Hungarian Central Statistical Office

Quality of Life Statistics Department

Living Standard Statistics Section

HU-1024 Budapest Keleti Károly u. 5-7.

30 May 2025

30 May 2025

10 October 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);
• Geographical code list (SCL Geo Code);
• The recommendations made by the United Nations in the Canberra Group Handbook on Household Income Statistics should also be taken into account.

For more details on the classifications used, please see Eurostat code list or Statistics explained glossary on classifications.

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 EU Regulation (EU) 2019/1700, EU Regulation 2019/2181, and EU Regulation 2019/2242. Additional information about microdata access is available in Statistics on Income and Living Conditions - Access to microdata - Eurostat 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 Hungary. Annex II of the EU regulation 2019/2242 defines specific statistical units per variable and specifies the content of the quality reports on the organization of a sample survey in the income and living conditions domain pursuant to EU regulation 2019/1700 of the European Parliament and of the Council.

The target population is private households and all persons composing these households having their usual residence in Hungary. A private household means a person living alone or a group of persons who live together, providing oneself or themselves with the essentials of living.

Definitions of reference population, household and household membership (specify any deviation from Eurostat definition in your country)

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.

Reference area is the territory of Hungary.

HU-SILC was introduced into the statistical system in Hungary in 2005.

Datasets are available from 2005 till the current year covered in this report namely 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

According to the regulation:

• Survey year indicated with T
• Income reference period is the previous calendar year: T-1

There was not any changes compared to the corresponding regulation.

EU regulation (EU) 2019/1700 was publish in the OJ on October 10, 2019, establishing a common framework for European statistics relating to persons and households, based on data at individual level collected from samples (IESS). The Annex to the Commission Implementing EU regulation (EU) 2019/2180 of December 16, 2019 specifies the detailed arrangements and content for the quality reports pursuant to EU regulation (EU) 2019/1700 and EU regulation 2019/2242.

Confidential microdata are not disclosed by Eurostat. Access to confidential microdata for scientific purposes may be granted on the  basis of EU regulation 557/2013 and EU regulation 223/2009 on European statistics.

We follow the correcponding EU regulation on confidentiality.

Our national regulations and any corresponding information are available on our website: Központi Statisztikai Hivatal (ksh.hu).

HU-SILC data collection has a standard data publication policy.

The fieldwork is carried out from February to April. The first dissemination of the results are in a form of publication on Income, Living Condition, Poverty and Social Exclusion in October year T.

The data tables are uploaded to STADAT database which is available on our website at the same time. Untill the official validation by Eurostat, all data are treated as preliminary, while after the official validation the data treated as final.

Information on the release available on this link: Központi Statisztikai Hivatal (ksh.hu).

European release of EU-SILC data is done according to predifined calendar which is publicly available on this link: 

Release calendar - Eurostat (europa.eu)

In line with the Community legal framework and the European Statistics Code of Practice, Eurostat disseminates European statistics on Eurostat's website (see section 10 - 'Accessibility and clarity'), respecting professional independence and in an objective, professional and transparent manner in which all users are treated equitably. The detailed arrangements are governed by the Eurostat protocol on impartial access to Eurostat data for users. Additional information about microdata access is available in Statistics on Income and Living Conditions - Access to microdata.

Annual

Standard data and publication is in October of data collection year. 

That time a comprehensive study and a STADAT database is available for users.

Helyzetkép | 2023 (ksh.hu)

A comprsensive study is avalaible on our website on Living Standars of households including income situation and poverty and social exclusion indicators.

Please note that according to the Hungarian data dissemination policy data are marked with refence year instead of EU standard of data collection year.

 The same time of the study publication data are available for users in STADAT database in our website.

 KSH Statinfo v39 | Témakör választó.

HCSO Dissemination Directorate continously collects information on number of visits on our website. This is available by statistcal themes, by month and annual breakdowns. 

Detailed information is available for researcher on the access to dataset for reserach purposes on our website.

In order to support scientific research, HCSO provides access to microdata files for scientific purposes only, subject to researcher accreditation, as follows.

1. Public used files: Publicly accessible micro-data files with strong anti-disclosure protection, of which two groups are available on our website. The sample files, which support research work, and educational micro-data files.
2. Release of anonymised mocrodata: Micro-data files containing data from statistical observation units with protection against disclosure that minimises the possibility of identifying the statistical unit concerned. Only for scientific research purposes, if there is a research institutional background.
3. Safe centre: Access to individual-level statistical data that cannot be directly identified, subject to a high level of data protection and strict compliance with data protection regulations. Access in a secure, camera-monitored environment. Access to data files requested for scientific research purposes only.
4. Remote Execution: This means the use of data files that can only be requested for scientific research. The researcher does not have direct access to the requested micro-data files, the query is run by the HCSO based on the program code/specification.

Dissemination uses the channels described in 10.2 - 10.4.

Not available.

HU-SILC follows the standard methodoly and meta information of HCSO.

Details and publications are avaliable on this link: Központi Statisztikai Hivatal (ksh.hu)

Annex 10 - provide information on metadata on Benefits.

For annexes, see Annexes section.

All required concepts are provided, 100%.

See details in 10.6.

All social surveys follows the general quality assurance policy in HCSO.

One of the basic documents of HCSO's commitment to quality is the Quality Policy (the relevant document is available on the link below). The Quality Policy issued in February 2023 follows the directions set out in the Office's strategy, the guidelines of the European Statistics Code of Practice and the National Statistics Code of Practice, and the requirements of the ISO 9001:2015 Quality Management System.

Quality management has unified concept in HCSO. It is based on GSBPM 5.0 model.

Quality assurance guidelines is avalabale on the link, in Hungarian only. 

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

Eurostat carried out a general User Satisfaction Survey (USS) in 2024 (User Satisfaction Survey by years) to obtain a better understanding of users’ needs and satisfaction with the services provided by Eurostat. The survey showed that EU-SILC is highly relevant to users. For the majority, both aggregates and microdata are considered 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 annual variables. Users emphasized their strong need for more detailed micro-data.

For more information, please consult the User Satisfaction Survey.

HU-SILC follows the corresponding regulations in the context of collected and transmitted data to Eurostat.

All the variables in the EU-SILC guidelines for 2024 operations are collected.

HY145N variable is not collected since our national taxation system has different concepts. Monthy deducted income have to be summarised in December of the given calendar year. If there is a overpayment or underpayment than it is corrected in December. 

PY035 variable was not collected during the 2024 data collection, however it is planned to be included in the 2026 data collection.

Not available.

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 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 the 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 designs 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.

Main indicators, standard error and CI at country level

Main indicators, standard error and CI at NUTS 2 level

Persistent-risk-of-poverty ratio over four years to the population, 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 for the whole units (households and/or persons) selected into the sample.
  • Item non-response: refers to the situation where a sample unit has been successfully enumerated, but not all required information has been obtained.

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. - Since 2022 the sampling frame of HU-SILC is the Register of Dwellings. It may have 15-20% over-coverage due to empty dwellings, non-residential buildings etc.
• Under-coverage: refers to units not included in the sampling frame. - Our sampling frame may have non-negligible under-coverage, but its' extent is not yet quantified.
• Misclassification: refers to the incorrect classification of units that belong to the target population - Misclassification may affect the stratification variables (especially that used for the stratification of adresses) but its' excent is not yet quantified.

Coverage error

Not appliacble. We use data only from the survey. 

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: unit non-response and item-non response.

1) Unit non-response which refers to the absence of information of the whole units (households and/or persons) selected into the sample. According to Annex VI of the EU regulation 2019/2242

• Household non-response rates (NRh) 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.

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.

Some explanation to the data: The most influential and controversial factor in the nonresponse rates is the denominator of "Ra": "number of valid addresses selected". SILC is a panel survey so we select the sample households when they first enter the survey. After that there is no sample selection, only panel attrition. The number of selected valid addresses for every rotational groups are the number of eligible addresses (existing addresses for living purpose) selected in the initial period of the rotational group. (We propose to avoid the usage of "response rate"/"non-response rate" definitions for t/(t-1) type ratios, because these do not reflect the possible effects of non-response on panel survey quality.) In our case there is an additional temporary factor which somewhat spoils our response rates. Until 2021 the SILC inherited the respondents' sample of HBS so only HBS had a selected samlpe. From 2022 the SILC has its own sample what is going to improve the response rates.

The computation of item non-response is essential to fulfil the precision requirements. The item non-response rate is provided for the main income variables, both at household and personal level.

Item non-response refers to the situation where a sample unit has been successfully enumerated, but not all the required information has been obtained.

See Annex 2.

 Description of data entry, coding controls and the editing system

Sample persons, co-residents and sample households are observed in the survey over the duration of the  four year panel period.

Sample persons moving to a private household within the national territory are covered in the survey and followed to the location of the household.

Sample persons who are no longer members of a private household, or who have moved outside the national territory are dropped from the survey.

Co-residents living in a household containing at least one sample person is followed.

A sample household shall be dropped from the survey in the following situations:

(a) the household was not enumerated for a single year due to either of the following reasons: (1) the address was impossible to locate; (2) the address was non-residential or unoccupied; (3) there was no information on what happened to the household (the household was lost); (4) the household refused to co-operate;

(b) the household was not contacted in the first year of the panel or in two consecutive years of the panel due to either of the following reasons: (1) it was not possible to access the address; (2) the whole household was temporarily away or unable to respond due to incapacity or illness or for other serious reasons.

Re-interview rates by wave

Not appliacable.

Timeliness of information reflects the length of time between its availability and the event or phenomenon it describes.

This section refers to the timing of dataset sending to Eurostat and first publication of results from the data collection.

HU-SILC 2024 dataset was set sent to Eurostat in 20 December 2024. At the time of compliling the report, the database has already been validated.

The first results of the data collection were published in 22 October 2024 in a form of a comprehensive study and diagram set. At the same time the data on the STADAT database was also avalable in the section Living conditions.  All data are considered as preliminary untill we receive the validation certificate from Eurostat.

Time lag between the reference period (income year 2023) and first results publication (October 22, 2024) is 9 months.

The final result will be published in the STADAT database in June 2024.

Time lag between the reference period (income year 2023) and final results publication is 17 months.

This section describes the lag between the actual time of the data delivery and the target delivery date.

The actual devilery date was 20 December 2024 to Eurostat. The target date was 31 December 2024. There was a difference of 11 days between the actual date and the target date.

The actual publication date was 22 October 2024 on our website. It was done according to the predefined internal schedule. No lag between the dates. 

The result are comparable in NUTS2 level. The key indicators are avalable by NUTS regions on our website in a time series from 2014 (see 5.1.2. chapters on the link). The key indicators are calculated according to the new AROPE concept.

Not applicable.

Due to the revision in 2025, there was a break in the series starting from the 2019 recording.

See Annex 8.

The last break in the series occurred at the 2019 recording, therefore the comparable period is 6 years from 2019 to 2024.

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.

Coherence with National Accounts for income variables

There are no known internal logical inconsistencies.

Mean (average) interview duration for selected respondents not applicable.

The revision of the annual datasets from the 2019–2024 data collections was carried out in the second quarter of 2025, in accordance with the pre-defined schedule.

The primary objective of the data revision was to reweight the datasets using benchmark figures derived from the results of the 2022 census. An additional aim was to improve methodological procedures, with a particular focus on fine-tuning the grossing-up, imputation, and correction methods for income data. These improvements were mainly intended to eliminate previously observed clustering in the income distribution, reduce excessive volatility in the at-risk-of-poverty gap, and resolve earlier inconsistencies between gross and net incomes. As a result of the revision, the accuracy of poverty-related indicators and the internal consistency of the data have significantly improved.

The new weights affected all indicators. The methodological adjustments and refinements in data processing concerned the income variables; therefore, the indicators that rely on income data were subject to more substantial changes.

Main Steps of the Revision Procedure

The revision process included verification of income classifications, logical and content consistency checks, determination of gross/net incomes, correction and imputation of income data, weighting, and quality checks with macro-level validation.

Detailed Description of the Revision Procedure

Income classifications were reviewed and corrected according to Eurostat definitions. Logical and consistency checks ensured demographic plausibility and coherence between employment status and income. Gross and net incomes were determined based on tax and social contribution rules for the relevant years. Missing income data were supplemented using aggregated administrative data as well as stratified averages and medians. Employee incomes were imputed through individual-level linkage with data from the tax authority. From 2023 onwards, individual-level administrative linkage for old-age pensions was also initiated. Survey weights were updated using the 2022 Population Census as a reference, and calibration ensured alignment with control totals. Quality checks included internal consistency analyses and reconciliation with macro-level aggregates to ensure plausibility and reliability of the estimates.

See more data about the revision in annex 11.

The revision of the 2019-2024 data collections was implemented in the second quarter of 2025.

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.

HU-SILC is direct data collection. All the information are derived from the data collected from the respondent during the field work. 

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.
We use two-staged stratified sampling.
At stage 1 we have a stratified sample of localities with pps selection. At stage 2 (in the sampled localities) we have a stratified simple random sample of addresses.
At stage 1 the population of localities are stratified. Each of the larger localities is a stratum of its own. These are the self-representing localities, the number of which is 126. Smaller localities are stratified by NUTS2 regions, personal income tax base per capita, the size of locality and the prevalence of internet usage.
At stage 2 the adresses are stratified by the estimated value of dwelling.
At stage 1 localities were selected with pps without replacement.
At stage 2 addresses within each strata were selected with srs without replacement.

Actual and achieved sample size:

Achieved sample size:

Number and percentage of Proxy interview:

Longitudinal information 2021-2024:

Achieved household sample size

Achieved individual sample size 

At stage 1 the sampling units are localities.

At stage 2 the sampling units are addresses.

Since 2022 the sampling frame of HU-SILC is the Register of Dwellings. 

HU-SILC is an annual data collection. It is carried out in every year in a fixed schedule time frame for the data collection.

Fieldwork timing and sample development over time HB050.

Mode of data collection

Description of collecting income variables

Fieldwork duration

Dataset validation is done by Eurostat.

Regarding income items we do use imputation. Completing the income variables during data collection is a condition for accepting the questionnaire. For the imputation procedure, the Hungarian Central Statistical Office utilizes administrative data obtained from the National Tax and Customs Administration and the Hungarian State Treasury.

The imputed rent is calculated by the Housing Statistics Section. 

The genaral approach we use for imputed rent determination:

Hungary has got a special housing market situation in the aspect of imputed rental calculation. The share of market rental sector is cca. 5-8 %. Owner occupiers constitute 95-92 % of the total housing market.  Personal attitudes and social circumstances make stronger the role of private property in the housing market. Geographical and physical attributes and foundemantally  the location of the dwelling within the country determines mostly the value of a dwelling, and possibility to let it on the rental market. Comparison of standard of living on the basis of EU-SILC survey between different social groups is not affected by the minor groups of market renters. The calculation of imputed rent is reasoned by international comparison of data within EU.

The imputed rent was calculated based on stratification method. The dwellings in HU-SILC were startified into 30 clusters according to geographical location of the dwelling since it is the key factor which determines the market rent. Then the avarage rents per 1 square meter corresponding to each geographical unit coming from Dwelling statistics were applied. The imputed rent was calculated as average rent per square meter in the given geo strata multiplied by actual square meter of the dwelling.   We consider this approach as the best available to estimate imputed rent for the time being in the context of small rental market.

See 18.5. and 13.3.4.

The weighting of EU-SILC is regulated in detail by chapter 8 of „Methodological Guidelines and Description of EU-SILC Target Variables” so we follow the principles laid down there. Our primary goal is to create cross-sectional weights for the current years’ sample and longitudinal weights for the 2, 3 and 4 years long panel subsamples. We create weights for every rotational groups separately to form the starting points (base weights) of final weights. The process starts with a common cross-sectional weighting of the new rotational groups. The initial weights are the design weights (the reciprocal of the inclusion probabilities) which are adjusted by calibration to get the cross-sectional weights. These weights are updated on personal level by logistic regression models to get the next periods’ base weights.  There are two parts of this modelling: first we classify persons with unknown status into „in scope” and „out of scope” categories to identify every persons who have left the target population (died, moved abroad or to an institution). Then we model the probability of remaining in respondents’ sample for every „in scope” persons (panel attrition). These models use various explanatory variables: geographical area, gender, age, economic activity, employment status, educational attainment, income, health related indicators, dwelling type, tenure status and some indicators of well-being. As we should avoid extreme dispersion of final weigths it is advisable to limit the weight range. The next step is the creation of weights of people arrived to the sample households by the next period.  Then these personal weights are transformed to household weights by generalized weight share method to form the initial weights of a calibration. The calibrated weights are reduced proportionally to the sample size of the rotational groups. The resulting weights are used again as primary weights  of a calibraton which renders the cross-sectional weights of a given year. If it is the final year of the panel then these are the RB050 and DB090 weights. The before mentioned calibrations are implemented by a raking ratio method. We apply a relatively large table of control totals which has two parts. The first part contains the demoghrapic control numbers by regions: 0-14 years old women/men, 15-29 years old women/men, 30-59 years old women/men, 60+ years old women/men (8*8 control numbers). The second part contains control totals related to economic activity and education broken down by 3 settlement types: Budapest – other bigger cities – other settlements. These are the number of employees, enterpreneurs, pensioners, unemployed, students, employed people broken down by schooling, number of households broken down by the number of children and single person households (3*16 control totals).  We calibrate iteratively to the 2 sets of control numbers and apply weight trimming to avoid influential records. The creation of longitudinal weights (RB062-RB064) is based on the initial years’ base weights and the modelling of panel attrition as described before.

See Annex 6.

Not applicable.

Not applicable.

Household definition: In our case the sample is based of the list of addresses. If there is a person (age 18+) who is studying and living away in a rented flat, other private address, than she/he is not considered as a household member in the selected and responding household.  Regarding students in dormitory - they are out of scope.