1.1. Contact organisation
Czech Statistical Office
1.2. Contact organisation unit
Social Surveys Unit
Household Surveys Department
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
Na padesatem 81, 100 82 Praha 10, Czech Republic
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
31 March 2024
2.2. Metadata last posted
31 March 2024
2.3. Metadata last update
31 March 2024
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).
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.
3.3. Coverage - sector
Data refer to all private households and individuals living in the private households in the national territory at the time of data collection.
The EU-SILC survey is a key instrument for the European Semester and the European Pillar of Social Rights, providing information on income distribution, poverty and social exclusion, as well as various related living conditions and poverty EU policies, such as on child poverty, access to health care and other services, housing, over indebtedness and quality of life. It is also the main source of data for microsimulation purposes and flash estimates of income distribution and poverty rates.
3.4. Statistical concepts and definitions
Statistical concepts and definitions for EU-SILC are specified in Regulation (EU) 2019/1700, Commission Implementing Regulation (EU) 2019/2181, and Commission Implementing Regulation (EU) 2019/2242. Additional information is available in the EU statistics on income and living conditions (EU-SILC) methodology and in the methodological guidelines and description of EU-SILC target variables (see CIRCABC).
Further details are provided in items 5, 15.1.1.1, 15.2.2 and 18.3.
3.5. Statistical unit
Statistical units are private households and all persons living in these households who have usual residence in the Member State. Annex II of the Commission implementing regulation (EU) 2019/2242 defines specific statistical units per variable and specifies the, content of the quality reports on the organization of a sample survey in the income and living conditions domain pursuant to Regulation (EU) 2019/1700 of the European Parliament and of the Council.
3.6. Statistical population
The target population is private households and all persons composing these households having their usual residence in the Member State. Private household means a person living alone or a group of persons who live together, providing oneself or themselves with the essentials of living.
3.6.1. Reference population
Definitions of reference population, household and household membership (specify any deviation from Eurostat definition in your country)
| Reference population |
Private household definition |
Household membership |
|---|---|---|
| fully comparable |
fully comparable |
fully comparable |
3.6.2. Population not covered by the data collection
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.
3.7. Reference area
The survey was carried out on the whole territory of the Czech Republic, none region was excluded.
3.8. Coverage - Time
Annual data, reference year 2024. Data are available for the survey years 2005-2024.
3.9. Base period
Not applicable.
The data involves several units of measure depending upon the variables. Income variables are transmitted to Eurostat in national currency. For more information, see methodological guidelines and description of EU-SILC target variables available on CIRCABC
Description of reference period used for incomes
| Period for taxes on income and social insurance contributions |
Income reference periods used |
Reference period for taxes on wealth |
Lag between the income ref period and current variables |
|---|---|---|---|
| calendar year 2023 |
calendar year 2023 |
calendar year 2023 |
The fieldwork started on the 3rd February and ended on 23rd June 2024. The lag is 2 to 6 months. |
6.1. Institutional Mandate - legal acts and other agreements
Regulation (EU) 2019/1700 was publish in OJ on 10 October 2019, establishing a common framework for European statistics relating to persons and households, based on data at individual level collected from samples (IESS). The Annex to the Commission implementing regulation (EU) 2019/2180 of 16 December 2019 specifies the detailed arrangements and content for the quality reports pursuant to Regulation (EU) 2019/1700 of the European Parliament and of the Council and Regulation (EU) 2019/2242.
6.2. Institutional Mandate - data sharing
Confidential microdata are not disclosed by Eurostat. Access to confidential microdata for scientific purposes may be granted on the basis of Commission Regulation 557/2013 and Regulation 223/2009 of the European Parliament and the Council on European statistics.
7.1. Confidentiality - policy
Czech Statistical Office follows European legislation concerning statistical confidentality.
7.2. Confidentiality - data treatment
Czech Statistical Office protects data according to European legislation. Anonymization at the national level means, for example, that some results are eligible to be used at NUTS 2 level as maximum.
8.1. Release calendar
Data are being published in the first quarter of the year following the survey year.
8.2. Release calendar access
Please refer to the Release calendar - Eurostat (europa.eu) publicly available on the Eurostat’s website.
8.3. Release policy - user access
In line with the Community legal framework and the European Statistics Code of Practice, Eurostat disseminates European statistics on Eurostat's website (see section 10 - 'Accessibility and clarity'), respecting professional independence and in an objective, professional and transparent manner in which all users are treated equitably. The detailed arrangements are governed by the Eurostat protocol on impartial access to Eurostat data for users. Additional information about microdata access is available in Statistics on Income and Living Conditions - Access to microdata.
Annual
10.1. Dissemination format - News release
Časopis Statistika&My | Statistika&My (statistikaamy.cz) - every year in June there are articles from SILC results.
10.2. Dissemination format - Publications
C ZSO. 2025. Household Income and Living Conditions - 2024. Household Income and Living Conditions - 2024 | CZSO
10.3. Dissemination format - online database
Public database from CZSO - Public database VDB (czso.cz)
10.3.1. Data tables - consultations
Not applicable.
10.4. Dissemination format - microdata access
Microdata are accessible in national version, more info at infoservis@czso.cz. Microdata are available for universities and research institutions for scientific reasons.
10.5. Dissemination format - other
Microdata are accessible in national version, more info at infoservis@czso.cz. Microdata are available for universities and research institutions for scientific reasons.
10.5.1. Metadata - consultations
Not applicable.
10.6. Documentation on methodology
CZSO. 2025. Methodology. Household Income and Living Conditions - 2024 (Household Income and Living Conditions - 2024 - Methodology | CZSO)
10.6.1. Metadata completeness - rate
Metadata were published just once, there wasn't any revision. All the required concepts of the SIMS are provided.
10.7. Quality management - documentation
Not applicable.
11.1. Quality assurance
Czech Statistical Office fulfills the commitment to quality as the principles of the European Statistics Code of Practice, which is being monitored regularly by means of a self-assesment and also by external assesment (peer reviews).
11.2. Quality management - assessment
Quality of data is being assessed by the 95% confidence intervals estimates of totals for households and individuals, also by response rate, comparison with macronumbers or variability of weights.
12.1. Relevance - User Needs
The main users of EU-SILC statistical data are policy makers, research institutes (e.g. CERGE_EI, Institute of Sociology) and students.
12.2. Relevance - User Satisfaction
Eurostat carried out an online general User Satisfaction Survey (USS) in the period between April and July 2019 to obtain a better knowledge about users, considering their needs and satisfaction with the services provided by Eurostat. The survey has shown that EU-SILC is of very high relevance for users. For the majority, both aggregates and micro-data were important or essential in their work irrespective of the purpose of their use. The use of the ad-hoc modules was less widespread than the use of the nucleus variables. Nevertheless, there was high interest to repeat these modules in order to have the possibility of comparing data over time. Users emphasized their strong need for more detailed micro-data, which is currently not possible. Under the new legal framework implemented from 2021, the NUTS 2 division will be available for the main indicators. Finally, users were satisfied with overall quality of the service delivered by Eurostat, which encompasses data quality and the supporting service provided to them.
For more information, please consult the User Satisfaction Survey.
12.3. Completeness
All variables according to the Regulation are being transmitted.
Not collected variables:
Income received by people aged under 16 (HY110G, HY110N) - In the Czech Republic it is not possible for children to have an employment contract. In very rare cases when child has some earnings, the earnings are included in the parent's income.
Optional variables not collected:
HY030G: Imputed rent (OPTIONAL)
RL080: Remote education (OPTIONAL)
HI130G: Interest expenses [not including interest expenses for purchasing the main dwelling] (OPTIONAL)
HI140G: Household debts (OPTIONAL)
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.
Annexes:
CZ_2024_Annex 3-Sampling_errors_13.2
13.2.1. Sampling error - indicators
The concept of accuracy refers to the precision of estimates computed from a sample rather than from the entire population. Accuracy depends on sample size, sampling design effects and structure of the population under study. In addition to that, sampling errors and non-sampling errors need to be taken into account. Sampling error refers to the variability that occurs at random because of the use of a sample rather than a census and non-sampling errors are errors that occur in all phases of the data collection and production process.
Annexes:
CZ_2024_Annex A EU-SILC - content tables
13.3. Non-sampling error
Non-sampling errors are basically of 4 types:
- Coverage errors: errors due to divergences existing between the target population and the sampling frame.
- Measurement errors: errors that occur at the time of data collection. There are a number of sources for these errors such as the survey instrument, the information system, the interviewer and the mode of collection.
- Processing errors: errors in post-data-collection processes such as data entry, keying, editing and weighting.
- Non-response errors: errors due to an unsuccessful attempt to obtain the desired information from an eligible unit. Two main types of non-response errors are considered:
- Unit non-response: refers to absence of information of the whole units (households and/or persons) selected into the sample.
- Item non-response: refers to the situation where a sample unit has been successfully enumerated, but not all required information has been obtained.
13.3.1. Coverage error
Coverage errors include over-coverage, under-coverage, and misclassification:
- Over-coverage: relates either to wrongly classified units that are in fact out of scope, or to units that do not exist in practice.
- Under-coverage: refers to units not included in the sampling frame.
- Misclassification: refers to incorrect classification of units that belong to the target population.
13.3.1.1. Over-coverage - rate
Coverage error
| Main problems | Population (sub-population) | Size of error | Comments |
|---|---|---|---|
| Over-coverage | 514 | 4.5% | |
| Under-coverage | 0 | 0% | |
| Misclassification | 0 | 0% |
13.3.1.2. Common units - proportion
Not requested by Reg. 2019/2180
13.3.2. Measurement error
Measurement error for cross-sectional data
Cross-sectional data
| Source of measurement errors |
Building process of questionnaire |
Interview training |
Quality control |
|---|---|---|---|
| It might occur as a consequence of many reasons, mostly of inaccurate methodological instructions, not respecting them by interviewers, wrong wording of questions, processing mistakes, unwillingness to participate in the survey or giving purposely biased answers. |
Data collection had the form of an interview and interviewers filled in the answers into paper questionnaires (PAPI data collection) or into electronic questionnaires (CAPI data collection). Data from paper questionnaires were then transcribed into CAPI questionnaire. Since 2023 the survey was conducted using electronic questionnaires with the assistance of programmatic system Survey Solutions. It is developed in the Data group of The World Bank and it is standard for questionnaire survey. The content of the survey was divided into four questionnaires with different units of reference: Questionnaire A (dwelling unit questionnaire): contained the roster with the list of all persons with usual residence in the selected dwelling, their basic demographic characteristics, information on sharing of expenses to determine household units and relationship of each person to the main user of the dwelling and to the head of household. Questionnaire B (household questionnaire): filled in for each household, contained information on housing, financial situation of the household, consumer durables, inter-household transfers paid and received, consumption from household own production (i.e. small scale farming and similar activities), family social benefits, rental income and paid regular taxes on wealth (buildings and land) and childcare. Questionnaire C (personal questionnaire): filled in for each household member aged 16+ as of 31 December 2023 (i.e. persons born in 2007 and earlier). This questionnaire contained information on labour status and employment, personal income, participation in private pension plans, health and selected biographical information.
|
Workers from regional departments conducted regular methodical training of fieldworkers. New interviewers attended detailed training from central staff. All interviewers obtained written methodical instruction. |
The raw data files are then subject to initial centrally performed checks – checking the integrity of identification numbers, consistency with the sample, completeness of the questionnaire sets for all dwellings. Central staff is responsible for further checking of the data, using a special software application containing a set of logical controls above all data, controls of derived variables. The controls contain consistency issues through all waves. Three kinds of errors are distinguished: critical errors (must be corrected, limited to a small set of key consistency issues), errors to verify (must be commented, involving contacting the interviewer in charge of that household, if additional information is necessary) and informative flags (extraordinary or unusual situations, which should be looked at). |
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
| Address (including phone, mail if applicable) contact rate | Complete household interviews | Complete personal interviews | Household Non-response rate | Individual non-response rate | Overall individual non-response rate | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| (Ra) | (Rh) | (Rp) | (NRh) | (NRp) | (NRp)* | ||||||||||||
| A | B | C | A | B | C | A | B | C | A | B | C | A | B | C | A | B | C |
| 99.93 | 99.82 | 100.0 | 78.75 | 54.40 | 97.72 | 100.00 | 100.00 | 100.0 | 21.31 | 45.70 | 2.28 | 0.00 | 0.00 | 21.31 | 45.70 | 2.28 | |
13.3.3.2. Item non-response - rate
The computation of item non-response is essential to fulfil the precision requirements. Item non-response rate is provided for the main income variables both at household and personal level.
Item non-response which refers to the situation where a sample unit has been successfully enumerated, but not all the required information has been obtained.
13.3.3.2.1. Item non-response rate by indicator
See Annex 2 - Item non-response
Annexes:
CZ_2024_Annex 2-Item_non_response_13.3.3.2.1
13.3.4. Processing error
Description of data entry, coding controls and the editing system
| Data entry and coding (if any used) |
Editing controls |
|---|---|
| In case of CAPI the basic checks are included in questionnaire. The electronic questionnaire has three types of control - longitudinal, warning and binding. Longitudinal control is engaged for checking information between years of the survey. Warrning control warn of the suspect value. Binding control point out wrong value and interweavers must fix the value. In case of PAPI checks on values are made during the transcription into CAPI. |
No information about the rates of falled edits for income variables and other relevant variables. |
13.3.5. Model assumption error
Not applicable.
14.1. Timeliness
Annex to Reg. (EU) 2019/1700: Information on:
- Date of the dissemination of national results – 6th March 2025
- Number of days between the end of fieldwork and the first fully validated delivery of data to the Commission (Eurostat) – 189
- Date of the first full delivery of data to the Commission (Eurostat). If the delivery of data does not meet the deadline laid down in Regulation (EU) 2019/1700, a reason for the delay should be given. – 29th December 2024
Please provide the link in national legislation, calendar of publication or other relevant information: Household Income and Living Conditions - 2024 | Products
14.1.1. Time lag - first result
National results were disseminated 6th March 2025 Household Income and Living Conditions - 2024 | Products
14.1.2. Time lag - final result
National results were disseminated 6th March 2025 Household Income and Living Conditions - 2024 | Products
14.2. Punctuality
The data was delivered on time according to the Regulation.
14.2.1. Punctuality - delivery and publication
The data was released on the time they were scheduled for release.
15.1. Comparability - geographical
In the Czechia EU-SILC results are eligible to use at NUTS 2 level as maximum. But not all the results, just some. Detailed classifications are for NUTS 2 ineligible.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not applicable.
15.2. Comparability - over time
See Annex 8 - Breaks in series.
Annexes:
CZ_2024_Annex 8-Breaks in series
15.2.1. Length of comparable time series
No breaks in series in last years.
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 hh gross income |
(HY010) |
F |
|
| Total disposable hh income |
(HY020) |
F |
|
| Total disposable hh income before social transfers other than old-age and survivors' benefits |
(HY022) |
F |
|
| Total disposable hh income before all social transfers |
(HY023) |
F |
|
| 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-hh cash transfers received |
(HY080) |
F |
|
| Alimonies received |
(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) |
NC |
|
| Regular taxes on wealth |
(HY120) |
F |
|
| Taxes paid on ownership of household main dwelling |
(HY121) |
F |
|
| Regular inter-hh transfers paid |
(HY130) |
F |
|
| Alimonies paid |
(HY131) |
F |
|
| Tax on income and social contributions |
(HY140) |
F |
|
| Repayments/receipts for tax adjustment |
(HY145) |
F |
|
| Value of goods produced for own consumption |
(HY170) |
F |
|
| Cash or near-cash employee income |
(PY010) |
F |
|
| Other non-cash employee income |
(PY020) |
F |
|
| Income from private use of company car |
(PY021) |
F |
|
| Employers social insurance contributions |
(PY030) |
F |
|
| Contributions to individual private pension plans |
(PY035) |
F |
|
| Cash profits or losses from self-employment |
(PY050) |
F |
|
| Pension 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 |
|
F= Fully comparable; L= Largely comparable; P= Partly comparable and NC= Not collected.
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.
Annexes:
CZ_2024_Annex 7-Coherence_15.3-15.3.2
15.3.1. Coherence - sub annual and annual statistics
Not applicable.
15.3.2. Coherence - National Accounts
See Annex 7 - Coherence.
15.4. Coherence - internal
National Accounts provided only preliminary data.
The differences imply from the different approach in collecting of the data. Methodology of national accounts (czso.cz).
Mean (average) interview duration per household = 59.2 minutes.
Mean (average) interview duration per person = 18.4 minutes.
17.1. Data revision - policy
Not applicable.
17.2. Data revision - practice
Not applicable.
17.2.1. Data revision - average size
Not applicable.
Detailed information concerning sampling frame, sampling design, sampling units, sampling size, weightings and mode of data collection can be found in this section (please see below). Such information is mainly used for the computation of the accuracy measures.
18.1. Source data
The source of EU_SILC data is fully interview.
18.1.1. Sampling Design
Type of sampling design
The survey was carried out on the whole territory of the Czech Republic. The sample size of newly selected dwelling (first wave in 2024) was 4 750 dwellings. The sample was obtained by applying a two-stage probability sampling scheme to each of the 14 administrative regions (NUTS3 regions) independently. The total number of dwellings selected in each region was proportional to the region's size. At the first sampling stage small geographical areas (CEUs - census enumeration units or districts) were selected by probability sampling. These CEUs served as a basis for the second-stage selection (a sample of 10 dwellings was drawn from each CEU). Before selecting the sample of dwellings, the sampling frame had to be adjusted to enable incorporation of small census enumeration units into the sampling process to reach the required full geographical coverage of the national territory. Small CEUs (with less than 20 inhabited dwellings) were merged with adjacent CEUs and the resulting larger CEUs entered the first stage of sampling. Therefore, in some cases, the 10 chosen dwellings could belong to two or more (in exceptional cases) CEUs.
18.1.2. Sampling unit
Census Enumeration Districts (CEUs) constitute the first-stage sampling units. CEUs are small geographical areas covering the whole territory of the country. They are used as enumeration districts during the census, but their use is more general. Continuously updated geographical register is maintained by the CZSO, where these units form the basic geographical layer, on which subsequent aggregations are based. This register is the base for an integrated hierarchical geographical information system and is the base for databases of regional indicators and statistical data.
For each CEU, a list of all buildings is maintained in the register. This list is updated from administrative data of the construction authorities (new buildings’, flats’ or commercial premises’ acceptation protocols, demolitions’ protocols). For each building, the number of dwelling units is recorded.
18.1.3. Sampling frame
CEUs vary considerably in size measured in number of dwelling units in them. Before drawing of the first stage sample, the sampling frame of CEUs had to be adjusted in two ways:
- As noted above, CEUs have wider use than sampling of dwellings and there are CEUs not containing any buildings dwellings (like industrial areas, railway stations and the like). These CEUs, where the number of dwellings is zero, are dropped from the sampling frame.
- In order to enable incorporation of small census enumeration units into the sampling process (to reach the required full geographical coverage of the national territory), small CEUs (with less than 20 inhabited dwellings) were merged with adjacent CEUs and this larger merged CEU entered the first stage of sampling. Therefore, in some cases, the 10 dwellings sampled in the second stage belong to two, in exceptional cases even more, real administrative CEUs. The survey design variable DB060 (PSU) is later coded according to this adjusted structure of the sampling frame, to keep the dwellings together as they were actually sampled.
In the second stage, 10 dwellings were sampled in each sampled CEU. CZSO’s regional fieldwork units (each covering one of the 14 NUTS3 administrative regions) received the list of selected dwellings (address + identification number of the flat in buildings with more than one flat). Before the actual fieldwork, the regional fieldwork units’ staff carried out identification of the selected dwellings and filled in the contact names on the list of selected dwellings for interviewers.
The ultimate sampling unit was the dwelling, i.e. all persons with usual residence in that dwelling (their only place of residence or their main place of residence, according to the EU-SILC definition) were included in the survey. This includes also foreign nationals and subtenants living in the selected dwelling.
The household definition is based on the sharing of expenditures concept – based on the declaration of the persons in sampled dwelling unit that they permanently live together and finance together expenditures to cover their needs.
18.2. Frequency of data collection
Fieldwork
Data collection lasted from 03 February to 23 June 2024.
Renewal of sample: rotational groups
The survey uses the integrated four-year rotational panel design. Since the 2005 operation was the first year of the survey, there was only one sample replication and no rotation was applied. The rotational scheme with four replications was begun in 2008. In 2009 first rotational panel was ended and the household from the 2005 operation was dropped from the sample. In 2024, households from the 2020 operation were dropped from the sample. Each next year, one sub-sample rotates out and a new one is drawn and substituted for.
The longitudinal dataset contains households sampled from 2021 (first interviews), 2022 (second interviews), 2023 (third interviews) and 2024 (fourth interviews).
|
|
new in 2021 |
|
|
|
|---|---|---|---|---|
| 2021 |
wave 1 |
new in 2022 |
|
|
| 2022 |
wave 2 |
wave 1 |
new in 2023 |
|
| 2023 |
wave 3 |
wave 2 |
wave 1 |
new in 2024 |
| 2024 |
wave 4 |
wave 3 |
wave 2 |
wave 1 |
Longitudinal sample: 2021 - 2024 2022 - 2024 2023 - 2024
18.3. Data collection
Mode of data collection
|
|
1-PAPI |
2-CAPI |
3-CATI |
4-CAWI |
5-PAPI proxy |
6-CAPI-proxy |
7-CATI-proxy |
8-CAWI proxy |
9-other |
|---|---|---|---|---|---|---|---|---|---|
| % of total |
48.4 |
18.6 |
6.7 |
|
16.3 |
6.3 |
3.8 |
|
|
Description of collecting income variables
| The source or procedure used for the collection of income variables |
The form (gross, net) in which income variables at component level have been obtained |
The method used for obtaining target variables in the required form |
|---|---|---|
| All the income variables are obtained by interview. The EU-SILC income target variables were divided to more subcomponents. The subcomponents were defined according to the Czech benefit system. These subcomponents were surveyed. |
Both alternatives (gross amounts, net amount – net of taxes and social insurance contributions) were available to respondents for income from employment and self-employment income. In addition, information on claimed tax deductions was collected from respondents. Algorithms based on detailed application of the national tax rules were then used to calculate the complementary net/gross amount. Social benefits are generally tax-exempt – therefore there is no difference between gross and net values – they can be collected as one value and assigned to both gross and net. |
Situation of missing income data for one of the household members was rare (10 cases). For these persons, the income was imputed by the simple hot-deck method (using randomly chosen person with similar characteristics from another household). Another source of bias, which needs to be taken into account, stems from the interviewing. Data on income obtained during interviews with household members have the tendency to underestimate certain sources of income or data on some components is missing (item non-response). Underestimation of income is a natural consequence of the fact, those respondents either tends to give lower then actual values or simply did not recall certain irregular or small incomes. It is, more or less, a non-sampling error, affected substantially by the incomes themselves and by their source. The possibilities to eliminate this underestimation of the survey data are limited. In the presented survey, only such adjustments were done, where there was sufficiently reliable external statistical source or which can be based on the legislation. Data on gross income from employment were compared with corresponding data from wage statistics broken into sectors of activity (NACE). Different from the last year's survey and in accordance with experience from other income surveys, income from work was underestimated. Primarily, this underestimation concerned those incomes that were recorded as yearly lump sums. Such incomes were moderately boosted so that the average monthly gross pay by sectors approached the data from wage statistics. There was no need for corrections with income from private enterprise. In case of social benefits for which there is a legal entitlement (parental leave benefit, child birth benefit, death grant provided to families of the deceased, to some extent also maternity leave benefit), a check on their receiving by the eligible households was applied and amounts provided were corrected according to the amounts fixed by the legislation. Old age benefits (pension from the social security system) were not corrected, since their underestimation is quite low. Amounts declared by the unemployed as unemployment benefits were overestimated. Unemployed respondents tend to report their income from social benefits as unemployment benefits and do not distinguish them from the minimum income support benefits (claimed on the basis of the legal minimum subsistence amounts). In cases where the duration of unemployment and the reported amounts did not match the rules of the unemployment benefits provision, the reported amounts were re-classified as minimum income support benefits. It was not possible to correct the underestimation of the sickness benefits (where respondents tend to forget spells of short-term illness over the 12 months income reference period), means-tested social benefits whose claims depend on the previous income (prior to the income reference periods), capital income and non-monetary income generated by own-consumption. The value of goods produced by own-consumption was an estimate of the household based on the amount of consumed food and other goods, own production and goods from own business during the year 2023 (for example food and animals from own small-scale non-commercial farming activity, value of meals from own restaurant, bread from own bakery and the like).
|
Annexes:
CZ_2024_Annex 4-Data_collection_18.3
CZ_2024_Annex 1 - questionnaire
18.4. Data validation
Data control
The raw data files are a subject to initial centrally performed checks – checking the integrity of identification numbers, consistency with the sample, completeness of the questionnaire sets for all dwellings. Central staff is responsible for further checking of the data, using a special software application containing a set of logical controls above all data, controls of derived variables. The controls contain consistency issues through all waves. Three kinds of errors are distinguished: critical errors (must be corrected, limited to a small set of key consistency issues), errors to verify (must be commented, involving contacting the interviewer in charge of that household, if additional information is necessary) and informative flags (extraordinary or unusual situations, which should be looked at).
18.5. Data compilation
Imputation procedure used
Situation of missing income data for one of the household members was rare (10 cases) in 2024. For these persons, the income was imputed by the simple hot-deck method (using a randomly chosen person with similar characteristics from another household). Access to administrative register information on individual level is not possible. We use our developed model for gross/net conversion, which was developed with regard to the Czech tax laws.
The item non-response of non-income variables is rare, so model approach development is unnecessary. We use the hot-deck method for new households and information from the previous year for households in subsequent waves of the survey. The amount of CZK 3000 was added to income in kind of an employee for each month of using a company car.
18.5.1. Imputation - rate
Imputation is the process used to assign replacement values for missing, invalid or inconsistent data that have failed edits. This includes automatic and manual imputations; it excludes follow-up with respondents and the corresponding corrections (if applicable). The unweighted imputation rate for a variable is the ratio of the number of imputed values to the total number of values requested for the variable.
18.5.2. Calculation of weighting factors and weight adjustments
See Annex 5.
Annexes:
CZ_2024_Annex 5-Weighting procedure
18.5.3. Estimation and imputation
See Annex 6.
Annexes:
CZ_2024_Annex 6-Estimation and Imputation (Country level description)
18.6. Adjustment
Not applicable.
18.6.1. Seasonal adjustment
Not applicable.
See Annex 9 - Rolling module
A note from the survey:
PC360: Feeling discriminated in public spaces (shop, café, restaurant, leisure facilities etc.) - Apart from defined possibilities, we added possibility "I have not been to these places in the last year", because our pretesting of the questionnaire showed this possibility as needed. And in the real survey more than 600 persons selected this possibility. We suggest to add it into Doc065.
Annexes:
CZ_2024_Annex 9-Rolling module
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).
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.
31 March 2024
Statistical concepts and definitions for EU-SILC are specified in Regulation (EU) 2019/1700, Commission Implementing Regulation (EU) 2019/2181, and Commission Implementing Regulation (EU) 2019/2242. Additional information is available in the EU statistics on income and living conditions (EU-SILC) methodology and in the methodological guidelines and description of EU-SILC target variables (see CIRCABC).
Further details are provided in items 5, 15.1.1.1, 15.2.2 and 18.3.
Statistical units are private households and all persons living in these households who have usual residence in the Member State. Annex II of the Commission implementing regulation (EU) 2019/2242 defines specific statistical units per variable and specifies the, content of the quality reports on the organization of a sample survey in the income and living conditions domain pursuant to Regulation (EU) 2019/1700 of the European Parliament and of the Council.
The target population is private households and all persons composing these households having their usual residence in the Member State. Private household means a person living alone or a group of persons who live together, providing oneself or themselves with the essentials of living.
The survey was carried out on the whole territory of the Czech Republic, none region was excluded.
Description of reference period used for incomes
| Period for taxes on income and social insurance contributions |
Income reference periods used |
Reference period for taxes on wealth |
Lag between the income ref period and current variables |
|---|---|---|---|
| calendar year 2023 |
calendar year 2023 |
calendar year 2023 |
The fieldwork started on the 3rd February and ended on 23rd June 2024. The lag is 2 to 6 months. |
According to Reg. (EU) 2019/1700 Annex II, precision requirements for all data sets are expressed in standard errors and are defined as continuous functions of the actual estimates and of the size of the statistical population in a country or in a NUTS 2 region. For the income and living conditions domain, the estimated standard errors of the following indicators are examined according to certain parameters set:
- · Ratio at‐risk‐of‐poverty or social exclusion to population;
- · Ratio of at‐persistent‐risk‐of‐poverty over four years to population;
- · Ratio at‐risk‐of‐poverty or social exclusion to population in each NUTS 2 region.
Further information is provided in section 13.2 Sampling error.
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
Imputation procedure used
Situation of missing income data for one of the household members was rare (10 cases) in 2024. For these persons, the income was imputed by the simple hot-deck method (using a randomly chosen person with similar characteristics from another household). Access to administrative register information on individual level is not possible. We use our developed model for gross/net conversion, which was developed with regard to the Czech tax laws.
The item non-response of non-income variables is rare, so model approach development is unnecessary. We use the hot-deck method for new households and information from the previous year for households in subsequent waves of the survey. The amount of CZK 3000 was added to income in kind of an employee for each month of using a company car.
The source of EU_SILC data is fully interview.
Annual
Annex to Reg. (EU) 2019/1700: Information on:
- Date of the dissemination of national results – 6th March 2025
- Number of days between the end of fieldwork and the first fully validated delivery of data to the Commission (Eurostat) – 189
- Date of the first full delivery of data to the Commission (Eurostat). If the delivery of data does not meet the deadline laid down in Regulation (EU) 2019/1700, a reason for the delay should be given. – 29th December 2024
Please provide the link in national legislation, calendar of publication or other relevant information: Household Income and Living Conditions - 2024 | Products
In the Czechia EU-SILC results are eligible to use at NUTS 2 level as maximum. But not all the results, just some. Detailed classifications are for NUTS 2 ineligible.
See Annex 8 - Breaks in series.
Annexes:
CZ_2024_Annex 8-Breaks in series
