1.1. Contact organisation
Institut national de la statistique et des études économiques du Grand-Duché du Luxembourg (STATEC)
1.2. Contact organisation unit
Social Statistics Division (SOC)
Unit "Living Conditions" (SOC1)
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
STATEC
B.P. 10
L-4401 Belvaux
1.6. Contact email address
Confidential because of GDPR
1.7. Contact phone number
Confidential because of GDPR
1.8. Contact fax number
Confidential because of GDPR
2.1. Metadata last certified
16 May 2025
2.2. Metadata last posted
16 May 2025
2.3. Metadata last update
10 June 2026
3.1. Data description
The European Union Statistics on Income and Living Conditions (EU-SILC) is a survey-based instrument aiming at collecting timely and comparable cross-sectional and longitudinal multidimensional microdata on income, poverty, social exclusion and living conditions. 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.
3.2. Classification system
- International Standard Classification of Education (ISCED'2011);
- International Standard Classification of Occupations (ISCO-08);
- Classification of Economic Activities (NACE Rev.2-2008);
- Common classification of territorial units for statistics (NUTS 2);
- SCL - Geographical code list;
- The recommendations made by the United Nations in the Canberra Group Handbook on Household Income Statistics should also be taken into account.
For more details on the classification used please, see EU Vocabularies, Eurostat's metadata server or CIRCABC.
3.3. Coverage - sector
Data refer to all private households and individuals living in the private households in the national territory at the time of data collection.
The EU-SILC survey is a key instrument for the European Semester and the European Pillar of Social Rights, providing information on income distribution, poverty and social exclusion, as well as various related living conditions and poverty EU policies, such as on child poverty, access to health care and other services, housing, over indebtedness and quality of life. It is also the main source of data for microsimulation purposes and flash estimates of income distribution and poverty rates.
3.4. Statistical concepts and definitions
Statistical concepts and definitions for EU-SILC are specified in Regulation (EU) 2019/1700, Commission Implementing Regulation (EU) 2019/2181, and Commission Implementing Regulation (EU) 2019/2242. Additional information is available in the EU statistics on income and living conditions (EU-SILC) methodology and in the methodological guidelines and description of EU-SILC target variables (see CIRCABC).
Further details are provided in items 5, 15.1.1.1, 15.2.2 and 18.3.
For years 2023 and 2024, the implementation of the PH030 deviates from the recommended 2-question instrument. Details are provided in Annex 12.
Annexes:
2024 LU Annex 12
3.5. Statistical unit
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.
3.6. Statistical population
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.
3.6.1. Reference population
Definitions of reference population, household and household membership
| Reference population |
Private household definition |
Household membership |
|---|---|---|
| The reference population comprises all the persons who currently resided within the national territory of Luxembourg in March 2024, except: - the persons living in collective households or institutions (retirement homes, prisons etc.) |
Same definition as that set out in the Regulation (EU) 2019/1700 of the European Parliament and of the Council of 10 October 2019 establishing a common framework for European statistics relating to persons and households, based on data at individual level collected from samples |
class="SpellE">Same class="SpellE">definition as in EU-SILC DocSILC065 (2024 operation version 6) |
3.6.2. Population not covered by the data collection
The sub-populations that are not covered by the data collection includes: those who moved out of the country’s territory; or those with no usual residence; or those living in institutions or who have moved to an institution compared to the previous year.
3.7. Reference area
The whole Luxembourg country is covered. There is no geographical area which is excluded.
3.8. Coverage - Time
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.
3.9. Base period
Not applicable.
The data involves several units of measure depending upon the variables. Income variables are transmitted to Eurostat in national currency. For more information, see methodological guidelines and description of EU-SILC target variables available on eusilc.
Description of reference period used for incomes
| Period for taxes on income and social insurance contributions |
Income reference periods used |
Reference period for taxes on wealth |
Lag between the income ref period and current variables |
|---|---|---|---|
| The whole year 2023. |
Calendar year: income received between the 1st January 2023 and the 31st December 2023. |
No more household wealth tax since 2007. |
The fieldwork was conducted between May 2024 and September 2024. Therefore, the lag between the income reference period and current variables ranges between 5 and 9 months. |
6.1. Institutional Mandate - legal acts and other agreements
Regulation (EU) 2019/1700 was publish in OJ on 10 October 2019, establishing a common framework for European statistics relating to persons and households, based on data at individual level collected from samples (IESS). The Annex to the Commission implementing regulation (EU) 2019/2180 of 16 December 2019 specifies the detailed arrangements and content for the quality reports pursuant to Regulation (EU) 2019/1700 of the European Parliament and of the Council and Regulation (EU) 2019/2242.
6.2. Institutional Mandate - data sharing
Confidential microdata are not disclosed by Eurostat. Access to confidential microdata for scientific purposes may be granted on the basis of Commission Regulation 557/2013 and Regulation 223/2009 of the European Parliament and the Council on European statistics.
7.1. Confidentiality - policy
STATEC does not do any additional manipulations on data concerning the statistical confidentiality applied to the data collection, transmission to Eurostat or publication.
7.2. Confidentiality - data treatment
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.
8.1. Release calendar
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
8.2. Release calendar access
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.
8.3. Release policy - user access
In line with the Community legal framework and the European Statistics Code of Practice, Eurostat disseminates European statistics on Eurostat's website (see section 10 - 'Accessibility and clarity'), respecting professional independence and in an objective, professional and transparent manner in which all users are treated equitably. The detailed arrangements are governed by the Eurostat protocol on impartial access to Eurostat data for users. Additional information about microdata access is available in EU statistics on income and living conditions - Microdata - Eurostat.
Annual
10.1. Dissemination format - News release
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
10.2. Dissemination format - Publications
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
10.3. Dissemination format - online database
STATEC publishes regularly tables on poverty and social exclusion. See link below for the available tables:
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.
10.3.1. Data tables - consultations
Not available.
10.4. Dissemination format - microdata access
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.
10.5. Dissemination format - other
Not available.
10.5.1. Metadata - consultations
Not available.
10.6. Documentation on methodology
Methodological information is available on STATEC's webportal.
10.6.1. Metadata completeness - rate
All required concepts are provided.
10.7. Quality management - documentation
Not applicable
11.1. Quality assurance
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.
11.2. Quality management - assessment
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.
12.1. Relevance - User Needs
The main users of EU-SILC statistical data are policy makers, research institutes, media, and students.
12.2. Relevance - User Satisfaction
Eurostat carried out an online general User Satisfaction Survey (USS) in the period between April and July 2019 to obtain a better knowledge about users, considering their needs and satisfaction with the services provided by Eurostat. The survey has shown that EU-SILC is of very high relevance for users. For the majority, both aggregates and micro-data were important or essential in their work irrespective of the purpose of their use. The use of the ad-hoc modules was less widespread than the use of the nucleus variables. Nevertheless, there was high interest to repeat these modules in order to have the possibility of comparing data over time. Users emphasized their strong need for more detailed micro-data, which is currently not possible. Under the new legal framework implemented from 2021, the NUTS 2 division will be available for the main indicators. Finally, users were satisfied with overall quality of the service delivered by Eurostat, which encompasses data quality and the supporting service provided to them.
For more information, please consult 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.
12.3. Completeness
LU-SILC is compliant with the EU-SILC regulation in terms of variables that are transmitted apart from variable of activity limitations PH030. However, from 2026 the recommended 2-question instrument for the variable on activity limitations will be implemented. All indicators which are based on variable PH030 will not be published for SILC 2024, and that respectively they will remain suspended for 2023. These indicators will be marked as "." in Eurobase for these years. More details are provided in Annex 12.
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)
12.3.1. Data completeness - rate
Not available.
13.1. Accuracy - overall
According to Reg. (EU) 2019/1700 Annex II, precision requirements for all data sets are expressed in standard errors and are defined as continuous functions of the actual estimates and of the size of the statistical population in a country or in a NUTS 2 region. For the income and living conditions domain, the estimated standard errors of the following indicators are examined according to certain parameters set:
- Ratio at‐risk‐of‐poverty or social exclusion to population;
- Ratio of at‐persistent‐risk‐of‐poverty over four years to population;
- Ratio at‐risk‐of‐poverty or social exclusion to population in each NUTS 2 region.
Further information is provided in section 13.2 Sampling error.
13.2. Sampling error
EU-SILC is a complex survey involving different sampling designs in different countries. In order to harmonize and make sampling errors comparable among countries, Eurostat (with the substantial methodological support of Net-SILC2) has chosen to apply the "linearization" technique coupled with the “ultimate cluster” approach for variance estimation.
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:
- BE, BG, CZ, IE, EL, ES, FR, HR, IT, LV, HU, PL, PT, RO, SI, UK and AL, whose sampling design could be assimilated to a two-stage stratified type we used DB050 (primary strata) for strata specification and DB060 (Primary Sampling Unit) for cluster specification;
- DK, DE, EE, CY, LT, LU, NL, AT, SK, FI, CH whose sampling design could be assimilated to a one stage stratified type we used DB050 for strata specification and DB030 (household ID) for cluster specification;
- MT, SE, IS, NO, whose sampling design could be assimilated to a simple random sampling, we used DB030 for cluster specification and no strata.
Annexes:
2024 LU Annex 3
13.2.1. Sampling error - indicators
The concept of accuracy refers to the precision of estimates computed from a sample rather than from the entire population. Accuracy depends on sample size, sampling design effects and structure of the population under study. In addition to that, sampling errors and non-sampling errors need to be taken into account. Sampling error refers to the variability that occurs at random because of the use of a sample rather than a census and non-sampling errors are errors that occur in all phases of the data collection and production process.
Annexes:
2024 LU Annex A
13.3. Non-sampling error
Non-sampling errors are basically of 4 types:
- Coverage errors: errors due to divergences existing between the target population and the sampling frame.
- Measurement errors: errors that occur at the time of data collection. There are a number of sources for these errors such as the survey instrument, the information system, the interviewer and the mode of collection.
- Processing errors: errors in post-data-collection processes such as data entry, keying, editing and weighting.
- Non-response errors: errors due to an unsuccessful attempt to obtain the desired information from an eligible unit. Two main types of non-response errors are considered:
- Unit non-response: refers to absence of information of the whole units (households and/or persons) selected into the sample.
- Item non-response: refers to the situation where a sample unit has been successfully enumerated, but not all required information has been obtained.
13.3.1. Coverage error
Coverage errors include over-coverage, under-coverage and misclassification:
- Over-coverage: relates either to wrongly classified units that are in fact out of scope, or to units that do not exist in practice.
- Under-coverage: refers to units not included in the sampling frame.
- Misclassification: refers to incorrect classification of units that belong to the target population
Annexes:
2024 LU Annex A1
13.3.1.1. Over-coverage - rate
Coverage error
| Main problems |
Population (sub-population) |
Size of error |
Comments |
|---|---|---|---|
| Over-coverage |
The EU-SILC sample of addresses comes from the Luxembourg's National Population Register (RNPP). The RNPP does not distinguish between the addresses that lead to a private dwelling (or private household) and those that correspond to an institution (or collective household). It is only when interviewers make contact during fieldwork that the addresses leading to collective households can be removed from the scope of the survey. However, the share of people living in collective households in Luxembourg is usually quite small. In addition, it might happen that some individuals who have left the country keep being recorded in the RNPP after several months. |
less than 1 % of cases |
We don't ajust for the over-coverage. |
| Under-coverage |
As LU-SILC 2024 data collecttion started only in May, and the refresher sample was drawn in March 2024, there is 2-month lag which may entail some under-coverage, caused by the population who arrived in the country between March and May 2024 and not being covered. |
The magnitude of such under-coverage errors cannot be measured accurately. However, demographic sources estimate 1% the share of the resident population who arrived in 2024 before the SILC survey fieldwork started. |
|
| Misclassification |
Not available. |
Not available. |
Not available. |
13.3.1.2. Common units - proportion
Not applicable, only one source used.
13.3.2. Measurement error
Measurement error for cross-sectional data
| Cross-sectional data |
|||
|---|---|---|---|
| Source of measurement errors |
Building process of questionnaire |
Interview training |
Quality control |
|
The questionnaire for 2024 was designed in the same way as those used in the previous waves of EU-SILC that is, using the Eurostat document EU-SILC 065/06 (Description of target variables). In a multilingual environment such as Luxembourg, the questionnaire was first drafted in French and then translated into German, Luxemburgish and English. The questionnaire is divided into three parts:
|
All CAPI / CATI interviewers received a data-mce-mark="1">training session (half a day) intended to present the main aspects of the work of interviewers. data-mce-mark="1">They also recieved an information about the EU-SILC questionnaires, the annexed documents and the MyGuichet system and tried the system by themselves. |
CAPI and CATI data collection are outsourced to an external service provider, who utilizes their internal procedures to manage their staff. To ensure data integrity, STATEC conducted random verification calls to households interviewed through CAPI and CATI. During these routine checks, we identified some irregularities. Although these findings had only minor effects on the overall data quality, STATEC decided to transition to a new service provider for the 2024 data collection, as a proactive step to further enhance data quality in 2024. |
13.3.3. Non response error
Non-response errors are errors due to an unsuccessful attempt to obtain the desired information from an eligible unit. Two main types of non-response errors are considered:
1) Unit non-response which refers to the absence of information of the whole units (households and/or persons) selected into the sample. According to Annex VI of the Reg.(EU) 2019/2242
- Household non-response rates (NRh) is computed as follows:
NRh=(1-(Ra * Rh)) * 100
Where Ra is the address contact rate defined as:
Ra= Number of address/selected person (including phone, mail if applicable) successfully contacted/Number of valid addresses/selected person (including phone, mail if applicable) selected
and Rh is the proportion of complete household interviews accepted for the database
Rh=Number of household interviews completed and accepted for database/Number of eligible households at contacted addresses (including phone, mail if applicable)
• Individual non-response rates (NRp) is computed as follows:
NRp=(1-(Rp)) * 100
Where Rp is the proportion of complete personal interviews within the households accepted for the database
Rp= Number of personal interview completed/Number of eligible individuals in the households whose interviews were completed and accepted for the database
• Overall individual non-response rates (*NRp) is computed as follows:
*NRp=(1-(Ra * Rh * Rp)) * 100
For those Members States where a sample of persons rather than a sample of households (addresses, phones, mails etc.) was selected, the individual non-response rates will be calculated for ‘the selected respondent.
2) Item non-response which refers to the situation where a sample unit has been successfully enumerated, but not all the required information has been obtained.
13.3.3.1. Unit non-response - rate
Unit non-response rate for cross-sectional
| Address (including phone, mail if applicable) contact rate | Complete household interviews | Complete personal interviews | Household Non-response rate | Individual non-response rate | Overall individual non-response rate | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| (Ra) | (Rh) | (Rp) | (NRh) | (NRp) | (NRp)* | ||||||||||||
| A | B | C | A | B | C | A | B | C | A | B | C | A | B | C | A | B | C |
| 66.67 |
55.76 | 99.12 | 64.40 | 54.42 |
82.25 |
100 | 100 |
100 |
57.07 | 69.66 | 18.48 |
0 | 0 |
0 | 57.07 |
69.66 | 18.48 |
where
A=total (cross-sectional) sample,
B =New sub-sample (new rotational group) introduced for first time in the survey this year,
C= Sub-sample (rotational group) surveyed for last time in the survey this year.
Annexes:
2024 LU Annex A3
13.3.3.2. Item non-response - rate
The computation of item non-response is essential to fulfil the precision requirements. Item non-response rate is provided for the main income variables both at household and personal level.
Item non-response which refers to the situation where a sample unit has been successfully enumerated, but not all the required information has been obtained.
13.3.3.2.1. Item non-response rate by indicator
See Annex 2
Annexes:
2024 LU Annex 2
13.3.4. Processing error
Description of data entry, coding controls and the editing system
| Data entry and coding (if any used) |
Editing controls |
|---|---|
In 2024, the fieldwork was conducted using CATI, CAPI and CAWI modes of data collection. At this level, several types of controls were performed:
|
|
Annexes:
2024 LU Annex A2
13.3.5. Model assumption error
Not applicable
14.1. Timeliness
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.
14.1.1. Time lag - first result
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.
14.1.2. Time lag - final result
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.
14.2. Punctuality
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.
14.2.1. Punctuality - delivery and publication
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.
15.1. Comparability - geographical
There is no problem of comparability between the regions of the country.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not applicable.
15.2. Comparability - over time
For 2024 data collection, there were no any breaks in series.
Annexes:
2024 LU Annex 8
15.2.1. Length of comparable time series
Currently, there is two reference periods in time series from last break.
15.2.2. Comparability and deviation from definition for each income variable
Comparability and deviation from definition for each income variable
| Income |
Identifier |
Comparability |
Deviation from definition if any |
|---|---|---|---|
| Total hh gross income |
(HY010) |
F |
|
| Total disposable hh income |
(HY020) |
F |
|
| Total disposable hh income before social transfers other than old-age and survivors' benefits |
(HY022) |
F |
|
| Total disposable hh income before all social transfers |
(HY023) |
F |
|
| Income from rental of property or land |
(HY040) |
F |
|
| Family/ Children related allowances |
(HY050) |
F |
|
| Social exclusion payments not elsewhere classified |
(HY060) |
F |
|
| Housing allowances |
(HY070) |
F |
|
| Regular inter-hh cash transfers received |
(HY080) |
F |
|
| Alimonies received |
(HY081) |
F |
|
| Interest, dividends, profit from capital investments in incorporated businesses |
(HY090) |
F |
|
| Interest paid on mortgage |
(HY100) |
F |
|
| Income received by people aged under 16 |
(HY110) |
F |
|
| Regular taxes on wealth |
(HY120) |
F |
|
| Taxes paid on ownership of household main dwelling |
(HY121) |
F |
|
| Regular inter-hh transfers paid |
(HY130) |
F |
|
| Alimonies paid |
(HY131) |
F |
|
| Tax on income and social contributions |
(HY140) |
F |
|
| Repayments/receipts for tax adjustment |
(HY145) |
F |
|
| Value of goods produced for own consumption |
(HY170) |
NC |
This decision was timely communicated to Eurostat |
| Cash or near-cash employee income |
(PY010) |
F |
|
| Other non-cash employee income |
(PY020) |
F |
|
| Income from private use of company car |
(PY021) |
F |
|
| Employers social insurance contributions |
(PY030) |
F |
|
| Contributions to individual private pension plans |
(PY035) |
F |
|
| Cash profits or losses from self-employment |
(PY050) |
F |
|
| Pension from individual private plans |
(PY080) |
F |
|
| Unemployment benefits |
(PY090) |
F |
|
| Old-age benefits |
(PY100) |
F |
|
| Survivors benefits |
(PY110) |
F |
|
| Sickness benefits |
(PY120) |
F |
|
| Disability benefits |
(PY130) |
F |
|
| Education-related allowances |
(PY140) |
F |
|
F= Fully comparable; L= Largely comparable; P= Partly comparable and NC= Not collected.
15.3. Coherence - cross domain
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.
15.3.1. Coherence - sub annual and annual statistics
Not applicable.
15.3.2. Coherence - National Accounts
Not available
15.4. Coherence - internal
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.
17.1. Data revision - policy
No established revision policy for LU-SILC.
17.2. Data revision - practice
The LU-SILC data have not been revised.
17.2.1. Data revision - average size
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.
18.1. Source data
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.
18.1.1. Sampling Design
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.
18.1.2. Sampling unit
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.
18.1.3. Sampling frame
- 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)
| DB075 | Number of households | Number of household interviews accepted (DB135=1) | Number of individuals aged 16 or more |
|---|---|---|---|
| 1 | 1210 | 767 | 1407 |
| 2 | 2324 | 1348 | 2484 |
| 3 | 824 | 556 | 1128 |
| 4 | 11489 | 3019 | 5593 |
Size of the longitudinal samples
| Number of household interviews accepted (DB135 = 1) | ||||
|---|---|---|---|---|
| DB075 | 2021 | 2022 | 2023 | 2024 |
| 1 | 0 | 2020 | 1103 | 767 |
| 2 | 0 | 0 | 2318 | 1348 |
| 3 | 2050 | 1130 | 750 | 556 |
| 4 | 806 | 464 | 321 | 3019 |
| Total | 2856 | 3614 | 4492 | 5690 |
Number of interviewed individuals aged 16 or more (sample persons and co-residents)
| DB075 |
RB100 |
2021 |
2022 |
2023 |
2024 |
|---|---|---|---|---|---|
| 1 |
1 |
0 |
3560 |
1921 |
1314 |
| 2 |
0 |
0 |
98 |
93 |
|
| 2 |
1 |
0 |
0 |
4045 |
2337 |
| 2 |
0 |
0 |
0 |
147 |
|
| 3 |
1 |
4537 |
2335 |
1508 |
1073 |
| 2 |
0 |
67 |
66 |
55 |
|
| 4 |
1 |
1824 |
976 |
638 |
5593 |
| 2 |
0 |
34 |
34 |
0 |
18.2. Frequency of data collection
The fieldwork period spanned from May to September 2024, as shown in the table below.
| Month | Number of household interviews accepted | % | cum. % |
|---|---|---|---|
| May 2024 | 634 | 11.1 | 11.1 |
| June 2024 | 2252 | 39.6 | 50.7 |
| July 2024 | 1766 | 31.0 | 81.7 |
| August 2024 | 761 | 13.4 | 95.1 |
| September 2024 | 277 | 4.9 | 100.0 |
| Total | 5690 | 100.0 | 100.0 |
18.3. Data collection
Mode of data collection
|
|
1-PAPI |
2-CAPI |
3-CATI |
4-CAWI |
5-PAPI proxy |
6-CAPI-proxy |
7-CATI-proxy |
8-CAWI proxy |
9-other |
|---|---|---|---|---|---|---|---|---|---|
| % of total |
0.0 |
17.8 |
13.9 |
68.3 |
0.0 |
25.1 |
28.9 |
21.8 |
0.0 |
Description of collecting income variables
| The source or procedure used for the collection of income variables |
The form (gross, net) in which income variables at component level have been obtained |
The method used for obtaining target variables in the required form |
|---|---|---|
| CAWI/CATI/CAPI interviewing |
Income variables were collected gross (before income taxes and social contributions) and net (after income taxes and social contributions). As total taxable income comprises all taxable sources of incomes, detailed income variables cannot be collected net. This is therefore a proxy of the net. |
Collection of gross income components |
Annexes:
2024 LU Annex 4
2024 LU Annex 1
18.4. Data validation
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.
18.5. Data compilation
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.
Annexes:
2024 LU Annex 5
2024 LU Annex 6
18.5.1. Imputation - rate
No additional information than one provided in the point 18.5 and 13.3.4.
18.5.2. Calculation of weighting factors and weight adjustments
See Annex 5
Annexes:
2024 LU Annex 5
18.5.3. Estimation and imputation
See Annex 6
Annexes:
2024 LU Annex 6
18.6. Adjustment
Not applicable.
18.6.1. Seasonal adjustment
Not applicable.
Annexes:
2024 LU Annex 9
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.
10 June 2026
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.
For years 2023 and 2024, the implementation of the PH030 deviates from the recommended 2-question instrument. Details are provided in Annex 12.
Annexes:
2024 LU Annex 12
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.
The whole Luxembourg country is covered. There is no geographical area which is excluded.
Description of reference period used for incomes
| Period for taxes on income and social insurance contributions |
Income reference periods used |
Reference period for taxes on wealth |
Lag between the income ref period and current variables |
|---|---|---|---|
| The whole year 2023. |
Calendar year: income received between the 1st January 2023 and the 31st December 2023. |
No more household wealth tax since 2007. |
The fieldwork was conducted between May 2024 and September 2024. Therefore, the lag between the income reference period and current variables ranges between 5 and 9 months. |
According to Reg. (EU) 2019/1700 Annex II, precision requirements for all data sets are expressed in standard errors and are defined as continuous functions of the actual estimates and of the size of the statistical population in a country or in a NUTS 2 region. For the income and living conditions domain, the estimated standard errors of the following indicators are examined according to certain parameters set:
- Ratio at‐risk‐of‐poverty or social exclusion to population;
- Ratio of at‐persistent‐risk‐of‐poverty over four years to population;
- Ratio at‐risk‐of‐poverty or social exclusion to population in each NUTS 2 region.
Further information is provided in section 13.2 Sampling error.
The data involves several units of measure depending upon the variables. Income variables are transmitted to Eurostat in national currency. For more information, see methodological guidelines and description of EU-SILC target variables available on eusilc.
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.
Annexes:
2024 LU Annex 5
2024 LU Annex 6
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.
Annual
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.
There is no problem of comparability between the regions of the country.
For 2024 data collection, there were no any breaks in series.
Annexes:
2024 LU Annex 8
