14.2. Punctuality
Punctuality includes the time lag between the actual delivery of the data and the target date when it should have been delivered. For 2023, most of the countries were able to meet the deadline. Please consult the national quality reports.
14.2.1. Punctuality - delivery and publication
Data were checked, validated, and disseminated as soon as they are received from countries.
15.1. Comparability - geographical
To ensure comparability of data and/or indicators, i.e., to ensure quality of data as defined by Eurostat, EU-SILC has adopted an ex-ante output harmonization strategy. When using output harmonization survey design and methods are flexible as long as the output requirements are met. Countries have to define suitable national concepts and measurement procedures with which the international concept can be portrayed. There are two different strategies depending on when the survey design is planned: with ex-ante harmonization, the surveys are created by the countries having in mind the output to produce; with ex-post harmonization, countries can adapt surveys already in place to produce comparable outcomes.
The EU-SILC common framework aims at ensuring standardisation at different levels. Conceptual standardisation is achieved because the common concepts/definitions underlying each measure/variable, the scope and time reference are defined and documented.
Implementation and process standardisation is achieved by editing data to ensure that recommendations are followed concerning collection unit, sample size to be achieved for each country, a recommended design for implementing EU-SILC (rotational panel), common requirements for sampling and tracing rules for the longitudinal components, common requirement for imputation and weighting procedures. International classifications aiming at maximising comparability of the information produced are also enforced.
Specific fieldwork aspects are also controlled by the framework: to limit the use of proxy interviews, to limit the use of controlled substitutions, to limit the interval between the end of the income reference period and the time of the interview, to limit to the extent for the total fieldwork of one-shot surveys, to define precise follow up rules of individuals and households in case of refusals, to limit noncontact. Eurostat and Member States work together to develop common guidelines and procedures aimed at maximising comparability.
The EU-SILC survey design is flexible. EU-SILC flexibility is a key aspect allowing for adaptation to national specificities in terms of infrastructure and measurement. The most important element of the flexibility is related to the data sources (administrative or interview) to be used. Eurostat encouraged the use of existing ones, whether they are surveys or registers. A second aspect of the flexibility is related to the survey and sampling design. The only constraint is that, for both, the cross-sectional and longitudinal components, all household and personal data have to be linkable at micro level. Countries can use survey vehicles already in place, set up a new survey possibly drawing on one recommended by Eurostat. Sampling design can draw on expertise for social surveys at national level. The third element of flexibility relates to the measure of self-employment income for which the diversity of the sources and practices did not allow to find common harmonised solutions.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not applicable.
15.2. Comparability - over time
Since 2005, comparability over time is ensured by a common data source (EU-SILC). Due to transition between end-ECHP (European Community Household Panel) and the start of the EU-SILC, there are further disruptions in series between 2001 and 2005.
Starting from 2020 and mostly from 2021, many countries were impacted partly or fully by breaks in time series (Belgium, Germany, Denmark, Ireland, France, Croatia, Lithuania, Luxembourg, Malta, Poland, Portugal, Slovenia, Finland, Sweden, Switzerland and Norway) (Please see concept 19).
In 2021, a new legislation on the implementation of EU-SILC came into force; revising and improving the survey (see section 3.4). The new legislation provided for multiple changes to EU-SILC data collection, in particular:
It enforced the need for the following improvements: improved timeliness, with shorter deadlines for EU-SILC data submission;
reformulated precision requirements at national and regional level (NUTS2) for the at-risk-of-poverty-or social-exclusion indicator and the persistent-risk-of-poverty rate;
additional/ changed EU-SILC variables;
data collection in three frequencies: nucleus, three-year module and six-year module, and the recommendation to extend the longitudinal panel.
The IESS implementation, the COVID-19, the changes to the sample, method of interview, number of rotations used, etc., influenced specific variables, indicators, or the national SILC. For some of the countries the impact was not related to COVID-19 or implementation of IESS, so the break in series occurred before 2020.
For detailed information about significant changes and breaks in time series, as well as other changes considered relevant, please see the overview of breaks in series on the Eurostat website.
15.2.1. Length of comparable time series
EU-SILC provides comparable results across years. Countries implement EU-SILC following the methodological guidelines where modalities and model questions are provided. Nevertheless, specific circumstances (i.e., the impact of COVID-19, moving to a larger rotation scheme, changing the mode of data collection, etc.) affected the time series.
These changes are highlighted by countries in their national quality reports as break in series.
For detailed information about significant changes and breaks in time series, please see the overview of breaks in series on the Eurostat website.
15.2.2. Comparability and deviation from definition for each income variable
Comparability and deviation from definition for each income variable are provided in the national quality reports and in the Annex 3 - Income variables, deviations from EU-SILC definitions . Some of the income variables are not collected (please see the national quality reports).
15.3. Coherence - cross domain
A comparison with external sources for all income target variables and the number of persons who receive income from each ‘income component’ are provided where the Member States concerned consider such external data to be sufficiently reliable.
The comparisons with other domains are done in national quality reports, mainly with the Household Budget Survey (HBS), the Labour Force Survey (LFS), National Accounts or/and Social Protection Accounts. In addition, some countries compared the data with administrative sources or with other sources.
The sets of weights available in EU-SILC datasets have been obtained using calibration techniques, which ensure the basic coherence of the estimates obtained from EU-SILC micro datasets and demographic counts. Additional information can be found in the national quality reports.
15.3.1. Coherence - sub annual and annual statistics
Not applicable.
15.3.2. Coherence - National Accounts
Most of the countries provided a comparison of the main target variables with national accounts. More information is provided in the national quality reports.
15.4. Coherence - internal
Countries provided information in case a lack of coherence in the EU-SILC data was visible. In 2023, countries did not report any significant inconsistencies in their national quality reports.
EU-SILC was designed to keep respondent burden controlled, to avoid a high non-response rate and to ensure the quality of the information collected. The method of interview significantly impacts the interview duration. The interview duration in 2023 is more than 90 minutes for Croatia, Romania and Germany, while for some countries the interview duration per household is less than 30 minutes (Netherlands, Italy, Latvia, and Denmark).
Annex 2 - Mode of data collection and fieldwork, provides more information about the length of interview by country.
All reported errors (once validated) result in corrections of the disseminated data.
Reported errors are corrected in the disseminated data as soon as the correct data have been validated.
Data may be published even if they are missing for certain countries or flagged as provisional or of low reliability for some of them. They are replaced with final data once transmitted and validated. European aggregates are updated accordingly.
Whenever new data are provided and validated, the already disseminated data are updated. There is no specific updating schedule for incorporating ‘spontaneously’ provided new data.
Revisions of previously released EU-SILC data may happen in case adjustments are implemented at national level (for example, the availability of new census data) or in other exceptional cases (for examples changes in the indicator definitions or in the EU-SILC methodology).
No substantial country-specific revisions are applied at the national level (the main driver of data revisions being changes that are coordinated within the ESS).
The EU-SILC team promptly shares information on any data revision with the Income and Living conditions Working Group members as well as with the Social Protection Committee – Indicators Sub-Group.
17.2.1. Data revision - average size
Not available.
18.1. Source data
EU-SILC combines survey and administrative data. Most countries use survey and administrative data combined; others use only survey data (e.g., Czechia, Germany, Greece, Luxembourg, Hungary, Poland, Portugal, Romania and Slovakia).
The Framework Regulation calls for the selection of nationally representative probability samples. The data are to be based on a nationally representative probability sample of the population residing in private households within the country, irrespective of language, nationality, or legal residence status.
All private households and all persons aged 16 and over within the household are eligible for the operation. Persons living in collective households and in institutions are generally excluded from the target population.
Figure 4: Data sources, 2023
Source: EU-SILC microdata 2023 (extraction August 2024).
Note: the information considers only individual interviews
18.1.1. Sampling Design
Following IESS regulation, starting from 2021 onwards, Eurostat recommends using at least a four-year rotational design. Belgium, Bulgaria, Ireland, Italy, Netherlands, and Sweden are using a six-year rotation design (please see table 5 in Annex 5). The sampling frame as well as the methods of sample selection should ensure that every individual and household in the target population is assigned a known probability of selection that is not zero.
18.1.2. Sampling unit
The sampling unit can be the address/dwelling, the household, or the individual, in accordance with the design chosen by the country. In case of a sample of dwellings /addresses, if more than one household shares the same dwelling, dwellings must be regarded as clusters of households. Households are clusters of individuals and all members aged 16 and over at the end of the income reference period of a selected household are eligible for inclusion in the sample. Countries that carry out a sampling of individuals, instead, only select persons of age 16 and over and the household is defined as the household of which the selected person is a member at the beginning of the survey. Denmark, Iceland, Finland, the Netherlands, Slovenia, Sweden, and Norway select a sample of individuals, while thirteen countries (Czechia, Germany, Spain, France, Croatia, Latvia, Luxembourg, Hungary, Austria, Poland, Portugal and Romania) select a sample of dwellings or addresses. The remaining countries selected a sample of households. More information is reported in the national quality reports.
18.1.3. Sampling frame
Concerning the SILC instrument, three different sample size definitions can be applied:
the ‘gross sample’ or the ‘initial sample’, which is the number of sampling units selected in the sample;
the ‘achieved sample size’ or the ‘net sample’, which is the number of observed sampling units (household or individual) with an accepted interview; and
the effective sample size, which is defined as the achieved sample size divided by the design effect regarding the at risk-of poverty rate indicator.
Please see Annex 5 - Sampling size, rotation, and non-response rateto get additional information.
18.2. Frequency of data collection
EU-SILC data are collected annually in each country. Additionally, a set of variables collected every three-year, six-year or ad-hoc are added to EU-SILC following the multiannual rolling planning, EU regulation 2020/256.
EU-SILC data collection is governed by a framework regulation of the Council and Parliament and implementation regulations of the Commission. Information can be collected either from registers or from interviews. In general, five different ways to collect data from interviews are allowed: Paper-Assisted Personal Interview (PAPI), Computer-Assisted Personal Interview (CAPI), Computer-Assisted Telephone Interview (CATI), Computer-Assisted Web Interview (CAWI), and self-administrated questionnaires.
Figure 5: The interview mode, 2023
Source: EU-SILC data 2023 (extraction August 2024)
Most of the countries use several modes of data collection, mainly CATI and CAPI, while PAPI, CAWI and self-administered modes are used respectively by fewer countries. In some countries, PAPI and CAPI remained the main data collection modes (e.g., Bulgaria, Czechia, Ireland, Greece, France, Romania, Portugal and Slovakia). Starting from 2021, many countries decided to change the mode of data collection from personal interviews to telephone interviews or web-based interviews. In 2023, CATI was the main data collection mode for most of the countries (Belgium, Estonia, Croatia, Cyprus, Latvia, Lithuania, Hungary, Malta, Poland, Slovenia, Finland, and Norway).
For Germany, Denmark, Luxembourg, the Netherlands, and Sweden the main mode of data collection was CAWI. It was widely used as well by Spain, Austria and Finland. Germany used partly a new mode of data collection, paper questionnaire with self-enumeration (PASI).
Annex two and figure two summarize the fieldwork period by country, where the figures correspond to the information on the month of the household interview. Fieldwork duration for the 2023 operation varies a lot between countries; it lasted from less than three months (Bulgaria, Denmark, France, Hungary, Malta, and Romania) to 8 months (Ireland). In addition, almost all countries started data collection before June (except Romania and Luxembourg) and most of them finished the fieldwork period by end of August. Among other countries which finished later: Germany, Ireland, Austria and the Netherlands ended data collection in September; Cyprus in October; Greece and Luxembourg in December.
18.4. Data validation
There is a comprehensive validation procedure applied prior to finalisation of the EU-SILC database for each cross-sectional and longitudinal "wave" (year of survey plus any re-working of prior year data). Source data is initially reviewed at the national level. It is subsequently submitted to Eurostat for multilateral validation together with a detailed quality report; bilateral contacts are pursued as necessary. All Member States validate the data before sending them to Eurostat. At the end of the data processing at the national level, the complete checking procedure that is applied at Eurostat is carried out using the SAS software, followed by the calculation of the indicators. A summary report of the errors produced by the checking tool is submitted to Eurostat. The rules that Eurostat applies are the same as those that countries apply when running the checking tool.
18.5. Data compilation
Estimates at the aggregate level (e.g., EU-27) are calculated as the population-weighted arithmetic average of individual national figures (Aggregates: EU-27 (from 2020), EU-28 (2013-2020), EU-27 (2007-2013), Euro area, Euro area -19 countries (from 2015), Euro area – 18 countries (2014)). Estimates of the EU aggregated indicators are calculated if the EU coverage in terms of the EU population is 70% or larger. Indicators are computed as the population-weighted average of national indicators. For 2023, EU-SILC data are available for EU-27 and, Norway (extraction August 2024).
Weights are provided by national statistical institutes as part of the data sets. All necessary imputations are done at the national level and the respective flag for the variable imputed is provided.
18.5.1. Imputation - rate
Imputation is the process used to assign replacement values for missing invalid or inconsistent data that have failed edits. The imputation rates for income variables are provided in the national quality reports. The imputations are applied at national level and not at EU level. The proper flag is used in cases where the variable is imputed. The imputation process is described by the countries in their national quality reports.
This metadata concept complements the information provided in points 18.5 and 13.3.4.
18.5.2. Weighting procedure
Weights are provided by each country in their data files. Eurostat does not calculate weights as calibration, and non-response adjustments are performed at the level of Member States. There are three types of weights: cross-sectional personal weights, cross-sectional household weights, and longitudinal personal weights. More details are available in the national quality reports. Additional weights are used in some of the modules to adjust for non-response or non-using proxy. The weighting procedure is described in the national quality reports for each country.
18.5.3. Estimation and imputation
Eurostat calculates all the aggregates from the microdata. Population-weighted arithmetic averages are applied to the aggregates. The EU-SILC weights (i.e., sum of the national population living in private households, broken down by the required dimensions of the indicators) are used for this exercise. Eurostat does not receive or publish, aggregates calculated by the NSIs. More information on the estimation and imputation procedures applied in each country are available in the national quality reports. The estimation and imputation methods used are described in the national quality reports for each country.
18.6. Adjustment
Missing survey data are imputed using procedures specified in EU-SILC EU regulation 1981/2003. This includes income data, household composition data and other elements.
18.6.1. Seasonal adjustment
Not applicable.
The metadata is issued in the SIMS format for European Union Statistics on Income and Living Conditions.
19.1 Some specific description from 2023 national quality reports
Reference population and household membership
Belgium: Tertiary students often residence at a private address in their university town, while coming back home during the weekend. They remain officially registered at their parents’ address. In BE-SILC, they belong to their parents’ household.
Estonia: Persons living in collective households are included in the reference population. The share of persons who are living in collective households and who are not at the same time members of some other private household is likely to be very low. Additionally, there is no feasible way to estimate their share in the total population. Thus, the exclusion of these persons is unlikely to affect the comparability and reliability of the estimates.
Malta: A person is a household member if s/he is usually resident in that particular dwelling and shares in household expenses. Persons who are temporarily absent for reasons of holiday, travel, work, health, education or similar are included as long as the persons do not intend to stay away for more than 6 months.
Portugal: Anyone living in the household who participates in the common budget and has no other address, even if they are away for less than 6 months.
Methods to calculate the variance, std error and CI for the main indicators (AROPE, AROP, SMDS, LWI)
There are countries that use their national methods to estimate precision requirements for the main indicators.
Belgium: Standard errors are estimated by jackknife repeated replication (JRR) method. The clusters are the groups, the strata made by two (or three) groups, using sampling order.
The design effect for the Median equivalised disposable income = 1.02.
France: For the variance at national level: Estimates with a linearisation of the AROPE and persistence in poverty were tested with no significant gain. In a conservative approach, more traditional precision estimators (Horvitz-Thompson-Narain estimators or calibrated estimators) were used.
For variance at the level of NUTS 2 regions: The small area method used is equivalent to regressing interest variables on the auxiliary variables used to construct the small area weights. This equivalence between the synthetic estimator and a regression is exploited when calculating the variance by calculating the variance of the estimate using the linearisation approach on regression coefficients (this allows to calculate a variance on the small area weights, which can be negative).
Croatia: The Sampling, Statistical Methods and Analyses Department calculates the indicators and the variance in the R programming language.
Italy: In calculating the standard errors, we take into account all the characteristics of the sample design.
Latvia: CSB of Latvia use own methodology for calculation of sampling errors using R (package vardpoor).
Lithuania: Variance for the main indicators (AROPE, etc.) is calculated using a different method than Eurostat (for reference Annex A in concept 13.2.1). Standard errors as well as calibrated weights are calculated using SAS macro CLAN (CLAN97.sas) developed by Statistics Sweden. CLAN computes estimate of a parameter q and an estimate - based on Taylor linearization - of the standard error . The generalised regression (GREG) estimator is used with auxiliary information.
Luxembourg: internal program mainly based on proc surveymeans.
Hungary: The standard error calculation is based on the linearization method, which takes into account the characteristics of the sampling plan and the effects of calibration.
Malta uses national method to calculate the standard error for the main indicators.
Netherlands uses national method to calculate variance for the main indicators (AROPE, etc.). Variances for the SILC indicators have been estimated taking into account the sampling design, various stages of non-response attrition, as well as the weighting to known population control totals.
Austria uses the calibrated bootstrap procedure using the R Package “surveysd” developed by the Methods Department of statistics Austria.
Romania: The variance for main indicators of EUSILC was computed using Taylor linearization method implemented in ReGenesees package; it was computed by considering the sampling plan and the calibration information.
Finland: Sampling errors are calculated with the estimation technique based on rescaling bootstrap for the indicators taking account of sampling design and weighting.
Sweden: Standard errors in Annex 3 were calculated using PROC SURVEYMEANS SAS procedure where DB030 was not used for cluster specification. Design was assimilated to a one stage stratified type. A variable available in the national dataset was taken for strata specification, DB090 and PB040 were chosen as weights. Note that all the standard errors published by Statistics Sweden as well as standard errors of the main indicators used with the quality assessment and presented in Annex A 13.2.1 were calculated in line with the national framework with stratification, clustering as well as household size taken into account. Calculations were made using SAS macro-ETOS (Estimation of Totals and Order Statistics) which was designed to compute point and standard error estimates of totals and order statistics (parameters) from sample surveys as well as rational functions of these parameters. According to ETOS 2.0 User’s guide (2012) the Estimating Equations (EE) technique was used for estimation of the variance of the order statistics, and the Taylor linearization method was used for the variance estimation of non-linear functions like ratios and products.
Break in time series by years
Indicators: “For Portugal, the methodology to calculate household type differ from the one used by INE (available on the website of Portugal Institute National of Statistics).”
Coverage: France: Since 2022, the survey also covers 4 overseas departments: Guadeloupe, Martinique, French Guiana and Réunion.
Methodological changes: Luxembourg: Design adjustments impacted by covid-19 crisis, as well as the introduction of a mixed mode of data collection resulted on break in series for 2020 and impacted the lack of four-year longitudinal data for 2020-2023.
For detailed information about significant changes and breaks in time series, please see the overview of breaks in series.
The European Union Statistics on Income and Living Conditions (EU-SILC) collects timely and comparable multidimensional microdata on income, poverty, social exclusion and living conditions.
The EU-SILC collection is a key instrument for providing information required by the European Semester ([1]) and the European Pillar of Social Rights, and the main source of data for microsimulation purposes and flash estimates of income distribution and poverty rates.
The EU-SILC instrument provides two types of data:
Cross-sectional data pertaining to a given time or a certain time period with variables on income, poverty, social exclusion and other living conditions.
Longitudinal data pertaining to individual-level changes over time, observed periodically over four‐or more year rotation scheme (Annex III (2) of 2019/1700).
EU-SILC collects:
annual variables,
three-yearly modules,
six-yearly modules,
ad-hoc new policy needs modules,
optional variables.
The variables collected are grouped by topic and detailed topic and transmitted to Eurostat in four main files (D-File, H-File, R-File and P-file).
The domain ‘Income and Living Conditions’ covers the following topics: persons at risk of poverty or social exclusion, income inequality, income distribution and monetary poverty, living conditions, material deprivation, and EU-SILC ad-hoc modules, which are structured into collections of indicators on specific topics.
([1]) The European Semester is the European Union’s framework for the coordination and surveillance of economic and social policies.
7 January 2025
Statistical concepts and definitions for EU-SILC are specified in the EU regulation 2019/1700, EU regulation 2019/2181, and EU regulation 2019/2242. The Regulation and its implementing and delegated acts provide for multiple changes to the EU-SILC data collection starting from 2021.
Countries shall follow the annex of EU regulation 2019/2242, where the list of variables is defined, including their modalities, flags, unit and reference period. A more detailed description of the list of variables as well as information on their implementation are available in the methodological guidelines.
The information collected in EU-SILC pertains to the following types of statistical units: private households and persons living in these households. Annex II of Commission EU regulation 2019/2242 defines the 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 EU regulation 2019/1700 of the European Parliament and of the Council.
The target population is the private households and all persons composing these households having their usual residence in the national territory. A 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 data refers to the Member States, Iceland, Norway, Switzerland, United Kingdom, Albania, Bosnia and Herzegovina, Montenegro, North Macedonia, Serbia, Türkiye and Kosovo([1]); national territory and NUTS II level.
EU-SILC may exclude small parts of the national territory amounting to no more than 2% of the national population and the national territories as defined in Article 6 of EU regulation 2019/2242.
Specific cases of coverage areas are for the following countries:
France (excluding Mayotte). Four overseas departments (Guadeloupe, Martinique, Guyane, la Réunion) have been included from 2022 data collection,
Ireland (Including the following offshore islands: Achill, Bull, Cruit, Gorumna, Inishnee, Lettermore, Lettermullan and Valentia),
Malta (Malta and Gozo),
Netherlands (Kingdom of the Netherlands excluding overseas territories),
Cyprus (Government-controlled areas of the Republic of Cyprus).
Portugal (The whole national territory, including the mainland and the two Autonomous Regions (Região Autónoma dos Açores and Região Autónoma da Madeira).
([1]) This designation is without prejudice to positions on status and is in line with UNSCR 1244/1999 and the ICJ Opinion on the Kosovo declaration of independence.
The reference period is the survey year. The nucleus or annual variables are collected yearly using the reference period as specified in Annex II, Article 7(1) of EU regulation 2019/2242 and as well as in the Methodological guidelines.
For all countries, the reference period for income variables in EU-SILC is the previous calendar year. Ireland, until 2019, collected income information for the 12-month period immediately preceding the sample household's interview date. For most of the countries, the fieldwork was carried out from January until July 2023. The lag between the income variables and the other variables varies across countries (Figure 2).
Figure 2: Lag between the income reference period and current variables by countries, 2023
Source: EU-SILC Micro-database 2023 (July 2024)
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 the structure of the population under study. In addition to that, sampling errors and non-sampling errors need to be considered. A 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.
From 2021
EU regulation 2019/1700 foresees the requirements relating to geographical coverage, detailed sample characteristics, including subsampling, in accordance with Annex III, common data gathering periods, common standards for editing and imputation, weighting, estimation and variance estimation.
More specifically, Annex II – Precision requirements, foresees the following:
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.
The estimated standard error of a particular estimate must not be bigger than the following amount:
The function shall have the form of .
The following values for parameters shall be used:
Indicator
N
a
b
Ratio at‐risk‐of‐poverty or social exclusion to population
Number of private households in the country in millions, rounded to 3 decimal digits
900
2600
Ratio of at‐persistent‐risk‐of‐poverty over four years to population
Number of private households in the country in millions, rounded to 3 decimal digits
350
1000
Ratio at‐risk‐of‐poverty or social exclusion to population in each NUTS 2 region (see *)
Number of private households in the NUTS 2 region in millions, rounded to 3 decimal digits
600
0
When countries obtain negative f(N) values with the parameters expressed above, they shall be exempt from the corresponding requirement.
(*) For the estimated ratio at‐risk‐of‐poverty or social exclusion to population in each NUTS 2 region, those requirements are not compulsory for NUTS 2 regions with less than 0,500 million inhabitants, provided that the corresponding NUTS 1 region complies with this requirement. NUTS 1 regions with fewer than 100 000 inhabitants are exempt from the requirement.
Before 2021
According to EU regulation 1982/2003 on sampling and tracing rules, for all components of EU-SILC (whether survey or register-based), the cross-sectional and longitudinal (initial sample) data were to be based on a nationally representative probability sample of the population residing in private households within the country, irrespective of language, nationality or legal residence status. The sampling frame and methods of sample selection ensured that every individual and household in the target population was assigned a known and non-zero probability of selection.
EU regulation 1177/2003 defined the minimum effective sample sizes to be achieved, i.e., the actual sample sizes had to be larger to the extent that the design effect exceeds 1.0 and to compensate for all kinds of non-response. Furthermore, the sample size referred to the number of valid households which were households for which (and all respective household members), all or nearly all the required information had been obtained. The allocation of the effective sample size was done according to the size of the country and ensuring minimum precision criteria for the key indicator at national level (absolute precision of the at-risk-of-poverty rate of 1%).
The data involves several units of measure, depending on the variable. For more information, see the methodological guidelines and description of EU-SILC target variables available in CIRCABC. Most indicators are reported as shares. Some are reported in other units (e.g., percent, thousands of persons, monetary units, etc.). More information is available in Eurobase, living condition database section.
Estimates at the aggregate level (e.g., EU-27) are calculated as the population-weighted arithmetic average of individual national figures (Aggregates: EU-27 (from 2020), EU-28 (2013-2020), EU-27 (2007-2013), Euro area, Euro area -19 countries (from 2015), Euro area – 18 countries (2014)). Estimates of the EU aggregated indicators are calculated if the EU coverage in terms of the EU population is 70% or larger. Indicators are computed as the population-weighted average of national indicators. For 2023, EU-SILC data are available for EU-27 and, Norway (extraction August 2024).
Weights are provided by national statistical institutes as part of the data sets. All necessary imputations are done at the national level and the respective flag for the variable imputed is provided.
EU-SILC combines survey and administrative data. Most countries use survey and administrative data combined; others use only survey data (e.g., Czechia, Germany, Greece, Luxembourg, Hungary, Poland, Portugal, Romania and Slovakia).
The Framework Regulation calls for the selection of nationally representative probability samples. The data are to be based on a nationally representative probability sample of the population residing in private households within the country, irrespective of language, nationality, or legal residence status.
All private households and all persons aged 16 and over within the household are eligible for the operation. Persons living in collective households and in institutions are generally excluded from the target population.
Figure 4: Data sources, 2023
Source: EU-SILC microdata 2023 (extraction August 2024).
Note: the information considers only individual interviews
Annual
IESS (EU regulation 2019/1700) establishes the timeliness of data transmissions from the national statistical institutes. In the first years of implementing the IESS regulation, some of the countries were granted a new deadline for data submission as specified in the annex to EU regulation 2020/2050.
Pursuing Annex V of the IESS regulation, Member States shall submit for the Income and Living Conditions’ domain pre-checked microdata without direct identifiers, according to the following deadlines:
(a) Variables for the data collection of year N should be transmitted by the end of the year N (cross-sectional and longitudinal variables, cross-sectional weights), but in exceptional cases, provisional microdata concerning income may be transmitted by the end of year N and revised data by 28 February of the year N+1;
(b) Variables related to the observation covering the years of the rotation scheme ending in year N (longitudinal weights), should be transmitted by 31 October of the year N+1.
According to the regulation, the aggregated data will be published on the Eurostat website as soon as possible and within six months of the transmission deadline for annual and infra‐annual data collection, and within 12 months of the transmission deadline for other data collection, save in duly justified cases.
To ensure comparability of data and/or indicators, i.e., to ensure quality of data as defined by Eurostat, EU-SILC has adopted an ex-ante output harmonization strategy. When using output harmonization survey design and methods are flexible as long as the output requirements are met. Countries have to define suitable national concepts and measurement procedures with which the international concept can be portrayed. There are two different strategies depending on when the survey design is planned: with ex-ante harmonization, the surveys are created by the countries having in mind the output to produce; with ex-post harmonization, countries can adapt surveys already in place to produce comparable outcomes.
The EU-SILC common framework aims at ensuring standardisation at different levels. Conceptual standardisation is achieved because the common concepts/definitions underlying each measure/variable, the scope and time reference are defined and documented.
Implementation and process standardisation is achieved by editing data to ensure that recommendations are followed concerning collection unit, sample size to be achieved for each country, a recommended design for implementing EU-SILC (rotational panel), common requirements for sampling and tracing rules for the longitudinal components, common requirement for imputation and weighting procedures. International classifications aiming at maximising comparability of the information produced are also enforced.
Specific fieldwork aspects are also controlled by the framework: to limit the use of proxy interviews, to limit the use of controlled substitutions, to limit the interval between the end of the income reference period and the time of the interview, to limit to the extent for the total fieldwork of one-shot surveys, to define precise follow up rules of individuals and households in case of refusals, to limit noncontact. Eurostat and Member States work together to develop common guidelines and procedures aimed at maximising comparability.
The EU-SILC survey design is flexible. EU-SILC flexibility is a key aspect allowing for adaptation to national specificities in terms of infrastructure and measurement. The most important element of the flexibility is related to the data sources (administrative or interview) to be used. Eurostat encouraged the use of existing ones, whether they are surveys or registers. A second aspect of the flexibility is related to the survey and sampling design. The only constraint is that, for both, the cross-sectional and longitudinal components, all household and personal data have to be linkable at micro level. Countries can use survey vehicles already in place, set up a new survey possibly drawing on one recommended by Eurostat. Sampling design can draw on expertise for social surveys at national level. The third element of flexibility relates to the measure of self-employment income for which the diversity of the sources and practices did not allow to find common harmonised solutions.
Since 2005, comparability over time is ensured by a common data source (EU-SILC). Due to transition between end-ECHP (European Community Household Panel) and the start of the EU-SILC, there are further disruptions in series between 2001 and 2005.
Starting from 2020 and mostly from 2021, many countries were impacted partly or fully by breaks in time series (Belgium, Germany, Denmark, Ireland, France, Croatia, Lithuania, Luxembourg, Malta, Poland, Portugal, Slovenia, Finland, Sweden, Switzerland and Norway) (Please see concept 19).
In 2021, a new legislation on the implementation of EU-SILC came into force; revising and improving the survey (see section 3.4). The new legislation provided for multiple changes to EU-SILC data collection, in particular:
It enforced the need for the following improvements: improved timeliness, with shorter deadlines for EU-SILC data submission;
reformulated precision requirements at national and regional level (NUTS2) for the at-risk-of-poverty-or social-exclusion indicator and the persistent-risk-of-poverty rate;
additional/ changed EU-SILC variables;
data collection in three frequencies: nucleus, three-year module and six-year module, and the recommendation to extend the longitudinal panel.
The IESS implementation, the COVID-19, the changes to the sample, method of interview, number of rotations used, etc., influenced specific variables, indicators, or the national SILC. For some of the countries the impact was not related to COVID-19 or implementation of IESS, so the break in series occurred before 2020.
For detailed information about significant changes and breaks in time series, as well as other changes considered relevant, please see the overview of breaks in series on the Eurostat website.
