1.1. Contact organisation
The Statistical Office of the Slovak Republic
1.2. Contact organisation unit
Social Statistics and Demography Department
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
Lamačská cesta 3/C , 840 05 Bratislava 45 , Slovak 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
23 May 2025
2.2. Metadata last posted
23 May 2025
2.3. Metadata last update
23 May 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). 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 Eurostat webpage.
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
| Reference population |
Private household definition |
Household membership |
|---|---|---|
|
Persons living in collective households are not included in the reference population. The share of persons who are living in collective |
|
|
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 Slovak Republic, none region was excluded.
3.8. Coverage - Time
Coverage time: 2005 - 2024
The fieldwork for SILC 2024 started in February and ended on the in July 2024.
Reference periods:
Period for taxes on income and social insurance contributions - calendar year 2023
Income reference periods used - calendar year 2023
Reference period for taxes on wealth - calendar year 2023
3.9. Base period
Period started from 2005.
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 |
|---|---|---|---|
| 2023 |
2023 |
2023 |
Therefore, the lag is at minimum 2 months and at maximum 7 months. (Start of survy is February 2024 and end July 2024) |
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
Confidentiality policy - description of any provisions in addition to European legislation that are relevant to the statistical confidentiality applied to the data.
7.2. Confidentiality - data treatment
Confidentiality – data treatment: general description of the rules applied to treating microdata and macrodata (including tabular data) with regard to statistical confidentiality.
8.1. Release calendar
Publications from EU SILC are releasing every year. Last publication will be published on 02 May 2024.
Calendar of publication is placed on web page of Stiatistical office of the Slovak republic
8.2. Release calendar access
Please refer to the Release calendar publicly available on the Eurostat’s website.
The calendar contains timetable of the first release of selected indicators. Data will be published in the given day at 9 o´clock on the Internet website of the SO SR in the part Information reports Catalogue of the SO SR and there will be also at the disposal at the spokesperson of the SO SR. You will also find notice for amendments of dates on the Internet website of the SO SR.
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 EU statistics on income and living conditions - Microdata - Eurostat.
Annual
10.1. Dissemination format - News release
Short news on our official webpage of Statistical office of Slovak Republic by end of February 2025.
10.2. Dissemination format - Publications
Date of dissemination of first results in a form of publication: 02 May 2025.
10.3. Dissemination format - online database
Online database of results on webpage DATAcube (on 02 May 2025)
10.3.1. Data tables - consultations
Not applicable
10.4. Dissemination format - microdata access
Anonymised microdata can be made for scientific purposes and at the individual request.
10.5. Dissemination format - other
Information service on request, according to the Rules of disemination of statistical products.
10.5.1. Metadata - consultations
Not applicable
10.6. Documentation on methodology
Methodolgy is available on our internal server for every person which is participated on survey.
10.6.1. Metadata completeness - rate
Not available.
10.7. Quality management - documentation
Not applicable
11.1. Quality assurance
Statistical Office of the Slovak Republic fulfils the commitment to quality as the principles of the European Statistics Code of Practice, which is regularly monitored by means of a self-assessment and also by external assessment (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, media, 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.
12.3. Completeness
All variables according to the Regulation are being transmitted.
Some variables which were optional were not collected, these are:
RL080: Remote education
HI130G: Interest expenses
HI140G: Household debts
12.3.1. Data completeness - rate
Not requested
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.
Slovakia use two-stage stratified type. DB050 (primary strata) for strata specification and DB060 (Primary Sampling Unit) for cluster specification;
Annexes:
Annex_3
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.
Main indicators, standard error and CI at country level
| AROPE | At risk of poverty | Severe Material and Social Deprivation | Very low work intensity | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Ind. | Stand. errors | 95% CI | Ind. | Stand. errors | 95% CI | Ind. | Stand. errors | 95% CI | Ind. | Stand. errors | 95% CI | |||||
| value | L | U | value | L | U | value | L | U | value | L | U | |||||
| Total | 18.3 | 1.0 | 16.3 | 20.2 | 14.5 | 1.0 | 12.6 | 16.4 | 7.6 | 0.8 | 6.1 | 9.1 | 3.8 | 0.5 | 2.9 | 4.8 |
| Male | 18.1 | 1.0 | 16.0 | 20.1 | 14.1 | 1.0 | 12.2 | 16.1 | 7.6 | 0.8 | 6.0 | 9.3 | 4.3 | 0.5 | 3.3 | 5.4 |
| Female | 18.4 | 1.0 | 16.4 | 20.5 | 14.8 | 1.0 | 18.9 | 16.8 | 7.6 | 0.8 | 6.0 | 9.1 | 3.3 | 0.5 | 2.4 | 4.3 |
| Age 0-17 | 22.6 | 1.8 | 19.0 | 26.2 | 19.8 | 1.8 | 16.3 | 23.3 | 10.7 | 1.5 | 7.7 | 13.7 | 4.9 | 1.0 | 2.9 | 6.8 |
| Age18-64 | 18.5 | 1.0 | 16.5 | 20.4 | 14.5 | 1.0 | 12.6 | 16.3 | 6.9 | 0.7 | 5.5 | 8.3 | 4.6 | 0.5 | 3.6 | 5.7 |
| Age 65+ | 12.8 | 1.0 | 10.8 | 14.8 | 8.9 | 0.9 | 7.7 | 10.7 | 6.8 | 0.8 | 5.2 | 8.4 | NA | NA | NA | NA |
| AROPE | At risk of poverty | Severe Material and Social Deprivation | Very low work intensity | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Ind. | Stand. errors | 95% CI | Ind. | Stand. errors | 95% CI | Ind. | Stand. errors | 95% CI | Ind. | Stand. errors | 95% CI | |||||
| value | L | U | value | L | U | value | L | U | value | L | U | |||||
| Bratislava region | 8.6 | 1.3 | 6.0 | 11.2 | 7.0 | 1.3 | 4.5 | 9.4 | 1.6 | 0.4 | 0.8 | 2.4 | 0.6 | 0.2 | 0.2 | 1.1 |
| Western Slovakia | 15.8 | 1.3 | 13.3 | 18.3 | 10.9 | 1.2 | 8.6 | 13.2 | 5.3 | 0.5 | 3.9 | 6.7 | 2.7 | 0.5 | 1.8 | 3.6 |
| Central Slovakia | 18.1 | 1.5 | 15.1 | 21.1 | 13.7 | 1.4 | 10.9 | 16.5 | 7.3 | 1.0 | 5.4 | 9.2 | 3.7 | 0.4 | 2.5 | 5.0 |
| Eastern Slovakia | 25.7 | 2.6 | 20.6 | 30.7 | 22.8 | 2.6 | 17.7 | 27.8 | 13.2 | 2.3 | 8.8 | 17.7 | 6.7 | 1.4 | 3.9 | 9.5 |
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 |
|
|
unknown over-coverage (nearly no over-coverage: There is only a very small difference between the frame and the target population) |
| Under-coverage |
|
|
unknown under-coverage (nearly no under-coverage: There is only a very small difference between the frame and the target population) |
| Misclassification |
|
|
there are no misclassifications |
13.3.1.2. Common units - proportion
Not requested
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 |
| Measurement errors are defined as the difference between the value of a specific variable (provided by the respondent) and the real, but unknown value of this variable. On the base of experience from EU SILC carried out in previous year there were several sources of errors, which also could occur too in EU SILC 2024. We focused on following sources of errors:
|
Central Statistical Office is responsible for methodological part of EU SILC survey. At national level it includes preparation of questionnaires, manual for interviewers including methodological documents needed for data collection and checking of data. The questionnaire of EU-SILC is standardised and was developed according to EU SILC regulations and EUROSTAT guidelines. In compiling of questionnaires EU SILC 2024 we resulted from until now proposed and applied questionnaires for the year 2023, where there were used and taken into account concrete knowledge from survey fieldwork and also changes made in some variables in accordance with Doc.065 for 2024 operation. Some changes in questionnaires were made at national level and most of them were rising from effort to make better harmonization of core variables with other household surveys within Social statistics.
When drawing up questionnaires we only took into account requirements and directions proposed in Doc. 065 (2023 operation) and also changes related to legislative on national level - i.e. adding or removing some income and tax components. Just like last year, we have renumbered the individual questions and grouped them into separate modules by reason of better handling, lucidity and securing more easily orientation of interviewers in questionnaires. As in previous years, questionnaires were also in this year printed in different colours shades. This colour distinguish of the questionnaires was evaluated by the interviewers positively - as it made it fieldwork much easier for them. In 2019, however, we proceeded to reorganize the implementation of the surveys in households EU SILC and HBS, which was subsequently reflected in the methodological preparation of the EU SILC survey. We continued in this focus in 2024 as well. Also national experts of Ministry of Labour, Social Affairs and Family and Institute for labour and family research have collaborated with us on preparation of questionnaires. They provided us very useful suggestions
|
The fieldwork for data collection for EU SILC 2024 was realized via external and also internal interviewers. Department of sample surveys statistics in Banska Bystrica ensured data collection in field for EU SILC survey 2024. It is coordinator of data collection (including such activities as forming of interviewer network, concluding contracts with external interviewers, including ensuring documentation and commenting relevant documents (manuals), taking over of questionnaire etc.), data recording and creation of regional databases within SILC survey. Department of sample surveys statistics has within regions - 8 departments of data collection from sample surveys, and one department for data processing of sample surveys, which is mainly aimed at coordination of activities in data collection and processing of data collected in households. For EU SILC 2024 there were approximately selected 7 000 households and during the months of February to July 2024 were interviewed by internal and external interviewers. External interviewers were persons, who ensured interviewing in EU SILC 2024 possibly in previous years of the survey or persons who proved themselves in previous national surveys realized in households. The fieldwork is very demanding in terms of physical but especially psychological aspects. In 2024 internal interviewers surveyed compulsorily the first visits in households. The situation was demanding, because the communication with households again slightly got worse and it was more difficult to look for household willing to cooperate, especially in time regarding with pandemic situation. The implementation conditions were made difficult mainly by the fact that it was not possible to estimate in advance how the pandemic situation would develop, to which it was necessary to respond promptly. Increased demands were placed mainly on the organization of the survey, when in some of the worst-affected areas the survey was also temporarily interrupted. Refusals for both first and repeated visits have an increasing trend. For repeated households it seem a lot - four visits and they are increasingly unwilling to provide data, while new households are afraid that their data will be misused. The most frequent reasons for refusal were also the length of the survey and the sensitivity of the collected data. The most difficult cooperation was with younger respondents and mainly with entrepreneurs. Older people were distrustful and for this reason they did not want to provide data. Geographically, cooperation in smaller municipalities is easier than in cities. Training of interviewers preceded one day training of head of departments from individual offices of SO SR focused chiefly on content aspect of manual for interviewers (with taking into consideration of changes, which have arisen compared previous year), on methodical aspect of newly included ad-hoc module, on quality and check of work interviewers, potentially on other organizational instructions related to survey. By the previous experiences for problem-free mastery of survey process there is necessary contact of interviewer (either personal or telephonic) with relevant head of department from section of statistics of fieldwork survey, who assisted in case of need in solution of methodical uncertainties or solution of other serious problems, which could arise in the course of survey. One of the factors that has influence on the successful course of the survey is the well-prepared advertising. It was done within whole Slovak territory by central statistical office (through media) and also at regional level (through regional media and newspapers, commune mayors and addressing letters to households). Especially in smaller villages cooperation with representatives of municipal authorities has proved successful. For this purpose, the official letter in which the survey will be carried out is sent to all municipalities before the start of the survey. The letter contains information about the deadlines, method and range of the survey. Addressing mayors of municipalities/lord mayors are required to cooperate in the implementation of this survey and provide assistance to the interviewers. The letter to the household is already sent by each interviewer on the specific address (name) of the selected household. In this letter, the household is informed that it has been selected in the survey and is asked to cooperate with stating a specific date of the visit yet (there is also stated a telephone and e-mail contact at the interviewer and on the relevant coordinator at the Regional Office of the Statistical Office SR). We try to motivate selected households to cooperate in survey through promotional gifts (promotional letters, leaflets, pens). Certainly informational small colourful posters (so-called folded leaflet) have proved successful, but also promotional gifts (especially pens with the logo of the Statistical Office), which also increase the credibility of the interviewers and have a positive impact on the response rate. Due to the scope of the survey, the interviewers suggest also other promotional gifts would be suitable to give to the households. The employees of the Department of standard of living statistics in cooperation with Department of the sample survey statistics in Banska Bystrica ensured the training of interviewers - separately for internal and external interviewers. The training was realized by means of two video-conferences. The interviewers, who could not participate in the training in the planned date, were trained separately and it was in competence of the department of relevant regional offices of the SO SR. During training interviewers (134 interviewers were trained in total) it was given special attention to methodological changes, namely it related to training of interviewers, who implemented this survey more times. The methodical changes were especially highlighted within individual chapters in Manual for interviewers The current information on situation in data collection in field (on number of recorded and non-response household) was available in the course of survey via monitoring, which was ensured by employees of Department of the sample survey statistics in the precisely fixed deadlines. After finishing of survey the head of departments ensured taking over of the completed questionnaires from interviewers. At the same time they realized formal check of the completeness of the returned and filled questionnaire and content check of data quality in the correspondence with elaborated manual on data collection and data check After realization of data collection we receive evaluative report from Department of sample surveys statistics, which is worked out on the base of all Regional Offices reports. It represents important feedback from experience from fieldwork data collection in households and gives us picture of severity of methodology and complications with relevance of data. |
With respect to data collected during the previous waves of the survey, interviewers were paying attention to quality of collected data, because in data processing there was underlined comparability of data in time. Data processing was realized on two levels: 1. The following actions has been realized on the decentralized level: a) taking questionnaires from interviewers, formal checking, preparation of questionnaires for data recording, b) data recording and editing: After organizational changes (year 2013) Department of sample surveys statistics plays important role in data recording and editing. It is responsible for preparation of some programs for data recording (including for EU SILC). For EU SILC 2024 survey Blaise software were used for data recording. Program was made in accordance with defined checks and requirements for all collected variables, which Eurostat update every year. And in addition there were controls included in accordance with our national specifications (for example fixed amount of some social benefits, checks in terms of respondent´s age and act.). These types of controls were used: checks on the data integrity, identification of duplicity, frequency checks, checks to the permissible values, the logic checks within a questionnaire and between questionnaires, special conditions for data recording and non-responses. All the defined checks are included in the technical project to data processing EU SILC 2024. The checks are divided into two types: informative checks and necessary checks. System of the checks also comprised of certain chosen checks from the checking software of Eurostat. Data recording is ensured at all Regional Offices. It is made by external employees in compliance with Manual for data recording, editing and auto-corrections. Data recording is ensured at all Regional Offices. It is made by external employees in compliance with Manual for data recording, editing and auto-corrections. Yearly as high as possible quality of the collected data is ensured, namely, via new controls in APV and improvements of existing controls too. The check activities are performed before the data editing. c) on this level, also the errors caused by data recording have been eliminated. There were mainly errors created by a shift in editing codes yes/no/don’t know and by not realizing a visual check sufficiently. By monitoring errors in the phase of data recording, the errors were analysed and subsequently the situation was improved. 2. On the centralized level a final database was created, i.e. data processing of delivered regional databases to final form for Eurostat is the task of Central Statistical Office (which includes creation of the final databases D-file, R-file, P-file and H-file for both cross-sectional and longitudinal component). Logic controls, corrections, over weighting and imputations were realized using SW of system SAS. Data transmission is done via Edamis together with calculation of poverty indicators. In addition to datasets yearly sent to Eurostat, Statistical Office of the Slovak Republic prepares on yearly basis national micro databases UDB_SR, which is more detailed in terms of content. These, in addition to all the variables defined by the regulation, include also income variables in more detailed structure (structural social benefits, number of months of benefit receiving) and variables collected on the base of requirement from external users for the purpose of calculating specific national indicators.
|
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 |
| 100 |
100 |
100 | 80,0 | 58,1 |
96,6 |
100 |
100 |
100 |
20 |
41,9 |
3,4 |
0,0 |
0,0 |
0,0 |
0,0 |
0,0 |
0,0 |
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.
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
13.3.4. Processing error
Description of data entry, coding controls and the editing system
| Data entry and coding (if any used) |
Editing controls |
|---|---|
| For EU SILC 2024 survey Blaise software were used for data recording, in which these types of controls were used: checks on the data integrity, identification of duplicity, frequency checks, checks to the permissible values, the logic checks within a questionnaire and between questionnaires, special conditions for data recording and non-responses. All the defined checks are included in the technical project to data processing EU SILC 2024. The checks are divided into two types: informative checks and necessary checks. System of the checks also comprised of certain chosen checks from the checking software of Eurostat. | The errors caused by data recording have been eliminated. There were mainly errors created by a shift in editing codes yes/no/don’t know and by not realizing a visual check sufficiently. By monitoring errors in the phase of data recording, the errors were analysed and subsequently the situation was improved. On the centralized level a final database was created. Logic controls, corrections, over weighting and imputations were realized using SW of system SAS. |
13.3.5. Model assumption error
Not applicable
14.1. Timeliness
Data collection took place from February 2024 to July 2024.
First transmission of data to Eurostat: 20 December 2024.
Final transmission of data to Eurostat: 19 February 2025.
14.1.1. Time lag - first result
First results concerning income poverty was published on official web page SO SR
14.1.2. Time lag - final result
Date of dissemination of first results in a form of publication: 02 May 2025.
14.2. Punctuality
Final delivery of data were on 19 February 2025.
14.2.1. Punctuality - delivery and publication
National publication: 02 May 2025.
15.1. Comparability - geographical
In the Slovak Republic SILC results are eligible to use at NUTS 2 level as maximum.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not applicable.
15.2. Comparability - over time
No breaks in series/significant changes in year 2024.
Annexes:
Annex_8
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) |
F |
|
| 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.
15.3.1. Coherence - sub annual and annual statistics
Not applicable.
15.3.2. Coherence - National Accounts
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.
According to the Regulation (EC) No 1177/2003 of the European Parliament and of the Council concerning EU-SILC: "Comparability of data between Member States shall be a fundamental objective and shall be pursued through the development of methodological studies from the outset of EU-SILC data collection, carried out in close collaboration between the Member States and Eurostat".
Although the best way for keeping the comparability of data is to apply the same methods and definitions of variables, small departures of the definitions given by Eurostat are allowed in EU-SILC. In this way, the mentioned Regulation in its article 16th says: "Small departures from common definitions, such as those relating to private household definition and income reference period, shall be allowed, provided they affect comparability only marginally. The impact of comparability shall be reported in the quality reports."
Basic concepts and definitions
- The reference period: no differences between the national and standard EU-SILC concept;
- The private household definition: no differences (there can be more households in one dwelling eligible for the survey);
- The household membership: no differences;
- The income reference period used: last calendar year (2023);
- The period for taxes and social contributions: taxes and social insurance contribution refer to the income received during the income reference period;
- The reference period for taxes on wealth: income reference period;
- Basic information on activity status during the income reference period: no differences.
Annexes:
Annex_7
15.4. Coherence - internal
No any lack of coherence in EU SILC.
Mean (average) interview duration per household = 49,6 minutes.
Mean (average) interview duration per person = 17,8 minutes.
Mean (average) interview duration for selected respondents (if applicable) = minutes.
17.1. Data revision - policy
Revision policy is described on offiical web page of SUSR
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
All data were collected by survey/interview.
18.1.1. Sampling Design
Type of sampling design
Two-stage stratified sampling was used in EU SILC 2024. The proportional number of households was selected by simple random sampling in individual strata.
Households with rotation groups 1, 2 and 4 were included into sample in EU SILC 2023 survey. Households included to 3-rd rotation group were excluded and substituted by new households for EU SILC 2024. Repeatedly stratified sampling was used for selection these new households and the proportional number of households was selected by simple random sampling in individual strata.
Stratification and sub stratification criteria
There are two criteria of area stratification in the sampling design:
- geographical stratification (8 standard administrative regions corresponding to the European NUTS 3 level.)
- degree of urbanization: 7 groups according to population size of municipalities and communes (number of inhabitants in municipalities and communes)
Totally 48 final strata were created (variable DB050) by using of those two stratification criteria.
Sample selection schemes
The information about population, which was obtained from sampling frame, the information about updating of sampling frame and the rules for proportional stratified sampling was used in creating of sample selection scheme for new rotational group.
In selection of households for the new rotational group we proceeded by analogy as in the first year of survey, i.e. in EU SILC 2005:
- up-to date sampling frame (list of households sharing of expenditures) was created,
- strata were created (households sharing of expenditures from list were put in strata by region and level of urbanisation of municipalities),
- required number of selected households sharing of expenditures for new rotational group was approximately 1 500 households,
- probability of sampling for given number of households sharing of expenditures was appointed,
- random numbers from interval (0,1) were generated in each strata for each unit, which was not included in sampling in previous period,
- units with random number lower or equal than was probability of sampling were included into sampled population.
Sample distribution over time
Survey was carried out in the period from 1st February to 31th July 2024.
18.1.2. Sampling unit
Households sharing of expenditures are the sampling units.
Households sharing of expenditures are private households comprised of persons in dwelling who live and manage together, including sharing in ensuring of the living needs. As manage together is considered: share in covering the basic household costs (catering, housing cost, costs of electricity, gas etc.).
The fullest list of households sharing of expenditures and permanently occupied dwellings and houses is available on the base of data from the 2021 Population and Housing Census (acronym - SODB).
18.1.3. Sampling frame
aktualizovat
Concerning the SILC instrument, three different sample size definitions can be applied:
- the actual sample size which is the number of sampling units selected in the sample: 7 222
- the achieved sample size which is the number of observed sampling units (household or individual) with an accepted interview: 5 781
- the effective sample size which is defined as the achieved sample size divided by the design effect with regards to the at-risk-of poverty rate indicator: 4 250
Given that the effective sample size has been already treated in the section dealing with sampling errors, in this section the attention focuses mainly on the achieved sample size.
Achieved sample size:
- no. of household: 5 781
- no. of all persons: 14 017
- no. of persons 16+: 11 986
18.2. Frequency of data collection
The fieldwork of data collection was planned for the period from February to July 2024, through internal as well as external interviewers in nearly 7,000 selected households.
Share of succesfull household interviews by month.
| Month | Total | in % |
|---|---|---|
| 2 | 829 |
14,3% |
| 3 | 1269 | 22,0% |
| 4 | 1643 | 28,4% |
| 5 | 1653 | 28,6% |
| 6 | 381 | 6,6% |
| 7 | 6 | 0,1% |
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 |
34.8 |
65,2 |
|
|
|
|
|
|
|
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 through interview. The target income variables were divided into more subcomponents according to the Slovak benefit system.
|
All income data was recorded as gross at component level. 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.
|
Income variables at component level were collected on the base of personal interview in private households. Regarding data on income obtained during interviews, household members have the tendency to underestimate individual sources of income or data on some income components is missing (item non-response). The elimination possibilities of this survey data underestimation 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). 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. In case of the unemployment benefits where the duration of unemployment and the reported benefits did not match the rules of the unemployment benefits provision, the reported benefits were re-classified as minimum income support benefits The value of goods produced by own-consumption was an estimated by the household and estimate was based on the amount of consumed food and other goods of own production and goods from own business during the year 2018. All income components were collected in common currency – EUR. |
Annexes:
Questionnares 2023
18.4. Data validation
Data control
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).
18.5. Data compilation
Database contains different types of weights:
- Household cross-sectional weight (DB090) to obtain the actual number of private households in Slovakia.
- Personal cross-sectional weight (RB050) to obtain actual number of persons in Slovakia.
- Personal cross-sectional weight for each household member aged 16 and more (PB040).
18.5.1. Imputation - rate
Estimation and Imputation
Imputation for within-household non-response
Data of non-responding persons or households were imputed by full record imputation.
18.5.2. Calculation of weighting factors and weight adjustments
Weighting procedure
In practice, the well-tried iteration method of weight calibration was utilized, which minimizes the difference between the known and the grossed up values of selected characteristics. Although it is a panel survey comprising data of four practically independent samples (waves 1-4), a simple calibration method was utilized which did not distinguish the waves but worked with all households together.
At the same time and according to the Eurostat’s recommendations the standard system of integrated weights was used in the survey, i.e. a single set of grossing-up coefficients that was subsequently used to produce results for both households and individuals.
As the basis for calculations the following calibration variables were used:
- Number of inhabited dwellings in each NUTS3 region, subdivided into family houses (detached and semi-detached houses) and apartments, based on the 2021 Census continuously updated from administrative sources of construction authorities
- Population characteristics:
- Population totals in each NUTS3 region (from demographic statistics)
- Economic activity characteristics in each NUTS3 region
- Number of employees – derived from the number of employees in the economy based on the Labour Force Survey (LFS) results and company reporting
- Economic activity characteristics in each NUTS3 region:
- Number of pensioners (excl. pensions for orphans) - based on the administrative data from the Ministry of Labour, Social Affairs and the Czech Social Security Administration and reduced the pensioners living out of the dwellings based on the 2011 Census
- Number of unemployed - registered unemployment from the administrative source of the Ministry of Labour and Social Affairs, corrected for unregistered unemployment using the Labour Force Survey data and for unemployment of the homeless and persons living in institutions or collective accommodation establishments (based on the 2011 Census)
- Number of self-employed - estimate based on the Labour Force Survey and on the administrative data from the Czech Social Security Administration
- Number of children aged 0-15 - from demographic statistics
- Demographic characteristics in each NUTS3 region (based on the demographic statistics):
- Age groups (0-15, 16-24, 25-34, 35-44, 45-54, 55-64, 65-74, 75+); Sex
- Municipality size (less than 2 000 inhabitants, 2 000-9 999, 10 000-49 999, 50 000 or more inhabitants) The target population of the survey was persons living in private households, therefore the data from demographic statistics was adjusted by subtracting institutionalized population (from social security administrative data and Ministry of Justice) and the persons living outside dwellings as based on the 2011 Census.
As the sampling unit is the dwelling, all weight coefficients were calculated for dwellings and subsequently assigned to all persons and households in them (integrated weights).
The method described above deals with non-response successfully, i.e. it corrects the bias due to the specific composition of households that did not respond. First of all, it improves demographic and social structure but, as a by-product, it also eliminates deformation of income indicators related to these structures.
18.5.3. Estimation and imputation
Imputation of income variables
Where possible, data from previous year 2023 was used for imputation. Data of 2023 was used only if association analysis showed that these two consequent year incomes are sufficiently closely related, based on households reported income in both years. If analysis indicated no correlation between the incomes of 2023 and 2024, values were not used in imputation. Before applying, income of 2023 was corrected for trend between 2023 and 2024.
If missing value could not be imputed with data from previous year, the following methods were used (in this order):
- Logical deduction of value, based on other data in questionnaire;
- Imputation with median or average, when only single values were missing;
For some income variable components, amount per month was imputed and then converted into amount per year.
If an income component was collected only net, then missing net values were imputed and then converted to gross using net/gross conversion algorithm. Respectively, if an income component was collected only gross, then a gross value was imputed and then converted to net.
For income components, where respondent could choose whether to provide a value net or gross (PY010, PY050 etc), gross values were converted to net prior to imputation. Missing values are thus imputed as net. Net/gross and gross/net conversion algorithms were based on local tax system.
18.6. Adjustment
Not applicable.
18.6.1. Seasonal adjustment
Not applicable.
No comments.
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.
23 May 2025
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 Slovak 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 |
|---|---|---|---|
| 2023 |
2023 |
2023 |
Therefore, the lag is at minimum 2 months and at maximum 7 months. (Start of survy is February 2024 and end July 2024) |
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
Database contains different types of weights:
- Household cross-sectional weight (DB090) to obtain the actual number of private households in Slovakia.
- Personal cross-sectional weight (RB050) to obtain actual number of persons in Slovakia.
- Personal cross-sectional weight for each household member aged 16 and more (PB040).
All data were collected by survey/interview.
Annual
Data collection took place from February 2024 to July 2024.
First transmission of data to Eurostat: 20 December 2024.
Final transmission of data to Eurostat: 19 February 2025.
In the Slovak Republic SILC results are eligible to use at NUTS 2 level as maximum.
No breaks in series/significant changes in year 2024.
Annexes:
Annex_8
