Institut national de la statistique et des études économiques du Grand-Duché du Luxembourg (STATEC)

Social Statistics Division (SOC)

Unit "Living Conditions" (SOC1)

STATEC

B.P. 10

L-4401 Belvaux

16 May 2025

16 May 2025

16 May 2025

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

The EU-SILC instrument provides two types of data:

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

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

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

Data 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 Luxembourg. 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 Luxembourg. Private household means a person living alone or a group of persons who live together, providing oneself or themselves with the essentials of living.

Definitions of reference population, household and household membership

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

The whole Luxembourg country is covered. There is no geographical area which is excluded.

Reference year 2024. SILC data are available for the years 2003-2024.

The income-related questions pertain to the previous calendar year, which is 2023.

The other questions refer to the current time of data collection, i.e. May-September 2024.

Not applicable.

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

Description of reference period used for incomes

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

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

STATEC does not do any additional manipulations on data concerning the statistical confidentiality applied to the data collection, transmission to Eurostat or publication.

STATEC makes tabular data available through its website. In addition, as set out in Article 16 of STATEC’s organic law, access to microdata files may be granted for research purposes only. In which case, the relevance of any request is carefully scrutinized and data manipulation can be made in order to better preserve data confidentiality (grouping of categories in order to prevent categories having too few observations, top-coding of extreme values etc.).

However, there is no unique data treatment policy at STATEC level which is applied to all surveys, and any data adjustment is decided on a case-by-case basis.

At national level: 

• Rapport Travail et cohésion sociale published each year. Last publication: 09/2025 - Analyses 03/2024 - Rapport Travail et cohésion sociale (TCS) - Statistiques - Luxembourg
• StatNews published on 12 March 2025: Près d’une personne sur cinq toujours en risque de pauvreté malgré une légère baisse - Près d’une personne sur cinq toujours en risque de pauvreté malgré une légère baisse - Statistiques - Luxembourg
  

Please refer to the this People at risk of poverty or social exclusion in 2024 - News articles - Eurostat publicly available on the Eurostat’s website.

Date of publishing the results: 30 Arpil 2025.

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

Annual

StatNews published on 12/03/25: Près d’une personne sur cinq toujours en risque de pauvreté malgré une légère baisse - Près d’une personne sur cinq toujours en risque de pauvreté malgré une légère baisse - Statistiques - Luxembourg

The EU-SILC data is published every year in the report "Rapport travail et cohésion sociale" which can be found here:

2024 Rapport travail et cohésion sociale

2023 Rapport travail et cohésion sociale

STATEC publishes regularly tables on poverty and social exclusion. See link below for the available tables:

LUSTAT Data Explorer 

The national SILC micro-data is available for scientific purposes, respecting the anonymity of the respondents.

In order to access this data, a researcher needs to fill a request form named “Micro-data transfer for scientific purposes” prepared by STATEC. In this form, one needs to describe the research project (i.e. its characteristics), define a list of variables and modalities, and provide a justification. 

Based on the information provided, a decision on whether such access is granted or not is made, including some potential modifications to the original request in terms of variables and their modalities.

Not available.

The national SILC micro-data is available for scientific purposes, respecting the anonymity of the respondents.

In order to access this data, a researcher needs to fill a request form named “Micro-data transfer for scientific purposes” prepared by STATEC. In this form, one needs to describe the research project (i.e. its characteristics), define a list of variables and modalities, and provide a justification. 

Based on the information provided, a decision on whether such access is granted or not is made, including some potential modifications to the original request in terms of variables and their modalities.

Not available.

Not available.

Methodological information is available on STATEC's webportal.

All required concepts are provided.

Not applicable

The survey has been designed using feedback from previous rounds of SILC data collection and using experienced interviewers who have been trained extensively on the main features of the survey and on its questionnaire.

The CAWI questionnaire was developed on MyGuichet, a govermental platform.

Statistical controls are regularly implemented on the data in order to improve their relevance and quality.

On a regular basis, a batch of survey responses are received from the fieldwork. A series of quality checks (syntax and plausibility checks) are applied to every new transmission of the LU-SILC data in order to detect any inconsistency in the responses and correct them.

At the end of fieldwork a report is prepared by the team in charge in order to list all the problems encountered and suggest adjustements for future rounds of data collection. 

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

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

For more information, please consult the User Satisfaction Survey.

LU-SILC is a complex and burdensome survey. This is even more an issue in a small size country such as Luxembourg, where individuals are regularly invited to participate in a high number of surveys. As a result non-response to LU-SILC remains high.

LU-SILC is compliant with the EU-SILC regulation in terms of variables that are transmitted.

Regarding the standard variables, HY170 is not collected.

Concerning the optional variables, due to the complexity of the LU-SILC questionnaire, following optional variables were not collected (relevant for the reconciled file 2021-2024): 

• HC300 (public transports are free in Luxembourg since 2020)
• HY030G (optional)

Not available.

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

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

Further information is provided in section 13.2 Sampling error.

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

LU relies on Eurostat calculations to estimate variance for the main EU-SILC indicators.

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

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

In particular, countries have been split into 3 groups:

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

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

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

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

Non-sampling errors are basically of 4 types:

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

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

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

Coverage error

Not applicable, only one source used.

Measurement error for cross-sectional data

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

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

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

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

Where Ra is the address contact rate defined as:

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

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

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

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

NRp=(1-(Rp)) * 100

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

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

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

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

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

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

Unit non-response rate for cross-sectional

where

A=total (cross-sectional) sample,

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

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

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

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

See Annex 2

 Description of data entry, coding controls and the editing system

Not applicable

Number of days between the end of fieldwork and the first fully validated delivery of data to the Commission: 74

Date of the dissemination of national results : 12 March 2025.

The end of income reference period was 31 December 2023 and the publication of the final results (including income) was on 12 March 2025, meaning that there was a lag of around 14 months.

The first results were published in 12 March 2025 that is, 6 months after the end of the fieldwork and around 14 months after the income reference period.

The final results were also published in March 2025 that is, 6 months after the end of the fieldwork and around 14 months after the income reference period.

The date for transmitting the final data (non-income and income) was end of November 2024.

For non-income and income data, the agreed deadline was respected.

There is no time lag between the number of months between the delivery/release date of data and the target date on which they were scheduled for delivery/release.

There is no problem of comparability between the regions of the country.

Not applicable.

 For 2024 data collection, there were no any breaks in series.

Currently, there is two reference periods in time series from last break.

Comparability and deviation from definition for each income variable

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

The most recent population census was in 2021 however the results are only partly available for comparison with other surveys. We did some comparison on education, sex and age. Except for education, LU-SILC 2024 results are closer to the 2021 census than those of previous LU-SILC surveys.

Except the population census, there are no other official sources referring to the same population as in the EU-SILC, which might serve as a benchmark for the EU-SILC target income variables.

Not applicable.

Not available

Not available

• Mean (average) interview duration per household = 26 minutes.
• Mean (average) interview duration per person = 21 minutes.

 In this case, the duration is calculated by taking the difference between the time of the first login to the questionnaire and the time when the final questionnaire is submitted. Possible session interruptions in the meantime are not taken into account. 

No established revision policy for LU-SILC.

The LU-SILC data have not been revised.

Not applicable

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.

From 2016 onwards, the EU-SILC sample is drawn from Luxembourg's National Population Register (RNPP - Registre National des Personnes Physiques). The RNPP covers the entire population residing within the national territory of Luxembourg, no matter its location, age or citizenship.

Sample selection scheme

All face-to-face surveys were stopped during the COVID lockdown period in 2020 in order to reduce social interactions between households and interviewers. That is why STATEC decided to conduct the SILC 2020 survey through solely CATI interviewing.

STATEC also decided not to select any new subsample in 2020 and to recontact all the addresses that had participated in the 2019 survey. The main reason for that is that the telephone numbers were available for nearly all the 2019 households, while a significant share of new addresses which would have been drawn in 2020, would not have had any telephone number to be used for contact purposes.

In order to restart the EU-SILC rotating scheme, it was decided to renew 50% of the 2020 sample in 2021 by adding two subsamples. In 2024 to maximize our chance to reach the longitudinal target we decided to keep the largest subsample from 2021 (rotational group X instead of Y).

Therefore, the LU-SILC 2024's database is composed of: 

• 556 addresses who participated in the survey for the first time in 2021; 
• 767 addresses who participated in the survey for the first time in 2022; 
• 1348 addresses who participated in the survey for the first time in 2023; 
• 11489 new addresses were added in 2024, of which 3019 responded to the survey.

The new SILC subsample used to be stratified according to the 12 geographical regions (canton) of the Grand-Duchy of Luxembourg, the canton of Luxembourg being further split into the city of Luxembourg and the rest. Thus, 13 stratum groups were defined. The sample was allocated among the strata proportionnaly to their size in number of individuals aged 18+. 

The newly selected addresses were split between a subsample CAPI / CATI and a subsample CAWI. Old households were dealt with using CAWI interviewing. However, CAWI households had the possibility to switch in CATI if they wanted.

A first longitudinal sample of individuals (DB075 = 3)

The first longitudinal sample comprises of 556 adresses who participated in EU-SILC for the first time in 2021. All the information related to the sampling design, the sampling units and the weighting procedure can be found in the quality report for the EU-SILC 2021 operation.

A second longitudinal sample of individuals (DB075 = 1)

The second longitudinal sample comprises of 767 adresses who participated in EU-SILC for the first time in 2022. All the information related to the sampling design, the sampling units and the weighting procedure can be found in the quality report for the EU-SILC 2022 operation.

A third longitudinal sample of individuals (DB075 = 2)

The third longitudinal sample comprises of 1 348 adresses who participated in EU-SILC for the first time in 2023. All the information related to the sampling design, the sampling units and the weighting procedure can be found in the quality report for the EU-SILC 2023 operation.

A forth sample of addresses (DB075 = 4)

This sample comprises of 11 489 newly selected addresses in 2024.

• The actual sample size for Luxembourg in 2024 is: 15 847
• The achieved sample size for Luxembourg in 2024 is: 5 690

Other results related to sample size in 2024 are illustrated below:

Size of the cross-sectional sample (DB010 = 2024)

Size of the longitudinal samples

Number of interviewed individuals aged 16 or more (sample persons and co-residents)

The fieldwork period spanned from May to September 2024, as shown in the table below.

Mode of data collection

Description of collecting income variables

The LU-SILC microdata has gone through a series of syntax and plausibility checks in order to increase their relevance and their statistical quality:

• Syntax checks: they aim at detecting incoherences between the responses and the syntax rules as set out in the questionnaire (question filters, bounds for numerical variables, eligible response categories etc.);
• Plausibility checks: they aim at detecting suspicious crossings of variables, for example retired with age lower than 30, having exactly the same amount for both salary and pension income, the household perceiving family allowances without any children etc.

Syntax and plausibility checks are dealt with on a case-by-case basis. 

STATA programs have been developed to implement those checks.

Missing income data are imputed using deductive or statistical imputation. Deductive imputation based on administrative rules are mainly used for social transfers such as family-related allowances. Missing data for other income components such as wages, salaries or pensions are imputed using statistical models.

In addition, the LU-SILC microdata are weighted in order to draw inference from the sample observations to the whole target population.

No additional information than one provided in the point 18.5 and 13.3.4.

See Annex 5

See Annex 6

Not applicable.

Not applicable.