Statistics Norway

Division for Social Statistics

Statistisk sentralbyrå
Postboks 2633 St. Hanshaugen
0131 Oslo

Norway

23 May 2025

23 May 2025

23 May 2025

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:

1. Cross-sectional data pertaining to a given time or a certain time period with variables on income, poverty, social exclusion and other living conditions;
2. 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.

• 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.

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.

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.

Definitions of reference population, household and household membership

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

Norway except Svalbard.

Annual data, reference year 2024. Income reference period is 2023. The survey has been conducted in Norway starting in 2003. 

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

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.

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.

The statitistics act states that:

Section 7. Statistical confidentiality in dissemination of official statistics

1. 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.
2. The first subsection shall not apply when the exception follows from an obligation to produce statistics pursuant to the EEA Agreement.
3. 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 

1. 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.
2. 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.

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. 

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.

Please refer to the Release calendar - Eurostat (europa.eu) publicly available on the Eurostat’s website.

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

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).

• Statistics on poverty-related problems
• Statisics on housing conditions
• Report on economy and living conditions

• Data tables poverty related problems
• Data tables housing conditions

These are the number of downloads:

 *Until 5 May 2025

National microdata is distributed by SIKT (The Norwegian Agency for Shared Services in Education and Research)  

Not available.

Not available.

Documentation for 2024: Levekårsundersøkelsen EU-SILC 2024. Documentation (in Norwegian)

Not available.

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).   

Peer review report, 2021. 

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

The quality of the output is presented in About the statistics on the website ssb.no.

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.

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).

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.

Not requested by Reg. 2019/2180.

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.

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:

1. 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;
2. 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;
3. 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

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

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.

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

Coverage error

Not requested by Reg. 2019/2180.

  Measurement error for cross-sectional data

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.

Unit non-response rate for cross-sectional

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

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.

See Annex 2 - Item non-response

Description of data entry, coding controls and the editing system

See Annex A for re-interview rates by wave.

Not applicable

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.

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.

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.

Not requested

The first data were disseminated in line with target date on a national level, before the end of the reference year N.

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.

Not applicable.

There are no significant breaks in time series in 2024.

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.

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

 Description of reference period used for incomes

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.

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.

Not applicable.

See Annex 7 - Coherence.

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.

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.

Not applicable.

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.

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

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. 

The sample units are residents aged 16 years and over at the income reference period living in private households (not living in institutions).

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.

Data collection is conducted once a year, during the first 6 months of the year.

Mode of data collection

 Description of collecting income variables

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

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.

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.

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.

See Annex 5 - Weighting procedure

See Annex 6 - Estimation procedure

Not applicable.

Not applicable.