1.1. Contact organisation
Institut National des Statistiques et des Etudes Economiques (INSEE)
1.2. Contact organisation unit
Direction des Statistiques Démographiques et Sociales
Département des ressources et des conditions de vie des ménages
Division Revenus des ménages
Timbre DG F350
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
INSEE - TIMBRE DG75-F350
88 avenue Verdier - CS 70058
92541 MONTROUGE CEDEX
France
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
26 May 2025
2.2. Metadata last posted
26 May 2025
2.3. Metadata last update
26 May 2025
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.
3.5. Statistical unit
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.
3.6. Statistical population
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.
3.6.1. Reference population
Definitions of reference population, household and household membership
| Reference population |
Private household definition |
Household membership |
|---|---|---|
|
The reference population for the survey is comprised of all ordinary households (excluding institutions) for which the primary residence is located in France. Since 2022, the survey also covers 4 overseas departments : Guadeloupe, Martinique, French Guiana and Réunion. In 2013, 2.3% of people in metropolitan France were living in communes or institutions or were homeless. |
The French survey uses the European definition of a household, which is based on the sharing of budgets rather than simply living in the same dwelling. This means that two living units that have separate budgets within the same dwelling would fill in two household questionnaires. A group of people (whether related or not) who usually share the same dwelling and who have a joint budget are considered to be a household, in other words: - they provide income to cover expenses incurred in the daily life of the household; - and/or simply benefit from these expenses Fewer than 1% of the dwellings included in the SILC survey contain several households constituting independent living units. |
Individuals living in the same habitual residence and sharing a budget are classed as belonging to the same household. In the first wave, we only question those living units whose primary residence falls within the sample (these are the dwellings drawn from the sample) If a household has multiple residences, we ask whether the dwelling being surveyed is their primary residence (the question is asked at the household level). All members of a household who consider the surveyed dwelling to be their primary residence are surveyed. Therefore, some members of the household may be questioned, even if they spend more than half of their time living in another dwelling: this may be the case for students living in a different dwelling during the week in order to undertake their studies, for example. |
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
France (excluding Mayotte).
Four overseas departments (Guadeloupe, Martinique, Guyane, la Réunion) have been included with effect from the 2022 survey.
3.8. Coverage - Time
SILC has been implemented in France every year since 2004.
About 2024 data :
Most of the data (on housing, living conditions, etc.) refers to the current period (or the default period defined in the guidelines).
The income variables refer to the previous year (2023).
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 CIRCABC
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 taxes paid in 2023 on income received during the course of that year are collected at the household level. The social security levies relate to income received in 2023. |
The survey was conducted between February and April 2024. The reference year for income is 2023. The income received during 2023 is collected at the individual and household level. |
We take account of the amount of property wealth tax paid in 2023. This relates to property held as at 1 January 2023. |
Some variables, such as variables relating to the cost of housing, relate to the collection period, i.e. the months of February to April 2024, which differs from the reference period for income relating to 2023 (declared income). |
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.
At the national level, the committees that have given INSEE the right and legitimacy to collect these data are the CNIS and, within CNIS, the Label Committee.
The CNIS examines each new project, whether it is a survey, a directory, an exploitation of administrative files, etc. These projects are presented by all the institutions that contribute through their work to the construction of official statistics. The discussion focuses in particular on the purpose of the project, its place in the information system and the planned conditions for its dissemination. Each operation must meet a need of general interest and not duplicate existing information sources. The CNIS issues an opinion on the opportunity.
The Label Committee, within the CNIS, ensures that the operation complies with statistical quality criteria. It ensures that the survey meets statistical quality criteria with regard to the collection and sampling method (sampling plan, data adjustment method, treatment of non-responses guaranteeing the reliability of the results, etc.), the relevance of the questioning and the adaptation of the dissemination to the announced objectives. It also ensures that the survey does not place an excessive burden on the respondents, that consultation has taken place with the partners concerned and that the wishes expressed by the CNIS during the debate on the opportunity to conduct the survey have been taken into account. The Label Committee gives a label of general interest and statistical quality.
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
Law No. 51-711 of 7 June 1951 on the obligation, coordination and secrecy of statistics.
Article 6 concerns statistical confidentiality. Subject to the provisions of Articles 40, 56, 76, 97 and 99 of the Code of Criminal Procedure and those of Article L. 213-3 of the Heritage Code, individual information contained in questionnaires bearing the visa provided for in Article 2 of this Act and relating to personal and family life and, in general, to private facts and behaviour may not, unless the archive administration decides otherwise, taken after consultation with the Statistical Confidentiality Committee and relating to a request made for the purposes of public statistics or scientific or historical research, may not be communicated by the depositary service before the expiry of a period of seventy-five years following the date on which the survey was carried out or a period of twenty-five years from the date of the death of the person concerned, whichever is shorter.
A Statistical Confidentiality Committee shall be set up. This committee is called upon to give its opinion on any question relating to statistical confidentiality. It shall give its opinion on requests for disclosure of individual data collected pursuant to this Act. The committee shall be chaired by a Councillor of State, appointed by the Vice-President of the Council of State. It shall include representatives of the National Assembly and the Senate. The composition and operating procedures of the committee shall be determined by decree in the Council of State. The beneficiaries of data communications resulting from ministerial decisions taken after advice from the Statistical Confidentiality Committee undertake not to communicate these data to anyone. Any breach of the provisions of this paragraph shall be punishable by the penalties provided for in Article 226-13 of the Criminal Code.
7.2. Confidentiality - data treatment
Responses to the questionnaire are protected by statistical secrecy and are intended for INSEE. Their use and access are strictly controlled and limited to the preparation of statistics or research work.
The General Regulation 2016/679 of 27 April 2016 on data protection (RGPD) and Law No. 78-17 of 6 January 1978 on information technology, files and freedoms apply to this survey.
Respondents may exercise a right of access, rectification or limitation of processing for the data concerning them during the period of conservation of identification data.
8.1. Release calendar
Not available.
8.2. Release calendar access
Please refer to the Release calendar - Eurostat (europa.eu) publicly available on the Eurostat’s website.
8.3. Release policy - user access
In line with the Community legal framework and the European Statistics Code of Practice, Eurostat disseminates European statistics on Eurostat's website (see section 10 - 'Accessibility and clarity'), respecting professional independence and in an objective, professional and transparent manner in which all users are treated equitably. The detailed arrangements are governed by the Eurostat protocol on impartial access to Eurostat data for users. Additional information about microdata access is available in Statistics on Income and Living Conditions - Access to microdata - Eurostat (europa.eu).
At the national level : availability of the study file to researchers via the CASD and of the production and research files (FPR) via the Quetelet network.
Annual
10.1. Dissemination format - News release
There was no press release during the past year at the national level.
10.2. Dissemination format - Publications
Recent studies
- on SILC 2024 data:
Gleizes F, Solard J, "Privation matérielle et sociale en 2024 : une personne sur huit est en situation de privation matérielle et sociale", Insee Focus n°353 - 2025
- on SILC 2023 data:
Duée M, Gleizes F, Solard J, "En France, la satisfaction dans la vie est la même qu'il y a 10 ans", Insee Focus n°347 - 2025
Gleizes F, Solard J, "La privation matérielle et sociale en 2023 : La part des personnes en situation de privation matérielle et sociale se stabilise à un niveau élevé", Insee Focus n°330 - 2024
- on SILC 2022 data:
Albouy V, Gleizes F, Solard J, "La part des personnes en situation de privation matérielle et sociale augmente en 2022", Insee Focus n°304 - 2023
- on SILC 2021 data:
Gleizes F, Pla A, "En 2021, un enfant sur dix ne part pas en vacances pour des raisons financières", Insee Focus, n°294 - 2023
Garnero M, Guillaneuf J, « En 2020, une mesure de la pauvreté compliquée par la crise sanitaire », Insee Analyse, n°77 - 2022.
- on previous years of the survey :
« Privations matérielles et sociales depuis 2013 », Insee Résultats - 2023 style="text-decoration: underline;">
Legleye S., Plat A., Gleizes F, « Une personne sur cinq est en situation de pauvreté monétaire ou de privation matérielle et sociale », Insee Focus, n°245, – 2021
Blasco J, Picard S, « Environ 2 millions de personnes en situation de grande pauvreté en France en 2018 », Insee Références, Les revenus et le patrimoine des ménages – Édition 2021, p.55 – 67
Gleizes F, Legleye S, Pla A, « Ordinateur et accès à Internet : les inégalités d’équipement persistent selon le niveau de vie », Insee Focus, N° 226 – 2021
Albouy V, Delmas F, « 70 % des personnes pauvres en 2016 le restent l’année suivante, une persistance en hausse depuis 2008 », Insee Focus, N° 208 – 2020
Blasco J, Gleizes F, « Qui est pauvre en Europe ? Deux figures différentes de la pauvreté, par l’approche monétaire ou par la privation matérielle et sociale », Insee Références, La France dans l’Union européenne– Édition 2019, p. 19-35
Contributions of periodical reference works :
Martin H, "Pauvreté monétaire, privation et difficultés financières : des situations qui ne se recouvrent que partiellement", Insee Analyses n°107 - 2025
Les revenus et le patrimoine des ménages, Insee Références, 2024
- Fiches: « Privation matérielle et sociale », « Grande pauvreté », « Pauvreté monétaire en Europe »
France, portrait social, Insee Références, 2024
- Fiches : « État de santé de la population », « Relations sociales », « Logement », « Les difficultés rencontrées lors des démarches administratives », « Satisfaction dans la vie et confiance envers les autres », « Salaires, niveaux de vie et pauvreté en Europe », « Consommation et conditions de vie en Europe ».
Tableaux de bord de l’économie française (interactive tool)
- Fiches : « Santé - Handicap - Dépendance », « Consommation et équipements des ménages », « Société - Vie sociale - Elections », « Loisirs - Culture », « Logement ».
10.3. Dissemination format - online database
Not available
10.3.1. Data tables - consultations
Not available
10.4. Dissemination format - microdata access
In the 3rd quarter N+1 :
A production and research file (FPR) is available to ministerial statistical services and certain public bodies (via Insee Info Service, forms must be completed, the first request being subject to the Statistical Confidentiality Committee) and to researchers (via the Quételet Centre). For other institutions and/or for requests for access to more detailed data, the requested data are made available via the Centre d'accès sécurisé distant aux données (CASD) after approval by the Confidentiality Committee.
Individual files are made available to researchers via the Centre d'Accès Sécurisé aux Données (CASD), subject to the agreement of the Comité du secret statistique.
Production and Research Files (FPR) are available to researchers via the Quetelet Diffusion network. Intermediate categories of detailed files concerning household surveys, these FPRs respect statistical secrecy but offer a greater level of detail than the « all public » files.
FPR files are also produced, under certain conditions, for organisations with a public service mission such as SSMs, international organisations and public services.
10.5. Dissemination format - other
Not available
10.5.1. Metadata - consultations
Not requested for SILC.
10.6. Documentation on methodology
See Annex 10 - Metadata on benefits.
10.6.1. Metadata completeness - rate
All required concepts are provided.
10.7. Quality management - documentation
Not available
11.1. Quality assurance
Since 2005, the European Statistics Code of Practice has been the benchmark for assessing the quality of the output of national statistical institutes. Periodic reviews by european peers are organised to ensure that the principles of this reference framework are implemented and to ensure that each institute is committed to continuous improvement. Within this framework, INSEE has adopted a process-based approach. A range of tools, pooled within the Official Statistical Service (SSP), has been created to describe statistical production processes, analyse their strengths and weaknesses, assess the risks involved, examine their documentation (metadata) or assess a particular stage (analysis of users' needs, data validation, etc.). The diagnoses resulting from these « quality approaches » lead to the establishment of action plans that are regularly monitored in the context of « process reviews » . In addition, INSEE regularly conducts satisfaction surveys on the indicators and data it produces. The results of these surveys are available on the insee.fr website.
In addition, with regard to surveys carried out by services producing official statistics (INSEE, ministerial statistical services, other related bodies such as INED, Céreq, Inserm, etc.), the Label Committee is responsible for examining, on behalf of the National Council for Statistical Information (CNIS), all projects for which the approval provided for in Article 2 of the Law of 7 June 1951 is requested. Over time, the Committee has developed a method and a body of case law for examining the files submitted to it. Starting from considerations expressed in terms of the burden or proportionality of the collection to the objectives pursued, the Committee has extended its examination rules to cover all dimensions of statistical quality, as formalised in the European Statistics Code of Practice. For the SSP, the Label Committee thus constitutes a lever for ensuring compliance with these principles, whether in terms of consultation, methodological quality, proportionate burden, dissemination or availability of duly documented statistical sources.
On a more strategic level, the INSEE Inspectorate General carries out evaluations, assessments and audits of the Institute's work, its operations and the organisation of its services. Some of these missions focus more pecifically on INSEE's key processes.
Quality procedures on FR-SILC include the following:
- Every year, both new and experienced interviewers are trained in the survey. New interviewers are trained for one and a half days and experienced interviewers for one day. The training sessions are organised in the month before the start of the survey.
- Once a year, the SILC design team organises a "user group" for the survey. This group brings together all of the partners, researchers and statisticians who work on the results of the survey.
- Once or twice a year, a steering committee is held that brings together all those involved in the FR-SILC survey.
- Finally, every 5 years, the survey design team presents a complete file on the FR-SILC survey to the label committee.
The Official Statistics Label Committee examines, on behalf of the National Council for Statistical Information (CNIS), the survey projects submitted to it by official statistics producing agencies. It evaluates the implementation methods planned by the survey department and, if the evaluation is favourable, awards a label of general interest and statistical quality. The Official Statistics Label Committee gives opinions on behalf of the National Council for Statistical Information and, at its request, on statistics produced by private-sector organisations; in this case, these opinions are forwarded to the President of the National Council for Statistical Information, who judges their contribution to the general interest.
On behalf of the Official Statistics Authority, and at the request of the latter, the Official Statistics Label Committee gives opinions on the processes of exploitation and dissemination, for general information purposes, of data collected by administrations, public bodies and private bodies entrusted with a public service mission; in the latter case, these opinions are forwarded to the President of the Official Statistics Authority, who may award the processes examined a label of general interest and statistical quality, with reference to the principles of the European Statistics Code of Practice.
11.2. Quality management - assessment
During data collection:
- Interviewers are invited to send any questions they wish to the questionnaire design team, which tries to answer them in a timely manner.
- The design team monitors the progress of the collection on a very regular basis and ensures that the response rate is satisfactory.
- Interviewer managers ensure that the interviewers' work run smoothly.
- Some of the questionnaires are proofread by the regional offices before being sent.
After data collection:
- In some years, post-collection questionnaires are sent to respondents to ensure that the established protocol has been followed by the interviewer.
- The results of the survey are then compared with those of previous surveys and other sources (administrative databases and other surveys).
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.
At the national level :
Once a year, the SILC design team organises a "user group" for the survey. This group brings together all the partners, researchers and statisticians, who work on the results of the survey.
12.3. Completeness
HY170G/N (value of goods produced for own consumption) is empty: the guidelines provide that :”For some countries and for some categories of households, the value of goods produced for own consumption does not constitute a significant component of income. In this case, related information does not need to be collected for this category of households and reported in the EU-SILC variable.” In France, the share of own consumption in total household expenditure has been steadily decreasing over the last few decades; it was only 0.3% in 2007 (See graph in this study, p:30). According to the 2021 national accounts, it remains at 0.3 % in the 2020s.
HY145N (repayments/receipts for tax adjustment): The administrative source that provides the income data declared in the FR-SILC survey does not provide the amounts of tax deducted directly at source: only the total amount of tax paid after adjustments is known. Consequently, variable HY145 is not filled in.
PY080 (pension from individual private plans) : PY080 was composed of only life annuity up to SILC2024 upwards. Since life annuity is available only at the household level now it is added in the HY040 aggregate (income from rental of a property or land).
The optional variables RL080, HI130G and HI140G were not collected.
12.3.1. Data completeness - rate
Not available
13.1. Accuracy - overall
According to Reg. (EU) 2019/1700 Annex II, precision requirements for all data sets are expressed in standard errors and are defined as continuous functions of the actual estimates and of the size of the statistical population in a country or in a NUTS 2 region. For the income and living conditions domain, the estimated standard errors of the following indicators are examined according to certain parameters set:
- · Ratio at‐risk‐of‐poverty or social exclusion to population;
- · Ratio of at‐persistent‐risk‐of‐poverty over four years to population;
- · Ratio at‐risk‐of‐poverty or social exclusion to population in each NUTS 2 region.
Further information is provided in section 13.2 Sampling error.
13.2. Sampling error
EU-SILC is a complex survey involving different sampling designs in different countries. In order to harmonize and make sampling errors comparable among countries, Eurostat (with the substantial methodological support of Net-SILC2) has chosen to apply the "linearization" technique coupled with the “ultimate cluster” approach for variance estimation.
Linearization is a technique based on the use of linear approximation to reduce non-linear statistics to a linear form, justified by asymptotic properties of the estimator. This technique can encompass a wide variety of indicators, including EU-SILC indicators. The "ultimate cluster" approach is a simplification consisting in calculating the variance taking into account only variation among Primary Sampling Unit (PSU) totals. This method requires first stage sampling fractions to be small which is nearly always the case. This method allows a great flexibility and simplifies the calculations of variances. It can also be generalized to calculate variance of the differences of one year to another.
The main hypothesis on which the calculations are based is that the "at risk of poverty" threshold is fixed. According to the characteristics and availability of data for different countries, we have used different variables to specify strata and cluster information.
In particular, countries have been split into 3 groups:
- BE, BG, CZ, IE, EL, ES, FR, HR, IT, LV, HU, PL, PT, RO, SI, UK and AL, whose sampling design could be assimilated to a two-stage stratified type we used DB050 (primary strata) for strata specification and DB060 (Primary Sampling Unit) for cluster specification;
- DK, DE, EE, CY, LT, LU, NL, AT, SK, FI, CH whose sampling design could be assimilated to a one stage stratified type we used DB050 for strata specification and DB030 (household ID) for cluster specification;
- MT, SE, IS, NO, whose sampling design could be assimilated to a simple random sampling, we used DB030 for cluster specification and no strata.
See Annex 3 - Sampling errors.
13.2.1. Sampling error - indicators
The concept of accuracy refers to the precision of estimates computed from a sample rather than from the entire population. Accuracy depends on sample size, sampling design effects and structure of the population under study. In addition to that, sampling errors and non-sampling errors need to be taken into account. Sampling error refers to the variability that occurs at random because of the use of a sample rather than a census and non-sampling errors are errors that occur in all phases of the data collection and production process
See Annex 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
13.3.1.1. Over-coverage - rate
Coverage error - not available
| Main problems |
Population (sub-population) |
Size of error |
Comments |
|---|---|---|---|
| Over-coverage |
|
|
|
| Under-coverage |
|
|
|
| Misclassification |
|
|
|
13.3.1.2. Common units - proportion
Not available.
13.3.2. Measurement error
Measurement error for cross-sectional data
Source of measurement errors
In order to limit measurement errors and improve the quality of individual income measurement in SILC, data concerning (taxable) income and social security benefits, which were collected by means of surveys until 2007, have since been collected by means of matching with tax and social security data (DGFIP, CNAF, CNAV and CCMSA), based on the Revenus Fiscaux et sociaux (Tax and social incomes) survey. However, this matching is not exhaustive: since matching takes place on the basis of addresses, young adults aged 18 to 25 who are included in their parents’ tax return at an address other than the reporting address may be difficult to find (these young people are asked about the amount of their wage). Likewise, people who have moved house since 1 January of the reporting year may be difficult to find. Where matching cannot be performed (around 4% of individuals for tax data), tax and social welfare income are imputed.
Finally, only taxable income is obtained by matching. As a result, the questionnaire still contains some questions about the amount of tax-free income. In the case of social security benefits, all family and housing benefits are covered. This is not the case for the minimum old-age pension. Indeed, CNAV and MSA only pay 70% of the total amount.
Building process of questionnaire
Structure of the questionnaire
The survey is made up of a household questionnaire (aimed at the household as a whole) and an individual questionnaire for all people within the household aged 16 or over (on 1 January of the survey year). The household questionnaire is preceded by the Common Core for household surveys, which forms the basis for all household surveys conducted by INSEE.
More specifically, the household questionnaire first surveys the income received at the household level and then focuses on children (income and care), housing and living conditions.
The individual questionnaire starts with questions about employment and professional life; it then focuses on income received at the individual level and finishes with living conditions.
Comprehensiveness and absence of double counting in the collection of income
Matching with tax and social security sources allows us to guarantee very good (near-comprehensive) coverage and to limit double counting.
For tax-free income and certain unmatched individuals (young adults), the questioning strategy for income remains the same as it was up until 2007 (see above). As the interviewer is not able to infer all of the sources of income of a household and since the latter may forget to mention one, a general “sweeping” strategy is implemented for income. This strategy consists of: -asking about the different types of income received by the household during the reference year, before collecting the corresponding amounts
-checking that the amount has not previously been included with another type of income in order to avoid double counting
-systematically attempting to obtain information in bands where the respondent was unable or unwilling to provide an amount.
Use of administrative and tax documents
Where an individual is matched using tax or social security data, the administrative data are systematically prioritised over the data concerning receipt or amounts collected via the questionnaire.
Questionnaire tests
The questionnaire benefited from the results of the 2004 to 2023 collections. Since 2020, following the redesign, field testing is no longer carried out between two surveys. An office test is conducted in order to experience specific or complex configurations. Prior to the introduction of the new questions (which the system launched in 2004 has never included), a qualitative test (focus group) is being performed on these questions. Upon the completion of each test and following the collection reports and the various comments made by interviewers, changes to the questionnaire (structure, wording) are proposed to the project manager by the design team. Such changes to the questionnaire are validated during a survey steering committee meeting.
A focus group was carried out for the preparation of the 2024 SILC 6-yearly module on access to services, as the module evolved since it was covered in 2016. The preparation of the questionnaire started at the beginning of 2023. Based on the guidelines, the FR-SILC team developed a first version of the questionnaire, which was submitted to experts working on the subject at INSEE and in several government departments and research centers. The redrafted version of the questionnaire was tested in April 2023 with five interviewers from the INSEE regional office based in Reims. Each interviewer was asked to administer the questionnaire on paper face-to-face to five households (neighbours, family, friends) with instructions to try to diversify the profiles of the households interviewed. Following the feedbacks received from the focus group, the questionnaire was improved and the instructions were clarified after some exchanges with Eurostat.
INSEE has a robust network of interviewers. Where possible, the Institute uses the same interviewers from one wave to the next, which has at least two advantages: the interviewers are very familiar with the survey and respondents are more likely to be loyal to an interviewer whom they already know.
Interview training
For the full-scale collection (February-April 2024), the design team does not directly train the interviewers, but instead trains the managers responsible for the survey within INSEE’s regional offices. In turn, the managers of the regional offices train the SILC interviewers, using the same training materials as were used to train the managers. The SILC design team provides the managers of the regional offices with slideshows and exercises on the collection application (CAPI) (in addition to the collection documents). In 2024, new managers and interviewers attended a 1.5-day training course and experienced managers and interviewers attended a 1-day training course.
During collection, each new interviewer is accompanied at least once by an INSEE agent (survey manager, designer, etc.).
Quality control
In order to limit collection errors, the design team has introduced filters and checks into the CAPI application, particularly for separate budgets.
Consistency checks can be performed from one wave to the next, which reduces measurement errors. These relate in particular:
- to the dates of certain events (work schedule, for example),
- to the non-receipt of a type of income received during the previous year,
- to changes in the amounts listed for types of income or the cost of housing (rent, rental costs, home insurance, etc.)
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 |
| 97,4 |
93,4 |
99,7 |
85,4 |
73,3 |
90,9 |
100 |
100 |
100 |
16,8 |
31,6 |
9,4 |
0 |
0 |
0 |
16,8 |
31,6 |
9,4 |
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 Annex A - content tables.
13.3.3.2. Item non-response - rate
The computation of item non-response is essential to fulfil the precision requirements. Item non-response rate is provided for the main income variables both at household and personal level.
Item non-response which refers to the situation where a sample unit has been successfully enumerated, but not all the required information has been obtained.
13.3.3.2.1. Item non-response rate by indicator
See Annex 2 - Item non response
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 survey was conducted by INSEE’s network of interviewers by means of face-to-face. The downstream tables containing the data from the questionnaires were created by an internal IT department at INSEE. Codification of professions and levels of education The coding of occupations - and the associated part of the questionnaire - changed in 2022 to correspond to the new PCS2020 nomenclature. Respondents can select their occupation from a list. If the occupation is not found, they can enter their occupation in plain text. |
The data processing programs incorporate data consistency and format checks (some check programs provided by Eurostat have been recoded in R within the processing chain). |
Re-interview rates by wave :
See Annex A - content tables
13.3.5. Model assumption error
All information about imputation is in Annex 6.
14.1. Timeliness
For more information, please see 14.1.1 and 14.1.2
14.1.1. Time lag - first result
The first version of the FR-SILC data was submitted in December 2024.
14.1.2. Time lag - final result
The final version of the FR-SILC data was submitted in May 2025.
14.2. Punctuality
FR-SILC 2024 data were published by Eurostat in April 2025.
At national level :
- First data on material and social deprivation were published in May 2025 (see Insee focus)
- Microdata will be available during the second semester of 2025
14.2.1. Punctuality - delivery and publication
Not available
15.1. Comparability - geographical
The sample size of the FR-SILC survey, around 25,000 respondents in metropolitan France, and the number of NUTS-2 regions, 26 (22 regions in metropolitan France and 4 overseas departments), 13 of which have fewer than 800 respondents, make it impossible to calculate reliable poverty indicators keeping only observations for each region. This is why INSEE has developed a small area estimation method, which will provide micro-data (weights) for each region, allowing the calculation of poverty rates (and AROPE) at regional level. With this method, all the observations in the database are used for each region. So, to calculate the regional indicators, it is important not to filter only on the observations from one region.
From FR-SILC 2022 onwards, the variables RB051_XXXX (with XXXX the NUTS2 or NUTS1 region identifier) contain regional weights calculated using a small area estimation method.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not applicable.
15.2. Comparability - over time
See Annex 8 - Break in series.
15.2.1. Length of comparable time series
There was a break in the series in 2022, linked to the inclusion of the overseas departments in the scope of the survey.
When processing the 2022 microdata and the following years, it is possible to neutralise the effect of the break in series by calculating the indicators on the basis of metropolitan France.
In 2023, the material deprivation question HD080 (Replacing worn-out furniture) was changed to distinguish households with a deprivation for financial reasons from households with a deprivation for other reasons. In order to avoid a break in the material and social deprivation indicator (and the AROPE) in 2023, INSEE has provided the variable HD080 backcast from 2020 to 2022 (in July 2024).
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) |
L |
Interests relates both to mortgages taken out for the purchase of the main residence and to loans for major repairs. |
| 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) |
NC |
The administrative source that provides the income data declared in the FR-SILC survey does not provide the amounts of tax deducted directly at source: only the total amount of tax paid after adjustments is known. Consequently, variable HY145 is not filled in. |
| Value of goods produced for own consumption |
(HY170) |
NC |
|
| Cash or near-cash employee income |
(PY010) |
L |
The amount of daily sickness, maternity and paternity leave pay is combined with wages in the tax return and on the pay slip. These therefore appear in aggregate PY010 rather than aggregate PY120. |
| 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) |
P |
The amount of daily sickness, maternity and paternity leave pay is combined with wages in the tax return and on the pay slip. These therefore appear in aggregate PY010. |
| 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 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.
At the national level, the external data used to control the income components are diverse.
We use mainly :
-
Exhaustive administrative databases, which provide us with target amounts of income from tax sources (declared amounts of wages, unemployment benefits, pensions, financial income, etc.) and from social security sources (housing benefits, family benefits, minimum social benefits)
- The results of the Tax and Social Incomes Survey (Enquête revenus fiscaux et sociaux – ERFS), which is the reference source, at national level, for the measurement of standards of living and monetary poverty. This source is constituted by matching the Labour Force Survey (LFS) (Enquête emploi en continu) with administrative data. The LFS is based on a large sample size and provides detailed statistics according to the main socio-demographic criteria.
ERFS has the advantage of being published on an annual basis, and of being based on large sample. For this reason, it is preferred to HBS for comparisons with FR-SILC.
The main FR-SILC results are also compared with those obtained using the INES national microsimulation model.
15.3.1. Coherence - sub annual and annual statistics
Not applicable.
15.3.2. Coherence - National Accounts
See Annex 7 - Coherence.
Several differences in the definition of the aggregates between FR-SILC and National Accounts explain the discrepancies. In particular, it can be pointed out that:
- In FR-SILC, paid sick leave could not be distinguished from wages either, whereas they are in the national accounts and are included in the D62 aggregate.
- The national accounts aggregate B3G includes fraud (i.e. underreporting of self-employed income to the tax authorities) while PY050G is calculated from income reported to the tax authorities.
- In variable HY140G, the employee's social contributions are included, while the employer's social contributions are excluded. In national accounts, on the other hand, employers' social contributions are included.
Each year, INSEE deals with administrative data on the distribution of social benefits and aids. This exhaustive information is used in order to have consistent estimates when comparing amounts of the various benefits calculated from FR-SILC and the amounts actually distributed according to administrative data.
15.4. Coherence - internal
Not requested for SILC.
Mean (average) interview duration per household = 44,2 minutes.
Mean (average) interview duration per person = 7,7 minutes.
17.1. Data revision - policy
There are no scheduled revisions.
17.2. Data revision - practice
There are no revisions to report.
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
The French data are taken from the statistics on income and living conditions (SILC): the majority of the data are taken from the SILC survey. The amounts for income and social security benefits are then obtained by matching with administrative data (taken from tax and social security sources).
18.1.1. Sampling Design
The survey design is a two-stage regionally stratified survey.
The first stage involves the drawing of the 541 Primary Units (PUs) selected as part of the Nautile Master Sample.
The second stage involves the systematic drawing of dwellings, stratified by PU and sorted by standard of living. Standard of living is the household’s disposable income divided by the number of consumption units.
18.1.2. Sampling unit
The sampling unit is the dwelling.
Upon the entry of the sample, all individuals aged 16 or over on 1 January of the year of the survey belonging to the main household of the dwelling are surveyed (therefore, where there is a separate budget within a dwelling the first time it is surveyed, only one household is surveyed).
18.1.3. Sampling frame
Since 2020, the incoming sample has been drawn from the new Nautile sampling frame. This sampling frame is established on the basis of administrative data (the previous sampling frame was established on the basis of data from the population census).
As the duration of the panel has been reduced to 4 years (it was a 9-year panel before 2020), the sample now includes only 4 sub-samples, all of which are mandatory.
The incoming sample was significantly increased in 2020 to compensate for the decrease in the number of samples undergoing repeat questioning. Part of this sample was only surveyed once and therefore was not included in the panel, which explains why some of the households questioned in 2020 are no longer present in 2021. Households that were only surveyed once were not selected until after the collection had been completed.
The overseas departments (except Mayotte) were added to the sample in 2022. In order to include them in the panel, the sample of overseas department has been divided into the 4 rotational groups - some households in these departments will therefore only be interviewed once, twice or three times (as they will leave the panel at the same time as their group).
In 2023, the sample size to be re-interviewed was larger than anticipated (following two years of high response rate). That is why some households in wave 2 and wave 3 were finally not re-interviewed. The first wave sample has also decreased somewhat. Despite the one-week extension of the collection period, collection rates were low. Thus the 2024 first wave sample has slightly increased in order to meet the precision requirements of the longitudinal indicators.
Achieved sample size for the regular data :
| Year |
Number of households |
Number of persons 16 + |
|---|---|---|
| 2020 |
10 899 |
20 822 |
| 2021 |
14 015 |
25 584 |
| 2022 |
17 451 |
31 064 |
| 2023 |
17 041 |
30 888 |
| 2024 |
17 305 |
31 354 |
3 314 households were present from 2021 to 2024, 7 200 households were present from 2022 to 2024, 11 567 households were present in 2023 and 2024;
7 267 persons (5 868 sample persons and 1 399 co-residents) were present from 2021 to 2024, 15 675 (12 634 sample persons and 3 041 co-residents) persons were present from 2022 to 2024 and 25 641 (20 346 sample persons and 5 295 co-residents) were present from 2023 to 2024.
18.2. Frequency of data collection
The collection of SILC survey takes place every year from February to April (and lasts 11 weeks).
Collection of the SILC 2024 survey took place in France from 29 January to 13 April 2024.
18.3. Data collection
See Annex 4 - Data collection.
The classic survey protocol was applied, the survey was conducted entirely face-to-face (with a few rare exceptions, around 0.8%)
The percentage of individual interviews conducted by proxy was 24 % in 2024.
Although the majority of the variables were obtained by means of surveys, income was obtained by means of matching with the tax and social security files.
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 |
|---|---|---|
| Income is obtained largely by matching with the tax and social security files and tax-free income is obtained by means of a survey, as is income for persons for whom matching is of poorer quality. |
Income variables are declared net of social security levies but including non-deductible social security contributions (non-deductible Social Debt Repayment Contribution (Contribution pour le remboursement de la dette sociale – CRDS) and Generalised Social Contribution (Contribution sociale généralisée – CSG)). |
Taxes are obtained by means of matching. |
18.4. Data validation
The format of the data is verified and validated through the check programs provided by Eurostat.
18.5. Data compilation
In 2020, the FR-SILC survey was redesigned in order to comply with the IESS regulation.
The whole statistical processing and editing chain has been rewritten (from SAS to R).
18.5.1. Imputation - rate
All information about imputation rates is in Annex 6.
18.5.2. Calculation of weighting factors and weight adjustments
See Annex 5 - Weighting procedure.
18.5.3. Estimation and imputation
See Annex 6 - Estimation and imputation.
18.6. Adjustment
Not applicable.
18.6.1. Seasonal adjustment
Not applicable.
See Annex 10 - Rolling module.
Annex 1 - Questionnaire (French)
Annex 1 - Questionnaire (English)
Annex 2 - Item non response
Annex 3 - Sampling errors
Annex 4 - Data collection
Annex 5 - Weighting procedure
Annex 6 - Estimation and Imputation
Annex 7 - Coherence
Annex 8 - Breaks in series
Annex 9 - Rolling module
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.
26 May 2025
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.
France (excluding Mayotte).
Four overseas departments (Guadeloupe, Martinique, Guyane, la Réunion) have been included with effect from the 2022 survey.
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 taxes paid in 2023 on income received during the course of that year are collected at the household level. The social security levies relate to income received in 2023. |
The survey was conducted between February and April 2024. The reference year for income is 2023. The income received during 2023 is collected at the individual and household level. |
We take account of the amount of property wealth tax paid in 2023. This relates to property held as at 1 January 2023. |
Some variables, such as variables relating to the cost of housing, relate to the collection period, i.e. the months of February to April 2024, which differs from the reference period for income relating to 2023 (declared income). |
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 CIRCABC
In 2020, the FR-SILC survey was redesigned in order to comply with the IESS regulation.
The whole statistical processing and editing chain has been rewritten (from SAS to R).
The French data are taken from the statistics on income and living conditions (SILC): the majority of the data are taken from the SILC survey. The amounts for income and social security benefits are then obtained by matching with administrative data (taken from tax and social security sources).
Annual
For more information, please see 14.1.1 and 14.1.2
The sample size of the FR-SILC survey, around 25,000 respondents in metropolitan France, and the number of NUTS-2 regions, 26 (22 regions in metropolitan France and 4 overseas departments), 13 of which have fewer than 800 respondents, make it impossible to calculate reliable poverty indicators keeping only observations for each region. This is why INSEE has developed a small area estimation method, which will provide micro-data (weights) for each region, allowing the calculation of poverty rates (and AROPE) at regional level. With this method, all the observations in the database are used for each region. So, to calculate the regional indicators, it is important not to filter only on the observations from one region.
From FR-SILC 2022 onwards, the variables RB051_XXXX (with XXXX the NUTS2 or NUTS1 region identifier) contain regional weights calculated using a small area estimation method.
See Annex 8 - Break in series.
