1.1. Contact organisation
Statistical office of the Republic of Slovenia
1.2. Contact organisation unit
Statistical office of the Republic of Slovenia
Demography Statistics and Level of Living
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
Statistical Office of the Republic of Slovenia
Litostrojska 54
SI – 1000 Ljubljana
Slovenia
1.6. Contact email address
Confidential because of GDPR
1.7. Contact phone number
Confidential because of GDPR
1.8. Contact fax number
Confidential because of GDPR
2.1. Metadata last certified
31 May 2025
2.2. Metadata last posted
31 May 2025
2.3. Metadata last update
31 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 EU Vocabularies, Eurostat's metadata server or CIRCABC.
3.3. Coverage - sector
Data refer to all private households and individuals living in the private households in the national territory at the time of data collection.
The EU-SILC survey is a key instrument for the European Semester and the European Pillar of Social Rights, providing information on income distribution, poverty and social exclusion, as well as various related living conditions and poverty EU policies, such as on child poverty, access to health care and other services, housing, over indebtedness and quality of life. It is also the main source of data for microsimulation purposes and flash estimates of income distribution and poverty rates.
3.4. Statistical concepts and definitions
Statistical concepts and definitions for EU-SILC are specified in Regulation (EU) 2019/1700, Commission Implementing Regulation (EU) 2019/2181, and Commission Implementing Regulation (EU) 2019/2242. Additional information is available in the EU statistics on income and living conditions (EU-SILC) methodology and in the methodological guidelines and description of EU-SILC target variables
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 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.
3.6.1. Reference population
Definitions of reference population, household and household membership
| Reference population |
Private household definition |
Household membership |
|---|---|---|
| The reference population is defined with the persons in the Central Register of Population, which are aged 16 years or more. The individuals with Slovenian citizenship as well as foreigners were included in the sampling frame. |
There were no divergences from the common definition. |
A household member must be a member for at least 12 months. Students are household member if they have regular and economic relations with the primary home, not taking into account if he/she live in dormitory or private address. If student do not have regular contact with the primary home and only come home to visit, they are not considered household members, in that case he/she has own household. There were no other divergences from the common definition. |
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.
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.
3.7. Reference area
The entire territory of Republic of Slovenia is covered.
3.8. Coverage - Time
Cross sectional exercise 2024 covers year 2024 and incomes from refrence period year 2023.
3.9. Base period
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)
| 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 |
|---|---|---|---|
| Year 2023 |
Year 2023 |
Year 2023 |
The fieldwork period (January-June 2024). Therefore, the lag is 1 to 6 months. |
6.1. Institutional Mandate - legal acts and other agreements
Regulation (EU) 2019/1700 was publish in OJ on 10 October 2019, establishing a common framework for European statistics relating to persons and households, based on data at individual level collected from samples (IESS). The Annex to the Commission implementing regulation (EU) 2019/2180 of 16 December 2019 specifies the detailed arrangements and content for the quality reports pursuant to Regulation (EU) 2019/1700 of the European Parliament and of the Council and Regulation (EU) 2019/2242.
In Slovenia the survey was conducted according to Annual programme of the statistical surveys. The legalisations is available only in Slovenian language.
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
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.
7.2. Confidentiality - data treatment
Cell suppression is used for protection of sensitive cells.
8.1. Release calendar
SURS's release calendar are published Slovenia statistical webpage.
8.2. Release calendar access
Please refer to the Release calendar - Eurostat (europa.eu) publicly available on the Eurostat’s website.
8.3. Release policy - user access
In line with the Community legal framework and the European Statistics Code of Practice, Eurostat disseminates European statistics on Eurostat's website (see section 10 - 'Accessibility and clarity'), respecting professional independence and in an objective, professional and transparent manner in which all users are treated equitably. The detailed arrangements are governed by the Eurostat protocol on impartial access to Eurostat data for users.
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.
10.1. Dissemination format - News release
- 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.
10.2. Dissemination format - Publications
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.
10.3. Dissemination format - online database
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.
10.3.1. Data tables - consultations
| ID | Title | Date of Publishing | Unique Views | Views |
|---|---|---|---|---|
| 13462 | Življenjski pogoji, 2024 | 20 February 2025 | 138 | 164 |
| 13462 | Living conditions, 2024 | 20 February 2025 | 15 | 17 |
| 13518 | Življenjski pogoji, podrobni podatki, 2024 | 19 March 2025 | 78 | 83 |
| 13518 | Living conditions, detailed data, 2024 | 19 March 2025 | 5 | 5 |
| 13464 | Kazalniki dohodka, revščine in socialne izključenosti, 2024 | 20 February 2025 | 670 | 882 |
| 13464 | Income, poverty and social exclusion indicators, 2024 | 20 February 2025 | 42 | 54 |
| 13487 | Energetska revščina, 2024 | 28 February 2025 | 123 | 134 |
| 13487 | Energy poverty, 2024 | 28 February 2025 | 19 | 22 |
| 12752 | Kazalniki dohodka, revščine in socialne izključenosti, podrobni podatki, 2024 | 19 March 2025 | 28 | 32 |
| 12752 | Income, poverty and social exclusion indicators, detailed data, 2024 | 19 March 2025 | 12 | 15 |
| 13557 | Življenjski pogoji otrok, 2024 | 10 April 2025 | 84 | 96 |
| 13557 | Living conditions of children, 2024 | 10 April 2025 | 3 | 3 |
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.
10.4. Dissemination format - microdata access
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.
10.5. Dissemination format - other
Not applicable
10.5.1. Metadata - consultations
Not applicable.
10.6. Documentation on methodology
Documentation on methodology can be found in the following Methodological explanations.
10.6.1. Metadata completeness - rate
Data are not available.
10.7. Quality management - documentation
Questionnaires, methodological explanations, and quality reports are available on the Statistical Office website.
11.1. Quality assurance
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.
11.2. Quality management - assessment
EU-SILC survey is produced in compliance with methodological requirements and standards.
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.
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.
12.3. Completeness
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.
12.3.1. Data completeness - rate
100% for obligatory variables.
13.1. Accuracy - overall
According to Reg. (EU) 2019/1700 Annex II, precision requirements for all data sets are expressed in standard errors and are defined as continuous functions of the actual estimates and of the size of the statistical population in a country or in a NUTS 2 region. For the income and living conditions domain, the estimated standard errors of the following indicators are examined according to certain parameters set:
- · Ratio at‐risk‐of‐poverty or social exclusion to population;
- · Ratio of at‐persistent‐risk‐of‐poverty over four years to population;
- · Ratio at‐risk‐of‐poverty or social exclusion to population in each NUTS 2 region.
Further information is provided in section 13.2 Sampling error.
13.2. Sampling error
EU-SILC is a complex survey involving different sampling designs in different countries. In order to harmonize and make sampling errors comparable among countries, Eurostat (with the substantial methodological support of Net-SILC2) has chosen to apply the "linearization" technique coupled with the “ultimate cluster” approach for variance estimation.
Linearization is a technique based on the use of linear approximation to reduce non-linear statistics to a linear form, justified by asymptotic properties of the estimator. This technique can encompass a wide variety of indicators, including EU-SILC indicators. The "ultimate cluster" approach is a simplification consisting in calculating the variance taking into account only variation among Primary Sampling Unit (PSU) totals. This method requires first stage sampling fractions to be small which is nearly always the case. This method allows a great flexibility and simplifies the calculations of variances. It can also be generalized to calculate variance of the differences of one year to another.
The main hypothesis on which the calculations are based is that the "at risk of poverty" threshold is fixed. According to the characteristics and availability of data for different countries, we have used different variables to specify strata and cluster information.
In particular, countries have been split into 3 groups:
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.
Annexes:
Sampling errors
13.2.1. Sampling error - indicators
The concept of accuracy refers to the precision of estimates computed from a sample rather than from the entire population. Accuracy depends on sample size, sampling design effects and structure of the population under study. In addition to that, sampling errors and non-sampling errors need to be taken into account. Sampling error refers to the variability that occurs at random because of the use of a sample rather than a census and non-sampling errors are errors that occur in all phases of the data collection and production process.
Annexes:
Sampling_errors_indicators
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
| Wave | Sample | Out-of-scope units | Overcoverage rate |
|---|---|---|---|
| 1 | 6393 | 119 | 1,86% |
| 2 | 3025 | 45 | 1,49% |
| 3 | 2458 | 44 | 1,79% |
| 4 | 2110 | 31 | 1,47% |
| Total | 13986 | 239 | 1,71% |
source SILC 2024
13.3.1.2. Common units - proportion
The sample size (net) is approximately 20 000 persons, meanwhile admnistrative sources cover whole population in Slovenia (app. 2 million persons).
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 |
| As in most surveys, the questionnaire can be one of the sources of potential measurement errors. Unsatisfactory organization and design of the survey may results in output different to the reality. The questionnaire of EU-SILC 2024 was developed on the basis of the EU-SILC regulations and the DOCSILC065 operation 2024. Some changes and adoptions to the prior questionnaire were made according to the changes of EUROSTAT’s requirements; experiences with last year’s surveys, like feedback from interviewers or data checking procedures which indicated misinterpretations of particular items. However, the wording and phrasing of the questions can lead to misunderstandings; also different ordering of the questions can result in different answers. But we implemented various methods and procedures to reduce such effects and errors. |
See annex in this concept (13.3.2) |
Training for interviewers who haven’t worked on the EU-SILC was each time split into two parts (two days): the theoretical part and practical exercises in Blaise done in small groups. A shorter training session for experienced EU-SILC interviewers was organised separately, where the majority of the focus was on the Ad hoc module 2024.
Training involved data protection and confidentiality, organization of the survey process, presentation of the survey, questionnaire and definitions, and practical exercises. The main focus in training sessions was on testing questions as this is the only way to assure data quality.
The training sessions for CATI interviewers were organised between 22 and 25 January 2024. The trainings were conducted in person in several rounds. Interviewers were divided into three groups according to their previous experience with interviewing: the most experienced 16 interviewers - those, who had already worked on EU-SILC in previous year(s), the second group – 19 interviewers who had experience with other surveys but not with EU-SILC, and 10 who did not have any experience with interviewing for the Statistical Office. We organised training for controllers togehter with interviewers in the CATI studio, who also need to gain some basic knowledge about the survey in order to help the interviewers in case of any problems. All together 45 interviewers participated in the training and 5 controllers.
EU-SILC is also conducted face-to-face in the field – Computer assisted personal interview (CAPI). For this we recruited 52 interviewers to cover all of the territory of Slovenia. All interviewers are obliged to participate in the training. As was the case with CATI, also here the training was organised according to their experience. The interviewers were divided in three groups according to their experience witch EU-SILC or any other survey conducted at SURS. The most experienced interviewers had shorter course, where we described in details the most “challenging” variables and all new variables (including the ad hoc modules). After theoretical part we prepared practical exercises where interviewers got a proper understanding about the objective for each variables. There we could also see if particular interviewer understood the concept of the survey. For the intermediately experienced interviewers who already had experience with interviewing, but not with EU-SILC, we additionally explained all the questions and concepts from the survey. For the 8 completely inexperienced interviewers, the training also involved lessons on data protection and confidentiality of the data, organization of the survey process, a technical part about the usage of BLAISE and lap-tops, presentation of the survey, questionnaire and definitions, and practical exercises. For those interviewers training was divided into two days and it lasted all together approximately 12 hours. The main focus of the training sessions was on survey questions and how to conduct the survey in practice as we found that this is the only way to insure the quality of the collected data. |
As in all surveys there is a high possibility that interviewer can influence respondent's answers. In the beginning of the conducting survey the methodologist was in survey studio but not allowed to listen the interviewers because of GDPR and it was not possible to correct their mistakes by interviewing. If some interviewers made mistakes, we can only find out after all the data are in the database. Interviewers had only possibility to ask the person present in the CATI studio after (or in rare emergency cases, during) an interview what to do in the case that he/she had a feeling that something was not clear or they had a doubt what to answer. The household respondent was chosen by the interviewer as the one who had the best knowledge of the household’s affairs. For part of questions for selected person the interviewers were instructed to prioritise interviewing the selected person whenever possible. In the case of a household that had already participated in EU-SILC, certain basic information was uploaded in the entry program prior to the new round of data collection and the interviewers then just verified the information. This way we reduced the burden, particularly on respondents. |
Annexes:
Building the questionnaire
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 |
| 91,37 |
84.40 |
99.03 | 70.00 |
55.79 | 92.18 |
100.00 |
100.00 |
100.00 |
36.04 |
52.91 |
8.71 | 0.00 |
0.00 |
0.00 |
36.04 |
52.91 |
8.71 |
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 in this concept
Annexes:
Item non response
13.3.4. Processing error
Description of data entry, coding controls and the editing system
| Data entry and coding (if any used) |
Editing 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. In 2014 SURS introduced new programs for data editing and imputations (SOP - statistical data processing). 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:
Hard syntax error:
Sex
Hard range: minimum: 1 Maximum : 2 Logical error:
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 errors are corrected. |
Here are some examples of checks at this stage:
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. |
Checks
| LK_label |
Table |
Error_decription |
Condition |
Remark |
|---|---|---|---|---|
| LK_E_006 |
GOSP |
Number of rooms are larger than 10 or smaller than 1 |
((GB5 < 1) or (GB5 > 10)) and not (GB5 in (-2 -1)) and status_gosp=10 |
|
| LK069 |
OSEB |
Child aged (6-12) and have less than 5 or more than 40 hours attending school. The child are also not "other inactive person" if they are handicaped. |
F ((AK8<=5) or (AK8 > 40)) and not (AK8 in (-2 -1)) and (STAR in (6 7 8 9 10 11 12)) and (AB1a ne 12) and (AK7 ne 3) |
|
| LK400 |
DOHODNINA |
Value cannot be negative |
if bruto1101<0 or neto1101<0 |
|
| LK022 |
GOSP |
Extreme value (costs can not be under or above definite amount) |
if ((GD19 < 10) or (GD19 > 450)) and not (GD19 in (-2 -1)) |
|
13.3.5. Model assumption error
Not applicable.
14.1. Timeliness
The national results were disseminated on SURS website according to calendar.
14.1.1. Time lag - first result
The data were not publish as first results (provisional). Statistical Office of the Republic of Slovenia published all other data only as final results.
14.1.2. Time lag - final result
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)
14.2. Punctuality
Not available.
14.2.1. Punctuality - delivery and publication
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.
15.1. Comparability - geographical
In Slovenia are no significant diffrences among NUTS2 regions.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not applicable.
15.2. Comparability - over time
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.
Annexes:
Break in series
Comparability over time
15.2.1. Length of comparable time series
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.
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.
Coherence with LFS for variable PL032 - self defined current economic status (%) - EU-SILC persons aged 16-89, LFS persons aged 15-89
| LFS average Q1-Q2_Y2024 | SILC 2024 | |
|---|---|---|
| TOTAL | 100,0 | 100,0 |
| Work | 54,3 | 54,3 |
| Unemployed | 3,6 | 4,2 |
| Pupil, student | 9,6 | 9,0 |
| Retired | 28,9 | 29,9 |
| Disabled for work | 1,7 | 1,2 |
| Fulfilling domestic tasks | 1,7 | 1,1 |
| Other | 0,2 | 0,3 |
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.
15.3.1. Coherence - sub annual and annual statistics
Not applicable.
15.3.2. Coherence - National Accounts
| EU-SILC variables |
National Accounts item (S14) |
Coverage rate (calculated as EU-SILC and NA ratio) |
EU-SILC growth rate (nominal, year to year) |
National accounts growth rate (nominal, year to year) |
|---|---|---|---|---|
| Employee income: PY010G Employee cash or near cash income+ PY021G Company car |
D11/rec Wages and salaries |
0.8913074 |
9.81 |
10.85 |
| Income from self-employment: PY050G Cash benefits or losses from self-employment |
B3g Mixed income, gross |
0.4576885 |
6.970 |
3.01 |
| Social benefits other than social transfers in kind: HY050G Family/children related allowances + HY060G Social exclusion not elsewhere classified + HY050G Family/children related allowances + HY060G Social exclusion not elsewhere classified + PY090G Unemployment benefits + PY100G Old-age benefits + PY110G Survivor’ benefits + PY120G Sickness benefits + PY130G Disability benefits +PY140G Education-related allowances + HY070G Housing allowances |
D62/rec: Social benefits, other than social transfers in kind |
0.8778344 |
8.38 |
5.19 |
| Social contributions and taxes on income paid: HY140G Tax on income and social contributions |
D61/use: net social contributions + D51/use: taxes on income |
0.5811348 |
9.85 |
9.51 |
| Total disposable household income HY020 |
B6 Gross disposable income |
0.7709560 |
9.64 |
8.40 |
15.4. Coherence - internal
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
| Obs | Country | Total duration (min) | Per person aged 16 and over (min) | Selected respondent (min) |
|---|---|---|---|---|
| 1 | SI | 31.1 | 2.5 | 5.9 |
17.1. Data revision - policy
Revision is not foreseen.
17.2. Data revision - practice
Revision is not foreseen.
17.2.1. Data revision - average size
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.
18.1. Source data
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
18.1.1. Sampling Design
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.
Annexes:
Rotational schemes DB075
18.1.2. Sampling unit
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.
18.1.3. Sampling frame
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:
- Non-urban settlement
- Smaller urban settlement
- Larger urban settlement
- Town with at least 10.000 inhabitants
- 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:
- Pomurska statistical region
- Podravska statistical region
- Koroška statistical region
- Savinjska statistical region
- Zasavska statistical region
- Posavska statistical region
- Jugovzhodna Slovenija
- Osrednjeslovenska statistical region
- Gorenjska statistical region
- Primorsko-notranjska statistical region
- Goriška statistical region
- Obalno-kraška statistical region
18.2. Frequency of data collection
| Month of interview |
Frequency |
Percent |
|
|---|---|---|---|
| Total |
|
8656 |
100,0 |
| 1 |
January |
439 |
5,1 |
| 2 |
February |
3899 |
45,0 |
| 3 |
March |
2601 |
30,0 |
| 4 |
April |
539 |
6,2 |
| 5 |
May |
606 |
7,0 |
| 6 |
June |
572 |
6,6 |
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 |
- |
25.4 |
57.8 |
- |
- |
6.6 |
10.2 |
- |
- |
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 |
|---|---|---|
|
Administrative source |
Directly from administrative soruce for majority of the variables. |
Annexes:
Data collection
18.4. Data validation
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
| LK_label |
Table |
Error_decription |
Condition |
Remark |
|---|---|---|---|---|
| LK074 |
OSEB |
Child cannot be at the same time in education at pre-school and at education at compulsory school |
if AK6>0 and AK8>0 |
|
| LK400 |
DOHODNINA |
Value cannot be negative |
if bruto1101<0 or neto1101<0 |
|
| LK022 |
GOSP |
Extreme value (costs cannot be under or above definite amount) |
if ((GD19 < 10) or (GD19 > 450)) and not (GD19 in (-2 -1)) |
|
18.5. Data compilation
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.
18.5.1. Imputation - rate
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:
| Variable |
missing |
not applicable |
questionnaire |
admnistrative source |
imputed |
Not possible to establish source |
comment |
|---|---|---|---|---|---|---|---|
| HH010 |
0.00 |
0.00 |
99.98 |
0.00 |
0.02 |
0.00 |
|
| HH021 |
0.00 |
0.00 |
99.65 |
0.00 |
0.35 |
0.00 |
|
| HH030 |
0.00 |
0.00 |
99.87 |
0.00 |
0.13 |
0.00 |
|
| PB140 |
0.00 |
0.00 |
0.00 |
100.00 |
0.00 |
0.00 |
|
| PB150 |
0.00 |
0.00 |
0.00 |
100.00 |
0.00 |
0.00 |
|
| PE010 |
0.00 |
0.00 |
0.00 |
100.00 |
0.00 |
0.00 |
|
| PE021 |
0.00 |
89.51 |
0.00 |
10.36 |
0.13 |
0.0 |
|
| PE041 |
0.00 |
0.00 |
0.00 |
99.49 |
0.51 |
0.00 |
|
| PL051A |
2.05 |
50.55 |
0.00 |
47.22 |
0.18 |
0.00 |
|
| PL060 |
0.00 |
50.55 |
49.07 |
0.00 |
0.38 |
0.00 |
|
| PL074 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
100.00 |
It is derived variable from PL211A-PL211L |
| PL211C |
0.00 |
0.00 |
14.24 |
82.31 |
3.46 |
0.00 |
|
Source: EU-SILC 2024
18.5.2. Calculation of weighting factors and weight adjustments
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 / Mh
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
|
|
HY020 |
|---|---|
| Design effect |
1.05 |
| Design factor |
1.03 |
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:
- 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
- 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 * Mh
The cross-sectional weight for children who were younger than 13 years on 31st December N-1 is RL070.
Weights are calculated in this way that we calculate for each age group a factor:
fi =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=fi*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.
18.5.3. Estimation and imputation
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.
18.6. Adjustment
Not applicable.
18.6.1. Seasonal adjustment
Not applicable.
Annexes:
Questionnaire (English, Slovenian), Ad hoc modules, Metadata on benefits.
Annexes:
Rolling_module_children
Rolling module on Access
Questionnaire - Slovenian
Questionnaire - English
The European Union Statistics on Income and Living Conditions (EU-SILC) is a survey-based instrument aiming at collecting timely and comparable cross-sectional and longitudinal multidimensional microdata on income, poverty, social exclusion and living conditions. In addition, it collects module variables every three years, six years or ad-hoc new policy needs modules.
The EU-SILC instrument provides two types of data:
- Cross-sectional data pertaining to a given time or a certain time period with variables on income, poverty, social exclusion and other living conditions.
- Longitudinal data pertaining to individual-level changes over time, observed periodically over four‐or more year rotation scheme (Annex III (2) of 2019/1700). Social exclusion and housing condition information is collected mainly at household level while labour, education and health information is obtained for persons aged 16 and over. The core of the instrument is income information at very detailed component level and mainly collected at personal level.
31 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
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.
The entire territory of Republic of Slovenia is covered.
Description of reference period used for incomes (cross sectional)
| 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 |
|---|---|---|---|
| Year 2023 |
Year 2023 |
Year 2023 |
The fieldwork period (January-June 2024). Therefore, the lag is 1 to 6 months. |
According to Reg. (EU) 2019/1700 Annex II, precision requirements for all data sets are expressed in standard errors and are defined as continuous functions of the actual estimates and of the size of the statistical population in a country or in a NUTS 2 region. For the income and living conditions domain, the estimated standard errors of the following indicators are examined according to certain parameters set:
- · Ratio at‐risk‐of‐poverty or social exclusion to population;
- · Ratio of at‐persistent‐risk‐of‐poverty over four years to population;
- · Ratio at‐risk‐of‐poverty or social exclusion to population in each NUTS 2 region.
Further information is provided in section 13.2 Sampling error.
The data involves several units of measure depending upon the variables. Income variables 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.
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 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
Annual for core data including income indicators and occasionally for ad hoc modules, which are published on the time when the data are collected.
The national results were disseminated on SURS website according to calendar.
In Slovenia are no significant diffrences among NUTS2 regions.
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.
Annexes:
Break in series
Comparability over time
