Federale Overheidsdienst Economie, KMO, Middenstand en Energie

Statbel (Algemene Directie Statistiek, Statistics Belgium)

Service Public Fédéral Economie, PME, Classes moyennes et Energie

Statbel (Direction Générale Statistique, Statistics Belgium)

DTS - Social Statistics

Koning Albert II laan 16

1000 Brussel

Belgium

7 February 2025

7 February 2025

7 February 2025

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

The EU-SILC instrument provides two types of data:

• 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).

Social exclusion and housing condition information is collected mainly at household level while labour, education and health information is obtained for persons aged 16 and over. The core of the instrument is income information at very detailed component level and mainly collected at personal level.

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

For more details on the classification used please, see Eurostat website.

No deviations for BE-SILC 2024.

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.

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

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

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

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

 Definitions of reference population, household and household membership

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

No Belgian geographical areas were excluded.

Income reference period is calendar year 2023.

Other variables have reference period: constant, current, last 12 months, a typical week, last situation, last 5 years (module variables).

Not applicable.

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

Description of reference period used for incomes

For all variables the reference period is consistent with the technical specifications.

However, a number of inconsistencies result from a mismatch between the composition of the household at the moment of the interview (between January and July of year x) and the income of the previous year (year x-1). This mismatch can bias the measurement of poverty status in several ways.  For example:

• Persons who were full-time students in year x-1 (and depending on their parents), but were employed at the time of the interview (and living independently in a one person household for example) will report an income equal to 0 in year x-1 and will be wrongly classified as a poor household.

Other examples can also occur for persons where the household composition changed:

• For a housewife who was married in year x-1, but divorced and is working at the time of the survey there will also be a mismatch.
• For a household which received family allowances for a student in year x-1, but where the student is no longer part of the household in year x there will also be a mismatch.
• For a household with a person working in year x-1, but retired at the moment of the survey (in year x) a mismatch will also occur. Take notice of the fact that, as the examples show the bias can go in both directions: under and over reporting of income. In each one of the examples, the choice to situate the income reference period in the past is the cause, however.

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

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

At the Belgian level, the dissemination of pseudonymised microdata is strictly regulated. The procedure is described on the website. In order to get the permission of Statbel's Data Protection Officer team and finally as data controller, Statbel's director-general, the third party should follow a procedure and sufficiently motivate the proportionality and relevance of its request. The more confidential the information requested, the better the need for it should be motivated.

The following anonymization rules apply for SILC-data shared by Statbel:

• DB040 on NUTS1 level
• HH010: code 5 is recoded as missing
• HB040, PB090 removed
• HB050, PB100 in three categories
• RB080, RB081, RB082, PB140 top coded on (equivalant of) '85 or older'
• RB280, RB290, PB230, PB240: recoded in BE, EU27 (excl. BE), OTHER
• PE021, PE041: top coded on 'master or higher'

Law of 4th of July 1962 regarding offical statistics and protection of data.

Privacy policy explained at the Statbel website.

Data treatment is strictly separated from data collection at Statbel. The data processing team does not have access to personal information of the respondents that would allow an identification. Linkages to register information is always done with a pseudonymized ID-variable.

Yearly at least three publications are done:

• January: All SILC indicators
• January: Material and social deprivation
• October: Module 

Sometimes additional publications are made based on specific SILC modules.

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

Statbel release calandar is published on the website.

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

National statistics are published on the Statbel website.

Annual

National results are published here.

More specifically:

• regarding material deprivation
• regarding other indicators
• regarding modules

Downloadable tables are published on Statbel website.

Online database Be.Stat available here.

Available SILC information will expand in the comming months.

Microdata are not published.

Not collected.

Microdata for research available. More information about the application procedure on the Statbel website. 

Not applicable.

Not applicable.

Methodological documentation available on the Statbel website.

100%, all the required concepts are provided.

Additional documentation not available.

The quality department is responsible for quality management in cooperation with and in support of the statisticians:

• Elaboration and updating of quality indicators
• Describing the different production steps in line with GSBPM
• Validation of results
• Metadata maintenance

The department is organized with a service in charge of quality which is consulted when significant changes are made to the production process of a statistic.

Since 2018, and starting with the most “critical” processes the ARIS software programme is used to describe statistical production in line with the BPMN standard (Business Process Model and Notation). The description is made available to the staff. Ultimately, the objective is to describe all existing processes, statistical and non-statistical, and to make the link between these descriptions and the national version of GSBPM.

The process descriptions in ARIS will complement the already existing task lines of the overall planning tool that is used to steer the production of statistics.

Risk management is applied at different levels:

• A risk analysis has been carried out for the strategic objectives and action plans have been set out to reduce residual risks;
• Risk analyses are carried out for operational processes and actions are identified to reduce residual risks.

 Links to public documents:

1. Specific section of the website of Statistics Belgium dedicated to quality.
   
2. Code of practice on website Statistics Belgium.

Relevance: high, more than 10 national stakeholders receive microdata annually, more than 10 annual ad hoc demands for microdata (often linkable with register information), +/- 150 ad hoc demands for detailed information per year (see point 12)

Accuracy: high, standard errors are within requirements (see point 13.2).

Timeliness: high, data were sent to Eurostat in December 2024 and published nationally in January 2025 (see point 14.1).

Punctuality: high, data were sent to Eurostat in December 2024 and published nationally in January 2025 (see point 14.2).

Comparability: high, time series deliver plausible trends, figures are mostly within the range of the figures of neighboring countries (see point 15).

Coherence: moderately high, coherent with NA and HBS (see point 15).

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

BE-SILC team has analyzed user needs and provides tailored solutions:

• At least 3 times a year a press release is published with SILC indicators: material deprivation, poverty indicators, SILC module. Each time users are provided with a short text, as well as with detailed downloadable excel tables and flat files. Since 2019 these tables have been extended with NUTS1, NUTS2 and other breakdowns.
• Additional excel tables and flat files are available for SDG-indicators.
• Each year users can receive 11 SILC microdatafiles (Dfile cross, Dfile long, Rfile cross, Rfile long, Hfile cross, Hfile long, Pfile cross, Pfile long, detailed additional Belgian variables in HHfile, detailed additional Belgian variables in INDVfile, Belgian file with mortgage variables) with a user-friendly application procedure. We have more than 10 users receiving these files annually, and more then 10 ad hoc applications a year for specific projects (often linkable to register information of Statbel).
• Indicators (with breakdowns and often confidence intervals) are provided for several public organizations such as: PPS Social Integration, Federal Planning Bureau, National Bank of Belgium, OECD, regions.
• Ad hoc data questions are treated by the team.  
• On a regular basis a user meeting is organised. 
• Ad hoc meetings are organised for users with additional questions or in need of more information.

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

For more information, please consult the User Satisfaction Survey.

 On a regular basis a meeting is organized for the BE-SILC key users. The BE-SILC team presents (future) novelties and discusses results. These meetings may also include questions / needs of the users. Overall user satisfaction survey of Statbel can be found on the Statbel website.

• In Dutch
• In French 

Optional variables not collected or variables that are not applicable:

• HY170G & HY170N: assumed negligible (cf. doc 065)
• HY145N: included in HY140 (cf. doc 065)
• HI130G
• HI140G
• PB060
• PB070
• PB080
• RL080

BE-SILC 2024:

• In the children module we always used RB082 for age - as for the RL-variables - instead of RB081. 
• HC240: For respondents not using professional home care, the question about unmet need (HC240) was not asked. Instead the routing went directely to the reason of unmet need (HC250). For respondents with codes 1 to 4, HC240 was imputed with 1. For respondents with code 5 and an additional remark indicating they did not need they, HC240 was imputed with 2. For all others with HC250 = 5 the variables HC240 and HC250 were set to missing.

100% of requested variables were transmitted.

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

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

Further information is provided in section 13.2 Sampling error.

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

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

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

In particular, countries have been split into 3 groups:

1) BE, BG, CZ, IE, EL, ES, FR, HR, IT, LV, HU, PL, PT, RO, SI, UK and AL, whose sampling design could be assimilated to a two-stage stratified type we used DB050 (primary strata) for strata specification and DB060 (Primary Sampling Unit) for cluster specification;

2) DK, DE, EE, CY, LT, LU, NL, AT, SK, FI, CH whose sampling design could be assimilated to a one stage stratified type we used DB050 for strata specification and DB030 (household ID) for cluster specification;

3) MT, SE, IS, NO, whose sampling design could be assimilated to a simple random sampling, we used DB030 for cluster specification and no strata.

Note that for Belgium, there is no unbiased estimator of the design variance for SYSPPS with replacement sampling. The large PSU are selected with probability 1, but may not be considered as self-representative, because the number of groups selected is random, and the sum of the sampling weights of selected household do not equal PSU size. 

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 = 1.16.

See annex 3.

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 sheet 13.2.1 in annex A.

Non-sampling errors are basically of 4 types:

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

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

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

In Belgium, the sampling frame is the Central Population Register.

As there was a period of 1.5 months between the drawing of households and the start of the fieldwork itself, over-coverage, under-coverage and misclassification could be happen.

Coverage error

For 73.2% of the sample (incluiding children) register income information was available. 

Measurement error for cross-sectional data

Proxy rate: 9.5%.

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

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

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

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

Where Ra is the address contact rate defined as:

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

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

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

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

NRp=(1-(Rp)) * 100

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

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

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

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

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

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

Unit non-response rate for cross-sectional

where

A=total (cross-sectional) sample,

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

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

 See sheet 13.3.3.1 annex A.

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.

Item non-response and number of observations at unit level of the common cross-sectional European Union indicators and for equivalised disposable income.

 Description of data entry, coding controls and the editing system:

See sheet 13.3.4 in annex A.

Not applicable.

National publication calendar is published on the Statbel website. The first national results were published on 29 January 2025.

For SILC 2024 fully checked data was sent to Eurostat through Edamis on 18 December 2024. Fieldwork ended on 14 July 2024, so that is a data processing time of 157 days.

Reference period SILC 2024 = calendar year 2024. First national results were published 29 January 2025.

This is 29 days after the end of the reference period.

National results are published on the Statbel website. 

Reference period SILC 2024 = calendar year 2024.

The first national results were published on:

• 29 January 2025 (all SILC indicators)
• 29 January 2025 (material deprivation)

A last publication is expected on 15 October 2025 (module 2024). This is 288 days after the end of the reference period.

National results are published on the Statbel website. 

For SILC 2024 fully checked data was sent to Eurostat through Edamis on 18 December 2024, respecting the IESS deadline.

National results were published on:

• 29 January 2025: as scheduled (1 month after end reference year 2024)

At the time of writing this QR: 100% of the publications were published on time.

An additional publication is foreseen in April 2025 and a last publication is scheduled on 15 October 2025. 

Direct results are available on NUTS1 level: all three regions are perfectly comparable.

Small area estimation (SAE) results are available on NUTS2 level for AROP, MSD, SMSD, LWI (EU2030), AROPE (EU2030), AROP before social transfers (pensions excluded from social transfers), inability to keep home adequately warm, inability to afford a meal with meat, fish or vegetarian equivalent every other day, ability to face unexpected expenses, arrears, housing cost overburden rate: all 11 NUTS2 regions are perfectly comparable. 

Not applicable.

SILC 2019: major break in time series because of fundamental reform (register information for income variables and revision of weighting model). 

More information: 

• Technical note
• Register income variables
• New weights

SILC 2020: impact of covid-19 on data collection, no break in time series.

SILC 2024: see annex 8

SILC 2019 - SILC 2024

Comparability and deviation from definition for each income variable:

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

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

See annex 7 for a comparison with HBS.

Not applicable.

See annex 7 for a comparison with NA.

No lack of internal coherence.

Mean (average) interview duration per household =  35 minutes:

• In CAPI mode: 40 minutes
• In CATI mode: 31 minutes

Mean (average) interview duration per person = 9 minutes.

• In CAPI mode: 11 minutes
• In CATI mode: 8 minutes

At the end of the interview, the household contact person was asked about the level of difficulty and the length of the questionnaire.

The data revision policy can be found on the website of Statistics Belgium.

No revisions of SILC 2024.

No revision of SILC 2024.

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.

Administrative sources:

• Register from PPS Social Integration for HY060: includes at the individuel level all social integration allowances that have been paid in the income reference period.
• Belcotax register from FPS Finances for PY010, PY020, PY021, PY050, PY080, PY090, PY100, PY110, PY120, PY130, HY050, HY110. This register is used to prefill the Belgian tax declaration. It is up to the tax payers to verify and to make adjustments. As such this is provisional tax data. However, the final tax data is only available one year later, too late for SILC. Analysis shows however that adjustments are extremely rare and if they happen extremely small. Undercoverage of this register is dealt with by using survey questions.
• Register regarding allowances for elderly PY100 from FPS Social Security, Flemish, Walloon and Brussels government.
• Register regarding allowances for disabled PY130 from FPS Social Security and Flemish government.

Other information comes from the household and personal interviews.

Type of sampling design

The Belgian EU-SILC 2024 survey is based on a stratified 2-stage sampling scheme drawn in 2004, followed by rotation since 2005. Until SILC 2018 rotation allowed to replace roughly one fourth of the sample each year. With SILC 2019 a first step was made towards a 6-year panel instead of a 4-year panel. Normally the rotational group started in 2015 would have been dropped for SILC 2019. However, this group remained in the survey as the fifth wave. With SILC 2020 a full 6-year panel is in place. The rotational group started in 2015 participated then as the sixth wave. In SILC 2024 a group left and a new group was added. Hence, households (ignoring split-offs) participating in 2024 have been drawn for participation in 2019, 2020, 2021, 2022, 2023 or 2024.

Stratification and sub stratification criteria

The main stratification criterion is the NUTS2 level. The 11 sampling strata are the 10 Belgian provinces (5 in Flanders – coded BE21-BE25 –  and 5 in Wallonia – coded BE31 to BE35) and the Brussels Capital Region (BE10). Further implicit stratification is obtained by sorting PSUs (sub-municipalities) on mean income and sorting SSUs (households) in selected PSUs on age of reference person.

In SILC 2024 one PSU in the province of Limburg was changed for another PSU in the same province because of fieldwork difficulties. The new PSU was selected to be as close as possible to the old PSU in terms of geography and income distribution of the population. 

New sampling method for Brussels from 2016 onwards

In the framework of Eurostat's evolving requirements, Statbel decided to modify the sampling design for the Brussels region as from SILC 2016. In order to improve the precision of the poverty indicators, we modified the sampling design for Brussels, by stratifying now according to the tax data of households in Brussels. Starting with a primary and then secondary unit drawing, as for the whole country, we have chosen to proceed with a stratified sampling based on the new administrative data available. We opted for stratification at household level using fiscal data instead of primary sampling units of geographical units for Brussels. We therefore decided to break down the households of Brussels into 5 tax quantiles, plus 1 strata for household without tax information. 

No substitution is applied in BE-SILC.

Sample size

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

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

In this section the attention focuses mainly on the achieved sample size.

Achieved sample size per nuts2 for 2024:

In 2024, 19 new households per group are randomly selected. In total 5190 new households are selected in 2024. These households are joined with the 5778 old households that remain from previous years (selected in 2019, 2020, 2021, 2022 or 2023). Hence 10.968 households are invited to participate in 2024. Given some attrition of old households and nonresponse of new households the number of participating households in 2024 is 6532.

Number of households for which an interview is accepted for the database (per rotational group and interview wave).

Number of persons of 16 years or older who are members of the households for which the interview is accepted for the database, and who completed a personal interview (per rotational group and interview wave).

Number of selected households (per rotational group and interview wave).

Sample size and achieved response by NUTS2-units

Sampling units and 2-stage sampling in 2004

In 2004, when organizing EU-SILC for the first time (ignoring the pilot survey in 2003), 2-stage sampling has been applied in each sampling stratum.

Stage 1 – Primary Sampling Units

The primary sampling units (PSUs) in stage 1 are the municipalities, or parts thereof in the larger ones. In each stratum, the PSUs in the frame are first descendingly sorted by average income; next, a fixed number of times a PSU is drawn according to a systematic PPS (probability proportional to size) selection scheme, where size is measured as the number of private households. This systematic sampling method generally causes some PSUs being selected repeatedly (e.g. Schaerbeek, a rather large municipality in stratum  BE10, turns out to be drawn 6 times).  In total, i.e. in all 11 sampling strata together, 275 PSU draws were made in 2004, once and for all (i.e. for the whole duration of EU-SILC).

In SILC 2024 one PSU in the province of Limburg was changed for another PSU in the same province because of fieldwork difficulties. The new PSU was selected to be as close as possible to the old PSU in terms of geography and income distribution of the population. 

Stage 2 – Secondary Sampling Units

The secondary sampling units (SSUs) in stage 2 are private households.  According to each single PSU draw, a group (generally of fixed size) of households is selected in this stage; notice that a group of households corresponds to each PSU draw.

In 2004, 40 households have been selected for each PSU draw (i.e. in each group); e.g. in Schaerbeek, 6 times 40 households were drawn. Systematic selection of households has been applied, after sorting the households in selected PSUs by age of reference person. Within each group, the selected households were numbered 1 to 40; households 1-10 constitute the first rotational group or replication, households 11-20 constitute the second rotational group or replication, and so on. The first replication was meant to participate in 2004 only, the second until 2005, and so on.

The initial household sample in 2004 was self-weighting, by the combination of (systematic) PPS sampling of sub-municipalities (PSUs) – size of PSUs being the number of private households – and (systematic) sampling of private households (SSUs), as explained.

Renewal of the sample by rotation, since 2005

Since 2005, a rotation scheme has been applied. Details for each year, from 2005 to 2020, can be found in the corresponding Quality Reports.

SILC 2005 - SILC 2018: The rotation pattern is such that the overlap between samples in any two successive years is roughly 75%, and that the sample is completely renewed after 4 years. Hence four replications or rotational groups in each year, one of which is replaced the year after. Since 2005, each new replication remains in the survey during the next 4 years, and since 2007, each of the four replications is in the survey during four consecutive years.

SILC 2019: With SILC 2019 a first step was made towards a 6-year panel. SILC 2019 more specifically consists of 5 rotational groups. As before, a new group entered. At the start of 2019, the replication that is in the survey since 2015 would under the old scheme entirely (i.e. irrespective of whether the households are responding or not) be dropped. However, under this new scheme they were kept in the survey as the fifth wave. So, the four replications which entered into the survey in 2015, 2016, 2017 and 2018, respectively, are retained (including their split-offs); the households belonging to these four replications will be designated ‘old’ hereafter.

From SILC 2020 onwards: With SILC 2020 the full a 6-year panel is in place. As before, a new group entered. At the start of 2020, the replication that is in the survey since 2015 would under the old scheme entirely (i.e. irrespective of whether the households are responding or not) be dropped in 2019, but was kept then in the survey as the fifth wave, and was also kept in 2020 als the sixth wave. For SILC 2024, the five replications which entered into the survey in 2019, 2020, 2021, 2022 and 2023 respectively, are retained (including their split-offs); the households belonging to these five replications will be designated ‘old’ hereafter.

The supplementary sample, i.e. the new replication that was added, is obtained by selecting, for each PSU draw, a fixed number of new households from the corresponding PSU. This selection is done again by systematic sampling, after sorting the households in each PSU on age of reference person. The number of new households for each PSU draw, is determined by considering some (expected) attrition of old households, some (expected) nonresponse for new households, and the required/desired minimum and maximum numbers of responding households, given some precision and budget constraints. 

Hence, the (cross-sectional) sample of SILC 2024 consists of

• “old” households: drawn between 2019 and 2023; and
• “new” households: drawn in 2024.

In Belgium, the sampling frame is the Central Population Register. This Register includes all private households and their current members officially residing in the territory (de jure population). Persons living in collective households and in institutions are excluded from the target population. The Central Population Register of 14 October 2023 was used. Updating actions: Central Population Register is updated two times during a month. The changes were communicated to the interviewers.

Note: During fieldwork the household composition can be changed by the household, so we end up with the de facto household composition. People registered there can be removed, people not registered there (or not even registered in Belgium) can be added.

The survey is led each year.

Fieldwork period SILC 2024: January- July 2024

Mode of data collection

See annex 4.

Fieldwork started on 02 January 2024 and ended on 14 July 2024.

Description of collecting income variables:

The questionnaire was available for respondents in Dutch, French, German and English (see annex 1: national questionnaires). The majority of the income variables was collected through registers. Information about the sources is included in the flag-variables. 

All administrative sources are checked before adding them to the SILC production flow. This is done in two steps:

• Automatic controle on number of beneficiaries, target variables and descriptive statistics of these target variables.
• Calculations of income variables and comparisons with previous years regarding number of beneficiaries and descriptive statistics.

Both input and output data is checked and validated multiple times throughout data collection and data processing:

• The presence of the interviewer during the entire interview is a first quality check.
• After some preliminary processing register income information is checked at the population and the sample level: number of beneficiaries and mean/median values are compared with those of previous years.
• The routing of the interview is designed as to avoid the maximum number of mistakes.
• During SILC-interview respondent’s input income values are compared with lower and higher boundaries. If one of these boundaries is crossed, a warning message appears and the response can be confirmed or corrected if needed.
• During SILC-interview respondent’s answers are cross-checked with answers to previous questions. If inconsistency is suspected an error (at least one of the responses should be modified) or a warning (responses can be modified) message appears.
• During data processing auxiliary variables are constructed an intensively used: (1) self-reported disposable monthly household income during the income reference period (1 question, household level), and (2) calculated disposable monthly household income (using all available data of all household members). The latter is iteratively corrected once raw input variables are corrected / imputed.
• During data processing income component variables are continuously checked for internal consistency between these income components, other income components, the auxiliary variables mentioned above, activity status variables and calendar variables. If inconsistency is suspected cases are sorted out for manual validation and corrected/imputed where necessary.
• Outliers are checked for plausibility and corrected where necessary.
• Once the target variables are constructed values are checked and compared with those of previous years.
• Indicators are calculated and compared with previous years for several detailed breakdowns.
• Data were prechecked with Eurostat validation tool before transfer.

Weighting is discussed in depth in section 18.5.2.

Estimation and imputation is discussed in depth in section 18.5.3.

See sheet 18.5.1 in Annex A.

See annex 5.

See annex 6 and annex 6b.

Not applicable.

Not applicable.

See annex 9.

See annex A.