1.1. Contact organisation
Statistical Office of the Republic of Serbia
1.2. Contact organisation unit
Department for Social Statistics and Sustainable Development Goals / Living Standard Statistics Unit
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
Milana Rakića 5, Belgrade, Serbia
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
30 September 2025
2.2. Metadata last posted
30 September 2025
2.3. Metadata last update
30 September 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 Geo Code - 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 the list of classification on the Eurostat webpage, Metadata and Statistics explained on classification.
3.3. Coverage - sector
Data refer to all private households and individuals living in the private households in the national territory at the time of data collection.
The EU-SILC survey is a key instrument for the European Semester and the European Pillar of Social Rights, providing information on income distribution, poverty and social exclusion, as well as various related living conditions and poverty EU policies, such as on child poverty, access to health care and other services, housing, over indebtedness and quality of life. It is also the main source of data for microsimulation purposes and flash estimates of income distribution and poverty rates.
3.4. Statistical concepts and definitions
Statistical concepts and definitions for EU-SILC are specified in Regulation (EU) 2019/1700, Commission Implementing Regulation (EU) 2019/2181, and Commission Implementing Regulation (EU) 2019/2242. Additional information is available in the EU statistics on income and living conditions (EU-SILC) methodology and in the methodological guidelines and description of EU-SILC target variables (see CIRCABC).
Further details are provided in items 5, 15.1.1.1, 15.2.2 and 18.3.
3.5. Statistical unit
Statistical units are private households and all persons living in these households who have usual residence in the Member State. Annex II of the Commission implementing regulation (EU) 2019/2242 defines specific statistical units per variable and specifies the, content of the quality reports on the organization of a sample survey in the income and living conditions domain pursuant to Regulation (EU) 2019/1700 of the European Parliament and of the Council.
3.6. Statistical population
The target population is private households and all persons composing these households having their usual residence in the Member State. Private household means a person living alone or a group of persons who live together, providing oneself or themselves with the essentials of living.
3.6.1. Reference population
Definitions of reference population, household and household membership
| Reference population |
Private household definition |
Household membership |
|---|---|---|
| All private households and their current members residing in the territory of the Republic of Serbia (excluding the territory of Kosovo and Metohija) at the time of data collection. Persons living in collective households and in institutions are excluded from the target population. |
A household is considered to be:
|
Household members are all persons that share household expenses, and:
|
3.6.2. Population not covered by the data collection
The sub-populations that are not covered by the data collection includes: those who moved out of the country’s territory; or those with no usual residence; or those living in institutions or who have moved to an institution compared to the previous year.
3.7. Reference area
The data relate to the territory of the Republic of Serbia.
Since 1999, the Statistical Office of the Republic of Serbia doesn't dispose of certain data for AP Kosovo and Metohia, so they are not contained in the data coverage for the Republic of Serbia (total).
3.8. Coverage - Time
EU-SILC data for the Republic of Serbia are available for the survey years 2013-2024.
3.9. Base period
Not applicable.
The data involves several units of measure depending upon the variables. Income variables are transmitted to Eurostat in national currency. For more information, see methodological guidelines and description of EU-SILC target variables available on CIRCABC
Description of reference period used for incomes
| Period for taxes on income and social insurance contributions |
Income reference periods used |
Reference period for taxes on wealth |
Lag between the income ref period and current variables |
|---|---|---|---|
| The reference period for taxes on income and social insurance contributions for EU-SILC 2024 was previous calendar year 2023. |
The income reference is a fixed twelve-month period. For EU-SILC 2024, the period was the year 2023. |
The reference period for taxes on wealth for EU-SILC 2024 was the previous calendar year, 2023. |
The lag between the income reference period and the household interview was 5-6 months. In EU-SILC 2024 the fieldwork lasted for two months, from May 9th to Jun 30th. |
6.1. Institutional Mandate - legal acts and other agreements
Regulation (EU) 2019/1700 was publish in OJ on 10 October 2019, establishing a common framework for European statistics relating to persons and households, based on data at individual level collected from samples (IESS). The Annex to the Commission implementing regulation (EU) 2019/2180 of 16 December 2019 specifies the detailed arrangements and content for the quality reports pursuant to Regulation (EU) 2019/1700 of the European Parliament and of the Council and Regulation (EU) 2019/2242.
6.2. Institutional Mandate - data sharing
Confidential microdata are not disclosed by Eurostat. Access to confidential microdata for scientific purposes may be granted on the basis of Commission Regulation 557/2013 and Regulation 223/2009 of the European Parliament and the Council on European statistics.
7.1. Confidentiality - policy
Data confidentiality is stipulated by:
- Official Statistics Law (Official Gazette of RS, number 104/2009), Articles 44, 45, 46, 47, 48, and 49. (Official Statistics Law)
- Rulebook on statistical data protection in the Statistical Office of the Republic of Serbia (Rulebook on statistical data protection in SORS)
- Guidelines on measures of data and information protection in the Statistical Office of the Republic of Serbia (Guidelines on measures of data and information protection in SORS )
7.2. Confidentiality - data treatment
Need to protect individual data is an extremely important issue. The results of the survey are published as aggregates, thus securing full confidentiality of information about households and individuals, according to the provisions of the Law on Official Statistics. Statistical micro-databases that are released for use for scientific research purposes contain reduced information about the respondents in order to prevent identification of the surveyed individuals or households.
8.1. Release calendar
Yearly publication of results, see Release Calendar | Statistical Office of the Republic of Serbia
8.2. Release calendar access
Please refer to the Release calendar - Eurostat (europa.eu) publicly available on the Eurostat’s website.
8.3. Release policy - user access
In line with the Community legal framework and the European Statistics Code of Practice, Eurostat disseminates European statistics on Eurostat's website (see section 10 - 'Accessibility and clarity'), respecting professional independence and in an objective, professional and transparent manner in which all users are treated equitably. The detailed arrangements are governed by the Eurostat protocol on impartial access to Eurostat data for users. Additional information about microdata access is available in Statistics on Income and Living Conditions - Access to microdata - Eurostat (europa.eu).
Annual
10.1. Dissemination format - News release
10.2. Dissemination format - Publications
SORS - Income and Living Conditions
10.3. Dissemination format - online database
Online Database - Income and living conditions
10.3.1. Data tables - consultations
Not available.
10.4. Dissemination format - microdata access
The data are available in national microdata form, e.g. for researchers, upon prior submission of an official request.
10.5. Dissemination format - other
Not applicable.
10.5.1. Metadata - consultations
Not available.
10.6. Documentation on methodology
Not available.
10.6.1. Metadata completeness - rate
All requested concepts are provided, 100%
10.7. Quality management - documentation
Not applicable.
11.1. Quality assurance
The SORS quality management system is relied on the Serbian official statistics mission and vision, as well as on the European Statistics Code of Practice – CoP and the Total Quality Management – TQM principles, which together make the common quality framework of the European Statistical System (ESS). For more information, please see the documents at System of quality management of the Statistical Office of the Republic of Serbia.
11.2. Quality management - assessment
Not available.
12.1. Relevance - User Needs
The main users of EU-SILC statistical data are policy makers, research institutes, media, and students.
The SILC survey regularly provides data for monitoring and comparing income, poverty, social exclusion and living condition statistics.
In accordance with the research methodology, two types of data are provided annually:
- Cross-sectional data - refer to a specific time or period of time, and provide information on income, social exclusion, poverty and living conditions
- Longitudinal data - refers to changes at the individual level over time, observed periodically, over a four-year period
Survey data are used for the analysis and research studies in standard of living, poverty etc. The users of data are: Statistical Office of the Republic of Serbia, The Government of the Republic of Serbia and government institutions, scientific and research institutions, international institutions, students, journalists, legal entities and individuals.
12.2. Relevance - User Satisfaction
Eurostat carried out a general User Satisfaction Survey (USS) to obtain a better understanding of users’ needs and their satisfaction with the services provided by Eurostat. The survey results indicated that EU-SILC data are of very high relevance to users. For the majority of respondents, both aggregated data and microdata were considered important or essential for their work, regardless of the purpose of use. The use of ad-hoc modules was less widespread compared to the use of annual variables. Users also highlighted a strong need for more detailed microdata.
For further information, please consult the Eurostat User Satisfaction Survey.
12.3. Completeness
The implementation of the survey, data processing and publication of the results are fully aligned with the research methodology defined by the EU regulations and methodological standrards of Eurostat related to the EU-SILC survey.
All variables required by Eurostat, as specified in EU-SILC methodological guidelines, are provided (with the exeption of the variables PY021N/G Company car and HY145N Repayments/receipts for tax adjustment, which have not been collected).
The following optional variable were not collected:
- RL080: Remote Education
- HI130G: Interest expenses [not included interest expenses for purchasing the main dwelling]
- HI140G: Household debts
12.3.1. Data completeness - rate
The item is not requested by Reg. 2019/2180.
13.1. Accuracy - overall
According to Reg. (EU) 2019/1700 Annex II, precision requirements for all data sets are expressed in standard errors and are defined as continuous functions of the actual estimates and of the size of the statistical population in a country or in a NUTS 2 region. For the income and living conditions domain, the estimated standard errors of the following indicators are examined according to certain parameters set:
- Ratio at‐risk‐of‐poverty or social exclusion to population;
- Ratio of at‐persistent‐risk‐of‐poverty over four years to population;
- Ratio at‐risk‐of‐poverty or social exclusion to population in each NUTS 2 region.
Further information is provided in section 13.2 Sampling error.
13.2. Sampling error
EU-SILC is a complex survey involving different sampling designs in different countries. In order to harmonize and make sampling errors comparable among countries, Eurostat (with the substantial methodological support of Net-SILC2) has chosen to apply the "linearization" technique coupled with the “ultimate cluster” approach for variance estimation.
Linearization is a technique based on the use of linear approximation to reduce non-linear statistics to a linear form, justified by asymptotic properties of the estimator. This technique can encompass a wide variety of indicators, including EU-SILC indicators. The "ultimate cluster" approach is a simplification consisting in calculating the variance taking into account only variation among Primary Sampling Unit (PSU) totals. This method requires first stage sampling fractions to be small which is nearly always the case. This method allows a great flexibility and simplifies the calculations of variances. It can also be generalized to calculate variance of the differences of one year to another.
The main hypothesis on which the calculations are based is that the "at risk of poverty" threshold is fixed. According to the characteristics and availability of data for different countries, we have used different variables to specify strata and cluster information.
In particular, countries have been split into 3 groups:
1) BE, BG, CZ, IE, EL, ES, FR, HR, IT, LV, HU, PL, PT, RO, SI, UK, AL and RS, 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.
In the case of the Republic of Serbia (RS) sampling errors for the indicators were calculated using the Bootstrap replication method.
Standard errors were calculated by using Bootstrap replication method, which represents type of repeated resampling method. The general characteristic of repeated resampling method and therefor also of Bootstrap is to draw (sub-) samples from the original sample and to calculate the population parameter of interest from each sample. The variance estimation is then based on the distribution of the several estimates.
The resampling methods have the major advantage of not requiring an explicit expression for the variance of each statistic. They are also more encompassing: by repeating the entire estimation procedure independently for each replication, the effect of various complexities, such as each step of a complex weighting procedure the impact of the sampling variability of the weights themselves can be incorporated into the variance estimates produced. Generally, this cannot be done with the linearization approach. The advantages of the replication methods are very relevant in the context of EU-SILC, because it has to deal with many statistics such as equivalized disposable income, Gini coefficient, poverty rates, etc., in their full complexity.
See Annex - Sampling errors
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.
Main indicators, standard error and CI at country level
| AROPE | At risk of poverty | Severe Material and Social Deprivation | Very low work intensity | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Ind. | Stand. errors | 95% CI | Ind. | Stand. errors | 95% CI | Ind. | Stand. errors | 95% CI | Ind. | Stand. errors | 95% CI | |||||
| value | L | U | value | L | U | value | L | U | value | L | U | |||||
| Total | 24.3 | 0.7 | 22.9 | 25.7 | 19.7 | 0.6 | 18.5 | 20.1 | 10.3 | 0.6 | 9.2 | 11.4 | 6.8 | 0.6 | 5.6 | 8.0 |
| Male | 23.7 | 0.8 | 22.2 | 25.2 | 19.5 | 0.7 | 18.2 | 20.9 | 9.9 | 0.6 | 8.7 | 11.1 | 7.3 | 0.6 | 6.1 | 8.5 |
| Female | 24.9 | 0.8 | 23.4 | 26.4 | 20.0 | 0.7 | 18.7 | 21.3 | 10.6 | 0.6 | 9.5 | 11.8 | 6.4 | 0.7 | 5.1 | 7.7 |
| Age 0-17 | 23.9 | 1.4 | 21.2 | 26.6 | 21.1 | 1.3 | 18.6 | 23.6 | 7.5 | 0.9 | 5.7 | 9.3 | 8.3 | 0.9 | 6.5 | 10.2 |
| Age 18-64 | 22.6 | 0.8 | 21.1 | 24.1 | 17.9 | 0.7 | 16.5 | 19.3 | 9.3 | 0.6 | 8.2 | 10.4 | 9.0 | 0.6 | 7.9 | 10.1 |
| Age 65+ | 29.4 | 0.9 | 27.6 | 31.3 | 23.6 | 0.9 | 21.9 | 25.4 | 14.9 | 0.8 | 13.4 | 16.4 | n/a | n/a | n/a | n/a |
Main indicators, standard error and CI at NUTS 2 level
| AROPE | At risk of poverty | Severe Material and Social Deprivation | Very low work intensity | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Ind. | Stand. errors | 95% CI | Ind. | Stand. errors | 95% CI | Ind. | Stand. errors | 95% CI | Ind. | Stand. errors | 95% CI | |||||
| value | L | U | value | L | U | value | L | U | value | L | U | |||||
| NUTS 2 RS11 |
15.4 | 1.2 | 13.0 | 17.8 | 8.7 | 0.9 | 6.9 | 10.5 | 9.7 | 1.1 | 7.6 | 11.8 | 3.9 | 0.8 | 2.3 | 5.5 |
| NUTS 2 RS12 |
26.8 | 1.8 | 23.4 | 30.2 | 21.0 | 1.6 | 17.8 | 24.2 | 13.9 | 1.2 | 11.6 | 16.2 | 8.2 | 1.2 | 5.9 | 10.5 |
| NUTS 2 RS21 |
25.5 | 1.4 | 22.8 | 28.2 | 23.0 | 1.3 | 20.4 | 25.6 | 7.6 | 0.9 | 5.9 | 9.3 | 6.7 | 1.2 | 4.4 | 9.0 |
| NUTS 2 RS22 |
30.9 | 2.0 | 26.9 | 34.9 | 27.7 | 2.0 | 23.8 | 31.6 | 10.1 | 1.3 | 7.5 | 12.7 | 8.9 | 1.5 | 5.9 | 11.9 |
Persistent-risk-of-poverty ratio over four years to the population, standard error and CI
| Persistent‐risk‐of‐poverty | ||||
|---|---|---|---|---|
| Ind. | Stand. Errors | 95% CI | ||
| value | L | U | ||
| Total | 11.3 | 0.5 | 10.3 | 12.3 |
13.3. Non-sampling error
Non-sampling errors are basically of 4 types:
- Coverage errors: errors due to divergences existing between the target population and the sampling frame.
- Measurement errors: errors that occur at the time of data collection. There are a number of sources for these errors such as the survey instrument, the information system, the interviewer and the mode of collection.
- Processing errors: errors in post-data-collection processes such as data entry, keying, editing and weighting.
- Non-response errors: errors due to an unsuccessful attempt to obtain the desired information from an eligible unit. Two main types of non-response errors are considered:
- Unit non-response: refers to absence of information of the whole units (households and/or persons) selected into the sample.
- Item non-response: refers to the situation where a sample unit has been successfully enumerated, but not all required information has been obtained.
13.3.1. Coverage error
Coverage errors include over-coverage, under-coverage and misclassification:
- Over-coverage: relates either to wrongly classified units that are in fact out of scope, or to units that do not exist in practice.
- Under-coverage: refers to units not included in the sampling frame.
- Misclassification: refers to incorrect classification of units that belong to the target population
13.3.1.1. Over-coverage - rate
Coverage error
| Main problems |
Population (sub-population) |
Size of error |
Comments |
|---|---|---|---|
| Over-coverage |
Persons in private households |
2.3% (142 addresses) |
142 addresses in the EU-SILC 2024 sample turned out to be not located/not found, unable to access, non-existent/non-residential or non-private/unoccupied/not principal residence |
| Under-coverage |
Not available. |
Not available. |
|
| Misclassification |
Not available. |
Not available. |
|
13.3.1.2. Common units - proportion
Not requested by Reg. 2019/2180
13.3.2. Measurement error
Measurement error for cross-sectional data
| Cross-sectional data |
|||
|---|---|---|---|
| Source of measurement errors |
Building process of questionnaire |
Interview training |
Quality control |
| The source of the measurement errors might be:
|
The basis for the development of the Questionnaires was the document EU-SILC 065 (2024 operation) with all of the variables stated in this document covered by the questions in the questionnaires. From 2018, SORS uses IST (Integrated system of data processing) application for entering data, processing and reporting. IST was entirely designed and developed by SORS experts. Many logical controls are built into application, which allows interviewers to keep a track of inconsistent responses and make necessary corrections immediately, in order to reduce as much as possible the number of errors in data entry.
|
The process of selection of interviewers and team leaders was organized in several rounds in 2024. Candidates were selected based on several criteria: prior experience in surveys conducted by SORS, education level, working status and availability during training and fieldwork. For the team leaders, centralized training course was organized, while the rest of interviewers for the main fieldwork took a decentralized training course in 15 Regional Offices and in the Central Office. The aim was to get acquainted with the new methodological guidelines and the survey itself. Examples were presented throughout the complete training course. It is important to mention that there was also a lecture on the organization of the fieldwork.
|
Each team of interviewers (2-4 members) had the team leader on the field and controller from the regional office. The Team Leader was, among other things, in charge of solving certain methodological issues and uncertainties (whether alone or with the help of the controller). In addition, the Team leader was in charge for keeping records on progress of his/her team in the field, which also helped in the better organization of work in the team. The Controller was in charge of supervising the work of one or two teams during the fieldwork, as well as of providing assistance, solving problems. Experienced statisticians from the SORS were engaged as the controllers. CAPI application had installed numerous of logical controls to reduce the mistakes. Field check tables were prepared and updated every day so the controllers can follow the progress of the fieldwork. Those reports were on the web and updated every day according to data submitted to server in SORS every day. |
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
| Address (including phone, mail if applicable) contact rate |
Complete household interviews |
Complete personal interviews |
Household Non-response rate |
Individual non-response rate |
Overall individual non-response rate |
||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| (Ra) |
(Rh) |
(Rp) |
(NRh) |
(NRp) |
(NRp)* |
||||||||||||
| A |
B |
C |
A |
B |
C |
A |
B |
C |
A |
B |
C |
A |
B |
C |
A |
B |
C |
| 99.62 |
98.89 |
99.92 |
90.70 |
78.55 |
97.37 |
100.0 |
100.0 |
100.0 |
9.65 |
22.33 |
2.70 |
0.00 |
0.00 |
0.00 |
9.65 |
22.33 |
2.70 |
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.
Longitudinal data
Response rate for households by wave
| Response rate for household | Wave 2 | Wave 3 | Wave 4 |
|---|---|---|---|
| Wave response rate | 80.6 | 89.7 | 90.4 |
| L follow-up rate | 94.3 | 95.7 | 96.5 |
| Follow-up ratio | 92.8 | 94.6 | 95.3 |
| Achieved sample size ratio | 92.8 | 94.6 | 95.3 |
Response rate for persons by wave
| Response rate for persons | Sample persons / co-residents | Wave 2 | Wave 3 | Wave 4 |
|---|---|---|---|---|
| Wave response rate | Sample persons | 100.0 | 100.0 | 100.0 |
| co-residents | 100.0 | 100.0 | 100.0 | |
| L follow-up rate | Sample persons | 100.0 | 100.0 | 100.0 |
| Achieved sample size ratio (persons aged 16 and over) | All persons | 92.0 | 95.1 | 95.7 |
| Sample persons | 91.0 | 94.1 | 94.6 | |
| co-residents | 0 |
141.1 | 135.2 | |
| Response rate for non-sample persons | co-residents | 100.0 | 100.0 | 100.0 |
Sample and response rate by wave
| Year of the survey | Sample of households | Sample of individuals 16+ | Response rate of the households | Response rate of individuals 16+ |
|---|---|---|---|---|
| Wave 1 | 1918 | 4511 | 77.7 | 100.0 |
| Wave 2 | 3361 | 8110 | 80.6 | 100.0 |
| Wave 3 | 4645 | 12286 | 89.7 | 100.0 |
| Wave 4 | 6117 | 16252 | 90.4 | 100.0 |
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
For the table containing the percentage of missing or partial information on income components before imputation as well as the percentage of full information, please refer to the attached annex.
In the case of EU-SILC in Serbia there are a few income components presented in the annex that need further explanation:
- HY145N: Data not collected
- PY021N/G: Due to low prevelence, data on Income from private use of company car are not being collected
See Annex - Item non-response
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 |
|---|---|
| There were many logical controls incorporated in the CAPI application including routings, ranges and inconsistences. In case of inconsistences, the message was shown with the suggestion to revise the answer. If the answer was correct, interviewers were supposed to insert a remark for that question explaining the reason for inconsistency. | After the data collection was over, a thorough cleaning of the data was performed. A set of logical controls was made and for all suspicious data remarks were checked. After our custom made logical control was applied, we also used the data checks made by the Eurostat for harmonized data sets. |
Re-interview rates by wave
Part I
| Re-interview rates | Wave 2 | Wave 3 | Wave 4 |
|---|---|---|---|
| (a) individuals in interviewed households % | 94.9 | 93.4 | 93.0 |
| (b) individuals out of scope % | 5.1 | 6.6 | 7.0 |
| (c) individuals not interviewed for reasons other than their being out of scope % | - | - | - |
Part II
| Re-interview rates | Wave 2 | Wave 3 | Wave 4 | |
|---|---|---|---|---|
| Re-interview rates for people leaving their household | total | 0.6 | 0.4 | 0.6 |
| males | 0.6 | 0.3 | 0.6 | |
| females | 0.7 | 0.5 | 0.6 | |
| Re-interview rates for young people (16-35) leaving their household | total | 1.3 | 0.7 | 1.3 |
| males | 1.0 | 0.6 | 1.3 | |
| females | 1.6 | 0.9 | 1.4 |
13.3.5. Model assumption error
Not applicable.
14.1. Timeliness
Date of the dissemination of national results. 20 Jun 2025
Number of days between the end of fieldwork and the first fully validated delivery of data to the Commission (Eurostat):
- End of field-work: 30 Jun 2024
- First data delivery: 30 Jun 2025
Days between the end of fieldwork and the first fully validated delivery: 365
14.1.1. Time lag - first result
The number of months from the last day of the reference period to the day of publication of first results: 18 months
14.1.2. Time lag - final result
The number of months from the last day of the reference period to the day of publication of complete and final results: 18 months
14.2. Punctuality
The data is published on time and in accordance with Statistical calendar
14.2.1. Punctuality - delivery and publication
Data gathered for year N are published by mid-Jun of year N+1 (where N = year of data collection)
15.1. Comparability - geographical
There are no conceptual differences between results on national and regional levels.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not applicable.
15.2. Comparability - over time
See Annex - Breaks in series.
Annexes:
Breaks in series
15.2.1. Length of comparable time series
12 reference periods.
15.2.2. Comparability and deviation from definition for each income variable
|
Comparability and deviation from definition for each income variable
F= Fully comparable; L= Largely comparable; P= Partly comparable and NC= Not collected.
|
15.3. Coherence - cross domain
The coherence of two or more statistical outputs refers to the degree to which the statistical processes, by which they were generated, used the same concepts and harmonised methods. A comparison with external sources for all income target variables and the number of persons who receive income from each ‘income component’ will be provided, where the Member States concerned consider such external data to be sufficiently reliable.
15.3.1. Coherence - sub annual and annual statistics
Not applicable.
15.3.2. Coherence - National Accounts
See Annex - Coherence.
Annexes:
Coherence
15.4. Coherence - internal
There is no information on lack of coherence in the EU-SILC 2024 dataset.
Mean (average) interview duration per household = 71.5 minutes.
Mean (average) interview duration per person = 20.5 minutes.
Mean (average) interview duration for selected respondents (if applicable) = n/a.
17.1. Data revision - policy
No revision applied on EU-SILC 2024 datasets.
17.2. Data revision - practice
No data revision.
17.2.1. Data revision - average size
No data revision.
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
Тhe sampling frame is the The Census of Population, Households and Dwellings in the Republic of Serbia, carried out in 2022. Тarget population consists of all persons living in private households in the Republic of Serbia . Persons living in collective households and in institutions are generally excluded from the target population.
18.1.1. Sampling Design
Type of sampling design:
The sample is based on two-stage stratified random sampling design.
Stratification and sub stratification criteria:
Stratification was done according to the type of settlement (urban and other) in four Regions (Belgrade, Vojvodina, Sumadija and Western Serbia and Southern and Eastern Serbia).
Sample selection schemes:
Sample consists of four independent sub-samples (rotational groups), same size and design, representative for the whole population. Primary sampling units (enumeration districts), were selected systematically with probability proportional to size within each stratum. Size measure for each ED was number of households, according to the Census 2022. Second stage units, households were selected randomly with equal probabilities. Every year one rotational group from the previous year is dropped from the sample and new one is added.
Sample distribution over time:
The sample is not distributed over time.
18.1.2. Sampling unit
Primary sampling units were enumeration districts and second stage units were households.
18.1.3. Sampling frame
Concerning the EU-SILC instrument, three different sample size definitions can be applied:
- the actual sample size which is the number of sampling units selected in the sample
- the achieved sample size which is the number of observed sampling units (household or individual) with an accepted interview
- the effective sample size which is defined as the achieved sample size divided by the design effect with regards to the at-risk-of poverty rate indicator
Given that the effective sample size has been already treated in the section dealing with sampling errors, in this section the attention focuses mainly on the achieved sample size.
Actual and achieved sample size
| Actual sample size |
Achived sample size |
|---|---|
| 6236 | 5527 |
Achieved sample size
| Number of households | Persons 16 over | Selected respondents |
|---|---|---|
| 5527 | 14128 | 0 |
| New rotational group | Number of households | Percentage |
|---|---|---|
| 3 | 1534 | 27.75 |
| Old rotational group | Number of households | Percentage |
|---|---|---|
| 4 | 1296 | 23.45 |
18.2. Frequency of data collection
Annual.
18.3. Data collection
| Mode of data collection
Description of collecting income variables
|
See Annex - Data collection
Annexes:
Data collection
18.4. Data validation
Control of source data is carried out in severeal iterations. Many numerical-logical controls are built into the CAPI application through hard errors and signals. That allowes interviewers to track inconsistent responses and make necessary corrections immediately, which contributes to better data quality. After the fieldwork, additional data cleaning and processing activities are performed during an extensive and comprehensive data cleaning process, which includes cheking the logic and consistency of the data, outliers analysis etc. Finaly, the national EU-SILC databases are verified and validated using Eurostat’s data chek and validation programs.
18.5. Data compilation
Weighting procedure and imputation were carried out according to regulation.
18.5.1. Imputation - rate
Not available.
18.5.2. Calculation of weighting factors and weight adjustments
See Annex - Weighting procedure.
Annexes:
Weighting procedure
18.5.3. Estimation and imputation
Imputation is used in order to complete missing information because of unit non-response (imputation of missing personal questionnaires) or because of item non-response (e.g. missing income information).
See Annex - Estimation and imputation.
Annexes:
Estimation and Imputation
18.6. Adjustment
Not applicable.
18.6.1. Seasonal adjustment
Not applicable.
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.
30 September 2025
Statistical concepts and definitions for EU-SILC are specified in Regulation (EU) 2019/1700, Commission Implementing Regulation (EU) 2019/2181, and Commission Implementing Regulation (EU) 2019/2242. Additional information is available in the EU statistics on income and living conditions (EU-SILC) methodology and in the methodological guidelines and description of EU-SILC target variables (see CIRCABC).
Further details are provided in items 5, 15.1.1.1, 15.2.2 and 18.3.
Statistical units are private households and all persons living in these households who have usual residence in the Member State. Annex II of the Commission implementing regulation (EU) 2019/2242 defines specific statistical units per variable and specifies the, content of the quality reports on the organization of a sample survey in the income and living conditions domain pursuant to Regulation (EU) 2019/1700 of the European Parliament and of the Council.
The target population is private households and all persons composing these households having their usual residence in the Member State. Private household means a person living alone or a group of persons who live together, providing oneself or themselves with the essentials of living.
The data relate to the territory of the Republic of Serbia.
Since 1999, the Statistical Office of the Republic of Serbia doesn't dispose of certain data for AP Kosovo and Metohia, so they are not contained in the data coverage for the Republic of Serbia (total).
Description of reference period used for incomes
| Period for taxes on income and social insurance contributions |
Income reference periods used |
Reference period for taxes on wealth |
Lag between the income ref period and current variables |
|---|---|---|---|
| The reference period for taxes on income and social insurance contributions for EU-SILC 2024 was previous calendar year 2023. |
The income reference is a fixed twelve-month period. For EU-SILC 2024, the period was the year 2023. |
The reference period for taxes on wealth for EU-SILC 2024 was the previous calendar year, 2023. |
The lag between the income reference period and the household interview was 5-6 months. In EU-SILC 2024 the fieldwork lasted for two months, from May 9th to Jun 30th. |
According to Reg. (EU) 2019/1700 Annex II, precision requirements for all data sets are expressed in standard errors and are defined as continuous functions of the actual estimates and of the size of the statistical population in a country or in a NUTS 2 region. For the income and living conditions domain, the estimated standard errors of the following indicators are examined according to certain parameters set:
- Ratio at‐risk‐of‐poverty or social exclusion to population;
- Ratio of at‐persistent‐risk‐of‐poverty over four years to population;
- Ratio at‐risk‐of‐poverty or social exclusion to population in each NUTS 2 region.
Further information is provided in section 13.2 Sampling error.
The data involves several units of measure depending upon the variables. Income variables are transmitted to Eurostat in national currency. For more information, see methodological guidelines and description of EU-SILC target variables available on CIRCABC
Weighting procedure and imputation were carried out according to regulation.
Тhe sampling frame is the The Census of Population, Households and Dwellings in the Republic of Serbia, carried out in 2022. Тarget population consists of all persons living in private households in the Republic of Serbia . Persons living in collective households and in institutions are generally excluded from the target population.
Annual
Date of the dissemination of national results. 20 Jun 2025
Number of days between the end of fieldwork and the first fully validated delivery of data to the Commission (Eurostat):
- End of field-work: 30 Jun 2024
- First data delivery: 30 Jun 2025
Days between the end of fieldwork and the first fully validated delivery: 365
There are no conceptual differences between results on national and regional levels.
See Annex - Breaks in series.
Annexes:
Breaks in series
