The Statistical Office of the Slovak Republic 

Social Statistics and Demography Department

Lamačská cesta 3/C , 840 05 Bratislava 45 , Slovak Republic  

23 May 2025

23 May 2025

23 May 2025

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

The EU-SILC instrument provides two types of data:

1. Cross-sectional data pertaining to a given time or a certain time period with variables on income, poverty, social exclusion and other living conditions;
2. Longitudinal data pertaining to individual-level changes over time, observed periodically over four‐or more year rotation scheme (Annex III (2) of 2019/1700). Social exclusion and housing condition information is collected mainly at household level while labour, education and health information is obtained for persons aged 16 and over. The core of the instrument is income information at very detailed component level and mainly collected at personal level.

• International Standard Classification of Education (ISCED'2011);
• International Standard Classification of Occupations (ISCO-08);
• Classification of Economic Activities (NACE Rev.2-2008);
• Common classification of territorial units for statistics (NUTS 2);
• SCL - Geographical code list;
• The recommendations made by the United Nations in the Canberra Group Handbook on Household Income Statistics should also be taken into account.

For more details on the classification used please, see Eurostat webpage.

Data refer to all private households and individuals living in the private households in the national territory at the time of data collection.

The EU-SILC survey is a key instrument for the European Semester and the European Pillar of Social Rights, providing information on income distribution, poverty and social exclusion, as well as various related living conditions and poverty EU policies, such as on child poverty, access to health care and other services, housing, over indebtedness and quality of life. It is also the main source of data for microsimulation purposes and flash estimates of income distribution and poverty rates.

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

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

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

The target population is private households and all persons composing these households having their usual residence in the 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.

Definitions of reference population, household and household membership

The sub-populations that are not covered by the data collection includes: those who moved out of the country’s territory; or those with no usual residence; or those living in institutions or who have moved to an institution compared to the previous year.

The survey was carried out on the whole territory of the Slovak Republic, none region was excluded.

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

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

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

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

Confidentiality policy - description of any provisions in addition to European legislation that are relevant to the statistical confidentiality applied to the data.

Confidentiality – data treatment: general description of the rules applied to treating microdata and macrodata (including tabular data) with regard to statistical confidentiality.

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

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.

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

Short news on our official webpage of Statistical office of Slovak Republic by end of February 2025.

Date of dissemination of first results in a form of publication: 02 May 2025.

 Online database of results on webpage DATAcube (on 02 May 2025)

Not applicable

Anonymised microdata can be made for scientific purposes and at the individual request.

Information service on request, according to the Rules of disemination of statistical products.

Not applicable

Methodolgy is available on our internal server for every person which is participated on survey.

Not available.

Not applicable

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).

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.

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

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.

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

Not requested

According to Reg. (EU) 2019/1700 Annex II, precision requirements for all data sets are expressed in standard errors and are defined as continuous functions of the actual estimates and of the size of the statistical population in a country or in a NUTS 2 region. For the income and living conditions domain, the estimated standard errors of the following indicators are examined according to certain parameters set:

• · Ratio at‐risk‐of‐poverty or social exclusion to population;
• · Ratio of at‐persistent‐risk‐of‐poverty over four years to population;
• · Ratio at‐risk‐of‐poverty or social exclusion to population in each NUTS 2 region.

Further information is provided in section 13.2 Sampling error.

EU-SILC is a complex survey involving different sampling designs in different countries. In order to harmonize and make sampling errors comparable among countries, Eurostat (with the substantial methodological support of Net-SILC2) has chosen to apply the "linearization" technique coupled with the “ultimate cluster” approach for variance estimation.

Linearization is a technique based on the use of linear approximation to reduce non-linear statistics to a linear form, justified by asymptotic properties of the estimator. This technique can encompass a wide variety of indicators, including EU-SILC indicators. The "ultimate cluster" approach is a simplification consisting in calculating the variance taking into account only variation among Primary Sampling Unit (PSU) totals. This method requires first stage sampling fractions to be small which is nearly always the case. This method allows a great flexibility and simplifies the calculations of variances. It can also be generalized to calculate variance of the differences of one year to another.

The main hypothesis on which the calculations are based is that the "at risk of poverty" threshold is fixed. According to the characteristics and availability of data for different countries, we have used different variables to specify strata and cluster information. 

Slovakia use two-stage stratified type. DB050 (primary strata) for strata specification and DB060 (Primary Sampling Unit) for cluster specification;

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

Non-sampling errors are basically of 4 types:

• Coverage errors: errors due to divergences existing between the target population and the sampling frame.
• Measurement errors: errors that occur at the time of data collection. There are a number of sources for these errors such as the survey instrument, the information system, the interviewer and the mode of collection.
• Processing errors: errors in post-data-collection processes such as data entry, keying, editing and weighting.
• Non-response errors: errors due to an unsuccessful attempt to obtain the desired information from an eligible unit. Two main types of non-response errors are considered:
  • Unit non-response: refers to absence of information of the whole units (households and/or persons) selected into the sample.
  • Item non-response: refers to the situation where a sample unit has been successfully enumerated, but not all required information has been obtained.

Coverage errors include over-coverage, under-coverage and misclassification:

• Over-coverage: relates either to wrongly classified units that are in fact out of scope, or to units that do not exist in practice.
• Under-coverage: refers to units not included in the sampling frame.
• Misclassification: refers to incorrect classification of units that belong to the target population

Coverage error

Not requested 

Measurement error for cross-sectional data

Non-response errors are errors due to an unsuccessful attempt to obtain the desired information from an eligible unit. Two main types of non-response errors are considered:

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

• Household non-response rates (NRh) is computed as follows:

NRh=(1-(Ra * Rh)) * 100

Where Ra is the address contact rate defined as:

Ra= Number of address/selected person (including phone, mail if applicable) successfully contacted/Number of valid addresses/selected person (including phone, mail if applicable) selected

and Rh is the proportion of complete household interviews accepted for the database

Rh=Number of household interviews completed and accepted for database/Number of eligible households at contacted addresses (including phone, mail if applicable)

• Individual non-response rates (NRp) is computed as follows:

NRp=(1-(Rp)) * 100

Where Rp is the proportion of complete personal interviews within the households accepted for the database

Rp= Number of personal interview completed/Number of eligible individuals in the households whose interviews were completed and accepted for the database

• Overall individual non-response rates (*NRp) is computed as follows:

*NRp=(1-(Ra * Rh * Rp)) * 100

For those Members States where a sample of persons rather than a sample of households (addresses, phones, mails etc.) was selected, the individual non-response rates will be calculated for ‘the selected respondent.

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

Unit non-response rate for cross-sectional

where

A=total (cross-sectional) sample,

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

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

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

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

See annex

 Description of data entry, coding controls and the editing system

Not applicable

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.

First results concerning income poverty was published on official web page SO SR

Date of dissemination of first results in a form of publication: 02 May 2025.

Final delivery of data were on 19 February 2025.

National publication: 02 May 2025.

In the Slovak Republic SILC results are eligible to use at NUTS 2 level as maximum.

Not applicable.

No breaks in series/significant changes in year 2024.

No breaks in series in last years.

Comparability and deviation from definition for each income variable

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

The coherence of two or more statistical outputs refers to the degree to which the statistical processes, by which they were generated, used the same concepts and harmonised methods. A comparison with external sources for all income target variables and the number of persons who receive income from each ‘income component’ will be provided, where the Member States concerned consider such external data to be sufficiently reliable.

Not applicable.

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.

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.

Revision policy is described on offiical web page of SUSR

Not applicable

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.

All data were collected by survey/interview.

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.      

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).

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

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.

Mode of data collection

Description of collecting income variables

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).

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).

Estimation and Imputation

Imputation for within-household non-response

Data of non-responding persons or households were imputed by full record imputation.

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.

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.

Not applicable.

Not applicable.

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