1.1. Contact organisation
Statistics Norway
1.2. Contact organisation unit
Division for Social Statistics
1.3. Contact name
Mari Lande With
1.4. Contact person function
Project manager
1.5. Contact mail address
Statistisk sentralbyrå
Postboks 2633 St. Hanshaugen
0131 Oslo
Norway
1.6. Contact email address
mlw@ssb.no
1.7. Contact phone number
Confidential because of GDPR
1.8. Contact fax number
Confidential because of GDPR
2.1. Metadata last certified
23 May 2025
2.2. Metadata last posted
23 May 2025
2.3. Metadata last update
23 May 2025
3.1. Data description
The European Union Statistics on Income and Living Conditions (EU-SILC) is an instrument aiming at collecting timely and comparable cross-sectional and longitudinal multidimensional microdata on income, poverty, social exclusion and living conditions. This instrument is anchored in the European Statistical System (ESS). In addition, are collected module variables every three year, six year 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, income at very detailed component level, is mainly collected at personal level.
3.2. Classification system
- International Standard Classification of Education (ISCED'2011);
- International Standard Classification of Occupations (ISCO-08);
- Classification of Economic Activities (NACE Rev.2-2008);
- Common classification of territorial units for statistics (NUTS 2);
- SCL - Geographical code list;
- The recommendations made by the United Nations in the Canberra Group Handbook on Household Income Statistics should also be taken into account.
For more details on the classification used please, see EU Vocabularies, Eurostat's metadata server or CIRCABC.
3.3. Coverage - sector
Data refer to all private households and individuals living in the private households in the national territory at the time of data collection.
The EU-SILC survey is a key instrument for the European Semester and the European Pillar of Social Rights, providing information on income distribution, poverty and social exclusion, as well as various related living conditions and poverty EU policies, such as on child poverty, access to health care and other services, housing, over indebtedness and quality of life. It is also the main source of data for microsimulation purposes and flash estimates of income distribution and poverty rates.
Data refers 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 providing information required by the European Semester and the European Pillar of Social Rights, in particular for 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 EU Regulation (EU) 2019/1700, EU regulation 2019/2181, and EU regulation 2019/2242. Additional information is available in the EU statistics on income and living conditions (EU-SILC) methodology and in the methodological guidelines and description of EU-SILC target variables (see CIRCABC).
Further details are provided in items 5, 15.1.1.1, 15.2.2 and 18.3.
3.5. Statistical unit
Statistical units are private households and all persons living in these households who have usual residence in the Member State. Annex II of the Commission implementing regulation (EU) 2019/2242 defines specific statistical units per variable and specifies the, content of the quality reports on the organization of a sample survey in the income and living conditions domain pursuant to Regulation (EU) 2019/1700 of the European Parliament and of the Council.
Statistical units are private households and all persons living in these households who have usual residence in the Member State. Specific statistical units per variable are defined in Annex II of the Commission implementing regulation (EU) 2019/2242 specifying content of the quality reports on the organization of a sample survey in the income and living conditions domain pursuant to Regulation (EU) 2019/1700 of the European Parliament and of the Council.
3.6. Statistical population
The target population is private households and all persons composing these households having their usual residence in the Member State. Private household means a person living alone or a group of persons who live together, providing oneself or themselves with the essentials of living.
3.6.1. Reference population
Definitions of reference population, household and household membership
| Reference population |
Private household definition |
Household membership |
|---|---|---|
| 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 people who live together providing themselves with the essentials for living. |
Persons usually residing in common dwelling and/or contributing to / benefiting from household income |
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
Norway except Svalbard.
3.8. Coverage - Time
Annual data, reference year 2024. Income reference period is 2023. The survey has been conducted in Norway starting in 2003.
3.9. Base period
Not applicable.
The data involves several units of measure depending upon the variables. Income variables are transmitted to Eurostat in national currency. For more information, see methodological guidelines and description of EU-SILC target variables available on CIRCABC
Description of reference period used for incomes
| Period for taxes on income and social insurance contributions |
Income reference periods used |
Reference period for taxes on wealth |
Lag between the income ref period and current variables |
|---|---|---|---|
| 2023 |
2023 |
2023 |
The income reference period is the previous calendar year (2023) and the current variables refer to the fieldwork period (January-June 2024). The lag is at minimum 1 month and at maximum 6 months. |
6.1. Institutional Mandate - legal acts and other agreements
Regulation (EU) 2019/1700 was publish in OJ on 10 October 2019, establishing a common framework for European statistics relating to persons and households, based on data at individual level collected from samples (IESS). The Annex to the Commission implementing regulation (EU) 2019/2180 of 16 December 2019 specifies the detailed arrangements and content for the quality reports pursuant to Regulation (EU) 2019/1700 of the European Parliament and of the Council and Regulation (EU) 2019/2242.
6.2. Institutional Mandate - data sharing
Confidential microdata are not disclosed by Eurostat. Access to confidential microdata for scientific purposes may be granted on the basis of Commission Regulation 557/2013 and Regulation 223/2009 of the European Parliament and the Council on European statistics.
7.1. Confidentiality - policy
The statitistics act states that:
Section 7. Statistical confidentiality in dissemination of official statistics
- Official statistics shall be disseminated in such a manner that it is not possible to directly or indirectly identify a statistical unit and thus disclose individual data.
- The first subsection shall not apply when the exception follows from an obligation to produce statistics pursuant to the EEA Agreement.
- An exception may be made from the first subsection if the statistical unit is a public authority, and the interests of the public sector are protected. An exception may also be made from the first subsection if the statistical unit has granted consent or if the data are available to the public.
Section 9. Information security
- Public authorities that process data covered by the obligation of secrecy pursuant to section 8 shall implement technological and organisational measures in order to achieve an adequate level of security. This includes providing adequate access control, logging and subsequent controls.
- Data that allow direct identification must be processed and stored separately from other data, unless this is inconsistent with the purpose of the processing or it is clearly unnecessary.
7.2. Confidentiality - data treatment
Statistics Norway never publishes statistics where it is possible to reveal information about specific persons or households. Data is stored in a safe way and in accordance with the legal demands for data storage.
Statistics Norway only grants accsess to anonymised microdata to public authorities and researchers affiliated with approved research institutions. See: Statistics Norways website for information on access to microdata.
An EU micro data file (EU scientific use file Norway) is made available by Eurostat.
8.1. Release calendar
Statistics Norway publishes results from EU-SILC on housing statistics every three years during the fall of the data collection, and results on poverty related issues every year in the spring the year after data collection. See Statistics Norway's release calendar for more information.
8.2. Release calendar access
Please refer to the Release calendar - Eurostat (europa.eu) publicly available on the Eurostat’s website.
8.3. Release policy - user access
In line with the Community legal framework and the European Statistics Code of Practice, Eurostat disseminates European statistics on Eurostat's website (see section 10 - 'Accessibility and clarity'), respecting professional independence and in an objective, professional and transparent manner in which all users are treated equitably. The detailed arrangements are governed by the Eurostat protocol on impartial access to Eurostat data for users.
Additional information about microdata access is available in EU statistics on income and living conditions - Microdata - Eurostat.
Annual
10.1. Dissemination format - News release
Statistics Norway have disseminated two articles based on the 2024 survey. See:
Article on poverty and housing conditions based on NO-SILC 2024, accessible from Statistics Norways website (only available in Norwegian).
Article on housing costs based on NO-SILC 2024 and earlier years, accessible from Statistics Norways website (only available in Norwegian).
10.2. Dissemination format - Publications
- Statistics on poverty-related problems
- Statisics on housing conditions
- Report on economy and living conditions
10.3. Dissemination format - online database
- Data tables poverty related problems
- Data tables housing conditions
10.3.1. Data tables - consultations
These are the number of downloads:
| 2018 |
2019 |
2020 |
2021 |
2022 |
2023 |
2024 |
2025 |
|
|---|---|---|---|---|---|---|---|---|
| Housing conditions |
|
|
|
|
|
|
|
|
| Downloads |
1367 |
1410 |
1333 |
1356 |
1074 |
1573 |
3556 |
1665 |
| API Downloads |
0 |
145 |
46 |
431 |
529 |
243 |
266 |
159 |
| Poverty related problems |
|
|
|
|
|
|
|
|
| Downloads |
521 |
1681 |
3077 |
3643 |
4662 |
8220 |
6774 |
2490 |
| API Downloads |
0 |
19 |
8 |
141 |
581 |
1593 |
2908 |
1257 |
*Until 5 May 2025
10.4. Dissemination format - microdata access
National microdata is distributed by SIKT (The Norwegian Agency for Shared Services in Education and Research)
10.5. Dissemination format - other
Not available.
10.5.1. Metadata - consultations
Not available.
10.6. Documentation on methodology
Documentation for 2024: Levekårsundersøkelsen EU-SILC 2024. Documentation (in Norwegian)
10.6.1. Metadata completeness - rate
Not available.
10.7. Quality management - documentation
Documentation on quality management at Statistics Norway can be found on the website Quality in official statistics, information on ssb.no.
Most recent publications on quality management at Statistics Norway:
System for quality assurance of official statistics (Documents 2021/36).
Report on the quality of official statistics, 2022 (Plans and reports 2022/8).
11.1. Quality assurance
The Norwegian Statistics Act states that Statistics Norway shall prepare an annual report for the Ministry of Finance on the quality of official statistics. According to the letter of allocation, Statistics Norway shall oversee the monitoring of compliance with the requirements for quality in official statistics and establish a system for following this up. The first report on the quality of official statistics was submitted to the Ministry in 2022.
In the annual report for Statistics Norway there are also reports on the quality indicators timeliness, response rate and response burden, for the production in Statistics Norway, referring to the performance requirements set by the Ministry.
Statistics Norway involves users in the development and refinement of new and existing products and services, and maintains regular contact with users through formalised committees, user groups and user forums.
Quality evaluations of official statistics at an institutional level are carried out at regular intervals at all statistical authorities, including Statistics Norway. In the quality evaluations, a questionnaire-based survey for self-assessment is combined with interviews among all producers of official statistics. The quality evaluation is based on the quality requirements in the Statistics Act and the quality principles in the European Statistics Code of Practice. Statistics Norway and the other national authorities has set up action plans to follow up on the recommendations from the quality evaluation, these actions will be followed up in the annual reports on quality in official statistics.
Quality reviews are systematic assessments of statistics or statistical domains, where emphasis is placed on the production process, output, and the user perspective. The review starts with a self-assessment based on the Statistical Act and quality principles in the European Statistics Code of Practice. The production process is mapped according to the Generic Statistical Business Process Model, GSBPM. The user perspective is covered with a focus group with main users, and reports on the use of the website.
Eurostat’s peer reviews are well known in the Norwegian statistical system. The last peer review of Norway was in 2021. In the report, the peer review team considers that the Norwegian statistical system overall demonstrates a strong commitment to the European Statistics Code of Practice. The peer review team presented recommendations that could allow Statistics Norway and the other national authorities to improve beyond compliance with the European Statistics Code of Practice. Statistics Norway and the other national authorities has set up action plans to follow up on these recommendations, and has started activities to fulfil the recommendations, while waiting for the action plan to be accepted by Eurostat.
Statistics Norway uses data from administrative information systems as a source for official statistics. Since 2012, Statistics Norway has been engaged in a standardised and formalised cooperation on quality with, inter alia, owners of administrative information systems.
Competence and training courses
Statistics Norway organizes regularly courses in quality and quality indicator subjects. The courses are open to both staff in Statistics Norway and members of the Committee for Official Statistics. There are also plans for a training course on statistical confidentiality. Furthermore, there has been arranged specialist seminars on the topics of dissemination, pseudonymisation, confidentiality, editing data and quality in the register. A series of seminars on big data and data minimization[1] have also been arranged. Participation on the ESTP[2]-training program do also contribute to the competence on quality in production of official statistics.
Planned improvements in the quality assurance system.
The combination of thorough quality reviews of selected statistics and self-assessments of the total production of statistics will provide a good basis for the annual quality report. Self-assessments based on the quality evaluation form will be adapted to function as a self-assessment of single statistical processes and output. [3]
Statistics Norway’ has developed a set of quality indicators, according to SIMS, to be used in production and dissemination of statistics. The implementation of these indicators has started. There are also plans to establish a reference database for metadata, including these indicators.
[1] The principle of “data minimization” means limiting the collection of personal information to what is directly relevant and necessary to accomplish a specified purpose. One should also retain the data only for as long as is necessary to fulfil that purpose.
[2] ESTP - European Statistical Training Programme
[3] Single Integrated Metadata Structure
11.2. Quality management - assessment
The quality of the output is presented in About the statistics on the website ssb.no.
12.1. Relevance - User Needs
The main users of EU-SILC statistical data are policy makers, research institutes, media, and students.
Data is used for policy development, research, and to inform the general public.
Users typically request information on poverty related problems experienced by different groups and housing conditions.
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
There are a few variables that are currently not transmited:
- HY051 This scheme does not exist at the national level
- HY053 This scheme does not exist at the national level
- HY061 This scheme does not exist at the national level
- HY071 This scheme does not exist at the national level
- HY072 This scheme does not exist at the national level
- HY074 This scheme does not exist at the national level
- HY120 (Regular taxes on wealth): Included in other income component. Only data for the sum of taxes is available, not indvidual tax components
- HY121 (Taxes paid on ownership of household main dwelling): Not available from registers. Currently not collected.
- HY131 Currently only included in HY130.
- HY145N (Repayments/receipts for tax adjustment): We have already adjusted for this in HY020. The value should therefore be zero for everyone.
- HY170 (Value of goods produced for own consumption): We do not have information about this, but the values would be negligible at the national level
- PL111B (Economic activity of the local unit (last job)): This is a very burdensome variable to collect and code. It is mainly relevant for Euromod (which Norway is not a part of) and therefore we do not collect it.
data-mce-mark="1"> data-mce-mark="1"> data-mce-mark="1"> data-mce-mark="1">Furthermore:
- HY030 is currently only transmitted for the three yearly housing module.
data-mce-mark="1"> data-mce-mark="1"> data-mce-mark="1"> data-mce-mark="1">Some income variables diverges somewhat from the Guidelines:
- HY080 (Regular inter-household cash transfer received) and HY130 (Regular interhousehold cash transfer paid): We get information about transfers between parents that are registered with the authorities (usually transfers agreed in a court settlement or similar). We do not have information about transfers that are not registered
- Net income variables are imputed as they are not available from registers. Imputation is based on information on taxable income and total tax burden. We get the income variables from adminstrative registers. We have detailed information about each income component, but only the sum of taxes paid. This is because tax deductions are computed on the sum of taxes and we can not tell how much should be deducted from each tax component.
12.3.1. Data completeness - rate
Not requested by Reg. 2019/2180.
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.
In terms of precision requirements, the representativeness of the sample and the effective sample size is to be achieved. The effective sample size combines sample size and sampling design effect which depends on sampling design, population structure and non-response rate.
13.2. Sampling error
EU-SILC is a complex survey involving different sampling designs in different countries. In order to harmonize and make sampling errors comparable among countries, Eurostat (with the substantial methodological support of Net-SILC2) has chosen to apply the "linearization" technique coupled with the “ultimate cluster” approach for variance estimation.
Linearization is a technique based on the use of linear approximation to reduce non-linear statistics to a linear form, justified by asymptotic properties of the estimator. This technique can encompass a wide variety of indicators, including EU-SILC indicators. The "ultimate cluster" approach is a simplification consisting in calculating the variance taking into account only variation among Primary Sampling Unit (PSU) totals. This method requires first stage sampling fractions to be small which is nearly always the case. This method allows a great flexibility and simplifies the calculations of variances. It can also be generalized to calculate variance of the differences of one year to another.
The main hypothesis on which the calculations are based is that the "at risk of poverty" threshold is fixed. According to the characteristics and availability of data for different countries, we have used different variables to specify strata and cluster information.
In particular, countries have been split into 3 groups:
- BE, BG, CZ, IE, EL, ES, FR, HR, IT, LV, HU, PL, PT, RO, SI, UK and AL, whose sampling design could be assimilated to a two-stage stratified type we used DB050 (primary strata) for strata specification and DB060 (Primary Sampling Unit) for cluster specification;
- DK, DE, EE, CY, LT, LU, NL, AT, SK, FI, CH whose sampling design could be assimilated to a one stage stratified type we used DB050 for strata specification and DB030 (household ID) for cluster specification;
- MT, SE, IS, NO, whose sampling design could be assimilated to a simple random sampling, we used DB030 for cluster specification and no strata.
See Annex 3 Sampling errors
Annexes:
Annex 3: Sampling errors
13.2.1. Sampling error - indicators
The concept of accuracy refers to the precision of estimates computed from a sample rather than from the entire population. Accuracy depends on sample size, sampling design effects and structure of the population under study. In addition to that, sampling errors and non-sampling errors need to be taken into account. Sampling error refers to the variability that occurs at random because of the use of a sample rather than a census and non-sampling errors are errors that occur in all phases of the data collection and production process.
The following tables are available in Annex A:
Main indicators, standard error and CI
Persistent-risk-of-poverty, standart error and CI
Annexes:
Annex A
13.3. Non-sampling error
Non-sampling errors are basically of 4 types:
- Coverage errors: errors due to divergences existing between the target population and the sampling frame.
- Measurement errors: errors that occur at the time of data collection. There are a number of sources for these errors such as the survey instrument, the information system, the interviewer and the mode of collection.
- Processing errors: errors in post-data-collection processes such as data entry, keying, editing and weighting.
- Non-response errors: errors due to an unsuccessful attempt to obtain the desired information from an eligible unit. Two main types of non-response errors are considered:
- Unit non-response: refers to absence of information of the whole units (households and/or persons) selected into the sample.
- Item non-response: refers to the situation where a sample unit has been successfully enumerated, but not all required information has been obtained.
13.3.1. Coverage error
Coverage errors include over-coverage, under-coverage and misclassification:
- Over-coverage: relates either to wrongly classified units that are in fact out of scope, or to units that do not exist in practice.
- Under-coverage: refers to units not included in the sampling frame.
- Misclassification: refers to incorrect classification of units that belong to the target population
13.3.1.1. Over-coverage - rate
Coverage error
| Main problems |
Population (sub-population) |
Size of error |
Comments |
|---|---|---|---|
| Over-coverage |
Over-coverage due to deaths and emigration between updating of the sampling frame and the interview is almost always discovered during the fieldwork. |
In 2024 85 selected respondents persons where classified as non-eligible because of emigration, death or living in institutions. |
|
| Under-coverage |
Under-coverage due to immigration between the updating of the sampling frame and interview is small. |
Insignificant |
Immigration is relatively small, and the new sampling frame is updated very frequently. |
| Misclassification |
There should be nearly no coverage errors connected to this frame, except for the extremely few cases of emigrations which are wrongly coded as non-response in stead of non-eligible because their emigration was not registered in the population register. |
|
No persons lived at an unknown adress |
13.3.1.2. Common units - proportion
Not requested by Reg. 2019/2180.
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 |
| In every survey there is a chance of respondents giving an incorrect answer. The question/answer process can be seen in four different phases. First there is the understanding and interpretation of the actual question. If there are difficult terms or complicated wording, this may cause errors. The second phase is where the respondent recalls information. Errors in this phase may rise if the information necessary is hard to retrieve because it is old, complicated or not available to the respondent. In EU-SILC some of the questions about housing costs are quite complicated even for the person responsible for the dwelling. This may affect the accuracy of the answers given. Apart from this, we have no suspicion of frequent errors caused by difficulties in information retrieval. The third phase is evaluating and selecting the information necessary to answer the question. In this phase, the respondent may actually have the right kind of information to answer the question correctly, but still end up with a wrong answer. This type of error is most frequent when the question is complicated and requires much information. Typical questions from EU-SILC may be questions requiring the respondent to select different economic components necessary for a specific question. The fourth and final phase is the actual formulating of the answer. This may cause errors if the respondents mastering of the language in use is weak, if the answer requires use of complicated terms or if the communication between the interviewer and the respondent is not optimal. |
The questionnaire was thoroughly evaluated before the data collection in 2021. All the questions were evaluated, and several were changed to make them easier for the respondents to answer. Several parts of the questionnaire have also been user tested over the years. HH021:The Norwegian question is more detailed. However it is quite clear how to aggregate categories to construct the Eurostat categories of owners and tenants. To distinguish between tenants paying rent at or below market price we asked whether the rent that is paid is market rent (question Husleie2) and whether this was because the rent was subsidised by the authorities, the employer or for some other reason (question Husleie2b). To distinguish households with a rent-free accommodation we asked whether the household pay rent (question Husleie1). HH070: Values are imputed in cases were the respondents do not know how much they spend on specific housing costs. Note that imputed often means that some housing costs are reported in the questionnaire, wheras others are imputed. PL032: The only difference is that the Norwegian question is only asked respondents working less than 32 hours a week. Persons working 32 hours or more a week are considered as 'carrying out a job or profession'. PL060: The question explicitly mentions that paid overtime and extra work at home shall be included. PH030: This is only asked to persons who suffer from chronic illness or condition (PH020). The variable is built on three questions to ensure that all the information needed for the variable is of good quality.1: ' Does this (chronic illness) lead to limitations in your daily activities' 2: ' Have these limitations lasted for at least six months' 3: ' Would you say that you are strongly limited or somewhat limited'? From 2025 we will not filter by PH020 anymore.
|
Interviewer effects may also be labelled under errors caused by interview. The interviewers used in EU-SILC were among the approximately 130 of the ordinary interviewer staff assigned to Statistics Norway. Approximately 60 of these interviewers are locally based interviewers who are part time employees with individual agreements ranging from 500 to 1200 hours of work per year. Theses interviewers are stationed in the sample areas according to the standard sampling frame. The approximately 70 centrally based interviewers are working from Statistics Norway’s call centres in Oslo and Kongsvinger (where Statistics Norway has offices). When hired, all interviewers must complete an education consisting of self-studies and written tasks in two stages. The interviewers are gathered to an obligatory three-day course before they are hired for a trial period of 6 months. The course can occur both digitally and on-site. Before the end of the trial period and permanent hiring, all new interviewers are given a personal follow-up talk. As part of the general follow-up and education of interviewers, shorter briefings are frequently held, where all interviewers are invited to participate. The interviewers also have a supervisor on each work shift they can communicate with. The specific training for EU-SILC consists of an obligatory interview guide following the survey. This guide contains information about the survey, description of the sample, time limits (start and end) and a mentioning and instructions for some of the questions. All interviewers are paid to read this instruction. In addition, they are paid a fixed price (estimated number of hours) for test interviewing before starting the actual work. In EU-SILC 2021, the estimated time destined to reading of instruction and training was 2 hours per interviewer. The interviewers are, in addition to reading the specific survey guide, given a presentation of the survey (briefing). This presentation is also made available as a video for the interviewers to rewatch when needed. The danger of systematic interviewer effects is reduced through training, but also by using a relatively large number of interviewers. |
In the Norwegian EU-SILC there have not been carried out any studies, such as re-interviews, record check studies, og split-sample experiments. |
13.3.3. Non response error
Non-response errors are errors due to an unsuccessful attempt to obtain the desired information from an eligible unit. Two main types of non-response errors are considered:
1) Unit non-response which refers to the absence of information of the whole units (households and/or persons) selected into the sample. According to Annex VI of the Reg.(EU) 2019/2242
- Household non-response rates (NRh) is computed as follows:
NRh=(1-(Ra * Rh)) * 100
Where Ra is the address contact rate defined as:
Ra= Number of address/selected person (including phone, mail if applicable) successfully contacted/Number of valid addresses/selected person (including phone, mail if applicable) selected
and Rh is the proportion of complete household interviews accepted for the database
Rh=Number of household interviews completed and accepted for database/Number of eligible households at contacted addresses (including phone, mail if applicable)
• Individual non-response rates (NRp) is computed as follows:
NRp=(1-(Rp)) * 100
Where Rp is the proportion of complete personal interviews within the households accepted for the database
Rp= Number of personal interview completed/Number of eligible individuals in the households whose interviews were completed and accepted for the database
• Overall individual non-response rates (*NRp) is computed as follows:
*NRp=(1-(Ra * Rh * Rp)) * 100
For those Members States where a sample of persons rather than a sample of households (addresses, phones, mails etc.) was selected, the individual non-response rates will be calculated for ‘the selected respondent.
2) Item non-response which refers to the situation where a sample unit has been successfully enumerated, but not all the required information has been obtained.
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.
13.3.3.1. Unit non-response - rate
Unit non-response rate for cross-sectional
| Cross sectional data |
|||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 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* |
| 89,96 |
79,25 |
97,15 |
87,69 |
68,18 |
98,5 |
100 |
100 |
100 |
21,11 |
45,97 |
4,3 |
0 |
0 |
0 |
21,11 |
45,97 |
4,3 |
A*- total sample, B*- New sub-sample, C*-Longitudinal 1 wave
Weighting
Data are weighted to correct for non-response / underepresentation by income group, age, immigration background, education level, county and family size.
See Annex A
13.3.3.2. Item non-response - rate
The computation of item non-response is essential to fulfil the precision requirements. Item non-response rate is provided for the main income variables both at household and personal level.
Item non-response which refers to the situation where a sample unit has been successfully enumerated, but not all the required information has been obtained.
13.3.3.2.1. Item non-response rate by indicator
See Annex 2 - Item non-response
Annexes:
Annex 2: Item non response 13.3.3.2.1
13.3.4. Processing error
Description of data entry, coding controls and the editing system
| Data entry and coding (if any used) |
Editing controls |
|---|---|
| The respondents report their current or last profession in the interview. PL051A and PL051B is coded based on this. The respondents also state their place of work. PL111A is then coded based on the NACE code of the employer. |
|
See Annex A for re-interview rates by wave.
13.3.5. Model assumption error
Not applicable
14.1. Timeliness
Data was first transmitted to Eurostat on February 21 2025, 252 days after the end of the field work.
Data was validated and accepted by Eurostat on February 26 2025, 257 days after the end of the field work.
14.1.1. Time lag - first result
National results were first disseminated on 19 November 2024, 5 months after the end of data collection. Link to statistics at Statistics Norway’s website. However, these tables mostly cover data from the national survey which is collected as part of the NO-SILC. First transmission of data to Eurostat was done on 21 February 2025.
14.1.2. Time lag - final result
National SILC results were disseminated on April 10 2025, 10 months after the end of data collection. Link to the statistics on poverty related issued on Statistics Norway’s website.
Final data transmission to Eurostat was the same as the first, made on February 21 2025.
14.2. Punctuality
Not requested
14.2.1. Punctuality - delivery and publication
The first data were disseminated in line with target date on a national level, before the end of the reference year N.
15.1. Comparability - geographical
There should be no problems comparing between geographical areas. However, some of the NUTS2 regions have relatively low number of respondents, which results in grater statistical uncertainty and should therfore be interpreted with care.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not applicable.
15.2. Comparability - over time
There are no significant breaks in time series in 2024.
Annexes:
Annex 8: Breaks in times series
15.2.1. Length of comparable time series
There was a break in time series in 2021 for all variables due to a change in weights, but the figures are largely comparable over time.
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. Any deviation from the standard definition should be reported.
Comparability and deviation from definition for each income
| Income |
Identifier |
Comparability |
Deviation from definition if any |
|---|---|---|---|
| Total hh gross income |
(HY010) |
F |
|
| Total disposable hh income |
(HY020) |
F |
|
| Total disposable hh income before social transfers other than old-age and survivors' benefits |
(HY022) |
F |
|
| Total disposable hh income before all social transfers |
(HY023) |
F |
|
| Imputed rent |
(HY030) |
NC |
|
| Income from rental of property or land |
(HY040) |
F |
|
| Family/ Children related allowances |
(HY050) |
F |
|
| Social exclusion payments not elsewhere classified |
(HY060) |
F |
|
| Housing allowances |
(HY070) |
L |
The benefit from renting a subsidised dwelling is not included in the income concept. Only state subsidies are included, not municipal. |
| Regular inter-hh cash transfers received |
(HY080) |
L |
Information on informal regular transmissions is not included |
| Interest, dividends, profit from capital investments in incorporated businesses |
(HY090) |
F |
|
| Interest paid on mortgage |
(HY100) |
F |
|
| Income received by people aged under 16 |
(HY110) |
F |
|
| Regular taxes on wealth |
(HY120) |
NC |
|
| Regular inter-hh transfers paid |
(HY130) |
P |
Information on informal regular transmissions is not included |
| Value of goods produced for own consumption |
(HY170) |
NC |
|
| Cash or near-cash employee income |
(PY010) |
L |
Payments to foster parents and severance and termination pay are included in wages and cannot be separated. These items are of a minor importance. |
| Other non-cash employee income |
(PY020) |
F |
|
| Income from private use of company car |
(PY021) |
F |
|
| Employers social insurance contributions |
(PY030) |
L |
Because of the allowance scheme which is per company (and every company has employees in the various zones and age groups) it is virtually impossible to calculate the payroll tax directly per person. Therefore, the calculation is done by companies where we have taken into account the allowance scheme, zones, age, sector and individual exceptions industries. |
| Cash profits or losses from self-employment |
(PY050) |
L |
It has not been possible to identify – and thus deduct from self-employment income – interest paid on business loans. |
| Unemployment benefits |
(PY090) |
F |
No information available on benefits (in-kind) related to vocational training. |
| Old-age benefits |
(PY100) |
F |
It was not possible to split the different types of occupational pensions into different functions, e.g. old-age, disability or survivor’s pension. Instead all types of occupational pensions have been included under the old-age function. Early retirement benefit is included in occupational pension, i.e. old-age function. |
| Survivors benefits |
(PY110) |
F |
Not possible to include funeral grants in the income concept. This benefit is transferred directly to the firm of undertakers. |
| Sickness benefits |
(PY120) |
F |
|
| Disability benefits |
(PY130) |
F |
|
| Education-related allowances |
(PY140) |
F |
|
| Gross monthly earnings for employees |
(PY200) |
NC |
|
Description of reference period used for incomes
| Period for taxes on income and social insurance contributions |
Income reference periods used |
Reference period for taxes on wealth |
Lag between the income ref period and current variables |
|---|---|---|---|
| The calendar year preceding the data collection. |
The calendar year preceding the data collection. |
December 31 the year preceding the data collection. |
0-6 months |
Information on informal regular transmissions is not included
Net income variables imputed based on total tax as this data is not available at the component level for individuals.
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.
15.3.1. Coherence - sub annual and annual statistics
Not applicable.
15.3.2. Coherence - National Accounts
See Annex 7 - Coherence.
Annexes:
Annex 7: Coherence
15.4. Coherence - internal
Not applicable
Mean (average) interview duration per household = 31,6 minutes.
Mean (average) interview duration per person = 10,7 minutes.
Mean (average) interview duration for selected respondents = 20,6 minutes.
17.1. Data revision - policy
Provisional data is transmitted at the end of the year based on income data from the survey year -2. Final data is transmitted by the end of february with income data from the survey year -1. In 2024 only the final data was transmitted.
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
Source of information for sampling: central population register
Source of information of EU-SILC data: interview and registers
Data collected from registers:
- Income variables
- Country of birth, citizenship
- Age, year of birth
- Sex
- Marital status
Data collected as a combination of registers and survey:
- Region, degree of urbanisation
- Housing variables
- Housing costs
- Education variables (education attained)
- Country of birth of parents
- NACE
- Grid
18.1.1. Sampling Design
Until 2008, the sample for EU-SILC in Norway was composed of an old sample for a longitudinal survey established in 1997, and a new sample with a different design in 2003 (se quality report for 2007). From 2008 on, the sample is selected only according to the new design because all respondent from the old sample were rotated out.
The sample in 2024 is drawn according to the rules for simple random sampling in one stage. There is still a systematic element, that stems from the arrangement of the population register.
The primary stratification criterion for the period 2003-2006 was age. The design chosen implicated that age was the central criterion for representativity. The sample was drawn as a proportion p of the population within one-year groups. Based on experience from analysing cross sectional EU-SILC data from 2003 to 2006, this way of stratification was problematic because the rotational groups were biased.
In 2007, the representativity based on one-year age groups was abandoned, and the new rotational groups are drawn as the proportion p of the population 16 years and over. The drawn sample consitutes the selected respondents. In addition, each existing rotational group is then supplemented with new 16 year olds and new immigrants to ensure representativity. The same system is still used. The sample is drawn from the population register, and this register is arranged to ensure geographical representativity. This is done by municipality and postal codes. The register is arranged by family number and personal code within the family before the actual selection of units.
The sample for the Norwegian EU-SILC before 2007 consisted of an existing sample for a longitudinal and a new sample selected according to a new design. For information on the old selection schemes, se previous intermediate quality reports.
Deleting one rotational group, adding a new rotational group and supplementing the old rotational groups resulted in a sample in 2024 of 11 810 persons. This included 159 16 year olds and 152 recent immigrants who were added to the previous rotational groups to ensure that each rotational group was representative of the target population.
To make the data collection effective, and to ensure a highest possible response rate among the new respondents in the sample, the sample was divided into 40 periodical groups with different start contact periods.
18.1.2. Sampling unit
The sample units are residents aged 16 years and over at the income reference period living in private households (not living in institutions).
18.1.3. Sampling frame
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 units (household or individual) with an accepted interview
- the effective sample size which is defined as the achieved sample size divided by the design effect with regards to the at-risk-of poverty rate indicator
Given that the effective sample size has been already treated in the section dealing with sampling errors, in this section the attention focuses mainly on the achieved sample size.
The actual sample size was 11810 individuals (selected respondents) for the cross-sectional component in the survey year 2024. The achieved sample size was 5762 individuals (selected respondents).
Renewal of sample: Rotational groups
Since 2012, the sampling design has been four-year panel. The sample has four rotational groups of equal size. Each year one group rotates out and a new rotation group is retracted. The sample is drawn as a random sample in one step. The number of new to be sample to be included each year is calculated on the total gross sample - the remaining three quarters of the sample.
In the transition between the old sampling plan and the new (2012-2014) the total sample consisted of rotating groups of different sizes.
For the total sample shall preserve its cross sectional characteristic from year to year, the sample is supplemented. 16-year-olds are drawn each year so that the number of 16 year olds in the sample corresponds to the proportion p of the population. The same applies to the recently immigrated. The supplemented into the sample will not be in the sample for four consecutive years, but from one to three years.
18.2. Frequency of data collection
Data collection is conducted once a year, during the first 6 months of the year.
18.3. Data collection
Mode of data collection
|
|
1-PAPI |
2-CAPI |
3-CATI |
4-CAWI |
5-PAPI proxy |
6-CAPI-proxy |
7-CATI-proxy |
8-CAWI proxy |
9-other |
|---|---|---|---|---|---|---|---|---|---|
| % of total |
|
|
100 |
|
|
|
|
|
|
Description of collecting income variables
| The source or procedure used for the collection of income variables |
The form (gross, net) in which income variables at component level have been obtained |
The method used for obtaining target variables in the required form |
|---|---|---|
| All income data in the EU-SILC are collected from various administrative and statistical registers, exept from hy100 and hy030 witch are collected from the interview. The main registers used are: (a) The Tax Return Register (Employee income, self-employment income, taxable pensions etc.) (b) The Tax Register for Personal Tax Payers (Assessed taxes, social security contributions) (c) National Insurance Service (Family allowances, attendance benefits, cash-for-care, child care benefits to single parents, sickness and maternity allowance)
(d) The a-ordning (Unemployment benefits, company car) (e) State Educational Loan Fund (Education related benefits) (f) The State Housing Bank (Dwelling support) (g) Social statistics (Social assistance) A comprehensive data file on income is created by linking the total resident population to all the different income registers. The key that links the individual to the registers is the Personal Identification Number. |
The register data only include gross income at component level. Total assessed taxes and contribution to social security are collected separately from tax registers. |
All income data recorded gross at component level. Net variables are calculated based on gross components and information on taxable income types and total taxes paid. |
The administrative income data is used for publication of income statistics at the national level and it's quality is checked by the corresponding unit at Statistics Norway.
See tables in Annex 4
Questionnaire in Annex 1a and Annex 1b
Annexes:
Annex 4 - Data collection
Annex 1: National Questionnaire Norwegian
Annex 1: National Questionnaire English
18.4. Data validation
Throughout the production process data is compared to previous years and other sources.
The Eurostat validation program is run by Statistics Norway before the data is sent in and error messages are fixed. We send a comment ot explain deviations or in cases were we are not able to fix errors.
18.5. Data compilation
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.
18.5.1. Imputation - rate
There is done no manual data editation. Blaise programming ensure unlikely responses are avoided. There is done some automatic editing for housing cost amounts that seem unlikely when compared to available register data. The editing mostly consists of removing unlikely responses and using imputation in stead. The imputation procedures then follow the same procedure as for respondents with item non response.
There is done some imputation for housing variables and income variables. For HH070 the imputation rate was 4,27 percent in 2024. This refers to households where the largest components of housing costs are imputed. For income variables, in 2024 5 individuals (0,05 percent) had imputed income data, 11 housholds (0,19 per cent) have imputed income for at least one household member, of which most are small children whose imputed income is set to 0.
A more detailed description of imputation procedures are included in Annex 6 Estimation and Imputation.
18.5.2. Calculation of weighting factors and weight adjustments
See Annex 5 - Weighting procedure
Annexes:
Annex 5: Weighting procedure
18.5.3. Estimation and imputation
See Annex 6 - Estimation procedure
Annexes:
Annex 6: Estimation and imputation
18.6. Adjustment
Not applicable.
18.6.1. Seasonal adjustment
Not applicable.
Annexes:
Annex 9: Rolling module
The European Union Statistics on Income and Living Conditions (EU-SILC) is an instrument aiming at collecting timely and comparable cross-sectional and longitudinal multidimensional microdata on income, poverty, social exclusion and living conditions. This instrument is anchored in the European Statistical System (ESS). In addition, are collected module variables every three year, six year 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, income at very detailed component level, is mainly collected at personal level.
23 May 2025
Statistical concepts and definitions for EU-SILC are specified in EU Regulation (EU) 2019/1700, EU regulation 2019/2181, and EU regulation 2019/2242. Additional information is available in the EU statistics on income and living conditions (EU-SILC) methodology and in the methodological guidelines and description of EU-SILC target variables (see CIRCABC).
Further details are provided in items 5, 15.1.1.1, 15.2.2 and 18.3.
Statistical units are private households and all persons living in these households who have usual residence in the Member State. Annex II of the Commission implementing regulation (EU) 2019/2242 defines specific statistical units per variable and specifies the, content of the quality reports on the organization of a sample survey in the income and living conditions domain pursuant to Regulation (EU) 2019/1700 of the European Parliament and of the Council.
Statistical units are private households and all persons living in these households who have usual residence in the Member State. Specific statistical units per variable are defined in Annex II of the Commission implementing regulation (EU) 2019/2242 specifying content of the quality reports on the organization of a sample survey in the income and living conditions domain pursuant to Regulation (EU) 2019/1700 of the European Parliament and of the Council.
The target population is private households and all persons composing these households having their usual residence in the Member State. Private household means a person living alone or a group of persons who live together, providing oneself or themselves with the essentials of living.
Norway except Svalbard.
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 |
|---|---|---|---|
| 2023 |
2023 |
2023 |
The income reference period is the previous calendar year (2023) and the current variables refer to the fieldwork period (January-June 2024). The lag is at minimum 1 month and at maximum 6 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.
In terms of precision requirements, the representativeness of the sample and the effective sample size is to be achieved. The effective sample size combines sample size and sampling design effect which depends on sampling design, population structure and non-response rate.
The data involves several units of measure depending upon the variables. Income variables are transmitted to Eurostat in national currency. For more information, see methodological guidelines and description of EU-SILC target variables available on CIRCABC
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.
Source of information for sampling: central population register
Source of information of EU-SILC data: interview and registers
Data collected from registers:
- Income variables
- Country of birth, citizenship
- Age, year of birth
- Sex
- Marital status
Data collected as a combination of registers and survey:
- Region, degree of urbanisation
- Housing variables
- Housing costs
- Education variables (education attained)
- Country of birth of parents
- NACE
- Grid
Annual
Data was first transmitted to Eurostat on February 21 2025, 252 days after the end of the field work.
Data was validated and accepted by Eurostat on February 26 2025, 257 days after the end of the field work.
There should be no problems comparing between geographical areas. However, some of the NUTS2 regions have relatively low number of respondents, which results in grater statistical uncertainty and should therfore be interpreted with care.
There are no significant breaks in time series in 2024.
Annexes:
Annex 8: Breaks in times series
