1.1. Contact organisation
Instituto Nacional de Estatística, I.P. (Portugal)
1.2. Contact organisation unit
Department of Demographic and Social Statistics/Living Conditions Statistics Unit
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
Av António José de Almeida, 5, 1000-043 Lisboa, Portugal
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
31 May 2025
2.2. Metadata last posted
31 May 2025
2.3. Metadata last update
31 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.
The variables regarding monetary income (labour, capital, transfers between households and social benefits), deducted from the taxes collected and social contributions when applicable, as well as the items of material and social deprivation and the low work intensity, allow for the calculations of the at-risk-of-poverty or social exclusion indicators, which are the main key indicators of the survey. Social exclusion and housing condition information are collected mainly at household level while labour, education and health information are obtained for persons aged 16 and over. The core of the survey, income at a very detailed component level, is mainly collected at personal level.
2 modules have been applied in 2024: a 3-year regular module about “Children's health and material deprivation” and a 6-year regular module about “Access to services".
The Portuguese implementation of EU-SILC corresponds to ICOR (Inquérito às Condições de Vida e Rendimento, or Survey on Living Conditions and Income in English), which is an annual survey on a representative sample of private households living in Portugal. It addresses the evaluation of several sources of households' income, its socio-economic characteristics and a set of items related to living conditions, such as health status and material, social and housing deprivation. It allows, inter alia, the estimation of official statistical results based on the income distribution, namely the at-risk-of poverty and inequality indicators, as well as Healthy life years and Energy poverty indicators. The survey, which is part of the harmonised program of European statistics on income and living conditions (EU-SILC), is being implemented annually by Statistics Portugal since the first EU-SILC edition in 2004, compliant to Regulation (EC) No 1177/2003 of the European Parliament and of the Council, of 16 June 2003, until 2020, and to Regulation (EU) 2019/1700 of the Parliament and of the Council, of 10 October 2019, as from 2021. As from 2018 the survey uses a NUTS 2 representative sample.
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 RAMON, Eurostat's metadata server.
The classification and modalities used for each variable follow the metodological guidelines 2024 operation.
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.
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 are 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 |
|---|---|---|
| Private households and all persons composing these households having their usual residence in the national territory in the reference period. The national definition is in accordance to the one established by Regulation 2019/2242, article 4, item 1. |
A group of people living at the same dwelling, with either "de jure" or "de facto" family relationships, occupying the all or part of a dwelling; or a single person that fully or partly occupies a dwelling. The national definition is in accordance to the one established by Regulation 2019/2242, article 4, item 2. |
Anyone living in the household who participates in the common budget and has no other address, even if they are away for less than 12 months. As from 2024, the national definition is strictly in accordance to the one established by Regulation 2019/2242, article 2, item 11 regarding the concept of usual residence. |
3.6.2. Population not covered by the data collection
The sub-populations that are not covered by the data collection includes: those who moved out of the country’s territory; or those with no usual residence; or those living in institutions or who have moved to an institution compared to the previous year.
3.7. Reference area
The reference area is composed by the whole national territory, including the mainland and the two Autonomous Regions (Região Autónoma dos Açores and Região Autónoma da Madeira).
3.8. Coverage - Time
The survey is implemented annually in Portugal since the beginning of the EU-SILC program in 2004.
In accordance with SILC guidelines, the annual survey carried out in a specific year collects data about the household's income distribution in the previous year. Otherwise, variables refer mostly to the date of interview.
The use of a 4-year rotational pattern allows for the estimation of longitudinal indicators.
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 (Euro). For more information, see methodological guidelines and description of EU-SILC target variables.
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 |
|---|---|---|---|
| Year 2023 |
Year 2023 |
Year 2023 |
Between 4 to 7 months depending on the household's date of interview. |
6.1. Institutional Mandate - legal acts and other agreements
Regulation (EU) 2019/1700 was publish in OJ on 10 October 2019, establishing a common framework for European statistics relating to persons and households, based on data at individual level collected from samples (IESS). The Annex to the Commission implementing regulation (EU) 2019/2180 of 16 December 2019 specifies the detailed arrangements and content for the quality reports pursuant to Regulation (EU) 2019/1700 of the European Parliament and of the Council and Regulation (EU) 2019/2242.
6.2. Institutional Mandate - data sharing
Confidential microdata are not disclosed by Eurostat. Access to confidential microdata for scientific purposes may be granted on the basis of Commission Regulation 557/2013 and Regulation 223/2009 of the European Parliament and the Council on European statistics.
7.1. Confidentiality - policy
Portuguese Law no. 22/2008, of 13th May (Law of the National Statistical System), article 6 on statistical confidentiality.
Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation).
Law no. 67/1998, of 26th October (Law on the protection of personal data).
Please refer to Statistics Portugal Policy of Statistical Confidentiality and Policy on Privacy and Data Protection, both publicly available.
7.2. Confidentiality - data treatment
Confidentiality is ensured by the suppression of the personal identification variables used in sampling selection and fieldwork. Top/bottom coding and grouping of several variables are also used to eliminate the risk of intrusion concerning anonymised data.
8.1. Release calendar
Release calendar is publicly available at Statistics Portugal website Statistics Portugal Release calendar
By the end of May 2025, 3 press releases related to the 2024 survey were available at Statistics Portugal website:
- Dec 3, 2024, “The at-risk-of-poverty rate decreased to 16.6% in 2023 - 2024”
- Feb 20, 2025, ” More than half of households with healthcare and home support needs did not have access to paid professional services - 2024”
- Mar 6, 2025, "The proportion of children who were unable to take a week's holiday away from home, paid for by the household, has increased - 2024"
Please refer also to the Release calendar - Eurostat (europa.eu) and publicly available on the Eurostat’s website.
8.2. Release calendar access
Release calendar is publicly available at Statistics Portugal website Statistics Portugal Release calendar
Please refer to the Release calendar - Eurostat (europa.eu) publicly available on the Eurostat’s website.
8.3. Release policy - user access
Please refer to Statistics Portugal Dissemination Policy which is publicly available and a detailed insight about Statistics Portugal means and services to access information.
In line with the European Union 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.
Annual
10.1. Dissemination format - News release
By the end of May 2025, 3 press releases related to the 2024 survey were published and are available at Statistics Portugal website:
- Dec 3, 2024, “The at-risk-of-poverty rate decreased to 16.6% in 2023 - 2024”
- Feb 20, 2025, ” More than half of households with healthcare and home support needs did not have access to paid professional services - 2024”
- Mar 6, 2025, "The proportion of children who were unable to take a week's holiday away from home, paid for by the household, has increased - 2024"
- Health data collected in the 2024 survey have been included in the press release published on Apr 4, 2025 by occasion of the World Health Day “World Health Day - 7 April”
10.2. Dissemination format - Publications
The survey information regarding health is also regularly included in an annual publication about "Health Statistics".
The 2024 survey information was included in the following Statistical Portugal regular publication: Health Statistics - 2023
10.3. Dissemination format - online database
Data tables are available at INE website.
Income and living conditions indicators are available at Statistics Portugal website.
10.3.1. Data tables - consultations
Not available.
10.4. Dissemination format - microdata access
According to the national statistical system law, article no. 6, individual statistical data on individuals and enterprises shall only be supplied for scientific purposes, in anonymised form to accredited researchers. To know the access conditions, visit the following page.
10.5. Dissemination format - other
Not applicable.
10.5.1. Metadata - consultations
Not available.
10.6. Documentation on methodology
Standard metadata in Portuguese is available at Statistics Portugal website (Portuguese only).
10.6.1. Metadata completeness - rate
100%
10.7. Quality management - documentation
Not applicable.
11.1. Quality assurance
Statistics Portugal follows a set of Ethics and policies which are publicly available in Statistics Portugal website , namely: Code of Ethics and Conduct, European Statistics Code of Practice, Quality Chart, Information Security Management System (Certification), Information Security Policy, Policy of Statistical Confidentiality, Policy on Privacy and Data Protection, Use of cookies, Dissemination Policy, Revisions Policy, Risk Management Plan for Corruption and Related Offenses.
Every survey carried out by Statistics Portugal follows those policies. The Quality Chart in particular formalizes Statistics Portugal’s public commitment to provide society with quality and reliable official statistics produced and disseminated bearing in mind the public service it provides, more so in relation to respondents, users of statistical information and to the public in general. An internal protocol is mandatory for the implementation of any survey, including the Survey on Living Conditions and Income, ensuring structuration, public transparency regarding methodology and calendar, internal communication, consistency and coherence, comparison with other statistics. It establishes that the action starts with planning, involving both the Department of Demographic and Social Statistics and the other departments collaborating in the action: the Department of Data Collection and Management and the Department of Methodology and Information Systems, and Planning, Control and Quality Unit. All tasks are scheduled in a specific electronic program, allowing for monitoring of the progress of action and any deviations from scheduled dates. The questionnaire and metadata must be approved and registered within the National Statistical System. This procedure is supervised by a coordination team, implying the consultation of the questionnaire and metadata document by all departments, where opinions and recommendations are collected and adopted if relevant. The interviewers are submitted to a training action before the beginning of data collection, backed up with support documentation, information about the necessary procedures to be taken and rules about what should be considered in each question, always following Eurostat recommendations. During the data collection, the interviewers’ work and the response rate are periodically supervised, the latest according to previously defined goals. Before sending the database to the Department of Methodology and Information Systems, for weighting and standard errors computation, the data is submitted to validation procedures both by the Department of Data Collection and Management and the Department of Demographic and Social Statistics, where therefore, confirmations with teamwork, (re)codifications and corrections can be made to the collected data.
11.2. Quality management - assessment
In 2024, data was collected through computer-assisted face-to-face interviews (CAPI, or Computer Assisted Personal Interviewing) and telephone interviews (CATI, or Computer Assisted Telephone Interviewing) between April and July. As in the previous year, administrative data of the Personal Income Tax (IRS – Model 3, Annex A) related to employees’ income have been used and, for the first time, those relating to old-age pensions in the contributory system were also used, in order to improve the consistency and quality of information before deduction of taxes and social contributions. However, the integration of the data in Annex A of Model 3 in the calculation of old-age pensions has an impact on the series relating to the monetary values of these pensions in a downward direction, compared to the survey data, resulting in a break in series.
From 2024 onwards, in order to apply the 2024 version of the Nomenclature of Territorial Units for Statistical Purposes (NUTS-2024), the sample was resized and a gradual increase plan was defined by updating the size of the new rotations over four years, from 2024 to 2027. By construction, in 2024, only 1/4 of the gradual resizing was ensured.
The questionnaire includes questions about the household and also about the personal characteristics of each member, in particular about the income of all members aged 16 years or older. In 2024, the survey addressed 19,815 families, of which 15,777 provided a complete response (collecting data on 37,524 people; 33,128 aged 16 and over). The overall household response rate decreased from 81% in 2023 to 80% in 2024.
The sampling design applied as of 2024 has been revised/increased in the context of the new NUTS-2024 classification, which solved the precision issue for AROPE in 2024, both for Portugal and by NUTS 2: for Portugal, it stood at 19,7% with a standard error of o,55 pp, lower than the 0.59 pp resulting from the IESS regulation (Regulation EU 2019/1700), Annex II, while all regional NUTS 2 values are also smaller than the criteria maximum reference standard error.
| Country |
Year |
Region |
AROPE (%) |
SE (pp) |
Max SE |
|---|---|---|---|---|---|
| PT |
2024 |
TOTAL |
19.7 |
0.55 |
0.59 |
| PT |
2024 |
PT11 |
21.0 |
1.10 |
1.50 |
| PT |
2024 |
PT19 |
18.9 |
1.45 |
1.73 |
| PT |
2024 |
PT1D |
19.1 |
1.54 |
2.06 |
| PT |
2024 |
PT1A |
16.5 |
1.17 |
1.55 |
| PT |
2024 |
PT1B |
21.8 |
1.97 |
2.18 |
| PT |
2024 |
PT1C |
18.7 |
1.36 |
2.35 |
| PT |
2024 |
PT15 |
18.7 |
1.55 |
2.36 |
| PT |
2024 |
PT20 |
28.4 |
2.00 |
3.34 |
| PT |
2024 |
PT30 |
22,9 |
1.30 |
3.05 |
For the indicator “Persistent at-risk-of-poverty”, the estimate is 7,1% in 2024 with a standard error of 0.9, above the national precision requirement, which can be explained by the fact that it was based on a sub-sample selected prior to the sample resizing.
As this indicator is based on a full 4-year rotation, it will not benefit from the gradual sample increase started in 2024 before 2027, so improving its quality in the two intermediate years (2025 and 2026) will be promoted by an action plan to increase the number of panel responses and eventually the reinforcement in the use of administartive data.
12.1. Relevance - User Needs
The main users of EU-SILC statistical data are policy makers, research institutes, media, and students.
The main users of the Portuguese Survey on Living Conditions and Income are the following national entities: Bank of Portugal, Regional Directorate of Statistics of Madeira, Azores Regional Statistics Service, Strategy and Planning Office of the Ministry of Labour, Solidarity and Social Security; Pordata; Business enterprises; Social Communication; Researchers and the general public, as well as Eurostat, OECD and European Anti Poverty Network.
New needs are mainly identified in the context of the Working Group on Living Conditions, the Statistical Council and considering the specific requests received by the Users Support Service, including Compliments, Suggestions and Complaints.
The Statistical Council, established by Law No 22/2008 of 13 May, is the entity of the State which guides and coordinates the National Statistical System.
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 User Satisfaction Survey.
Statistics Portugal users satisfaction is regularly assessed through the Satisfaction Survey for the Services Provided and also taking into account Compliments, Suggestions and Complaints.
12.3. Completeness
All 2024 mandatory variables have been collected or derived (in the case of PY030G).
None of the 2024 optional variables have been collected.
Two national questions about anxiety have been included, based on the GAD-2, Generalized Anxiety Disorder 2-item model.
12.3.1. 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 persistent at-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. […]
- 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; DB050 was used 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; DB050 was used 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; DB030 was used for cluster specification and no strata.
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, both sampling errors and non-sampling errors need to be taken into account. Sampling error refers to the variability that occurs at random due to the use of a sample rather than a census, while non-sampling errors occur at all phases of the data collection and production process.
Standard error estimates are obtained using the Jackknife resampling method, which makes it possible to calculate variances for totals (linear estimators) and for quotients of totals and differences of quotients (non-linear estimators). This method is recommended when a complex sampling design and calibrated estimators are involved, as is the case for the Portuguese EU-SILC survey implementation, known as ICOR.
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.
The sampling frame (National Dwellings Register) was created based on data from the 2011 Census. It is regularly updated based on data collected by Statistics Portugal in the context of SIOU (urban operations indicator system) and on external administrative data: IMI (Property Tax), ADENE (Energetic Certification), Social Security, BDIC/CCIC (Civil Identification Database) and IRS (Personal Income Tax).
13.3.1.1. Over-coverage - rate
Coverage error
| Main problema | Population (sub-population) | Size of error | Comments |
|---|---|---|---|
| Over-coverage | The ratio of selected units not corresponding to private households (DB120=21,23) | 0,1% | |
| Under-coverage | Not known | ||
| Misclassification | Not known |
13.3.1.2. Common units - proportion
Of a total of 33,128 individuals aged 16 or over registered in the 2024 survey, 15,728 earned employees’ income in 2023. Among these, the amount of employees’ income in 2023 for 12,120 employees was based on administrative fiscal data (77.1% of employees).
13.3.2. Measurement error
Measurement error for cross-sectional data
| Cross-sectional data |
|||
|---|---|---|---|
| Source of measurement errors |
Building process of questionnaire |
Interview training |
Quality control |
| The complexity of income components collection, leading to misinterpretation and confusion between components – such as the one associated with old-age and survivors’ benefits –, rough self-estimates by interviewed persons and missing or not credible values. Distinguishing between gross and net income concepts is not easily perceived by interviewed persons and a special case of income – incomes that are not clearly classified in self-employment category or in employees’ category – produces considerable longitudinal instability. An increased difficulty when collecting self-employment income components in comparison to employee’s components. |
The questionnaire is built using the EU annual guidelines, as well as previous national SILC questionnaires and, in specific situations, such as the ones regarding employmente or health variables, using other surveys as a reference (LFS or EHIS). |
All members of the data collection team and the interviewers follow a specific training program previously to fieldwork. |
Measurement errors are prevented by the use of a specific software that includes online routing and validations and specific training sessions, which are implemented as follows:
Along data collection, the supervision team controlled the quality of data collected, namely the number of missing values and unusual answers/situations, mainly by listening the interviews. |
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 refers to the absence of information of the whole units (households and/or persons) selected into the sample, according to Annex VI of 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 addresses/persons successfully contacted (including phone, mail if applicable) / Number of valid addresses/persons selected (including phone, mail if applicable)
and Rh is the proportion of complete household interviews accepted for the database:
Rh = Number of household interviews completed and accepted / 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 households accepted for the database:
Rp = Number of personal interviews completed / Number of eligible individuals in households whose interviews were completed and accepted
Overall individual non-response rates (*NRp) is computed as follows:
*NRp = (1 - (Ra * Rh * Rp)) * 100
For Member States where a sample of persons rather than households (addresses, phones, mails, etc.) was selected, the individual non-response rates will be calculated for the selected respondent.
- Item non-response refers to the situation where a sample unit has been successfully enumerated, but not all the required information has been obtained.
13.3.3.1. 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 |
| 100% |
100% |
100% |
82.8% |
84.6% |
78.7% |
100% |
100% |
100% |
17.2% |
15.4% |
21.3% |
0% |
0% |
0% |
17.2% |
15.4% |
21.3% |
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.
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 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
Please refer to "Annex 2 – Item non-response".
13.3.4. Processing error
Description of data entry, coding controls and the editing system
| Data entry and coding |
Editing controls |
|---|---|
| Data entry is ensured by the interviewers using the data collection computer application, which supported by automatic validations. Coding, when applicable, is ensured by the Data Collection team. |
The following 3 steps of validation are subsequently applied to data collected:
|
13.3.5. Model assumption error
Not applicable.
14.1. Timeliness
The national results have been published on Dec 03, 2024, 4 months after data collection was finished.
14.1.1. Time lag - first result
Considering the income reference period, 11 months.
14.1.2. Time lag - final result
Considering the income reference period, one year and 5 months.
14.2. Punctuality
No delay, as the dissemination calendar occurred as planned on Dec 03, 2024.
14.2.1. Punctuality - delivery and publication
No delay, as the dissemination calendar occurred as planned on Dec 03, 2024.
15.1. Comparability - geographical
From 1 January 2024, the release of statistical results uses the 2024 version of the Nomenclature of Territorial Units for Statistical Purposes (NUTS-2024).
In 2023, considering the national poverty threshold and the new classification, Grande Lisboa wasthe region in which the risk of poverty was lowest (12.9%). Also on the mainland, Centro, Oeste e Vale do Tejo, Alentejo and Algarve regions had poverty risks below the national average, while in the Norte and in Península de Setúbal the incidence of poverty reached, respectively, 18.0% and 18.7% of the population.
The risk of poverty was, as in previous years, higher in the Região Autónoma dos Açores, with 24.2%, and in Região Autónoma da Madeira, with 19.1%, the latter standing out for the largest reduction in the poverty rate between 2022 and 2023 when considering the national poverty threshold.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not applicable.
15.2. Comparability - over time
The results of the Survey on Living Conditions and Income, carried out in 2024 on incomes from the previous year, indicate that 16.6% of people were at-risk-of-poverty in 2023, 0.4 percentage points (pp) less than in 2022.
15.2.1. Length of comparable time series
21 years.
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.
|
Income
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.
The information on the 2021 income distribution indicators obtained by the national HBS have been made available on Jun 19, 2024.
15.3.1. Coherence - sub annual and annual statistics
Not applicable.
15.3.2. Coherence - National Accounts
Coherence indicators with National Accounts are presenetd in Annex 7.
15.4. Coherence - internal
Nothing to report.
Mean (average) interview duration per household = 16,5 minutes.
Mean (average) interview duration per person = 11,4 minutes.
17.1. Data revision - policy
Please refer to the Statistics Portugal Revisions Policy page
17.2. Data revision - practice
Not applicable.
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 Portuguese SILC data is mostly obtained by CAPI or CATI interviewing, except for the use of administrative data of the Personal Income Tax in the case of employees’ income data, which has been used since the 2022 survey. In 2024, for the first time, those relating to old-age pensions in the contributory system were also used.
18.1.1. Sampling Design
Type of sampling design (stratified, multi-stage, clustered)
Households are selected by stratified two-stage sampling, from a sampling frame of dwellings of usual residence.
The longitudinal nature of the sample, as well as the limitation of the statistical burden on respondents, are ensured by setting up an annual rotational scheme involving four independent sub-samples, each one being replaced every year. Hence, each household is interviewed four times at most, and thus the overlapping of ¾ of respondents vis-à-vis the previous year is guaranteed.
Up to 2012 the sample was selected exclusively from the Master Sample (MS). However, from 2013 onwards a gradual transition of the latter to the new sampling frame was initiated, based on the National Dwellings Register (NDR). This transition took place over four years: between 2013 and 2015 dwellings selected from both sampling frames co-existed in the sample. As from 2016, the survey annual sample, i.e. all four sub-samples is selected from the sampling frame based on the NDR.
Sampling selection follows a NUTS 2 stratified multistage sampling design, with primary sampling units (INSPIRE grid cells of 1km2) being selected with probability proportional to the number of dwellings of usual residence, and secondary sampling units (dwellings) selected systematically in each primary sampling unit. All households and individuals residing in the selected dwellings are interviewed.
Sample selection schemes
The sample selection is different for the MS and for the new sampling frame:
- In the MS, the 542 areas were drawn in each stratum systematically with a sampling interval given as the ratio between the number of areas defined to the EU-SILC and the number of areas in the MS. The dwellings were selected in block in order to reduce the travel costs. In each area the dwellings are arranged according to their census enumeration, ensuring that the units are geographically closer. The first dwelling of the block was selected at random and we assume that all dwellings have equal probability of being selected.
- In the new sampling frame the 624 PSU were drawn in each stratum with PPS (number of private dwellings of usual residence). The dwellings were selected systematically in each PSU.
Sample distribution over time
A rotational design comprising four panels is used (the design recommended by Eurostat). Each of the panels is kept in the sample for four consecutive years before being replaced by another panel of the same size. Exception is made for the first three years where one panel is surveyed only once, one panel two times and one panel three times. This design ensures an overlap of 75% between two consecutive years, 50% between three consecutive years and finally 25% between four years.
At the first year (2004) the total sample size was 6504 dwellings, a value calculated to achieve a national representativeness for the poverty rate. Three dwellings per panel were allocated to each of the 542 areas selected for the EU-SILC.
From the second year onwards, the sample size is a random variable because of the tracing rules (Commission Regulation (EC) No 1982/2003). The sample size comprises the three-fourths of the sample that are to be follow-up, plus one-fourth of new dwellings entering the sample (in this case 3 dwellings are drawn in each area).
Due to losses in the sample, in 2009 the sample size was revised in order to ensure, in 2012, the minimum effective sample size (4,500 households) required by the regulation. Thus, from 2009 till 2012 a top-up sample had been added with the new panel.
From 2013 onwards the transition between sampling frames implied adjustments in the number of PSU (from 542 to 624) and in the sample size (each new rotation has 2,409 dwellings).
In 2015, a new gradual increase started to be implemented in order to achive a full NUTS 2 representative sample in 2018. For its determination, we considered: a relative sampling error of 10.4% (corresponding to an absolute error of 2.5 pp); a 24% benchmark for the at-risk-of-poverty and social exclusion rate; a sample design effect of 1.6; a sample correction rate equal to the average response rate obtained in the ICOR by region for the period 2008-2012 (ranging from 34% to 93%).
In 2020, taking into account the impact of the measures to mitigate the COVID-19 pandemic, namely the change from the CAPI to CATI collection method, it was decided to reinforce the dimension of the new sub-sample, which included 8,397 dwellings instead of 4,830.
The revision of the sampling plan in 2021 to consider the precision criteria established by Annex II of the IESS regulation, establishes a sample size of 6,096 dwellings for the new sub-samples as from 2021, with a view to obtaining a total sample of 24,384 dwellings as from 2024.
18.1.2. Sampling unit
For the new sub-samples selected up to 2012 the primary sampling units are the areas of the Master Sample (see 3.1.1). Each area comprised one or more contiguous census enumeration areas in order to achieve a minimum of 240 dwellings as usual residence per area.
As from 2013, the new sub-samples selected are from the new sampling frame based in National Dwellings Register (see 3.1.1). Each PSU compromises one or more grid INSPIRE cells.
In both situations, the secondary sampling units (and also the ultimate sampling units) are the dwellings, each one identified by an address and the name of the household representative.
18.1.3. Sampling frame
A description of the sampling frame (reference period, updating actions, quality review actions)
- Before 2013 the sampling frame was the Master Sample 2001 (MS). From 2013 to 2015 two sampling frames coexisted: the MS and the new sampling frame drawn from the National Dwellings Register (NDR). As from 2016 all units are selected from the NDR.
- The MS was designed and selected using the information of the 2001 Census of Population and Housing (Census 2001). It was constituted by private dwellings, and it excluded collective households and institutions since they represented 1% of the total population residing in Portugal. The MS had almost 750 thousand private dwellings (535 thousand of which were of usual residence, the remaining being vacant, seasonal or for secondary use).
- The sampling frame used for the 2021 data collection was selected from the NDR which in turn uses information collected in the 2011 Census. It is also constituted by private dwellings of usual residence and excludes collective households and institutions. Its size is approximately 1,4 million dwellings of usual residence.
Both sampling frames are stratified one-stage cluster samples. In each stratum (NUTS 3) the clusters were selected systematically with probability proportional to size (number of private dwellings of usual residence). However the clusters were constructed differently:
- n MS they were geographical areas constituted by one or more contiguous statistical sections (census enumeration areas).
- In the new sampling frame the clusters were constituted by one or more contiguous grid INSPIRE[1]cells with 1 Km2 of area.
There is no information about coverage problems in both sampling frames.
[1] Oficial GRID developped by EUROSTAT for the European territory - Grid_ETRS89_LAEA_1K
18.2. Frequency of data collection
Annually.
In 2024, data collection was carried out between Apr 11, 2023 and Jul 27, 2023.
18.3. Data collection
| Mode of data collection
Description of collecting income variables
|
18.4. Data validation
A specific software used in the data collection includes a set filters and question cycles (if applicable) that ensure the right path of questioning. There are also basic validation rules included in the software that allow to detect errors of domain and coherence, and prevent the response to a question that is inconsistent to a previous one. In addition, the data collection team ensure an a priori validation regarding the coherence of individual data on time, focusing on sex, age, education, activity status and unplausible income values.
The Subject matter team ensures the final validations, mainly focused on the consistency of time changes, on income and other monetary variables, and in comparison to activity characteristics such as occupation, economic sector, working time (full or part), etc. When data is classified as probably not valid the data collection team is asked to relisten to the interview or to recontact the household. Income estimates, including change in time, are validated by comparison to National Accounts data, other oficial statistics on wages and salaries, and administrative based data from the Social Security.
18.5. Data compilation
Statistical outcomes correspond to weighted totals and proportions based on these totals, as well as means, medians or other income percentiles. All estimates are obtained by the application of an adequate weight that ensure total non-response adjustment and calibration to the households and individuals know distributions using external sources.
18.5.1. Imputation - rate
The use of administrative data of the Personal Income Tax in the case of employees’ income data has been used for the first time in the 2022 survey. In 2024, for the first time, those relating to old-age pensions in the contributory system were also used.
18.5.2. Calculation of weighting factors and weight adjustments
Further details are provided in Annex 5 - Weighting procedure.
18.5.3. Estimation and imputation
Imputation procedure
The net series of income data is obtained by the application of a specific gross-to-net micro simulation model[1]. This model was presented and is available on the Proceedings of the EU-SILC Conference, Helsinki, 6-8 November 2006, on Comparative EU Statistics on Income and Living Conditions: Issues and Challenges (Eurostat Methodologies and Working papers), pages 157-172, “Income in EU-SILC – Net/Gross Conversion Techniques for Building and Using EU-SILC Databases”.
Because HY025 is not to be used as from 2013, a procedure for imputation of partial non-response was developed. For individuals not responding in t (t being the current survey collection year) but responding in t-1 (previous year), values of t were made equal to values of t-1. For individuals not responding either in t and in t-1, a donor with similar characteristics in terms of sex, age group and household size was randomly choosen.
Company cars
For each person referring the personal use of a company car, the survey collects data on its Manufacturer, Model, Engine displacement, Engine power, Number plate and the number of months the company car had been used by the respondent during the relevant income reference period. For company cars younger than 10 years of number plate, data on the car characteristics is used to get information about the updated commercial value using a used car valuation database. The remaining years of useful life are calculated by the difference between the year of number plate plus 10 and the income reference year. The remaining value of the car by the end of the reference period corresponds to the quotient between the updated commercial value and the remaining years of useful life. The remaining value of the car is divided by 12 in order to get the monthly remaining value of the car. By convention, all cars with a number plate older than 10 years are allocated a null commercial value. Based on this information, the annual benefit of the respondent is estimated by multiplying the monthly remaining value of the car per the number of months the company car had been used by the person during the relevant income reference period.
[1] Carlos Farinha Rodrigues, PhD, ISEG/ULisboa and consultant of Statistics Portugal
18.6. Adjustment
Not applicable.
18.6.1. Seasonal adjustment
Not applicable.
No comments.
Annex 3 - Sampling errors
Annex 1 - National questionnaire
Annex 1 - National questionnaire
Annex 1 - National questionnaire
Annex 1 - National questionnaire
Annex 2 – Item non-response
Annex 4 - Data collection
Annex 7 - Coherence
Annex 8 - Break in series
Annex 9 - Rolling modules
Annex A - Content tables
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.
The variables regarding monetary income (labour, capital, transfers between households and social benefits), deducted from the taxes collected and social contributions when applicable, as well as the items of material and social deprivation and the low work intensity, allow for the calculations of the at-risk-of-poverty or social exclusion indicators, which are the main key indicators of the survey. Social exclusion and housing condition information are collected mainly at household level while labour, education and health information are obtained for persons aged 16 and over. The core of the survey, income at a very detailed component level, is mainly collected at personal level.
2 modules have been applied in 2024: a 3-year regular module about “Children's health and material deprivation” and a 6-year regular module about “Access to services".
The Portuguese implementation of EU-SILC corresponds to ICOR (Inquérito às Condições de Vida e Rendimento, or Survey on Living Conditions and Income in English), which is an annual survey on a representative sample of private households living in Portugal. It addresses the evaluation of several sources of households' income, its socio-economic characteristics and a set of items related to living conditions, such as health status and material, social and housing deprivation. It allows, inter alia, the estimation of official statistical results based on the income distribution, namely the at-risk-of poverty and inequality indicators, as well as Healthy life years and Energy poverty indicators. The survey, which is part of the harmonised program of European statistics on income and living conditions (EU-SILC), is being implemented annually by Statistics Portugal since the first EU-SILC edition in 2004, compliant to Regulation (EC) No 1177/2003 of the European Parliament and of the Council, of 16 June 2003, until 2020, and to Regulation (EU) 2019/1700 of the Parliament and of the Council, of 10 October 2019, as from 2021. As from 2018 the survey uses a NUTS 2 representative sample.
31 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.
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 are 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.
The reference area is composed by the whole national territory, including the mainland and the two Autonomous Regions (Região Autónoma dos Açores and Região Autónoma da Madeira).
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 |
|---|---|---|---|
| Year 2023 |
Year 2023 |
Year 2023 |
Between 4 to 7 months depending on the household's date of interview. |
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 persistent at-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 (Euro). For more information, see methodological guidelines and description of EU-SILC target variables.
Statistical outcomes correspond to weighted totals and proportions based on these totals, as well as means, medians or other income percentiles. All estimates are obtained by the application of an adequate weight that ensure total non-response adjustment and calibration to the households and individuals know distributions using external sources.
The Portuguese SILC data is mostly obtained by CAPI or CATI interviewing, except for the use of administrative data of the Personal Income Tax in the case of employees’ income data, which has been used since the 2022 survey. In 2024, for the first time, those relating to old-age pensions in the contributory system were also used.
Annual
The national results have been published on Dec 03, 2024, 4 months after data collection was finished.
From 1 January 2024, the release of statistical results uses the 2024 version of the Nomenclature of Territorial Units for Statistical Purposes (NUTS-2024).
In 2023, considering the national poverty threshold and the new classification, Grande Lisboa wasthe region in which the risk of poverty was lowest (12.9%). Also on the mainland, Centro, Oeste e Vale do Tejo, Alentejo and Algarve regions had poverty risks below the national average, while in the Norte and in Península de Setúbal the incidence of poverty reached, respectively, 18.0% and 18.7% of the population.
The risk of poverty was, as in previous years, higher in the Região Autónoma dos Açores, with 24.2%, and in Região Autónoma da Madeira, with 19.1%, the latter standing out for the largest reduction in the poverty rate between 2022 and 2023 when considering the national poverty threshold.
The results of the Survey on Living Conditions and Income, carried out in 2024 on incomes from the previous year, indicate that 16.6% of people were at-risk-of-poverty in 2023, 0.4 percentage points (pp) less than in 2022.
