Statistics Netherlands

Social-economic and spatial statistics

Labour, income and quality of life statistics

Centraal Bureau voor de Statistiek (CBS)

Postbus 4481
6401 CZ Heerlen

22 May 2024

28 May 2025

28 May 2025

The European Union Statistics on Income and Living Conditions (EU-SILC) is a survey-based instrument aiming at collecting timely and comparable cross-sectional and longitudinal multidimensional microdata on income, poverty, social exclusion and living conditions. In addition, it collects module variables every three years, six years or ad-hoc new policy needs modules.

The EU-SILC instrument provides two types of data:

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 is income information at very detailed component level and 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.

Statistical concepts and definitions for EU-SILC are specified in Regulation (EU) 2019/1700, Commission Implementing Regulation (EU) 2019/2181, and Commission Implementing Regulation (EU) 2019/2242. Additional information is available in the EU statistics on income and living conditions (EU-SILC) methodology and in the methodological guidelines and description of EU-SILC target variables (see CIRCABC).

Further details are provided in items 5, 15.1.1.1, 15.2.2 and 18.3.

Statistical units are private households and all persons living in these households who have usual residence in the Member State. Annex II of the Commission implementing regulation (EU) 2019/2242 defines specific statistical units per variable and specifies the, content of the quality reports on the organization of a sample survey in the income and living conditions domain pursuant to Regulation (EU) 2019/1700 of the European Parliament and of the Council.

The target population is private households and all persons composing these households having their usual residence in the Netherlands. Private household means a person living alone or a group of persons who live together, providing oneself or themselves with the essentials of living. A person has his/her usual residence in the Netherlands if the person has his/her actual place of residence in the Netherlands. The person must be listed in the Dutch population register.

Definitions of reference population, household and household membership

The sub-populations that are not covered by the data collection includes: those who moved out of the country’s territory; or those with no usual residence; or those living in institutions or who have moved to an institution compared to the previous year. Homeless people are excluded from the statistical population. In the Netherlands, this concerns about 30 thousand people.  style="text-align: justify;">In 2023, 276 thousand people lived in an institution. 

Territorial coverage: Kingdom of the Netherlands excluding overseas territories.

Annual data from 2005 to 2024.

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.

To protect the data on natural persons or enterprises that is subject to confidentiality, it is ensured that such data cannot be disclosed directly or indirectly in the published statistics. With regard to personal data the Official Statistics Act and the EU General Data Protection Regulation apply.

Statistics Netherlands (CBS) collects data from people, companies and institutions. Upon receipt of these data, all directly identifying personal details are removed as soon as possible and replaced by a pseudo key. CBS uses these so-called pseudonymised data to conduct statistical research. CBS will only publish statistical information without identifiable or traceable personal data. Furthermore, CBS has taken measures to ensure protection from theft, loss or abuse of personal data.
CBS will never supply identifiable data to third parties, including other government institutions. However, (academic) institutions may, under strict conditions, be given access to pseudonymised personal or corporate data. These are referred to as microdata.

Measures

Statistics Netherlands protects the data with technical and logistical measures. Following are the most important measures:

• When completing a survey or submitting any data, this information is delivered to CBS in encoded form. The data are received by CBS in a secured environment. Only authorised personnel shall have access to these data.
• At the earliest possible stage in the process, all directly identifiable personal data are removed from the files. This means survey files will never contain data such as names, addresses or Burgerservicenummers (national ID numbers).
• All CBS employees are bound by an obligation of confidentiality. All our employees have signed a confidentiality agreement.
• CBS uses the data solely for statistical and scientific purposes. CBS is excluded by law from using the data for fiscal, administrative, verification and legal purposes. Furthermore, CBS is not allowed to use data for marketing.
• CBS was certified Privacy Audit Proof in 2017. This audit is carried out by an accredited external party in concordance with DPA standards.

Personal Data Protection Act

CBS is bound by the European General Data Protection Regulation (GDPR). This Regulation helps protect the privacy of citizens. In addition, CBS adheres to the privacy stipulations in the Statistics Netherlands ACT, the European Code of Practice, the Statistical Law and its own code of conduct.

CBS has its own Data Protection Officer. This officer monitors the use of personal data by CBS and keeps a register of all personal data processing at CBS. 

The Dutch EU-SILC follows Statistics Netherlands' Confidentiality Policy.

Personal Data Protection Act

CBS is bound by the European General Data Protection Regulation (GDPR). This Regulation helps protect the privacy of citizens. In addition, CBS adheres to the privacy stipulations in the Statistics Netherlands ACT, the European Code of Practice, the Statistical Law and its own code of conduct.

CBS has its own Data Protection Officer. This officer monitors the use of personal data by CBS and keeps a register of all personal data processing at CBS. 
The Dutch Data Protection Authority (DPA) supervises compliance with regulations of the law concerning personal data protection.

Aggregated tabels are published annually, at he end of May, in Statline, Statistics Netherlands' online database

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 Netherlands did not issue a press release linked to EU-SILC data collection 2024

EU-SILC Subjective Poverty Indicators are published on CBS website.

Aggregated tables are published in Statline, Statistics Netherlands' online database on CBS website.

Not available.

Microdata are can be made available to Dutch universities, scientific organisations, planning agencies and statistical authorities within the EU under strict conditions for statistical research. The guiding principle here is safeguarding privacy and preventing disclosure of persons or companies. To gain access, a number of steps must be completed. Researchers can analyse themicrodata via a secure internet connection. To do so, they will receive a personal token on loan. They will only have access to the data which are needed for their research. All the microdata will remain within this secure CBS IT-environment. If researchers wish to export results from the secure environment, CBS will check whether the results do not contain any disclosure risk.

Not applicable.

Not available.

For metadata of the income benefits, see Annex 10.

The Metadata completeness-rate is 100%.

Not available.

CBS’ mission is to publish reliable and coherent statistical information that meets the needs of Dutch society. A prerequisite of this mission is that the quality of this statistical information is assured. To this end, CBS has set up a quality management system that is based on the international ISO 9001:2015 standard. 

European Statistics Code of Practice

The European Statistics Code of Practice is a self-regulatory instrument for quality assurance and applies to the production of official statistics within the European Statistical System. Compliance with the Code is monitored through a system of peer evaluations, consisting of self-assessments, peer reviews and annual progress reports.

The peer reviews are conducted once every five years under the responsibility of the ESS by senior expert teams from the fields of science and statistics. Not only CBS is being interviewed, but its principal users (ministries, planning agencies, universities, media, etc.), principal respondents/data suppliers and academic institutions are also invited to participate in the peer review.

The whole project (data collection, data processing, and data analysing) has two project managers and several statisticians/researchers.

The project can be supported - if necessary - by a senior statistical researcher within the unit and - also if needed - by the methodological or IT department of SN. 

No part of the work was subcontracted.

The data collection is a regular data collection. The possible problems can be:

• Low response rate: this is diverted by sending sufficient reminders, lengthening the CAWI data collection period, dividing the CAWI fieldwork into smaller portions  instead of one portion, using incentives in the form of iPad lottery, and monitoring the fieldwork on the daily base.
• Misunderstanding by respondents of certain new questions. In the worst case, it might be decided that the results of this question(s) will not be published.
• Emergency (sickness, not meeting deadlines etc.): there are complementary resources available.

The main users of EU-SILC statistical data are policy makers, research institutes, media, and students.

User of the national datatables, presented at Statline tables, are ministries and researchers. Customized tables, if reliable, can be compiled for users (no dataset). 

Eurostat carried out an online general User Satisfaction Survey (USS) in the period between April and July 2019 to obtain a better knowledge about users, considering their needs and satisfaction with the services provided by Eurostat. The survey has shown that EU-SILC is of very high relevance for users. For the majority, both aggregates and micro-data were important or essential in their work irrespective of the purpose of their use. The use of the ad-hoc modules was less widespread than the use of the nucleus variables. Nevertheless, there was high interest to repeat these modules in order to have the possibility of comparing data over time. Users emphasized their strong need for more detailed micro-data, which is currently not possible. Under the new legal framework implemented from 2021, the NUTS 2 division will be available for the main indicators. Finally, users were satisfied with overall quality of the service delivered by Eurostat, which encompasses data quality and the supporting service provided to them.

For more information, please consult the User Satisfaction Survey.

The core and module variables have all been transmitted. Information on the value of goods produced for own consumption (HY170) has not been collected. This value does not constitute a significant component of income. 

Target variable HY145N is not collected either. This variable should be filled when the country has recorded only net income at the component level. Taxes on income and social contributions (Target variable HY140G) are based on the 'income received' in the income reference year (accrual basis) and do not refer to the amounts actually paid in the income reference year. Tax adjustments have been recorded in the variable HY140G.

The Netherlands have no tax on wealth (HY120G), but there is a tax on the income generated from wealth. For tax purposes, a fixed return on savings and investments is presumed. These taxes are collected in target variable HY140G.

Concerning Target variable HY121G (Taxes paid on ownership of the household main dwelling): Part of the value of the mail dwelling counts as income. This is known as the imputed rental value (in Dutch: ‘eigenwoningforfait’) and amounts to a percentage of the home’s value. This tax cannot be distinguished from other taxes and is also included in target variable HY140G.

Optional variables were not collected, with the exception of:

HI130G: Interest expenses [not including interest expenses for purchasing the main dwelling]

All target variables mandatory by regulation were transmited to Eurostat.

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.

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.

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.

A table of standard errors for the main indicators is attached in Annex A. The standard errors for the indicators 'low work intensity' and "material and social deprivation" were calculated by Eurostat. Regarding AROP, AROPE and work intensity (LWI), they are calculated by Statistics Netherlands. These variances have been estimated taking into account the sampling design, various stages of non-response attrition, as well as the weighting to known population control totals. The weighting not only reduces bias due to selective non-response, but also reduces the variances of most SILC indicator estimates. The latter is due to the use of strongly related covariates in the weighting scheme. In particular, the Dutch income register is employed to derive whether a person's household has an income below the at-risk-of-poverty threshold. Here both the threshold and the equivalized household incomes are derived from the income register in such a way so as to approximate the EU-SILC concept as well as possible. Using this covariate in the weighting scheme has the effect of reducing the variances of the ARPR and AROPE by approximately a factor of 4 (and therefore the standard errors by a factor of 2). The gain is even somewhat higher for ARPR and AROPE estimates by province, due to the fact that the weighting scheme also contains the interaction of the register-based below-ARPT concept by province. For other indicators there are also clear, albeit somewhat smaller, accuracy gains.

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

Regarding all sample persons, the information is based on both interview and register data.

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.

Number of households/selected respondents for which an interview is accepted for the database by rotational group and wave

 Number of persons aged 16 or older for which an interview is accepted for the database by rotational group and wave

Distribution of households by 'record of contact at address (DB120)'and wave

Distribution of households by 'household questionnaire result (DB130)' and wave

Distribution of households by 'household questionnaire result (DB135)' and wave

Unit non-response rate for cross-sectional

where:

A = total (cross-sectional) sample,

B = New sub-sample (new rotational group) introduced for first time in the survey this year,

C = Sub-sample (rotational group) surveyed for last time in the survey this year.

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

Not applicable.

Data collection: 16 February 2024 - 9 September 2024.

Date of the dissemination of national results: as yet unknown

First transmission to Eurostat: 18 December 2024

Number of days between the end of fieldwork and the first transmission (Eurostat): 105

Second transmission to Eurostat: 18 February 2025

In the second transmission, some small errors identified by Eurostat were corrected.

First (and final) results will be  published on March 14, 2025.

Final results were published on 28 February 2023, 28 days after the end of the reference period.

Provisional transmission : 25 December 2023

Final transmission: 24 May 2024, almost 3 months after the deadline.

Final results were published by the end of March, according to the schedule.

The main indicators are comparable at NUTS2 level.

Not applicable.

See Annex 

See Annex 8.

There have been no significant breaks in series between 2016 and 2023. 

Comparability and deviation from definition for each income variable

F= Fully comparable; L= Largely comparable; P= Partly comparable and NC= Not collected.

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

Methodological background for comparisons is provided in the Methodological note Comparison of household income: European Union Statistics on Income and Living Conditions and National Accounts.

There is no clear lack of coherence in the EU-SILC dataset.

Mean (average) interview duration per household = 18,1 minutes.

Mean (average) interview duration per person = 5.6 minutes.

Mean (average) interview duration for selected respondents (if applicable) =  10.2 minutes.

No specific data revision policy is related to the Dutch EU-SILC.

There are no revisions to report related to the Dutch EU-SILC 2024.

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.

Data is collected by computer-assisted web interviewing and computer-assisted telephone interviewing (CAWI and CATI) and then supplemented with data from administratieve sources/registers.

The following administrative registers are used:

• Municipal Population Register (BRP): Country of birth (respondent, father, mother), martital status, citizenship
• Income and Taxation Register: All income components
• Rent allowance register: HH060 (Current rent) and HY070G (Housing allowances)
• Rent register: HH060
• Register on mandatory services and charges like sewage removal, refuse removal and local taxes: HH070 (Housing costs)
• Register on use of utilities (electricity, gas): HH070 (Housing costs)

Number of households/selected respondents for which an interview is accepted for the database by rotational group and wave

The sampling unit is individuals aged 16 or older (selected respondents).

The EU-SILC survey is an annual survey with a rotational panel and has been carried out as an integrated survey, covering both cross-sectional and longitudinal primary target variables by a single operation. In 2021, CBS applied for the first time a fifth wave in SILC, in 2022 a sixth wave. The cross-sectional sample of EU-SILC 2024 consists of six rotational groups. Group R2 (DB075=2) consists of sample persons who were drawn in 2019 (W6). Group R3 has entered the survey for the first time in 2020 (W5), sample persons in group R5 were interviewed for the first time in 2021 (W4). Sample persons in group R6 in 2022(W3), R7 consists of respondents who were interviewed in 2023 for the first time (W2) and R1 entered the survey in 2024 (W1). 

In order to improve the timeliness, Statistics Netherlands has redesigned the data collection in the first wave. From 2016 onwards, the first wave was conducted as a stand-alone survey with a new sampling design and mixed mode data collection (CAWI and CATI) as a main feature. Sample persons were invited to fill in the questionnaire by means of CAWI. Subsequently, non-respondents had been contacted by phone to conduct the interview by means of CATI. The timeliness of the data transmissions was also took into account in the redesign, as the starting date of the field work changed from July to March/February.

The sample covers the total population in the Netherlands, aged 16 years and older, living in private households and registered in the Municipal Population Register. (BRP). This population register is the sampling frame for statistics in the Netherlands.

The sampling frame is the Municipal Population Register (BRP). This population register contains all individuals who are registered in Dutch municipalities. It does not include homeless people or people living in institutions.

The sampling design can be classified as a stratified random sampling design.

Stratification involves the division of the population into sub-groups, or strata, from which independent samples are taken. In the new sampling design for the first wave in 2016, a stratified sampling frame of persons aged 16 and over was constructed. Sample persons were divided into 21 strata. These strata were defined on the basis of household income, age and number of household members belonging to the target population: 

Ten deciles of the equivalised household income

The number of household members aged 16 or over, two classes: 1, > 1

Age, two classes : 16, > 16

Within each income decile, sample persons were classified according to the number of persons aged 16 and over in the household. A distinction was made between households with 1 and households with two or more members aged 16 and over. At the sampling stage, the inclusion probability for sample units in the first group was twice as large as that of sample units in the second group. In this way, the sample design resembles a household sample with regard to (first order) inclusion probabilities. Households with only one person aged 16 or over have the same inclusion probability as households with two people in the target population.

The inclusion probabilities of the sample design were inversely proportional to pre-estimated response probabilities per stratum. Sample persons aged 16 were oversampled with a factor 2. This prevents them from being under-represented in the panel and in the cross-sectional component.

Anually, from 2005 onwards.

2024 data were collected from February 2024 to September 2024

Mode of data collection

The data collection is a mixed-mode design: starting with internet mode (CAWI), followed by telephone interviewing (CATI) of non-respondents. The strategy is composed of the following steps:

• All sample units receive a letter containing the internet address where the web questionnaire can be found and a personal login to gain access to the web questionnaire. In the letter the sample person is requested to complete the questionnaire through the internet. The letter is sent on Thursday so as to ensure sample units receive it just before the weekend.
• One week and two weeks after the first letter sample units that did not yet fill in the questionnaire receive a letter reminding them to do so. Again both letters are sent on Thursday.

One week after the second reminder the web questionnaire is closed. Non-respondents to the web questionnaire are re-approached by telephone if their telephone number is available at Statistics Netherlands. This approach is not announced in a preceding letter. However, in the letters mentioned above sample units are notified that an approach by telephone may be carried out if they do not respond to the web questionnaire. The fieldwork period for telephone interviewing starts 4,5 weeks after the first invitation letter and takes about a month. For each sample units nine call attempts are made at maximum.

The CATI interviewers receive instruction about the design and the content of the survey by means of e-learning. In the e-learning module a test is included that must be completed by interviewers. Interviewers were specifically trained in convincing respondents and to apply prescribed interview techniques. Only interviewers with EU-SILC experience were involved in the 2022-opereation.

In the first wave, incentives (iPad lottery) were used. In wave 2, 3 and 4 sample units received an unconditional incentive (5 euros) which was sent with the invitation letter.

Description of collecting income variables

Assesment during data collection

During the interviews, checks are carried out to ensure that the answers are correct. If the respondent provides a response that clearly deviates from what is considered reasonable, a message appears asking to check or correct the answer.

Assesment of microdata

Data processing is mainly automated. Data-checks are developed for new modules, but also comparing results with previous years.

Standard error analyses is conducted in order to ensure the sample remains above the prescribed limits. 

Estimates are compared with the corresponding values from previous years.

Estimation and Imputation

As income data are based on register information – except for the questions concerning some inter-household transfers (paid and received)– the income variables do not consist of partial unit non-response or item non-response. If the household respondent refused to answer or did not know the amount of the inter-household transfers mean value imputation was used.

Company car

The estimation of the value of ‘company car’ has been specified by the amount of benefit for which the recipient is assessed for tax purposes. The calculation of the employee income component ‘company car’ follows the rules of the tax authorities. The additional wages or additional income is a percentage of the car’s (original) value.

Current rent related to the occupied dwelling

Current rent related to the occupied dwelling (HH060) is imputed when missing and estimated based on the number rooms in the households’ main residence.

Total housing cost

Electricity and gas consumption costs are part of housing costs. Data on household energy consumption are obtained from energy companies. Where consumption information is missing, it is imputed based on characteristics of the dwelling and the household.

See Annex 2: Item_nonresponse.

The weighting procedure is described in Annex 5  - Weighting.

Not available.

Not applicable.

Not applicable.

See Annex 9_Rolling Module for metadata to the 2022-Modules.