1.1. Contact organisation
National Statistics Institute (INE-Spain).
1.2. Contact organisation unit
Directorate of Social Statistics.
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
Avenida de Manoteras, 50-52. Madrid. Spain
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
5 March 2025
2.2. Metadata last posted
5 March 2025
2.3. Metadata last update
5 March 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, every 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 EU regulation 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);
- Geographical code list (SCL Geo Code);
- 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 for 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 EU Regulation (EU) 2019/1700,EUregulation 2019/2181, and EU regulation 2019/2242. Additional information is available in the EUstatistics 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 EU regulation 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 EU regulation 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. A private household means a person living alone or a group of persons who live together, providing oneself or themselves with the essentials of living.
3.6.1. Reference population
Definitions of reference population, household and household membership
| Reference population |
Private household definition |
Household membership |
|---|---|---|
| The target population was members of private households residing at main family addresses, and the households themselves. (No differences between national and EU-SILC concept) |
An individual or a group of people occupying in common a main family address or a part of it, and consuming and/or sharing food or other goods paid for out of a common budget. (No differences between national and EU-SILC concept) |
The reference in the definition of ‘household member’ is to apply the Eurostat guidelines |
3.6.2. Population not covered by the data collection
The sub-populations that are not covered by the data collection include: 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 entire Spanish territory.
3.8. Coverage - Time
The statistics are carried out on an annual basis. There are results available since 2004.
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 the 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 |
|---|---|---|---|
| Taxes received/paid during the income reference period are considered. In the case of tax adjustments, these taxes usually refer to income received during the income reference period. (No differences between national and EU-SILC concept) |
The income reference period is the previous calendar year. (No differences between national and EU-SILC concept) |
We considered the tax received/paid during the income reference period. In the case of the taxes paid on ownership of the household main dwelling (IBI) the reference period is the last 12 months. |
From 31 December of the year prior to the survey to the time of data collection (February-June). The lag thus ranged from 1 to 5 months. |
6.1. Institutional Mandate - legal acts and other agreements
EU regulation (EU) 2019/1700 was publish in the OJ on October 10, 2019, establishing a common framework for European statistics relating to persons and households, based on data at the individual level collected from samples (IESS). The Annex to the Commission Implementing EU regulation (EU)2019/2180 of December 16, 2019 specifies the detailed arrangements and content for the quality reports pursuant to EU regulation 2019/1700 and EU regulation 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 EU regulation 557/2013 and EU regulation 223/2009 on European statistics.
7.1. Confidentiality - policy
The Statistical Law No. 12/1989 specifies that the INE cannot publish, or make otherwise available, individual data or statistics that would enable the identification of data for any individual person or entity. Regulation (EC) No 223/2009 on European statistics stipulates the need to establish common principles and guidelines ensuring the confidentiality of data used for the production of European statistics and the access to those confidential data with due account for technical developments and the requirements of users in a democratic society.
7.2. Confidentiality - data treatment
INE provides information on the protection of confidentiality at all stages of the statistical process:
INE questionnaires for the operations in the national statistical plan include a legal clause protecting data under statistical confidentiality. Notices prior to data collection announcing a statistical operation notify respondents that data are subject to statistical confidentiality at all stages.
For data processing, INE employees have available the INE data protection handbook, which specifies the steps that should be taken at each stage of processing to ensure reporting units' individual data are protected.
The microdata files provided to users are anonymised.
8.1. Release calendar
The link of the calendar of publications in INE-Spain is: Calendar.
8.2. Release calendar access
Please refer to the release calendar which is publicly available on 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.
Annual
10.1. Dissemination format - News release
The results of the statistical operations are normally disseminated by using press releases that can be accessed in the web. Here is the last press release.
10.2. Dissemination format - Publications
All relevant documents related to the ES-SILC (methodology, indicators, etc.) are published by INE-Spain.
10.3. Dissemination format - online database
INEbase is the system the INE uses to store statistical information on the Internet. It contains all the information the INE produces in electronic formats. The primary organisation of the information follows the theme-based classification of the Inventory of Statistical Operations of the State General Administration. The basic unit of INEbase is the statistical operation, defined as the set of activities that lead to obtaining statistical results on a determined sector or subject based on the individually collected data. Also included in the scope of this definition are synthesis preparation.
Here you can access the on-line database of the ES-SILC in INEbase.
10.3.1. Data tables - consultations
The number of data table consultations in 2023 was 738,581.
10.4. Dissemination format - microdata access
ES-SILC microdata are available free of charge for downloading in the INE website Microdata Section.
The survey provides, free of charge, duly anonymised microdata files (Cross-sectional files and longitudinal files) that are available on the INE website.
10.5. Dissemination format - other
Customised requests are made of exploitations not included in the detailed results that are published.
These customised requests take into account both the confidentiality of the data and their robustness, so that requests that might infringe any of the above points are not dealt with.
The request is made through the User Service Area on the INE website.
10.5.1. Metadata - consultations
Not available.
10.6. Documentation on methodology
The available methodological documentation is the following:
See also Annex 10 -Metadata on benefits
10.6.1. Metadata completeness - rate
All requested concepts are provided, 100%.
10.7. Quality management - documentation
Not applicable.
11.1. Quality assurance
Quality assurance framework for the INE statistics is based on the ESSCoP, the European Statistics Code of Practice made by EUROSTAT. The ESSCoP is made up of 16 principles, gathered in three areas: Institutional Environment, Processes and Products. Each principle is associated with some indicators which make possible to measure it. In order to evaluate quality, EUROSTATprovides different tools: the indicators mentioned above, self-assessment based on the DESAP model, peer review, user satisfaction surveys and other proceedings for evaluation.
The ECV (Encuesta de Condiciones de Vida), as source of the EU-SILC of Spain, is based on a framework regulation(2019/1700) which establishes a common framework for European statistics relating to persons and households, based on data at individual level collected from sample.
11.2. Quality management - assessment
Eurostat carries out a review of the survey data before the results are published. Subsequently a comparative quality report is generated showing the strengths and weaknesses of the survey.
One of the weak points of the ES-SILC is the relatively high percentage of proxy interviews (interview with a member of the household responding on behalf of another person).
As a strong point of the ES-SILC is the adequate adjustment of the definitions of income variables.
For more information on these aspects, please refer to the Standardised Methodological Report prepared by INE- Spain.
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 (repeated in June-July 2022) to obtain better understanding about users’ 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 microdata 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. Users emphasized their strong need for more detailed microdata.
For more information, please consult the User Satisfaction Survey by years).
12.3. Completeness
ES-SILC covers all the variables required in the survey Regulation, except the variable HY121 (Taxes paid on ownership of household main dwelling), which is included in HY120.
The optional variables are not collected in the 2024 survey, except HY030G ‘Imputed rent’.
12.3.1. Data completeness - rate
ES-SILC covers all the variables required in the survey Regulation.
Data completeness rate=100%
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 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 the asymptotic properties of the estimator. This technique can encompass a wide variety of indicators, including EU-SILC indicators. The "ultimate cluster" approach is a simplification consisting 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 the variance of the differences from one year to another.
The main hypothesis on which the calculations are based is that the "at risk of poverty" threshold is fixed. According to the characteristics and availability of data for different countries, we have used different variables to specify strata and cluster information.
In INE-Spain we use:
- 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.
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 the 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.
Variance estimation calculations on indicators are provided by ESTAT. See Annex A - List of tables attached to concepts.
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 the absence of information for the whole units (households and/or persons) selected into the sample.
- Item non-response: refers to the situation where a sample unit has been successfully enumerated, but not all 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 the incorrect classification of units that belong to the target population.
13.3.1.1. Over-coverage - rate
Coverage error
| Main problems |
Population (sub-population) |
Size of error |
Comments |
|---|---|---|---|
| Over-coverage |
Addresses do not exist or non-residential or are unoccupied or not principal residence (DB120 =23) over the total original address (household) selected: 4.4%. |
|
|
| Under-coverage |
|
|
Information not available |
| Misclassification |
|
|
Information not available |
13.3.1.2. Common units - proportion
The merge of the persons of the survey and the administrative files is carried out by the NIF (tax identification number). The rate of adults of sample persons with NIF found is 99.05%.
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 |
| Information not available. |
The questionnaire was constructed so as to elicit sufficient information to determine the target variables set forth in the Regulation. We did not include additional questions to cover other areas at the national level. The questionnaire design was worked on by experts of the originating unit and of the IT and Fieldwork departments. It was then reviewed by experts working on other surveys. The questionnaire was later tested by various people.
|
Training followed a cascade pattern. First, Area Heads received a course in Madrid. At their Provincial Offices, Area Heads then taught a course to their staff using a range of training manuals. |
Information not available |
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: unit non-response and item-non response.
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 EU regulation 2019/2242
- Household non-response rates (NRh) are computed as follows:
NRh=(1-(Ra * Rh)) * 100
Where Ra is the address contact rate defined as:
Ra= Number of addresses/selected person (including phone, mail if applicable) successfully contacted/number of valid addresses/selected person (including phone, mail if applicable) selectedand Rh is the proportion of complete household interviews accepted for the database.
Rh=Number of household interviews completed and accepted for the database/Number of eligible households at contacted addresses (including phone, mail if applicable)
- Individual non-response rates (NRp) are 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 interviews completed/Number of eligible individuals in the households whose interviews were completed and accepted for the database.
- Overall individual non-response rates (*NRp) are computed as follows:
*NRp=(1-(Ra * Rh * Rp)) * 100
For those Member 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 data
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 |
| 99.79 |
99.42 |
100.0 |
82.40 |
70.09 |
91.99 |
99.58 |
99.51 |
99.82 |
17.78 |
30.32 |
8.01 |
0.42 |
0.49 |
0.18 |
18.12 |
30.66 |
8.18 |
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.
Unit non-response rate for longitudinal data
See Annex A- List of tables attached to concepts.
13.3.3.2. Item non-response - rate
The computation of item non-response is essential to fulfil the precision requirements. The item nonresponse rate is provided for the main income variables, both at the household and personal level.
Item non-response 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 |
|---|---|
| Questionnaires have been completed by CAWI-CATI-CAPI (Compute Aided Interviewing) since 2021. We convert the data to the format required by Eurostat and apply the set of checks developed by Eurostat.
|
Due to the mode of collection (CAPI - CATI), and, since 2021, CAWI, some of the traditional sources of errors have disappeared or have been reduced. |
See also Annex A- List of tables attached to concepts. The re-interview rates by wave for people leaving their household (part II of the table 13.3.4 of the Annex A) are 0.0 in Wave 2 because in 2022 survey the split-off households are not followed in the panel component (for more details see 18.1.1 Sampling Design).
13.3.5. Model assumption error
Not applicable.
14.1. Timeliness
The link of the calendar of publications in INE-Spain website.
14.1.1. Time lag - first result
ES-SILC 2024 data are published 14 months after the end of the income reference period.
14.1.2. Time lag - final result
There was not a revised publication.
14.2. Punctuality
2024 ES-SILC data have been released as scheduled.
14.2.1. Punctuality - delivery and publication
2024 ES-SILC data have been released as scheduled.
15.1. Comparability - geographical
The processing of this statistic in all its phases is the same for the whole territory. In this way, the results are fully comparable for any geographical breakdown.
In the construction of the variables relating to household income, the tax administrative files in Alava have not been used. In order to ensure geographical comparability, corrective factors have been applied in this province.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not applicable.
15.2. Comparability - over time
See Annex 8 – Breaks in series
15.2.1. Length of comparable time series
ES-SILC was first carried out in 2004.
From 2004 until the 2012 survey there have been no significant methodological changes. As of the 2013 survey there is a significant methodological change consisting of the use of administrative files for the construction of the income variables. For this reason, retrospective estimates have been prepared since 2008, comparable with the 2013 data.
Therefore, the number of comparable elements of the time series related to income information is since the 2008 survey.
15.2.2. Comparability and deviation from definition for each income variable
|
Comparability and deviation from definition for each income variable
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.
Comparison of total household income with HBS is made.
See Annex 7 -Coherence
15.3.1. Coherence - sub annual and annual statistics
Not applicable.
15.3.2. Coherence - National Accounts
Comparison of with NA is made.
See Annex 7 -Coherence
15.4. Coherence - internal
The EU-SILC growth rate (nominal, year to year) is included in Annex 7 – Coherence.
Mean (average) interview duration per household = 33.3 minutes.
Mean (average) interview duration per person = 9.5 minutes.
17.1. Data revision - policy
INE-Spain has a policy which regulates the basic aspects of statistical data revision, seeking to ensure process transparency and product quality. This policy is laid out in the document approved by the INE board of directors on 13 March of 2015, which is available on the INE website, in the section "Methods and projects/Quality and Code of Practice/INE’s Qualitymanagement/INE’s Revision policy".
This general policy sets the criteria that the different type of revisions should follow:
- routine revision: it is the case of statistics whose production process includes regular revisions.
- more extensive revision: when methodological or basic reference source changes take place.
- exceptional revision: for instance, when an error appears in a published statistic.
17.2. Data revision - practice
There is currently no revisions in ES-SILC.
17.2.1. Data revision - average size
There is currently no revisions in ES-SILC.
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 administrative sources used are:
- Geographical frame of addresses ("Marco de direcciones georrefenciadas") for the sampling frame.
- Register of Public Social Benefits (RPSP). Centralized database with benefits paid by different public bodies.
- Personal Income Tax (Form 100 and file of imputations). Declarations of Income Tax of Individuals and families in the Form 100. Also information for those who have not filled the Tax Returns (imputations)
- Tax withholding at source system (Form 190). Annual summary of withholdings of Income Tax of Individuals.
List of final variables with use of administrative files
- Old age benefits (RPSP)
- Survivors' benefits (RPSP)
- Disability benefits (RPSP)
- Capital income (Form 100 and file of imputations)
- Property income (Form 100)
- Result of the income tax return (Form 100)
- Income from self-employment (Form 100)
- Cash employee income (Form 190)
- Non-cash employee income (Form 190)
- Sickness benefits (Form 190)
- Private pension plans (Form 190)
- Unemployment benefits (Form 190)
18.1.1. Sampling Design
Type of sampling design
The Survey on Income and Living Conditions (Spanish “ECV”) is an annual survey with a rotational-group design. The sample comprises four independent sub-samples, each of which is a four-year panel. Each year, the sample is rotated in one of the panels.
The new sub-sample is selected following a two-stage design; the first-stage units are stratified. The first stage is made up of census sections. The second stage comprises main family addresses. There was no sub-sampling within those units; all households usually residing in those addresses were surveyed. The other sub-samples are formed with the households of the previous waves that have collaborated.
In the 2020, 2021 and 2022 surveys the split-off households are not followed in the panel component (in the COVID-19 crisis context, together with the change of the Data Collection program and the mode of data collection, it has only been possible to develop the procedure to follow the movement of the entire household, but not the case of the split-off households.).
Since the 2016 survey a supplementation of the sample is added in Catalonia in the new-subsample for the cross-sectional operation. From the 2021 survey this sample will be followed up in the subsequent years following the rotating scheme (the new-subsample has 116 census sections).
The sample size has been duplicated in the context of the precision requirements introduced in the new frame regulation. The process has been initiated in 2019 and is consolidated in 2022. The new-subsamples are duplicated (some adjustments have been carried out in the allocation by Autonomous Communities) in the period 2019-2022.
Renewal of sample: Rotational groups
As indicated earlier, the sample design takes the form of four annual panels: individuals in each panel remain in the sample for four consecutive years. Therefore we divided the 4000 sections (after the duplication in the period 2019-2022) into four groups –called rotational groups – of 1000 sections corresponding to the four panels of the sample.
Every year, we replace all the sample of addresses in the sections belonging to a given rotational group (the sections don´t change, new addresses are selected). Hence the year’s sample has a three-quarters overlap with the previous year’s sample.
The values used in the variable DB075 (rotational group) are 1,2,3 and 4. In the 2023 survey, the rotational group of the new sub-sample is “4”.
Stratification and sub stratification criteria
In each Autonomous Community [self-ruling region], first-stage units were stratified by the size of the municipality to which the census section belonged.
The following strata were considered:
Stratum 0: Municipalities of over 500,000 population.
Stratum 1: Provincial capitals (other than the above).
Stratum 2: Municipalities of over 100,000 population (other than the above).
Stratum 3: Municipalities of 50,000 to 100,000 population (other than the above).
Stratum 4: Municipalities of 20,000 to 50,000 population (other than the above).
Stratum 5: Municipalities of 10,000 to 20,000 population.
Stratum 6: Municipalities of under 10,000 population.
An independent sample was designed in each Autonomous Community to represent it, because one of INE’s survey objectives isto provide data at this level of disaggegration.
Sample selection schemes
To achieve the survey objective of producing acceptably reliable estimates at both the national and at the Autonomous Community (regional) level, we selected, in the new sub-sample, a sample of 12,000 addresses spread over 1000 census sections. We distributed the sample across Autonomous Communities by allocating one part uniformly and another part in proportion to Autonomous Community size. The uniform part accounted for about 40% of sections.
In each section, 12 addresses are selected originally.
The number of sections in each Autonomous Community and stratum group was always a multiple of four, to ensure that all rotations had the same notional-sample distribution across Autonomous Communities and strata.
In the new sub-sample, census sections were selected in each stratum by a probability in proportion to size (family dwellings). In each section,addresses were selected with equal probability by systematic sampling initiated at random. This procedure produces self-weighted samples in each stratum.
Method of selection of substitutions
Since the 2014 survey substitutions are eliminated. In each section twelve sections are selected instead of eight.
Sample distribution over time
There is no itemised distribution for sample collection in the period February-June 2024.
Concerning the SILC instrument, three different sample size definitions can be applied: - the actual sample size which is the number of sampling units selected in the sample - the achieved sample size which is the number of observed sampling unitsv(household or individual) with an accepted interview - the effective sample size which is defined as the achieved sample size divided by the design effect.
In this section the attention focuses mainly on the achieved sample size.
Cross-sectional information year 2024
Actual and achieved sample size
| Obs |
DB020 |
Actual_SSize |
Achieved_SSize |
|---|---|---|---|
| 1 |
ES |
37118 |
29781 |
Cross-sectional information year 2024
Achieved sample size
| Obs |
DB020 |
number_of_hh2024 |
persons_16_over2024 |
selected_respondents2024 |
NewRG |
|---|---|---|---|---|---|
| 1 |
ES |
29871 |
61526 |
. |
4 |
| Obs |
newRG_number_of_hh2024 |
percent2 |
OldRG |
OldRG_number_of_hh2024 |
percent3 |
|---|---|---|---|---|---|
| 1 |
8946 |
30.04 |
1 |
6531 |
21.93 |
Longitudinal information years 2021 - 2024
Achieved household sample size
| Obs |
country |
DB020 |
wave2324 |
wave222324 |
wave21222324 |
|---|---|---|---|---|---|
| 1 |
ES |
ES |
20328 |
12241 |
6058 |
Longitudinal information years 2021 - 2024
Achieved individual sample size
| Obs |
RB020 |
TOTAL2324 |
SAMPLEPER2324 |
CORES2324 |
TOTAL222324 |
SAMPLEPER222324 |
|---|---|---|---|---|---|---|
| 1 |
ES |
50525 |
41001 |
9524 |
29653 |
24319 |
| Obs |
CORES222324 |
TOTAL21222324 |
SAMPLEPER21222324 |
CORES21222324 |
|---|---|---|---|---|
| 1 |
5334 |
14511 |
11993 |
2518 |
18.1.2. Sampling unit
The first-stage units are census sections. Each section is made up of around 400 addresses.
The second-stage units are the principal family addresses selected for the sample in the census section.
18.1.3. Sampling frame
The sampling frame used in the ES-SILC-2024 has been the Geographical frame of addresses ("Marco de direcciones georrefenciadas") with reference date July 2023. This frame is based in the Municipal register (Padrón), but has information from the tax registry of dwellings ("catastro") and other sources that improve the quality of the previously used sampling frame of dwellings. This frame is now used in all population and household surveys conducted by the NSI-Spain (INE).
Additionally to the dwelllings information we have information of all persons registered in those dwellings with some demographic and identification information of them.
Rotation scheme: The sample comprises four independent sub-samples, each of which is a four-year panel. Each year, the sample is rotated in one of the panels.
18.2. Frequency of data collection
There is no itemised distribution for sample collection in the period February-June 2024.
Sample distribution (collected household questionnaire) over the time
| Month | Day range | Number | Percentage |
|---|---|---|---|
| February | 1 to 10 | 1 | 0.0 |
| 11 to 20 | 1294 | 4.3 | |
| 21 to 31 | 2690 | 9.0 | |
| March | 1 to 10 | 2896 | 9.7 |
| 11 to 20 | 4148 | 13.9 | |
| 21 to 31 | 2895 | 9.7 | |
| April | 1 to 10 | 4030 | 13.5 |
| 11 to 20 | 2859 | 9.6 | |
| 21 to 31 | 2931 | 9.8 | |
| May | 1 to 10 | 1991 | 6.7 |
| 11 to 20 | 1821 | 6.1 | |
| 21 to 31 | 2215 | 7.4 | |
| June | 1 to 10 | 7 | 0.0 |
| 11 to 20 | 3 | 0.0 |
18.3. Data collection
Since 2021 the mode of data collection has been changed to multichannel (CAWI, CATI, CAPI).
Since the 2017 survey the data collection has been externalised although all the implementation tools have been provided by the National Statistical Institute (INE). The data collection has been exhaustively supervised by INE.
Spain uses administrative data in the production of the income variables since the 2013 survey. The strategy is to use a mixed methodology taking mainly income data from the registers and also from questionnaires when the register information is insufficient.
Cross-sectional information year 2024
Mode of data collection (Cross-sectional 2024)
|
|
2-CAPI |
3-CATI |
4-CAWI |
5-Other |
|---|---|---|---|---|
| Mode of coll |
Mode of coll |
Mode of coll |
Mode of coll |
|
| ES |
6.5 |
44.2 |
49.2 |
0.0 |
Description of collecting income variables
|
See Annex 1: National questionnaires.
See Annex 4: Data collection.
18.4. Data validation
The two sources of ES-SILC are initially validated according to basic statistics:
- Data from questionnaires: Once the fieldwork is started, from the first collected households the basic statistics are calculated and compared with the time series.
- Data from administrative files: Once the administrative data are received, the basic statistics of the administrative variables are calculated, comparing with the time series.
See also item 13.3.4.
18.5. Data compilation
Data editing. The final target variables are constructed:
- Non income variables. Normally they are constructed directly from the updated and checked information of the questionnaires (see data validation).
- Income variables: The construction integrates the information of the questionnaires and the register data. After the combination of the information, the income data are imputed if the value is still unknown.
18.5.1. Imputation - rate
See information provided in the point 18.5.3. and 13.3.4.
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.
Information on the quality of the rolling module is available in Annex 9- Rolling module.
Annex 1 – National Questionnaire
Annex 2 – Item non-response rate
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, every 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 EU regulation 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.
5 March 2025
Statistical concepts and definitions for EU-SILC are specified in EU Regulation (EU) 2019/1700,EUregulation 2019/2181, and EU regulation 2019/2242. Additional information is available in the EUstatistics 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 EU regulation 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 EU regulation 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. A private household means a person living alone or a group of persons who live together, providing oneself or themselves with the essentials of living.
The entire Spanish territory.
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 |
|---|---|---|---|
| Taxes received/paid during the income reference period are considered. In the case of tax adjustments, these taxes usually refer to income received during the income reference period. (No differences between national and EU-SILC concept) |
The income reference period is the previous calendar year. (No differences between national and EU-SILC concept) |
We considered the tax received/paid during the income reference period. In the case of the taxes paid on ownership of the household main dwelling (IBI) the reference period is the last 12 months. |
From 31 December of the year prior to the survey to the time of data collection (February-June). The lag thus ranged from 1 to 5 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 the methodological guidelines and description of EU-SILC target variables available on CIRCABC.
Data editing. The final target variables are constructed:
- Non income variables. Normally they are constructed directly from the updated and checked information of the questionnaires (see data validation).
- Income variables: The construction integrates the information of the questionnaires and the register data. After the combination of the information, the income data are imputed if the value is still unknown.
The administrative sources used are:
- Geographical frame of addresses ("Marco de direcciones georrefenciadas") for the sampling frame.
- Register of Public Social Benefits (RPSP). Centralized database with benefits paid by different public bodies.
- Personal Income Tax (Form 100 and file of imputations). Declarations of Income Tax of Individuals and families in the Form 100. Also information for those who have not filled the Tax Returns (imputations)
- Tax withholding at source system (Form 190). Annual summary of withholdings of Income Tax of Individuals.
List of final variables with use of administrative files
- Old age benefits (RPSP)
- Survivors' benefits (RPSP)
- Disability benefits (RPSP)
- Capital income (Form 100 and file of imputations)
- Property income (Form 100)
- Result of the income tax return (Form 100)
- Income from self-employment (Form 100)
- Cash employee income (Form 190)
- Non-cash employee income (Form 190)
- Sickness benefits (Form 190)
- Private pension plans (Form 190)
- Unemployment benefits (Form 190)
Annual
The link of the calendar of publications in INE-Spain website.
The processing of this statistic in all its phases is the same for the whole territory. In this way, the results are fully comparable for any geographical breakdown.
In the construction of the variables relating to household income, the tax administrative files in Alava have not been used. In order to ensure geographical comparability, corrective factors have been applied in this province.
See Annex 8 – Breaks in series
