Statistical office of the Republic of Slovenia

Statistical office of the Republic of Slovenia

Demography Statistics and Level of Living

Statistical Office of the Republic of Slovenia
Litostrojska 54
SI – 1000 Ljubljana
Slovenia

31 May 2025

31 May 2025

31 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 EU Vocabularies, Eurostat's metadata server or CIRCABC.

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

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 Slovenia. 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 population moved out of territory of country, the population that have not a usual residence, living in institutions or who have moved to an institutions from the previous wave are not covered.

The entire territory of Republic of Slovenia is covered. 

Cross sectional exercise 2024 covers year 2024 and incomes from refrence period year 2023.

Not applicable.

The data involves several units of measure depending upon the variables. Income variables were collected and then transmitted to Eurostat in Euro. For more information, see methodological guidelines and description of EU-SILC target variables available on CIRCABC.

Description of reference period used for incomes (cross sectional)

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.

In Slovenia the survey was conducted according to Annual programme of the statistical surveys.  The legalisations is available only in Slovenian language.

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.

All data collected and published by the Statistical Office are governed by the National Statistical Act (OJ) RS No. 45/95 and No. 9/01.

Cell suppression is used for protection of sensitive cells.

SURS's release calendar are published Slovenia statistical webpage.

Please refer to the Release calendar - Eurostat (europa.eu) publicly available on the Eurostat’s website.

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.

Statistical Office of the Republic of Slovenia disseminate data for Slovenia on SURS's website  and Sistat database with detailed data.  The data are available for all users on the day of the release at 10:30 am in Slovenian and English. 

Annual for core data including income indicators and occasionally for ad hoc modules, which are published on the time when the data are collected.

• 20 February 2025 Living conditions.
• 19 March 2025 Living conditions, detailed data .
• 10 April 2025 Living conditions of children.
• 22. May 2025 Access to services. 
• 10 June 2025 Access to services, detailed data.
• 20 February 2025 Income, poverty and social exclusion indicators.
• 28. February 2025 Energy poverty. 
• 19 March 2025 Income, poverty and social exclusion indicators, detailed data.

The data were used in several special releases during the year, for example - International Day of Happiness, International day of women, World Health Day, etc. The data from EU-SILC are used also on social media as Facebook, Instagram, LinkedIn.

SiStat - Statistical database of the Statistical Office of the Republic of Slovenia.  

It is an open database with integrated search. You can access the data in the SiStat Database in two ways: with the search engine or with a tree view of statistical themes. In the PX-Web tool, you can select the categories for each variable (municipalities, gender, year, etc.) that you want to display in the table.

The data presented in the table can be edited and calculated, shown on charts, sorted and/or exported in various file formats (Excel, CSV, JSON, etc.).

You can also access the data in the SiStat Database using the API, which enables you to automate the reading and use of the data.

Note: The views are counted from the date of publication until 23 April 2025. The numbers reflect views of the Slovenian language release and the English language release, respectively.

For tables in the SiStat Database, see Annex A under concept 13.2.1.

In the Slovenian statistical website can be found more information for  is available the researchers. 

The Statistical Office of the Republic of Slovenia (SURS) enables researchers to access data for the purpose of research, i.e. to all data collected with statistical surveys planned in the current annual programme of statistical surveys (LPSR). SURS also enables researchers to access data that researchers transmit to SURS with the purpose of linking them in a secure environment.

The use of the data is according to the data sensitivity in the following ways:

• Access in SURS's secure room
• Remote access
• Using statistically protected microdata via Big file exchange system (SOVD)

For researcher's access to data in the secure room or via remote access, SURS prepares individual microdata databases by removing identifiers. Via Big file exchange system (SOVD), researchers receive only statistically protected microdata that are the result of some statistical surveys conducted on a small sample. SURS enables researchers to access statistically unprotected aggregated data.

In addition, SURS can also transmit to researchers a limited set of individual data that they need for conducting surveys (name and family name, residence, year of birth and sex) but only in the form of a so-called sample of persons.

Researchers can also access methodological explanations and questionnaires for individual statistical surveys conducted by SURS. Selected examples of research analyses on national statistics are available on the website.

Researchers who use data or information produced by the Statistical Office of the Republic of Slovenia in their research papers, presentations, posters and other material, please take into account that you need to acknowledge the source and follow the guidelines for correct use of the emblem.

Individual data collected by national statistics for statistical processing are strictly confidential and can be used exclusively for statistical purposes, irrespective of whether they refer to natural or legal persons. Therefore, they have to be handled very carefully and responsibly, and any enabling of access to such data to researchers must be in line with National Statistics Act.

Not applicable

Not applicable.

Documentation on methodology can be found in the following Methodological explanations.

Data are not available.

Questionnaires, methodological explanations, and quality reports are available on the Statistical Office website.

Quality reports for EU-SILC statistics can be reached on SURS website.  

Theme: Quality of life. There is an automatic link to the methodological explanations, quality report and questionnaire.

EU-SILC survey is produced in compliance with methodological requirements and standards. EU-SILC is conducted according to standards at SURS.

EU-SILC survey is produced in compliance with methodological requirements and standards.

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.

For more information, please consult  User Satisfaction Survey.

SURS regularly monitoring user satisfaction and needs with the help of various methods and tools. The latest available results from the user satisfaction survey are from 2023.

Respondents assessed general satisfaction with SURS with the average score of 8.0 (on a scale from 1 – disagree completely to 10 – agree completely). The complete report are available here on the Slovenia statistical website. In November 2024, a new round of the user satisfaction survey was carried out.

All  obligatory required variables were transmitted.

We did not collect the following variables:

HS022 Reduced utility cost

HY030G imputed rent

RL080 Remote education

HI130G Interest expenses

HI140G Household debts

We did not collect these variables because we did not have legal basis to collect them. Exception was HS022 where such scheme does not exist in the country and RL080 which depends more or less on COVID-19 pandemic situation.

100% for obligatory variables.

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. 

In particular, countries have been split into 3 groups:

1) BE, BG, CZ, IE, EL, ES, FR, HR, IT, LV, HU, PL, PT, RO, SI, UK and AL, whose sampling design could be assimilated to a two-stage stratified type we used DB050 (primary strata) for strata specification and DB060 (Primary Sampling Unit) for cluster specification;

2) DK, DE, EE, CY, LT, LU, NL, AT, SK, FI, CH whose sampling design could be assimilated to a one stage stratified type we used DB050 for strata specification and DB030 (household ID) for cluster specification;

3) MT, SE, IS, NO, whose sampling design could be assimilated to a simple random sampling, we used DB030 for cluster specification and no strata.

The data are provided in attached Annex an_1. We provide the cross sectional data for whole country, and for NUTS2 regions. 

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.

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

 source SILC 2024

The sample size (net) is approximately 20 000 persons, meanwhile admnistrative sources cover whole population in Slovenia (app. 2 million persons).

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 in this concept

 Description of data entry, coding controls and the editing system

Checks

Not applicable.

The national results were disseminated on SURS website according to calendar.

The data were not publish as first results (provisional). Statistical Office of the Republic of Slovenia published all other data only as final results.

Final results were published several times on different topics:

20 February 2025 Living conditions  (M+14)

19 March 2025 Living conditions, detailed data  (M+15)

10 April 2025 Living conditions of children  (M+16)

22 May 2025 Access to services  (M+17)

10 June 2025 Access to services, detailed data (M+18)

20 February 2025 Income, poverty and social exclusion indicators (M+14)

28 February 2025 Energy poverty  (M+14)

19 March 2025 Income, poverty and social exclusion indicators, detailed data  (M+15)

Not available.

All data were published according to release calendar as it was planned in September previous year.

The final data were delivered to Eurostat on 12 February 2025. 

In Slovenia are no significant diffrences among NUTS2 regions.

Not applicable.

In 2024 we have the organizational part of the survey without specifics, similar to year before covid-19 situation. Data were collected in normal share by CAPI and CATI. We finished with collection period at the end of June (as usual). Anyway, the majortiy of the questionnaires were filled in the first part of the year.

Published results from the Living Conditions survey (SILC) refer to the individual year of the survey. Most of the indicators in the SiStat database refer to years from 2005 onwards. In the case of indicators where data accuracy is guaranteed, they are also broken down by cohesion and statistical regions.

The indicators "degree of long-term risk of poverty", "degree of overburdened with housing costs", "median burden with housing costs", data on household incomes and indicators broken down by cohesion and statistical regions refer to the years from 2008 onwards. Due to a break in the time series, the "dwelling overcrowding rate" indicator by cohesion and statistical regions refers to the years from 2011 onwards. The indicator "level of material and social deprivation" refers to the years from 2014 onwards, due to the inclusion of 7 new variables in the Living Conditions survey. The indicators for monitoring the achievement of the objectives of the EU strategy until 2030, which were calculated for the first time in 2022 according to a slightly modified methodology (level of risk of social exclusion, level of serious material and social deprivation, level of very low work intensity), refer to the years from 2014 onwards.

The indicators from the Household Budget Survey (HBS) refer to the period 1997-2004.

For the additional explanations and factors influencing comparability over time see Annexes.

EU-SILC survey is conducted in Slovenia from 2005. Thus length of the time series is 19 years. Last bigger change was in 2021 with the change of regulation.

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.

Coherence with LFS for variable PL032 - self defined current economic status (%) - EU-SILC persons aged 16-89, LFS persons aged 15-89 

Source: EU-SILC, LFS 2024

Because SILC data collection was not equally distributed over time, the data are not completely comparable with LFS.  

Coherence with administrative sources

We do not compare EU-SILC data with administrative sources, because administrative sources are source for EU-SILC data.

Not applicable.

Data are comparable from year to year, except years 2020 and 2021 due to COVID-19, where some data a little bit diffriete because of COVID-19.

Duration of interviewing SILC 2024

Revision is not foreseen.

Revision is not foreseen.

Revision is not foreseen

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.

The sources of EU-SILC data are:

• EU-SILC questionnaire (CATI, CAPI);
• Income database;
• Persons in employment;
• Registered unemployed persons;
• Administrative evidence of inactive persons;
• Statistical Population Database.
• Real estate register
• Surveys on education
• Administrative data about sickness leave

As in previous year the sample design for Slovenian EU-SILC 2024 was two-stage stratified design. In each stratum primary sampling units (PSUs) were firstly systematically selected, and in the second stage 7 persons were selected in each PSU.

We have used rotational design, meaning that three waves were preserved from the previous year and just one wave was additionally selected using the described design.

In the first stage primary sampling units were selected. Primary sampling units are clusters of enumeration areas, which are approximately of the same size. In the second stage 7 persons were selected in each of the selected primary unit. Unit of observation are selected persons living in private households in Slovenia and their households. The data are collected from all household members who were on 31st December 2023 aged 16 years or more. The selected person is also the sample person; other household members are not sample persons.

The sampling frame of persons aged 16 years or more is divided into 5 strata, which are defined according to the size of the settlement and it's characteristics:

1. Non-urban settlement
2. Smaller urban settlement
3. Larger urban settlement
4. Town with at least 10.000 inhabitants
5. Ljubljana

When selecting the primary sampling units, explicit stratification according to the type of settlement was used (5 strata). Since we wanted to maintain regional representativeness, implicit stratification according to the statistical region was applied. It means that the list of units within strata was sorted according to statistical regions. In Slovenia, there are 12 statistical (NUTS3) regions:

1. Pomurska statistical region
2. Podravska statistical region
3. Koroška statistical region
4. Savinjska statistical region
5. Zasavska statistical region
6. Posavska statistical region
7. Jugovzhodna Slovenija
8. Osrednjeslovenska statistical region
9. Gorenjska statistical region
10. Primorsko-notranjska statistical region
11. Goriška statistical region
12. Obalno-kraška statistical region

Mode of data collection

Description of collecting income variables

Procedures, used for checking and validating the source and output data are data logical controls and Eurostat checking.

Examples of data logical controls:

As in previous years checking of the data was done in several stages: data-entry checks, data control and data editing for all separate sources (questionnaire and registers data), and finally the data control on integrated database. For data editing and imputations, the statistical data processing was used, i.e MetaSOP application. The program is based on SAS.

The questionnaire was programmed in Blaise, so data entry controls were built into the electronic questionnaire, what reduced the need for post data control. Control of data in the entry program was done in various ways. All numeric variables had absolute limits for data entry. We had a lot of syntax checks, some of them were signals (soft errors) which gave a warning to the interviewers if the answer was either unlikely because it was extreme or because it did not correspond to answer given to the earlier asked questions. These signals could be overridden if the answer in question was confirmed. And similar hard errors, which it was impossible to override. We also had a lot of logical checks.

Here are examples of syntax checks and one logical check: 

Soft syntax error:

• Variable (PL060): Number of hours usually worked per week in main job: if interviewer entered less than 8 or more than 70 hours there was a signal: Really less than 8 or more than 70 hours per week in main job? The answer could be yes – suppress or no - correct the number of hours.

Hard syntax error:

• Variable PL200 If ASP17-AGE =<14 then HARD ERROR! Person could not work before age of 14.

 Logical error:

• Variable PL032: Self-defined current economic status: if interviewer entered the person aged 16 and more is a preschool child there was an error: The person is 6 or more year old so he/she can not be a preschool child.

The second stage was done in our office by checking and correcting all sources separately. The system of processing, checking and correcting was programmed in SAS. We had various logical and consistency checks, we checked the extreme values of all income components and variables with amounts from questionnaire (for example total housing costs). During the editing procedures the detected syntax and logical errors are corrected.

Here are some examples of checks at this stage:

Checks

The system of processing, checking and correcting was programmed in SAS. We had various logical and consistency checks, we checked the extreme values of all income components and variables with amounts from questionnaire (for example total housing costs). During the editing procedures the detected syntax and logical errors are corrected.

After editing the data from all sources separately, we compose so called integrated database with all the data. In the case of logical mistakes and inconsistency of the data, we edited the data to the most probably value. We also compared the data with data from previous waves on micro level (for those household that had already participated in the survey) and corrected errors.

The data about imputations rate for income variables are available in concept 13.3.3.2. Item non-response - rate.

The data about source and imputation rate for some key non-income variables are follows:

Source: EU-SILC 2024

As in previous years the cross-sectional weights for the first wave were calculated differently as those for the consecutive waves.

Cross-sectional weights for the first wave

The weights were calculated in three consecutive steps. In the first step the sampling weight (design factor), in the second the non-response adjustment factor and in the third the calibration factor was calculated. The final weight was the product of all three factors. The weights were calculated for the selected household (selected person of the household) and for all the persons included in the survey.

In EU-SILC the sample of persons aged 16 years or more was selected from the Central Register of Population. Sample persons and their households were interviewed.

Design factor

The sampling weight for the sample person PB070 is inversely proportional to the probability of selection and the weight is calculated when the person is selected in the sample. For the persons that were in the sample also in the previous year, the sampling weight is taken from the previous year, yet the sampling weights are to be calculated just for the persons that are new in the sample. Since the PPS 2-stage sampling was used, the sampling weight for the selected person in the particular stratum, can simple be calculated as, where  is the stratum number of the persons in the sampling frame and  is the stratum number of the persons in the sample.

The sampling weight of the household of the selected person: DB080

Since SURS doesn’t yet owns a household register that could be used for sampling purposes, the selection of the households is done through the selection of the persons. Since households with more persons aged 16 years or more have a larger probability of selection then smaller households, this has to be corrected with weighting in such way that all households have equal probability of being selected in the sample. Thus the probability of selection of the household is equal to the probability of selection of the person divided by the number of eligible persons (aged 16+) in the household M:

DB080=PB070 / M_h

The sampling weight for the households has to be calculated for all households in the sample, not only for the responding households. Since for the households that did not respond we do not know their size, we have calculated the average size of the household of persons aged 16 or more according to different statistical regions and type of settlement (47 classes) and we imputed this value to households that did not respond.

Table: Design effect and design factor for variable HY020

Source: SILC 2024

Non-response adjustments

The non-response factor was calculated for each stratum. First, the sample was divided into three categories: responses, non-responses and out-of-scope units. The non-response adjustment factor is calculated: , where  is the number of the responses in the stratum and  number of the non- responses in the stratum. With the reference year 2024, we are introducing a novelty to the procedure for non-response adjustments. Namely, before 2024 the strata for the non-response adjustment were defined by variable Type of Settlement. Now we added new stratification variable, aiming to dived response as well as non-responses into two groups based on administrative data: those below the at-risk-of-poverty threshold and those above it. Somewhat simplified, we could say that we divided the sampled households into "poor" and "non-poor" based on administrative data. This new stratification variable, which has two categories, is then combined with the existing one—Settlement Type (which has five categories)—resulting in a total of ten stratification cells for each rotational group (survey wave).

When matching data on whether a household is “administratively poor” or not, from the administrative source to the sample data, a small proportion of units did not match. This meant that we could not assign a value to the additional stratification variable for these units. To address this issue, we imputed the missing data using the hot-deck method, where the donor was randomly selected within the "old" stratification variable, Settlement Type.

Adjustments to external data (level, variables used and sources)

The final step of the calculation of the weights was the calculation of the calibration factors. By the calibration procedures the weighted sums of some key variables are set to the known population values. These population values are obtained from the different administrative sources. For the calibration of weights we used SAS Macro Calmar. We performed calibration on the level of households, as well as on the level of the persons.

For the calibration we used:

1. for households:

• Number of households in the population, obtained from the demography statistics
• Family and children related allowance (HY050) from the administrative source for family and children related allowances

1. for persons:

• Sex- age classes distribution from the Central Register of Population
• Employee cash or near cash income minus sickness benefits from the administrative source for incomes
• Pensions from the administrative sources for pensions
• Unemployment benefits (PY090)  from the administrative source for unemployment benefits
• Education related allowances from the statistical source about scholarships

Final cross-sectional weights

The cross-sectional weight for the household (DB090) is equal to the calibrated weight. The sum of weights is equal to the sum of the estimated number of households in Slovenia.

With the selected person also the household which has to be interviewed is defined. All household members have the same weight, this is the cross-sectional weight. The cross-sectional weight of the person RB050, which all persons get in the household register, and the cross-sectional weight of persons aged 16 years or more PB040 in the person register are equal to the cross-sectional weight of the household.

RB050= PB040=DB090

The cross-sectional weight for the selected person PB060 is equal to the cross-sectional weight of the household of this person multiplied by the number of persons aged 16+:

PB060= DB090 * M_h

The cross-sectional weight for children who were younger than 13 years on 31^st December N-1 is RL070.

Weights are calculated in this way that we calculate for each age group a factor:

f_i =number of children in the population/weighted number of children in the survey, i=1,2,…,12.

With this factor we multiply the cross-sectional weight RB050 of a child in the corresponding age group.

                      RL070=f_i*RB050 , i=1,2,…,12

The base weights for the persons in the first wave are equal to the cross-sectional weights for the persons.

Cross-sectional weights for the consecutive waves

Base weights

The Base weights for the persons were calculated by taking the base weights from the previous year and then adjust these weights for the attrition in the Sex- age classes. Using the weight-share method we then calculated the weights for the immigrants, re-entries and newborns. After that for each of the rotational groups the weights were adjusted to the adequate longitudinal population counts in each Sex- age class.

Final cross-sectional weights

The cross-sectional weights for the households were calculated by firstly taking the average of the base weights for the belonging persons and then calibrate these weights for each rotational group to the same margin values as used in 2.8.1.3. The cross-sectional weights for the persons and selected persons were calculated by the same procedure as used for the first wave.

Longitudinal weights

The longitudinal weights were calculated by taking the base weights and then calibrate these weights to the Sex-age structure of the corresponding longitudinal population which was determined as the overlap of the register population in the consecutive years.

Substitutions

In EU-SILC we did not have substitute units.

The computation of item non-response is essential to fulfil the precision requirements concerning publication as stated in the Commission Regulation No (EU) 2019/1700. Item non-response rate is provided for the main income variables both at household and personal level in item 18.5.1.

The data file from Tax authority was edited in advance. Before we began the data processing for EU-SILC we checked the data from tax data file. We edited impossible values (for example negative values) and some very extreme values. Some imputations were also made in advance – we did logical checks and in the case of inconsistency of imputed values. These imputations are not included into the imputation factor in EU-SILC database.

Also other sources, which are special surveys in the Statistical Office, are edited and imputed in advance (register of active population, demographic database). By EU-SILC we edited the data, which are differ with the data from questionnaire.

In the first stage we imputed:

In the case of partial non-response the next income variables were imputed:

• Income from farming (in the questionnaire)
• Regular inter household transfers received
• Regular inter household transfer paid
• Contribution to private pensions plans
• Interests paid for mortgage (components to calculate interests)
• Consumption from own production (all components to calculate own production)

In  the case of missing data, we also imputed the following non income variables:

• Number of rooms
• Ability to keep home adequately warm
• Current rent related to occupied dwelling
• Total housing costs (all components from the questionnaire)
• Arrears on utility bills
• Arrears on hire purchase installments or other loan payments
• Capacity to afford paying for one week annual holiday away from home
• Capacity to afford a meal with meat, chicken…
• Capacity to face unexpected financial expenses
• PC
• Car
• Ability to make ends meet
• Financial burden of the repayment of debts from hire purchases or loans
• Child care (variables RL)
• Activity status during the income reference period (PL211A-PL211L)
• Occupation (ISCO-08) in main job
• Occupation (last job)
• Numbers of hours usually worked per week in main job
• Economic activity of the local unit (NACE Rev.2) for the main job
• Economic activity of the local unit (last job)
• Number of years spent in paid work
• Full or part-time job
• General health
• Material deprivation variables (variables HD, PD)
• All variables from Ad hoc modules -Children and Access to services (in the case that "do not know" was not answer category).

We used different types of imputation methods for different kinds of variables. In general we used four different methods with different parameterizations: Hot-deck method (or Nearest Neighbor version) with different imputation cells defined; Trimmed average method with different imputation cells and different trim-threshold defined; Logical imputations; Historical data imputations.

In the second stage of imputations we imputed:

PY050 in the case that self-employed person do not have any income (no profit, no wage, no social or family benefits, unemployed benefits). In such cases we imputed the values of minimal social benefits.

For income variables where we collected the data in the questionnaires by open questions and after that we have a scale as help, the imputations factors were calculated according to the open question. This means that in the case that person answered the question on the scale, looks like that the whole amount was imputed. Imputations factors also include manual editing and corrections of the extreme values.

We found out that it is very difficult to ask all questions about mortgage (HY100G/N). There we had several questions about mortgage and we found out that in the most cases the interest rate which we need to calculate interest of mortgage was missed. We asked also some other necessary variables to calculate the interest, but usually other variables do not make troubles to participants. We asked all questions only in the first wave, for following waves we transmitted the data from previous year and all these are look as 100% imputation. In the year 2022 we add also data about other loans (beside mortgage) for main dwelling where household live.

It is quite large share of households where HY020 (disposable income) was decreased after imputations. The reason was imputation of the variable HY120G/N (tax on wealth) which caused the decreasing of disposable income.

The share of imputed and partly imputed data about income components for year 2024 are in item 18.5.1 of this QR.

It is necessary to mentioned that some share of imputations are not real. Such cases are variable HY100, HY170. For HY100  we transmit the data from previous waves and it looks like that almost everything is imputed in the wave 2, 3 and 4. HY170 is special case, because we prepare the data outside SILC databases with the modelling and all corrections were made there and therefore we can not calculate (real) imputations factors.

Not applicable.

Not applicable.

Annexes:

Questionnaire (English, Slovenian), Ad hoc modules, Metadata on benefits.