Reference metadata describe statistical concepts and methodologies used for the collection and generation of data. They provide information on data quality and, since they are strongly content-oriented, assist users in interpreting the data. Reference metadata, unlike structural metadata, can be decoupled from the data.
Directorate: Social Statistics Unit: Social and Living Conditions
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
Guglgasse 13, 1110, Vienna, Austria
1.6. Contact email address
Confidential because of GDPR
1.7. Contact phone number
Confidential because of GDPR
1.8. Contact fax number
Confidential because of GDPR
2.1. Metadata last certified
28 May 2025
2.2. Metadata last posted
18 November 2025
2.3. Metadata last update
24 October 2025
3.1. Data description
The European Union Statistics on Income and Living Conditions (EU-SILC) is a survey-based instrument aiming at collecting timely and comparable cross-sectional and longitudinal multidimensional microdata on income, poverty, social exclusion and living conditions. In addition, it collects module variables every three years, six years or ad-hoc new policy needs modules.
The EU-SILC instrument provides two types of data:
Cross-sectional data pertaining to a given time or a certain time period with variables on income, poverty, social exclusion and other living conditions;
Longitudinal data pertaining to individual-level changes over time, observed periodically over four‐or more year rotation scheme (Annex III (2) of 2019/1700). EU-SILC Austria provides longitudinal data pertaining to a four year rotation scheme.
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.
3.2. Classification system
International Standard Classification of Education (ISCED'2011);
International Standard Classification of Occupations (ISCO-08);
Classification of Economic Activities (NACE Rev.2-2008);
Common classification of territorial units for statistics (NUTS 2);
SCL - Geographical code list;
The recommendations made by the United Nations in the Canberra Group Handbook on Household Income Statistics should also be taken into account.
For more details on the classification used please, see EU Vocabularies, Eurostat's metadata server or CIRCABC.
Note that due to a very small number of respondents in Austria with ISCED level 0, i.e., less than primary education (primary education is compulsory in Austria), these respondents are lumped together with the ISCED level 1 category, i.e. primary education.
3.3. Coverage - sector
Data refer to all private households and individuals living in the private households in the national territory (Austria) 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.
Further details are provided in items 5, 15.2.2 and 18.3.
3.5. Statistical unit
Statistical units are private households and all persons living in these households who have usual residence in Austria.
Annex II of the Commission implementing regulation (EU) 2019/2242 defines specific statistical units per variable and specifies the, content of the quality reports on the organization of a sample survey in the income and living conditions domain pursuant to Regulation (EU) 2019/1700 of the European Parliament and of the Council.
3.6. Statistical population
The target population is private households and all persons composing these households having their usual residence in Austria. Private household means a person living alone or a group of persons who live together, providing oneself or themselves with the essentials of living.
3.6.1. Reference population
Definitions of reference population, household and household membership:
Reference population
Private household definition
Household membership
The reference population of EU-SILC is all private households and their current members residing in the territory of Austria at the time of data collection. Persons living in collective households and in institutions are generally excluded from the target population.
There is no difference to the standard EU-SILC concept.
Accommodations in with at least one person aged 16 or over has their main place of residence (Hauptwohnsitzmeldung). Institutional housing facilities and dwellings in which no person aged 16 or over has their main residence are not included.
There is no difference to the standard EU-SILC concept.
Person living in the dwelling on a permanent basis, i.e. for at least twelve months.
There is no difference to the standard EU-SILC concept.
3.6.2. Population not covered by the data collection
The sub-populations that are not covered by the data collection include:
those who have moved out of Austria's territory; or
those who have no usual place of residence; or
those living in institutions; or
those who have moved into an institution compared to the previous year.
3.7. Reference area
EU-SILC in Austria covers all of Austria, i.e., the whole geographical area.
3.8. Coverage - Time
Annual data, reference year 2024. The income reference period is the calendar year 2023.
EU-SILC in Austria has been carried out annually since 2004 based on a rotational panel design . The income reference period is always the year preceding the survey year.
Please also refer to the descriptions of reference periods in section 5.
3.9. Base period
Not applicable.
The data involves several units of measure depending upon the variables. Income variables are transmitted to Eurostat in national currency (EUR).
For more information, see methodological guidelines and description of EU-SILC target variables available on CIRCABC.
Description of reference period used for incomes:
Period for taxes on income and social insurance contributions
Income reference periods used
Reference period for taxes on wealth
Lag between the income reference period and current variables
The reference period for taxes on income and social insurance contributions for EU-SILC 2024 is 2023. For the survey years 2021-2023, which are part of the longitudinal component of the reconciled data, the reference period is also the year preceding the survey year.
There is no difference to the standard EU-SILC concept.
The income reference year for EU-SILC 2023 was 2022. For the survey years 2020-2022, which are part of the longitudinal component of the reconciled data, the income reference period is also the year preceding the survey year.
There is no difference to the standard EU-SILC concept.
There are no taxes on wealth in Austria.
This refers to the lag between the income reference period and the household interview date.
The fieldwork for EU-SILC 2024 lasted from February to July. The fieldwork for EU-SILC 2023 lasted from March to August. The fieldwork for EU-SILC 2022 and 2021 lasted from February to July.
6.1. Institutional Mandate - legal acts and other agreements
EU Regulation (EU) 2019/1700 was published 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 regulation (EU) 2019/2180 of December 16, 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.
At national level, the Ordinance of the Federal Minister of Labour, Social Affairs and Consumer Protection on Statistics on Income and Living Conditions (Income and Living Conditions Statistics Ordinance - ELStV; Federal Law Gazette II No. 277/2010) was issued on 31 August 2010, which regulates the collection and linking of administrative data records. This ordinance was amended in 2013 (Federal Law Gazette II, No. 230/2013), in 2018 (Federal Law Gazette II, No. 313/2018), in 2019 (Federal Law Gazette II No. 319/2019), in 2021 (Federal Law Gazette II No. 38/2021) and most recently in 2024 (Federal Law Gazette II No. 80/2024). The currently valid version can be found at the Legal Information System of the Republic of Austria (in German).
6.2. Institutional Mandate - data sharing
Confidential microdata are not disclosed by Eurostat. Access to confidential microdata for scientific purposes may be granted on the basis of Commission Regulation 557/2013 and Regulation 223/2009 of the European Parliament and the Council on European statistics.
At national level, Statistics Austria provides selected anonymised microdata standardised data sets (SDS) and as task-specific data sets (ADS) for use in scientific research and education. In the sense of statistical secrecy and as a measure of data protection, the data are anonymised so that a direct or indirect identification of a concrete individual case is de facto impossible.
SDS can be downloaded after registration and activation. By registering, users agree to the terms of use. For more information, see part 8. Additional information about microdata access is available at Center for science and especially part 10.4.
7.1. Confidentiality - policy
Data published or made available as micro-data are fully anonymized. No confidential data are involved.
7.2. Confidentiality - data treatment
Linking to other data sources (register data) is done according to the national regulation using a fully anonymized key.
User data are fully anonymized, regional variables below NUTS2 and some other variables (e.g. day and month of birth) are excluded, and several variables (e.g. age above 80 years, country of birth, size of dwelling etc.) are recoded to prevent from identification.
For tables and indicators small cells are masked (based on sample size or standard error).
8.1. Release calendar
Austrian EU-SILC data are published annually, in April the following year. The data for EU-SILC 2024 were published on 29th of April 2024.
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.
At national level, Statistics Austria provides selected anonymised microdata standardised data sets (SDS) and as task-specific data sets (ADS) for use in scientific research and education. In the sense of statistical secrecy and as a measure of data protection, the data are anonymised so that a direct or indirect identification of a concrete individual case is de facto impossible.
SDS can be downloaded after registration and activation (for more information on how to gain access see part 10.4).
Besides these EU SILC-specific publications, there are also other publications containing results from EU-SILC in other thematic publications of Statistics Austria (e.g. housing, migration and immigration, education, SDGs etc.).
Micro-data for national users (for scientific research and education purposes) is available after registration, by providing information about the purpose of use, as well as identifying the persons that will gain access. By registering, users agree to the terms of use. Additional information about microdata access is available at the Center for science (see 10.4).
The use of Austrian data from EU-SILC can be requested from Statistics Austria (Contact: +43 (1) 71128 8285, richard.heuberger@statistik.gv.at)
The cross-sectional data sets have been available since the start of the survey (2003). The anonymised datasets contain the non-monetary questionnaire variables as well as the aggregated and imputed income components according to the EUROSTAT target variable logic (EUROSTAT). The data are made available free of charge for scientific purposes. Compliance with the terms of use by the project staff must be ensured by the respective project management.
For the use of the data in courses, compliance with the terms of use of the data must be ensured by the course instructor through appropriate measures (e.g. signature list). This includes in particular that the data is not used beyond the purpose of the course.
The data from EU-SILC is also used for academic theses. In this case the professor who supervises the thesis must be named as the project leader and must therefore comply with the the data usage conditions. The data usage agreement must also be signed by the supervisor.
By registering, users agree to the terms of use. Additional information about microdata access is available at the Center for science (more EU-SILC relevant information is available on the German version of the website).
The EU SILC results are of great interest to the policy makers in Austria (for e.g. the Federal Ministry for Social Affairs, Health, Care and Consumer Protection), researhers, as well as the media. Thus, next to the data published in the publications described in parts 10.1 to 10.4, EU-SILC results can be also found in
other thematic publication formats of Statistics Austria (e.g. housing, migration and immigration, education, SDGs etc.)
Publication of articles in policy and scientific papers (for example the Social report 2024 (German only))
Scientific user conferences organized by Statistics Austria every few years
For more information on the metadata on the income benefits, please refer to Annex 10: Metadata on benefits.
10.6.1. Metadata completeness - rate
100%
10.7. Quality management - documentation
Product quality is at the centre of Statistics Austria's quality work. In order to be able to achieve a sufficient quality for statistical products, it is necessary to comply with specified standards in the respective process steps. In order to ensure this, Statistics Austria has created guidelines that relate to all topics that are relevant to the statistical production process. The aim of the quality guidelines is, on the one hand, to give the employees of Statistics Austria a reference for all relevant process steps and, on the other hand, to convey to users that compliance with certain standards is mandatory for the products of Statistics Austria.
The quality management unit as shown in the organizational structure of Statistics Austria is a centralized unit reporting directly to the Director General for Statistics. The competences and tasks of the quality management unit comprise all components of the quality policy of Statistics Austria.
Product quality is a central part of the Quality Commitment Statement. Quality reports are available for all statistical products (part of the Standard-Documentations) for monitoring product quality, as well as Statistics Austria’s quality guidelines, Since 2004 Statistics Austria performs a series of so called "Feedback-Talks" on Quality based on the Standard Documentations (including quality reports). All major statistical products are covered by the set of Standard-Documentations. The feedback talks are conducted in close cooperation with the Quality Subcommittee of the Statistics Council.
11.2. Quality management - assessment
In 2010 Statistics Austria elaborated quality guidelines covering all stages of the statistical production process. The full version of quality guidelines is available on the website of Statistics Austria:
Statistics Austria participated in the latest round of European Peer Reviews from April 4th to 8th, 2022. All information about the peer review, including previous rounds and links to the report can be found on Statistics Austria’s web site.
12.1. Relevance - User Needs
The main users of EU-SILC statistical data are policy makers, research institutes, media, and students.
On average, the EU-SILC team at Statistics Austria processes about 60 data requests per year. Users are mainly universities, research institutes and scientific departments of public institutions. EU-SILC data are mainly used for scientific research and academic degrees. Statistics Austria provides user with the income target variables, the national questionnaire variables for non-income variables and some calculated indicator variables.
12.2. Relevance - User Satisfaction
Eurostat carried out an online general User Satisfaction Survey (USS) in 2024 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, since the topic “Population and social conditions” was the one in which the users were most interested in (page 17). For the majority, both aggregates and micro-data were important or essential in their work irrespective of the purpose of their use. Finally, users were satisfied with overall quality of the service delivered by Eurostat, which encompasses data quality and the supporting service provided to them.
The following variables have not been collected for EU-SILC in Austria. The reason for not collecting is explained below.
PY020G/PY020N:
From 2014 onwards Statistics Austria decided to exclude all questions on non-cash employee incomes, since modelling was starting to get problematic and since incomes from these sources are not that important in Austria. Additionally, these incomes are not included in the computation of household incomes.
PY021G/PY021N
is fully included in PY010G/PY010N.
HY120G/HY120N:
There are no taxes on wealth in Austria.
HY121G/HY121N:
There are no taxes on wealth in Austria.
HY145N:
HY145N is not delivered because this variable is filled only for countries that record net income at the component level only.
HY170G/HY170N:
From EU-SILC 2011 onwards HY170G is no longer collected because of it is not regarded as a relevant component of the household income (amounts are too small).
Optional variables:
RL080:
Not collected in order to reduce respondent burden.
HI130G:
Not collected in order to reduce respondent burden.
HI140G:
Not collected in order to reduce respondent burden.
12.3.1. Data completeness - rate
Not requested by Reg. 2019/2180.
13.1. Accuracy - overall
According to Reg. (EU) 2019/1700 Annex II, precision requirements for all data sets are expressed in standard errors and are defined as continuous functions of the actual estimates and of the size of the statistical population in a country or in a NUTS 2 region. For the income and living conditions domain, the estimated standard errors of the following indicators are examined according to certain parameters set:
Ratio at‐risk‐of‐poverty or social exclusion to population;
Ratio of at‐persistent‐risk‐of‐poverty over four years to population;
Ratio at‐risk‐of‐poverty or social exclusion to population in each NUTS 2 region.
Further information is provided in section 13.2 Sampling error.
13.2. Sampling error
EU-SILC is a complex survey involving different sampling designs in different countries. In order to harmonize and make sampling errors comparable among countries, Eurostat (with the substantial methodological support of Net-SILC2) has chosen to apply the "linearization" technique coupled with the “ultimate cluster” approach for variance estimation.
Linearization is a technique based on the use of linear approximation to reduce non-linear statistics to a linear form, justified by asymptotic properties of the estimator. This technique can encompass a wide variety of indicators, including EU-SILC indicators. The "ultimate cluster" approach is a simplification consisting in calculating the variance taking into account only variation among Primary Sampling Unit (PSU) totals. This method requires first stage sampling fractions to be small which is nearly always the case. This method allows a great flexibility and simplifies the calculations of variances. It can also be generalized to calculate variance of the differences of one year to another.
The main hypothesis on which the calculations are based is that the "at risk of poverty" threshold is fixed. According to the characteristics and availability of data for different countries, we have used different variables to specify strata and cluster information.
In particular, countries have been split into 3 groups:
BE, BG, CZ, IE, EL, ES, FR, HR, IT, LV, HU, PL, PT, RO, SI, UK and AL, whose sampling design could be assimilated to a two-stage stratified type we used DB050 (primary strata) for strata specification and DB060 (Primary Sampling Unit) for cluster specification;
DK, DE, EE, CY, LT, LU, NL, AT, SK, FI, CH whose sampling design could be assimilated to a one stage stratified type we used DB050 for strata specification and DB030 (household ID) for cluster specification;
MT, SE, IS, NO, whose sampling design could be assimilated to a simple random sampling, we used DB030 for cluster specification and no strata.
For more information, please refer to Annex 3 on 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.
Statistics Austria uses a calibrated bootstrap procedure for standard error estimation of indicators in EU-SILC. Recalibration of bootstrap weights is carried out by considering geographic, sociodemographic as well as income and activity status characteristics that are also marginal distribution for the calibration of household weights. The R Package “surveysd” developed by the Methods Department of Statistics Austria is applied for this work. In EU-SILC 2024 this recalibration procedure of the bootstrap replicates weights was extended to the longitudinal weight RB064 in order to take into account the effect of the calibration of longitudinal weights on the standard error of the persistent at-risk-of-poverty-rate (the calibration procedure for longitudinal weights is described in Annex 5).
The standard errors, and the confidence intervals of the main indicators at country level (AT) and NUTS2 levels are presented in Annex A. Annex A also included the standard errors and confidence intervals of the persistent-risk-of-poverty ratio over four years.
For more information on sampling errors, please also refer to Annex 3.
13.3. Non-sampling error
Non-sampling errors are basically of 4 types:
coverage errors: errors due to divergences existing between the target population and the sampling frame
measurement errors: errors that occur at the time of data collection. There are a number of sources for these errors such as the survey instrument, the information system, the interviewer and the mode of collection
processing errors: errors in post-data-collection processes such as data entry, keying, editing and weighting
non-response errors: errors due to an unsuccessful attempt to obtain the desired information from an eligible unit. Two main types of non-response errors are considered
Unit non-response: refers to absence of information of the whole units (households and/or persons) selected into the sample
Item non-response: refers to the situation where a sample unit has been successfully enumerated, but not all required information has been obtained
Non-sampling errors are basically of 4 types:
coverage errors: errors due to divergences existing between the target population and the sampling frame
measurement errors: errors that occur at the time of data collection. There are a number of sources for these errors such as the survey instrument, the information system, the interviewer and the mode of collection
processing errors: errors in post-data-collection processes such as data entry, keying, editing and weighting
non-response errors: errors due to an unsuccessful attempt to obtain the desired information from an eligible unit. Two main types of non-response errors are considered
unit non-response: refers to absence of information of the whole units (households and/or persons) selected into the sample
item non-response: refers to the situation where a sample unit has been successfully enumerated, but not all required information has been obtained
13.3.1. Coverage error
Coverage errors include over-coverage, under-coverage and misclassification:
Over-coverage: relates either to wrongly classified units that are in fact out of scope, or to units that do not exist in practice.
Under-coverage: refers to units not included in the sampling frame.
Misclassification: refers to incorrect classification of units that belong to the target population
Frequency of timing between the sampling frame and the target population:
In 2024 there was anywhere between four months (start of data collection) and ten months (end of data collection) discrepancy between the drawing of the sample and the target population. The sample frame was drawn from the central residence register with cut-off date 30 September 2023. People who moved in or out of a private household in Austria between this date and the period of data collection (February to July 2024; for more see parts 3.3, 5, 14.1, and 18) are under, i.e., overrepresented in the sample, respectively.
This discrepancy may lead to both over- and under-coverage (see 13.3.1.1).
13.3.1.1. Over-coverage - rate
Coverage error
Main problems
Population (sub-population)
Size of error
Comments
Over-coverage
Persons in private households in Austria
2.2% (214 addresses)
214 addresses in the gross sample of EU-SILC 2024 turned out to be non-existent/non-residential or non- private/unoccupied/not principal residences.
Under-coverage
not available
not available
The extend of this issue is not known. Some addresses might be incorrectly registered as businesses. Other addresses maybe added between the drawing of the sample (30.september 2023) and the end of the field work (30.july2024). People who are unregistered in the central register are also potentially missed by the sampling design.
Misclassification
no known issues with misclassification
no known issues with misclassification
no known issues with misclassification
13.3.1.2. Common units - proportion
Not requested by Reg. 2019/2180.
13.3.2. Measurement error
Measurement error for cross-sectional data
Source of measurement errors
Building process of questionnaire
Interview training
Quality control
Measurement errors are defined as the difference between the value of a certain variable (provided by the respondent) and the true, but unknown value of this variable. If the distribution of the error made at each single response is not random, the resulting statistic is biased. Elements affecting measurement are:
The questionnaire of EU-SILC is standardized and was developed according to EU-SILC regulations and EUROSTAT guidelines. The newly implemented questionnaire is identically implemented for CATI, CAPI and CAWI mode.
EU-SILC in Austria applies several quality control measures during the fieldwork:
The questionnaire (e.g. the design, content, question wording, sensitivity of questions)
The standardized question wording should include all necessary information to answer the question. If respondents or interviewers need further support to answer the question additional definitions and explanations are integrated in the electronic questionnaire and written remarks for clarification are allowed for each question in CAPI and CATI.
A large number of automatic plausibility checks are integrated in the questionnaire. Since only computer assisted interviewing is used (CAPI, CATI and CAWI)1, the programming of the questionnaire ensures that values which are defined as implausible or incorrect cannot be entered in the questionnaire. Additionally, the majority of the questions are supplemented with help information and examples for the defined terms.
The interviewer (e.g. characteristics, behavior, experience, workload, explanations, probing)
In order to reduce interviewer effects; it is necessary to provide interviewers with sufficient training and supporting measures.
153 CAPI interviewers and 22 CATI interviewers (including one supervisor) conducted the interviews for EU-SILC 2024. CAPI interviewers who have already worked for previous EU-SILC waves did not receive a conventional training at Statistics Austria but were required to make a test interview on their laptop computer to learn about revisions of the questionnaire and especially the questions for the modules 2024.
Results were presented and discussed during an assessment meeting, where interviewers could also share their experience from previous years.
First time CAPI interviewers were fully trained in small groups for eight hours (either personally or by video recording). Additionally, the interviewers received written training material.
CATI interviewers received a two-day theoretical and practical training course.
The trainings and assessments (via test questionnaires) took place on the 12th, 16th and 19th of February 2024, as well as the 13th of March. The debriefing with CATI supervisors took place on the 07th of May 2025.
The questionnaire is adapted yearly based on the experiences of previous years' fieldwork collected during the interviewer debriefings as well as by written feedback written directly in the feedback fields in the questionnaire.
The new CAWI mode eliminates the interviewer effects for a large part of the sample (see 18.3).
The respondents (e.g. problems arising during the cognitive response process, proxy interviews)
As was the case for previous years, pre-tests were also carried out for the 2024 questions of the ad-hoc module. The standard questionnaire launched in its current form for the first time in 2022 (with the introduction of a CAWI module) had already been subject to various test procedures (expert evaluation, cognitive testing, friendly user test, etc.).
CATI interviewing is carried out in a separate telephone studio at Statistics Austria allowing for a controlled and supervised interview situation. During team meetings, the agents have an additional possibility to report problems occurring during the interviews.
The interview situation (e.g. environment, presence of other persons, pressure of time)
Field control: if problems with certain questions or interviews arise, questionnaires are reviewed and corrected by an experienced supervisor, if necessary.
Incomplete data from public registers
In EU-SILC 2024 register data were used to collect 83% of the income data. Since several registers were available, in some cases one register could be used to evaluate if an income component was missing in another register.
1) CAPI = Computer Assisted Personal Interviewing; CATI = Computer Assisted Telephone Interviewing; CAWI = Computer Assisted Web Interviewing
13.3.3. Non response error
Non-response errors are errors due to an unsuccessful attempt to obtain the desired information from an eligible unit. Two main types of non-response errors are considered:
1) Unit non-response which refers to the absence of information of the whole units (households and/or persons) selected into the sample. According to Annex VI of the Reg.(EU) 2019/2242
Household non-response rates (NRh) is computed as follows:
NRh=(1-(Ra * Rh)) * 100
Where Ra is the address contact rate defined as:
Ra= Number of address/selected person (including phone, mail if applicable) successfully contacted/Number of valid addresses/selected person (including phone, mail if applicable) selected
and Rh is the proportion of complete household interviews accepted for the database
Rh=Number of household interviews completed and accepted for database/Number of eligible households at contacted addresses (including phone, mail if applicable)
Individual non-response rates (NRp) is computed as follows:
NRp=(1-(Rp)) * 100
Where Rp is the proportion of complete personal interviews within the households accepted for the database
Rp= Number of personal interview completed/Number of eligible individuals in the households whose interviews were completed and accepted for the database
Overall individual non-response rates (*NRp) is computed as follows:
*NRp=(1-(Ra * Rh * Rp)) * 100
For those Members States where a sample of persons rather than a sample of households (addresses, phones, mails etc.) was selected, the individual non-response rates will be calculated for ‘the selected respondent.
2) Item non-response which refers to the situation where a sample unit has been successfully enumerated, but not all the required information has been obtained.
13.3.3.1. Unit non-response - rate
Unit non-response rate for cross-sectional sample
Address (including phone, mail if applicable) contact rate
Complete household interviews
Complete personal interviews
Household Non-response rate
Individual non-response rate
Overall individual non-response rate
(Ra) A
(Ra) B
(Ra) C
(Rh) A
(Rh) B
(Rh) C
(Rp) A
(Rp) B
(Rp) C
(NRh) A
(NRh) B
(NRh) C
(NRp) A
(NRp) B
(NRp) C
(NRp) A
(NRp) B
(NRp) C
99.90
99.89
99.77
65.78
44.23
92.53
100.0
100.0
100.0
34.28
55.81
7.68
0.00
0.00
0.00
34.28
55.81
7.68
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.
Unit non-response which refers to the absence of information of the whole units (households and/or persons) selected into the sample. According to the Commission implementing regulation (EU) 2019/2180 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.
For the longitudinal unit-non-response data and the response rate please refer to Annex A.
Note. In 2024, 75 Households were excluded for quality reasons even though they did initially participate in the survey (or at least some of the adult household members participated). Despite participation they returned incomplete interviews and thus were excluded from the net sample. These interviews are coded with db135 = 2, interview rejected and in this variable are indistinguishable from those that refused to participate completely.
13.3.3.2. Item non-response - rate
The computation of item non-response is essential to fulfil the precision requirements. Item non-response rate is provided for the main income variables both at household and personal level.
Item non-response which refers to the situation where a sample unit has been successfully enumerated, but not all the required information has been obtained.
Description of data entry, coding controls and the editing system
Data entry and coding
Editing controls
Checks to detect processing errors have been implemented in the electronic questionnaire (programmed in "STATsurv", the survey tool of Statistics Austria), where the entry of a response is checked for ranges and inconsistencies. Problems are indicated to the interviewer and corrected if needed. Checks in the electronic questionnaire have to be commented by the interviewer and in case of CAWI by the respondents themselves, for example when according to the activity calendar the respondent has been self-employed during the last year but does not declare any income from self-employment.
Correction of unaccepted values and inconsistencies that are indicated during the interview is possible. In this case the question is repeated and an option is given to correct the value (in case of misunderstanding) or to re-enter the value (in case the original value is correct).
Another option is to comment the problem in a remark field which is accounted for during data-editing. The same applies to obligatory (interviewer) comments.
During post-data-collection-processing some of the checks included in the questionnaire are repeated and additional checks are conducted. They include formal data checks (e.g. checking of completeness of data copies, correctness of routings and ranges, ratios and balances of entered or computed values, frequencies of new variables) but also checks which use cross-sectional, longitudinal or external information to evaluate plausibility and consistency. Interviewer comments are also taken into account. If necessary, collected values are altered or the value is deleted and thus marked to be imputed later on. Interviewer remarks can also give background information which supports the collected value. Repeated comments on the same question indicate the need to either adjust the question, the definitions and filters for the question, or the checks to be implemented in the next survey.
Distributions and frequency tables of the main variables are produced after each major step in the processing to assess the impact of each procedure and to assure that the distribution has not become biased. The final distributions of income variables, European and national indicators are compared with various data sources such as the previous EU-SILC waves, Microcensus data, LFS, HBS, tax statistics and national accounts. The comparison is done in order to identify implausible distributions. As the last step the EUROSTAT target variables are checked by the EUROSTAT SAS checking program to detect errors in computation and coding. Cases which are identified by the checking program are checked in detail for plausibility.
Processing errors that arise during post-data-collection-processing mostly can be corrected by the adaptation of the existing procedures, which are repeated after being modified.
When evaluating major changes in procedures or newly integrated features, dissemination of documentation and reports to all team members for review and discussion prove to be useful.
For the Austrian EU-SILC 2024 data the regular data transmission including all variables was sent on February 28th 2025.
The data editing process of EU-SILC 2024 involves a top- and bottom recoding for income variables. Here, particularly extreme values are restricted to a maximum value that is defined as a multiplier of the median of the distribution. An additional step in the imputation process ensures that imputed values are not higher than the highest recorded value.
For re-interview rates by wave, sex and age of the respondents please refer to Annex A.
13.3.5. Model assumption error
There are no models used in treatment of specific sources of error.
14.1. Timeliness
Pursuing annex V of the IESS regulation, Member States shall submit for the Income and Living Conditions domain pre-checked microdata without direct identifiers, according to the following deadlines (IESS Regulation (EU) 2019/1700 establishing a common framework for European statistics relating to persons and households, based on data at individual level collected from samples):
By the end of year N, submission of cross-sectional and longitudinal variables for the data collection of year N, including cross-sectional weights.
In exceptional cases, microdata concerning income variables may be submitted as provisional data.
By 28 February of year N+1, receipt of revised, final income data is expected.
By the end of October of year N+1, at the latest, longitudinal weights are to be submitted to complete the data files.
Number of days between the end of fieldwork and the first fully validated delivery of data to the Commission (Eurostat):
End of field-work: 30 July 2024
First data delivery: 20 December 2024
Days between the end of fieldwork and the first fully validated delivery: 213 days.
Date of the first full delivery of data to the Commission (Eurostat). 28 February 2025.
Months between end of reference year N (2024) and the first fully validated delivery: 0
Months between end of reference year N (2024) and the final fully validated delivery: 2
Date of the dissemination of national results: 29 Aprtil 2025
Time lag between the actual delivery of the data and the target date when it should have been delivered.
First delivery (due date 31 December 2024, delivery 20 December 2024): -11 days
Final delivery (due date 28 February 2025, delivery 28 February 2025): 0 days
14.2.1. Punctuality - delivery and publication
The number of months between the delivery/release date of (national) data and the target date on which they were scheduled for delivery/release.
National publication (due date 29 April 2025, publication 29 April 2025): 0 days
The number of months after end of reference year N (2024), data were published at national level: 5 months.
The percentage of data release delivered on time: 100%
15.1. Comparability - geographical
There are no conceptual differences between results on national level and NUTS2 level except for the size of the confidence intervals. The standard errors are larger for the NUTS region, since the regions and the resulting sample sizes are smaller. To adjust for this discrepancy, the national publication strategy is to publish 3-year averages for NUTS2-results. This strategy aims to smooth yearly differences due to small sample sizes.
15.1.1. Asymmetry for mirror flow statistics - coefficient
There are no significant breaks because of data collection methodology since 2008 (when data collection via data registers was introduced)
A break in series for the main (AROPE) indicator from 2021 onwards, due to the new definition and implementation of the new regulation. Nationally however, the new AROPE indicator is back calculated and published from 2018 onwards.
15.2.2. Comparability and deviation from definition for each income variable
Comparability and deviation from definition for each income variable
Income
Identifier
Comparability
Deviation from definition if any
Total household gross income
(HY010)
F
Total disposable household income
(HY020)
F
Total disposable household income before social transfers other than old-age and survivors' benefits
(HY022)
F
Total disposable household income before all social transfers
(HY023)
F
Income from rental of property or land
(HY040)
F
Family/ Children related allowances
(HY050)
F
Social exclusion payments not elsewhere classified
(HY060)
F
Housing allowances
(HY070)
F
Regular inter-household cash transfers received
(HY080)
F
Alimonies received [compulsory + voluntary]
(HY081)
F
Interest, dividends, profit from capital investments in incorporated businesses
(HY090)
F
Interest paid on mortgage
(HY100)
F
Income received by people aged under 16
(HY110)
F
Regular taxes on wealth
(HY120)
NC
There are no taxes on wealth in Austria.
Taxes paid on ownership of household main dwellings
(HY121)
F
Regular inter-household transfers paid
(HY130)
F
Alimonies paid [compulsory + voluntary]
(HY131)
F
Tax on income and social contributions
(HY140)
F
Repayments/receipts for tax adjustment
(HY145)
NC
Value of goods produced for own consumption
(HY170)
F
Cash or near-cash employee income
(PY010)
F
Other non-cash employee income
(PY020)
NC
From 2014 onwards, Statistics Austria decided to exclude all questions on non-cash employee incomes because modelling was becoming problematic and because income from these sources is not that significant in Austria. Additionally, this income is not part of the household income computation.
Income from private use of company car
(PY021)
F
Employers social insurance contributions
(PY030)
F
Cash profits or losses from self-employment
(PY050)
F
Pensions from individual private plans
(PY080)
F
Unemployment benefits
(PY090)
F
Old-age benefits
(PY100)
F
Survivors benefits
(PY110)
F
Sickness benefits
(PY120)
F
Disability benefits
(PY130)
F
Education-related allowances
(PY140)
F
15.3. Coherence - cross domain
The coherence of two or more statistical outputs refers to the degree to which the statistical processes, by which they were generated, used the same concepts and harmonised methods. A comparison with external sources for all income target variables and the number of persons who receive income from each ‘income component’ will be provided, where the Member States concerned consider such external data to be sufficiently reliable.
Detailed Income information in the Austrian HBS is imputed from EU-SILC based on a statistical matching procedure. Therefore, comparison of EU-SILC and HBS in terms of income variables is not meaningful for Austria.
For more information, please refer to Annex 7 on coherence.
15.3.1. Coherence - sub annual and annual statistics
Not applicable.
15.3.2. Coherence - National Accounts
Comparison between National Accounts (NA) 2023 and EU-SILC 2024 (in Mio. Euro)
Gross incomes of private households
Gross incomes of private households without property income
Disposable income
Basic Value from national accounts
346 284
323 551
250 274
Deduction for non-profit organisations1)
-
-
5 869
Deduction for persons not living in private households2)
5 298
4 950
3 829
Deduction for value of goods self-consumption3)
4 502
4 206
3 254
Deduction for imputed rents4)
10 706
10 706
10 706
Estimate from National Accounts
325 778
303 689
226 616
Estimate from EU-SILC 2024
312 232
303 629
237 277
Difference between National accounts and EU-SILC 2024 in %
4,2
0,0
-4,7
Source: Statistics Austria EU-SILC 2024 and national accounts 2023
estimated value, as for disposable income only one estimate is produced for NPOs and private households
estimated on the basis of the population prognosis
estimate for 1.3% of the total consumption expenditures based on the Household budget survey (HBS) 2019/20
National accounts 2023
For additional comparison and growth rate information for the income variables please refer to Annex 7 - Coherence.
There are no known inconsistencies within the data sets of EU-SILC 2024.
Mean (average) interview duration per household = 41.9 minutes.
Mean (average) interview duration per person = 15.7 minutes.
17.1. Data revision - policy
No foreseen data revision for EU-SILC 2024 as of May 2025.
Potential new data deliveries, if necessary, are unlikely to affect the central indicators (e.g. if some changes to hy022 or hy023 are necessary). When results remain unchanged this is no revision. Since micro-data have not yet been disseminated to users (other than Eurostat), these potential changes are not relevant for users.
If under any circumstances the need for a data revision occurs this will be communicated with full transparency (e.g. by adding an “Erratum”- Section in the Book of Tables and by providing the date and type of change, the variables and/or indicators that were affected etc.).
17.2. Data revision - practice
No revisions planned.
For the general revision policy see 17.1
17.2.1. Data revision - average size
No revisions planned.
For the general revision policy see 17.1
Detailed information concerning sampling frame, sampling design, sampling units, sampling size, weightings and mode of data collection can be found in this section (please see below). Such information is mainly used for the computation of the accuracy measures.
18.1. Source data
The sampling frame for the first wave (for more see 18.1.1) households of EU-SILC 2024 was based on the central residence register (Zentrales Melderegister - ZMR). The ZMR is a continuously updated population register based on the registration of the main residence. It contains basic demographic information about the person (for e.g. date and place of birth, citizenship, etc.) as well as the address(es) of a person. The ZMR is administrated by the Federal Ministry of the Interior (BMI). Data of the ZMR are delivered quarterly to Statistics Austria.
In order to facilitate more complex sampling and estimation procedures, the ZMR information For EU SILC is supplemented by socioeconomic and demographic variables from administrative registers. Linkage for the merging of the ZMR information with administrative register data was carried out by a pseudonymized ID key (bereichsspezifisches Personenkennzeichen - bPK).
The ZMR was filled in for the first time after the census in 2001 by merging the municipal population registers and is since continuously updated on the basis of the municipalities' residence notifications. It contains the respective address data of the registered main and secondary residences for all persons registered in Austria. When merging the address data of different persons of a household, different spellings of the address may lead to unrecognized residential connections. As a rule, there is exactly one household at a given address. In rare cases, however, there may be several households, understood as economic units, at one address. Whether an address contains several households can only be clearly determined in the course of the data collection. Furthermore, it must be taken into account that the ZMR information does not always correspond to the reality, i.e. sometimes the actual household composition collected during the interview differs from that in the ZMR.
In 2024, 4,479 addresses were selected at the beginning of the fieldwork to constitute the rotational group 1 (and 1,492 addresses were drawn as a reserve). The reference date for the sampling frame of EU-SILC 2024 was the 30th of September 2023. Addresses sampled in the previous waves of EU-SILC 2021-2023 as well as addresses selected for other household surveys where the fieldwork overlapped by up to plus or minus 3 months with the EU-SILC fieldwork were excluded from the sampling frame. This was carried out by using socio-demographic variables in combination with the available income information. This so-called "rich frame" was used to train a machine learning model for estimating the AROPE for the whole frame before the field work. This predicted AROPE was used as a sub-stratification criterion within each NUTS2 province variable.
As in the previous years, for EU-SILC 2024, the data was not only collected via household interviews. Essential components of the household income were calculated from administrative data sources. About 87% of the volume of the total income of a household is calculated from administrative data. The rest (e.g. income from self-employment) is surveyed during the interviews.
The table below describes the income data that is collected from administrative registers data and is used for the measurement of the income components for EU-SILC.
Data that is collected from administrative registers data
Data set
Description
Wage tax dataset
Contains all income from employment and pensions, regardless of whether income tax was paid on them or not. This data set also contains information about the receipt of care allowance and maternity allowance. Before use of the data from this dataset, extensive plausibility checks are carried out. All the he paychecks per person are merged and the respective incomes are totaled.
Pension data
Contains information on all the pensions under insurance law (e.g. old-age pensions, invalidity pensions and survivors' benefits). This data set of the Main Association of Austrian Social Insurance Institutions contains comprehensive information on all pensions in force in a calendar year, new additions and retirements, and it makes it possible to classify pensions with regard to different individual characteristics such as the type of pension.
Social insurance agency data
Contains the social insurance law notifications to the Main Association of Austrian Social Insurance Institutions. This data set does not contain income information, but a variety of information on the respective status under social insurance law, e.g. unemployment notifications.
Transfer data
Contains information on benefits paid by the Public Employment Service, i.e. mainly payments from unemployment insurance such as unemployment benefits or unemployment assistance.
Employee tax assessment record
This data set contains the repayments or subsequent payments of wage tax from the employee assessment.
Family allowance dataset
Contains the data on family allowances paid out.
Student Allowance Dataset
Contains information on study grants paid out by the study grant authority in the calendar year. Furthermore, any repayment to the authority is also recorded.
Pupil Allowance Dataset
Contains the merged data set of pupil grant disbursements of the Austrian pupil grant authorities.
Childcare allowance dataset
Contains the disbursement of the childcare allowance and the family time bonus administered by the Austrian health insurance fund (Österreichische Gesundheitskassa -ÖGK) Lower Austria. The record also contains any repayments to the authority.
Accident pension data set
Contains accident pensions and survivors' benefits paid out under accident insurance.
18.1.1. Sampling Design
Sampling design
EU-SILC in Austria uses an integrated rotational design meaning that each year about one fourth of the sample is replaced by a new rotational group. EU-SILC 2023 was the 20th year of EU-SILC in Austria as a panel. Each rotational group of the sample 2023 entered the survey in a different year: 2020 (R4), 2021 (R1), 2022 (R2) and 2023 (R3).
Stratification and sub stratification criteria
The first wave sample of EU-SILC 2024 is a one-stage stratified probability sample. The sample of the first wave was stratified according to 18 strata that where comprised of the 9 Austrian provinces (NUTS 2 units) subdivided into 2 groups that were defined by an estimation of the main indicator "at risk of poverty or social exclusion" (AROPE). To accomplish this, a machine learning model (random forest) for estimating AROPE for the entire rich frame was applied and thus made it possible to use this estimated dichotomous variable as a sub-stratification criterion. The aim of the sub-stratification with characteristics that are highly correlated with the main income-based indicators of EU-SILC was to achieve a more efficient sample and a smaller standard error for the AROPE indicator.
Sample size and allocation criteria
The first wave sampling process was carried out according to a stratified one-stage probability sample with systematic sample selection without replacement and disproportional allocation. 4 479 addresses for the first wave rotational group of 2024 (R4/24) were planned for selection. The number of selected households was determined as approximately 0.1% of all eligible addresses. The sample design for the initial EU-SILC 2024 survey was adapted with regard to the allocation per province compared to the previous year. Since 2021, the allocation criterion has been the precision of the risk of poverty or exclusion (AROPE) indicator in the cross-section (based on the latest available results from EU-SILC). The aim of this sample design is to obtain the most precise sample possible (in terms of the standard error of AROPE). Therefore, the allocation of the sample for SILC 2024 per province was adjusted with respect to Eurostat's precision specifications. The precision of the sample defined by Eurostat in terms of the standard error (SE) of AROPE per province was taken into account. For each province, it was calculated how much larger the sample would have to be to meet the precision requirements. The resulting distribution of addresses to be drawn was then scaled to the required gross sample.
In the end, the gross sample consisted of 9783 households, i.e., 4901 households for the first rotational year (2024) and 4822 households for the follow-up years (of which 2029 were in the second year, 1464 in the third year, and 1329 in the forth year). The 4901 households included 419 extra addresses added from the reserve adresses (see 18.1) in order to achieve the required sample size, as well as three additional households in the first wave, which were identified as shared flats.
The follow-up addresses are the addresses that were successfully interviewed in 2023, i.e., part of the EU SILC 2023 database.
Note that 20 sample households in wave 2+ that couldn’t be accessed due to temporary conditions during the field work in previous years (2022 or 2023) were not followed in 2024, although they should have been followed according to Doc 65 (p. 625 f). This processing error will be fixed from 2025 onwards.
The achieved sample size was 6193 households, i.e., 2071 households who took part in EU SILC for the first time in 2024, and 4122 household who were already part in the previous waves of EU SILC (1625, 1283, and 1214 for the second, third and fourth year respectively). 12672 people were registered in these 6193 households, 10625 of which as 16 or older and thus potentially eligible for a personal interview. From them 12556 interviews were accepted in the EU SILC 2024 database, 12514 of which for household members 16 or older. 37 of these 12514 interviews were fully imputed.
18.1.2. Sampling unit
Sampling units are dwelling units registered in the central residence register (ZMR). The sampling frame consisted of all accommodations with at least one person aged 16 years or older who has their main registered residence (Hauptwohnsitzmeldung) in these accommodations. Institutional housing facilities, dwelling units where no person with a main residence in the dwelling is 16 years or older were excluded from the sample. Units that have been selected for the prior samples of EU-SILC were excluded as well.
18.1.3. Sampling frame
The sampling frame of the first wave households of EU-SILC 2024 was based on the the central residence register (Zentrales Melderegister -ZMR). The ZMR is a continuously updated population register based on the registration of the main residence. It contains information on the person (date and place of birth,etc.) and on the address(es) of a person. The ZMR is administrated by the Federal Ministry of the Interior (BMI). Data of the ZMR are delivered quarterly to Statistics Austria. In order to facilitate more complex sampling and estimation procedures of the ZMR information needed for sampling was enriched by socioeconomic and -demographic variables from administrative registers. Linkage was carried out by a pseudonymized key (bereichsspezifisches Personenkennzeichen - bPK).
18.2. Frequency of data collection
Number of interviews per month and wave
Month of data collection 2024
Interviewed
in %
cum. %
First wave interviews
Second wave interviews
Third wave interviews
Forth wave interviews
February
1 326
21.4
21.4
197
374
399
356
March
2 353
38.0
59.4
517
729
555
552
April
1 074
17.3
76.7
488
251
169
166
May
540
8.7
85.5
262
122
84
72
June
512
8.3
93.7
321
97
44
50
July
388
6.3
100.0
286
52
32
18
Total
6 193
100.0
100.0
2 071
1 625
1 283
1 214
18.3. Data collection
Mode of data collection
Mode of data collection
CAPI
CATI
CAWI
Total
% of total interviews
47.1
17.0
35.9
100.0
Proxy interviews pro mode
Mode of proxy interviews
CAPI
CATI
CAWI
Total
% of total, within own mode
10.3
11.2
11.1
11.3
Description of collecting income variables
The source or procedure used for the collection of income variables
The form (gross, net) in which income variables at component level have been obtained
The method used for obtaining target variables in the required form
Register information was used for the majority of income components. The following components are calculated mainly on the basis of register information:
Employment income (PY010),
Unemployment benefits (PY090),
Old-age benefits (PY100),
Survivor's benefits (PY110),
Sickness benefits (PY120),
Disability benefits (PY130),
Education related benefits (PY140),
Family related benefits (HY050),
Income received by people aged under 16 (HY110).
If there was no income information available from the registers, this information as asked from the respondents. To collect the required information to fill the EU-SILC target variables, the income components are split into more differentiated sub-components. These sub-components are defined according to the Austrian regulations and benefit system.
For income components where register information could be used, gross and net values were directly obtained or calculated from the registers. For all variables where no register information was available, the net and the gross values were asked from the respondents directly. An exception was the self-employment income, for which only the net income was asked.
For all variables, the net and the gross values are in the dataset. If either the net or the gross value was missing for PY010 or PY100, the missing value was calculated on the basis of a net-gross conversion and vice versa. Missing gross values for incomes from self-employment (PY050) were calculated on the basis of the tax payments and social contributions stated by the respondents. If neither gross or net values could be surveyed, gross values for income from employment (PY010) or pension incomes (PY100) were calculated on the basis of the wage tax statistics.
For persons over the standard retirement age (women 60; men 65) the values for PY100 were taken from wage tax register, all values for PY110 were taken from the wage tax register and the accident benefits register.
Note. The pension age for women in Austria is being gradually increased from 60 to 65 by six months each year starting from January 1, 2024. Consequently, in January 2024 the retirement age for women increased to 60.5 years from previously 60 years. In January 2025 it will be 61, in January 2026 it will be 61.5 etc. The total “harmonization” to 65 years will be concluded in 2033. SILC will make year adjustments to the pension age accordingly. However, since the reference year for the income variables in 2024 is 2023, there were no adjustments yet needed.
For the national questionnaires (German and English), please refer to Annex 1 – National questionnaires. For the mode of data collection per rotation, please refer to Annex 4 - Data collection.
Checks to detect processing errors have been implemented in the electronic questionnaire. Validation applied during post-data-collection-processing includes formal data checks as well as checks for plausibility and consistency which use longitudinal or external information. Detected errors or inconsistencies are fed back for validation to the interviewer concerned, they are either corrected or approved and can also lead to an adjustment of questions or interviewer guidelines in the next year’s survey.
18.5. Data compilation
For further information, please refer to the Annexes of this section.
18.5.1. Imputation - rate
Please see Parts 13.3.4, 18.5 and the information provided in the Annex 6 on imputation (Part 18.5.3.).
18.5.2. Calculation of weighting factors and weight adjustments
Please see the information provided in the Annex 5 on the weighting procedure.
The European Union Statistics on Income and Living Conditions (EU-SILC) is a survey-based instrument aiming at collecting timely and comparable cross-sectional and longitudinal multidimensional microdata on income, poverty, social exclusion and living conditions. In addition, it collects module variables every three years, six years or ad-hoc new policy needs modules.
The EU-SILC instrument provides two types of data:
Cross-sectional data pertaining to a given time or a certain time period with variables on income, poverty, social exclusion and other living conditions;
Longitudinal data pertaining to individual-level changes over time, observed periodically over four‐or more year rotation scheme (Annex III (2) of 2019/1700). EU-SILC Austria provides longitudinal data pertaining to a four year rotation scheme.
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.
Further details are provided in items 5, 15.2.2 and 18.3.
Statistical units are private households and all persons living in these households who have usual residence in Austria.
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 Austria. Private household means a person living alone or a group of persons who live together, providing oneself or themselves with the essentials of living.
EU-SILC in Austria covers all of Austria, i.e., the whole geographical area.
Description of reference period used for incomes:
Period for taxes on income and social insurance contributions
Income reference periods used
Reference period for taxes on wealth
Lag between the income reference period and current variables
The reference period for taxes on income and social insurance contributions for EU-SILC 2024 is 2023. For the survey years 2021-2023, which are part of the longitudinal component of the reconciled data, the reference period is also the year preceding the survey year.
There is no difference to the standard EU-SILC concept.
The income reference year for EU-SILC 2023 was 2022. For the survey years 2020-2022, which are part of the longitudinal component of the reconciled data, the income reference period is also the year preceding the survey year.
There is no difference to the standard EU-SILC concept.
There are no taxes on wealth in Austria.
This refers to the lag between the income reference period and the household interview date.
The fieldwork for EU-SILC 2024 lasted from February to July. The fieldwork for EU-SILC 2023 lasted from March to August. The fieldwork for EU-SILC 2022 and 2021 lasted from February to July.
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.
The data involves several units of measure depending upon the variables. Income variables are transmitted to Eurostat in national currency (EUR).
For more information, see methodological guidelines and description of EU-SILC target variables available on CIRCABC.
For further information, please refer to the Annexes of this section.
The sampling frame for the first wave (for more see 18.1.1) households of EU-SILC 2024 was based on the central residence register (Zentrales Melderegister - ZMR). The ZMR is a continuously updated population register based on the registration of the main residence. It contains basic demographic information about the person (for e.g. date and place of birth, citizenship, etc.) as well as the address(es) of a person. The ZMR is administrated by the Federal Ministry of the Interior (BMI). Data of the ZMR are delivered quarterly to Statistics Austria.
In order to facilitate more complex sampling and estimation procedures, the ZMR information For EU SILC is supplemented by socioeconomic and demographic variables from administrative registers. Linkage for the merging of the ZMR information with administrative register data was carried out by a pseudonymized ID key (bereichsspezifisches Personenkennzeichen - bPK).
The ZMR was filled in for the first time after the census in 2001 by merging the municipal population registers and is since continuously updated on the basis of the municipalities' residence notifications. It contains the respective address data of the registered main and secondary residences for all persons registered in Austria. When merging the address data of different persons of a household, different spellings of the address may lead to unrecognized residential connections. As a rule, there is exactly one household at a given address. In rare cases, however, there may be several households, understood as economic units, at one address. Whether an address contains several households can only be clearly determined in the course of the data collection. Furthermore, it must be taken into account that the ZMR information does not always correspond to the reality, i.e. sometimes the actual household composition collected during the interview differs from that in the ZMR.
In 2024, 4,479 addresses were selected at the beginning of the fieldwork to constitute the rotational group 1 (and 1,492 addresses were drawn as a reserve). The reference date for the sampling frame of EU-SILC 2024 was the 30th of September 2023. Addresses sampled in the previous waves of EU-SILC 2021-2023 as well as addresses selected for other household surveys where the fieldwork overlapped by up to plus or minus 3 months with the EU-SILC fieldwork were excluded from the sampling frame. This was carried out by using socio-demographic variables in combination with the available income information. This so-called "rich frame" was used to train a machine learning model for estimating the AROPE for the whole frame before the field work. This predicted AROPE was used as a sub-stratification criterion within each NUTS2 province variable.
As in the previous years, for EU-SILC 2024, the data was not only collected via household interviews. Essential components of the household income were calculated from administrative data sources. About 87% of the volume of the total income of a household is calculated from administrative data. The rest (e.g. income from self-employment) is surveyed during the interviews.
The table below describes the income data that is collected from administrative registers data and is used for the measurement of the income components for EU-SILC.
Data that is collected from administrative registers data
Data set
Description
Wage tax dataset
Contains all income from employment and pensions, regardless of whether income tax was paid on them or not. This data set also contains information about the receipt of care allowance and maternity allowance. Before use of the data from this dataset, extensive plausibility checks are carried out. All the he paychecks per person are merged and the respective incomes are totaled.
Pension data
Contains information on all the pensions under insurance law (e.g. old-age pensions, invalidity pensions and survivors' benefits). This data set of the Main Association of Austrian Social Insurance Institutions contains comprehensive information on all pensions in force in a calendar year, new additions and retirements, and it makes it possible to classify pensions with regard to different individual characteristics such as the type of pension.
Social insurance agency data
Contains the social insurance law notifications to the Main Association of Austrian Social Insurance Institutions. This data set does not contain income information, but a variety of information on the respective status under social insurance law, e.g. unemployment notifications.
Transfer data
Contains information on benefits paid by the Public Employment Service, i.e. mainly payments from unemployment insurance such as unemployment benefits or unemployment assistance.
Employee tax assessment record
This data set contains the repayments or subsequent payments of wage tax from the employee assessment.
Family allowance dataset
Contains the data on family allowances paid out.
Student Allowance Dataset
Contains information on study grants paid out by the study grant authority in the calendar year. Furthermore, any repayment to the authority is also recorded.
Pupil Allowance Dataset
Contains the merged data set of pupil grant disbursements of the Austrian pupil grant authorities.
Childcare allowance dataset
Contains the disbursement of the childcare allowance and the family time bonus administered by the Austrian health insurance fund (Österreichische Gesundheitskassa -ÖGK) Lower Austria. The record also contains any repayments to the authority.
Accident pension data set
Contains accident pensions and survivors' benefits paid out under accident insurance.
Annual.
Pursuing annex V of the IESS regulation, Member States shall submit for the Income and Living Conditions domain pre-checked microdata without direct identifiers, according to the following deadlines (IESS Regulation (EU) 2019/1700 establishing a common framework for European statistics relating to persons and households, based on data at individual level collected from samples):
By the end of year N, submission of cross-sectional and longitudinal variables for the data collection of year N, including cross-sectional weights.
In exceptional cases, microdata concerning income variables may be submitted as provisional data.
By 28 February of year N+1, receipt of revised, final income data is expected.
By the end of October of year N+1, at the latest, longitudinal weights are to be submitted to complete the data files.
Number of days between the end of fieldwork and the first fully validated delivery of data to the Commission (Eurostat):
End of field-work: 30 July 2024
First data delivery: 20 December 2024
Days between the end of fieldwork and the first fully validated delivery: 213 days.
Date of the first full delivery of data to the Commission (Eurostat). 28 February 2025.
Months between end of reference year N (2024) and the first fully validated delivery: 0
Months between end of reference year N (2024) and the final fully validated delivery: 2
Date of the dissemination of national results: 29 Aprtil 2025
There are no conceptual differences between results on national level and NUTS2 level except for the size of the confidence intervals. The standard errors are larger for the NUTS region, since the regions and the resulting sample sizes are smaller. To adjust for this discrepancy, the national publication strategy is to publish 3-year averages for NUTS2-results. This strategy aims to smooth yearly differences due to small sample sizes.
