1.1. Contact organisation
National Statistical Institute
1.2. Contact organisation unit
Statistics on Living Conditions Department
Demographic and Social Statistics Directorate
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
2 P.Volov street, 1038 Sofia, Bulgaria
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
25 April 2025
2.2. Metadata last posted
25 April 2025
2.3. Metadata last update
25 April 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.
EU-SILC provides four basic files containing target variables based on common concepts and definitions.
Annual data for the countries contain the following components:
- Household register (D-file);
- Personal register (R-file)
- Basic data;
- Child care;
- Household data (Н-file)
- Basic data;
- Housing;
- Material deprivation;
- Income at household level;
- Personal data of people aged 16 and more (Р-file)
- Basic data;
- Education;
- Health status;
- Economic activity;
- Individual income.
Each year additional data on the household and household members on specific topics is collected, the so-called ad-hoc modules.
The indicators on poverty and social inclusion are calculated on the basis of the survey "Statistics on income and living conditions" and a common methodology for data collection, target variables obtaining and calculating of common indicators, approved by Eurostat. The poverty rate is the share of households that are below the poverty line which is defined as 60% of the median equivalised disposable income.
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 information on the classification used, please refer to the list of classification on the Eurostat webpage and Statistics explained.
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.
The following social fields are included in the survey methodology:
- Basic demographic and other characteristics of the household and its members;
- Monetary indicators of living standards and social stratification of the population: data on income from different sources;
- Non-monetary indicators of living standard: basic data on housing conditions; problems related to housing or neighborhood (location); access to education; health status and access to healthcare;
- Economic activity, employment and unemployment of persons aged 16 and more;
- Social services and programs and the participation of the household or its members in them.
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 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 BG-SILC target population consists of all private households and their current members residing in the country.
Usual residence is a place where a person normally spends their daily period of rest, regardless of temporary absences for purposes of recreation, holidays, visits to friends and relatives, business, medical treatment or religious pilgrimage.
The following persons shall be considered to be the usual residents of Bulgaria:
- those who have lived in their place of usual residence for a continuous period of at least 12 months before the reference date (as defined for a specific data collection); or
- those who arrived in their place of usual residence during the 12 months before the reference date (as defined for a specific data collection) with the intention of staying there for at least 1 year.
3.6.1. Reference population
Definitions of reference population, household and household membership
| Reference population |
Private household definition |
Household membership |
|---|---|---|
| The reference population of BG-SILC is all private households and their members residing in the territory of the country at the time of data collection. The source of the sample is the sample frame based on the Population Census 2021. The data base includes all private households and their current members residing in the territory, independently of any socio-economic characteristics they may have. Persons living in collective households and in institutions are excluded from the target population. |
The definition of household that Eurostat recommends is used. Household is defined as a person living alone or a group of people who live together in the same dwelling and share expenditures including the joint provision of the essentials of living. Family members living together but not sharing their income and expenditure with other family members make up separate households. |
All household members aged 16 years and more at the time of the interview, are selected for a personal interview. |
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.
Persons living in collective households and in institutions are generally excluded from the target population. The population moved out of territory of country, the person that have not a usual residence or who have moved to an institution from the previous wave are not covered.
3.7. Reference area
Entire territory of Republic of Bulgaria
3.8. Coverage - Time
2008-2024
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 the methodological guidelines and description of EU-SILC target variables available in 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 income tax repayment and compulsory social insurance contributions is the previous calendar year. |
The income reference period is the previous calendar year. |
The reference period for taxes on wealth is the previous calendar year. |
The lag between the income reference period and current variables is at minimum 3 months and at maximum 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.
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
- Law on Statistics;
- Regulation (EC) No 223/2009 on European statistics (recital 24 and Article 20(4)) of 11 March 2009 (OJ L 87, p. 164), stipulates the need to establish common principles and guidelines ensuring the confidentiality of data used for the production of European statistics and the access to those confidential data with due account for technical developments and the requirements of users in a democratic society.
7.2. Confidentiality - data treatment
According Art. 25 of the Statistics Act individual data are not published (they are suppressed). Dissemination of individual data is possible only according to Art. 26 of the Statistics Act.
8.1. Release calendar
Results are published once a year
Annexes:
Release Calendar
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 EU statistics on income and living conditions - Microdata - Eurostat
Annual
10.1. Dissemination format - News release
Poverty and Social Inclusion Indicators.
Annexes:
Poverty and Social Inclusion Indicators
10.2. Dissemination format - Publications
Research results are published as a press release only.
10.3. Dissemination format - online database
Detailed results are available to all users of the NSI website under the heading Social Inclusion and Living Conditions - Poverty and Social Inclusion Indicators.
Annexes:
Poverty and Social Inclusion Indicators
10.3.1. Data tables - consultations
2
10.4. Dissemination format - microdata access
Anonymised individual data can be made available for scientific research purposes, and at the individual request of the Rules for the provision of anonymised individual data for scientific and research purposes.
10.5. Dissemination format - other
Information service on request, according to the Rules for the dissemination of statistical products and services to NSI.
10.5.1. Metadata - consultations
Number of times a published metadata file is viewed.
10.6. Documentation on methodology
Available methodology on the NSI internet site and ESMS reference metadata.
- Survey methodology
- Indicators - Methodological notes
Annexes:
Survey and Indicator Methodology
10.6.1. Metadata completeness - rate
All required concepts are provided, 100%
10.7. Quality management - documentation
Quality report in national level is available on the NSI website.
Annexes:
Quality Report
11.1. Quality assurance
The National Statistical Institute has developed, documented, implemented and maintained a quality management system and is working to continuously improve it in accordance with the requirements of ISO 9001. The methodological framework of the SILC survey is fully in line with European and other international standards, guidelines and good practices. About 70% of the definitions and concepts used by administrative sources are close to those used for statistical purposes.
National Statistical Institute (NSI) is certified according to ISO 9001 standards. The certification confirms that NSI fullfills the quality requirements for statistics production.
In practical terms for the BG-SILC survey, this means:
- all activities follow the well described ISO procedures for main phases of the statistical business process (on the base of the GSBPM 5.1);
- all activities are documented;
- methodological documents, software and data files are well-structured and in order;
- checks are carried out in critical business process steps;
- continuous improvement is integrated as a routine in the daily work: internal quality revisions assures the compliance to the ISO-standards requirments;
- issues are documented using the PDCA-method (plan, do, check, act);
- regular internal and external audits carry out.
Also NSI follows the European statistics Code of Practice.
11.2. Quality management - assessment
Data are accompanied with quality reports analysing the accuracy, coherence and comparability of the data.
The quality of the BG-SILC survey can be assumed to be high. Its concepts and methodology have been developed according to European and international standards and using best practices from all EU Member States. BG-SILC indicators are considered to be sufficiently accurate for all practical purposes they are put into. The indicators are disseminated following a predetermined Release calendar.
Further work is ongoing to improve the quality and in particular the comparability of the indicators. Key priorities are greater harmonisation of methods for quality adjustment and sampling.
There is a yearly ISO 9001 internal and external audits for the whole department.
12.1. Relevance - User Needs
The main users of EU-SILC statistical data are policy makers, research institutes, media, and students.
BG-SILC the main users are:
- Institutional users like other Commission services, other European institutions (such as the ECB), national administrations (mainly those in charge of the monitoring of social protection and social inclusion, or other international organisations);
- Eurostat, ministries and government agencies;
- Research organizations and institutes;
- End users - including the media - interested in living conditions and social cohesion in the EU.
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 the User Satisfaction Survey.
12.3. Completeness
All target variables in the BG-SILC survey are fully in line with the methodological guidelines (Doc065 2024 operation year) and the Commission (Eurostat) requirements.
12.3.1. Data completeness - rate
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:
- 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;
- 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;
- 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 2024 survey year, Bulgaria used bootstrap variance estimation technique to obtain standard error for the main indicator of interest AROPE. In total, 100 replicate samples were drawn. The replicates weights were obtained by using calibration approach. The calibration approach was identical to the calculation of RB050 weights. This additional procedure gives unbiased estimator for the population totals and increases the precision. Standard error was calculated using the information from 100 replicate samples where the PSU sample size is reflation of sample size of the main survey for the corresponding year.
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 -60% |
Severe Material and Social Deprivation |
Very low work intensity |
||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Ind.value |
Stand. errors |
Half CI (95%) |
Ind.value |
Stand. errors |
Half CI (95%) |
Ind.value |
Stand. errors |
Half CI (95%) |
Ind.value |
Stand. errors |
Half CI (95%) |
|
| Total |
30.3 |
0.7 |
30.3 |
21.7 |
0.8 |
21.7 |
16.6 |
0.7 |
16.6 |
7.9 |
0.7 |
7,9 |
| Male |
28.9 |
0.9 |
28.9 |
20.8 |
0.8 |
20.8 |
15.5 |
0.8 |
15.5 |
7.9 |
0.6 |
7,9 |
| Female |
31.6 |
0.9 |
31.6 |
22.6 |
0.8 |
22.6 |
17.6 |
0.8 |
17.6 |
8.0 |
0.7 |
8.0 |
| Age0-17 |
35.1 |
1.8 |
35.1 |
28.2 |
1.7 |
28.2 |
18.2 |
1.6 |
18.2 |
10.4 |
1.3 |
10.5 |
| Age18-64 |
26.3 |
0.9 |
26.3 |
18.3 |
0.8 |
18.3 |
14.3 |
0.8 |
14.3 |
7.1 |
0.5 |
7.1 |
| Age 65+ |
36.6 |
0.8 |
36.6 |
25.5 |
0.7 |
25.5 |
21.0 |
0.7 |
21.0 |
|
|
|
Main indicators, standard error and CI at NUTS 2 level
|
|
AROPE |
At risk of poverty -60% |
Severe Material and Social Deprivation |
Very low work intensity |
||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Ind.value |
Stand. errors |
Half CI (95%) |
Ind.value |
Stand. errors |
Half CI (95%) |
Ind.value |
Stand. errors |
Half CI (95%) |
Ind.value |
Stand. errors |
Half CI (95%) |
|
| BG31 |
35.8 |
2.4 |
35.9 |
26.3 |
2.2 |
26.5 |
17.9 |
2.0 |
18.2 |
16.3 |
2.4 |
16.8 |
| BG32 |
36.6 |
1.9 |
36.7 |
24.5 |
1.9 |
24.7 |
21.6 |
1.9 |
21.8 |
9.2 |
1.7 |
9.7 |
| BG33 |
26.3 |
1.9 |
36.4 |
20.0 |
1.8 |
20.2 |
13.9 |
1.6 |
14.2 |
4.4 |
1.1 |
5.0 |
| BG34 |
35.8 |
2.4 |
36.0 |
25.1 |
2.1 |
25.3 |
22.7 |
2.1 |
22.9 |
11.7 |
2.1 |
12.3 |
| BG41 |
20.1 |
1.3 |
20.3 |
13.8 |
1.2 |
14.0 |
9.1 |
1.0 |
9.3 |
3.1 |
0.6 |
3.3 |
| BG42 |
38.3 |
2.1 |
38.4 |
28.8 |
2.2 |
30.0 |
22.1 |
2.3 |
22.4 |
10.6 |
2.1 |
11.3 |
Persistent-risk-of-poverty ratio over four years to the population, standard error and CI
| Persistent-risk-of-poverty | ||||
|---|---|---|---|---|
| 95% CI | ||||
| Ind. value | Stand. Errors | L | U | |
| Total | 15.7 | 1.0 | 13.8 | 17.6 |
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 | 172122 | 6.2 | Non-contacted addresses for the following reasons:
|
| 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
As with any other statistical survey, BG-SILC may be burdened with non-sampling errors which occur at various stages of the survey and which cannot be eliminated completely. This mainly applies to interviewers’ errors at the stage of collecting the information, errors due to the respondents’ misunderstanding of questions and inaccurate or sometimes even false answers as well as the errors taking place at the stage of data recording.
BG-SILC is a non-obligatory, representative survey of individual households, performed by a face-to-face interview technique with the use of the CAPI method. Two types of questionnaires: individual and household questionnaire were applied. In order to finalize the questionnaires, any observations made on the questionnaires of the previous years were taken into account. The data collected from the survey were compared to the data obtained from the registers. Some of the persons, who according to the register receive minimum income, defined themselves as unemployed or non-active in the survey, because they assess their current activity as temporary and did not indicate their income. Income from interests, dividends in unincorporated businesses is in general not provided from the households.
Measurement error for cross-sectional data
| Cross-sectional data |
|||
|---|---|---|---|
| Source of measurement errors |
Building process of questionnaire |
Interview training |
Quality control |
| This mainly applies to interviewers’ errors at the stage of collecting the information, errors due to the respondents’ misunderstanding of questions and inaccurate or sometimes even false answers as well as the errors taking place at the stage of data recording. |
Households and individuals are interviewed by electronic devices (CAPI). The data entry program was developed on Visual Basic.NET (MS Visual Studio 2022). The program is currently running on Windows 10 based tablet PCs. We used the following components when installing the program:
A large number of edit checks (hard and soft) between questions in both questionnaires were implemented for ensuring data correctness and consistency. For example, two external files (at household and personal level) were used for verifying correctness of identifiers and for checking against previously collected information – household composition and questions such as day, month and year of birth, sex etc. for those individuals who are not observed for the first time. All gross income values were checked if they are equal or greater than net values (hard error) and if net values are greater or equal than gross values divided by two (soft error). In order to check the consistency of data on child allowances an additional check has been implemented – the program checks if the number and age of children in the household corresponds to the child allowances received in the household (hard error). Another check that has been added is between the salary of an individual, his/her profession and the minimum insurance income (soft error). According to national legislation the minimum insurance income is set to a certain level according to the profession type. For checking purposes, lower and upper boundaries, narrower than absolute, were set for most of the questions on income (e.g. social benefits, pensions) based upon national legislation. Internal files (implemented in the database) that hold valid ISCO-08 and NACE codes and descriptions were included. During data entry phase, data entry operators were enabled to generate progress report by using SQL queries. The report contained form IDs, form status, number of errors and number of suppressed signals. A report for the number of individuals and households been interviewed or not grouped by interviewee had been added. The IT application has been updated in line with the new questions from the modules. New logics related to the ad-hoc subject module were added. The application was sent for testing by the regional coordinators and all comments and corrections were reflected. The IT tools was installed on the technical devices. The application allows work in on- and off-line mode. The system allows to periodically correct or introduce logical controls, which are immediately reflected in the work. |
The training of 28 supervisors and 6 IT experts was held in the training center of the NSI in the period March 05-07, 2025. Particular attention was paid to the specifics of the questions from the modules, as well as to taking measures to reduce the number of non-responding households and individuals. |
After data-entry phase, further data checking and editing was performed, using SPSS scripts. Initially, data were checked whether all questionnaires have been entered and completed. Special attention was paid to split-off households. Next, all suppressed signals and remarks made by data entry operators were checked up and corrections were made if necessary. After that, data were converted to SPSS data sets. Extreme income values were compared with data provided by National Social Security Institute or administrative data sources and data from previous waves where possible and corrected if necessary. All SILC target variables were computed after checking original variable(s). Finally, four transmission files were converted to .csv format and verified by Eurostat` SAS checking programs. The main errors detected in the post-data-collection process were related to double registration of child allowances and personal income from agriculture, property or land. Both of them were recorded in household` and individual` questionnaires. All gross income values were checked if they are equal or greater than net values (hard error) and if net values are greater or equal than gross values divided by two (soft error). |
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
| Cross-sectional data | |||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 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 |
| 99.6 | 98.5 | 100.0 | 90.0 | 70.3 | 98.2 | 99.8 | 99.6 | 100.0 | 10.4 | 30.8 | 1.8 | 0.2 | 0.4 | 0.1 | 10.5 | 31.1 | 1.9 |
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 | wave2_20 | wave3_21 | wave4_22 | wave5_23 | wave6_24 |
|---|---|---|---|---|---|
| Wave response rate | 96.98 | 97.32 | 96.69 | 88.27 | 89.64 |
| L follow-up rate | 93.72 | 95.70 | 95.33 | 97.16 | 97.20 |
| Follow-up ratio | 89.94 | 92.31 | 91.54 | 94.92 | 94.46 |
| Achieved sample size ratio | 89.94 | 92.31 | 91.54 | 94.92 | 94.46 |
Responce rate for persons by wave
| Response rate for persons | Sample persons/ co-residents | wave2_20 | wave3_21 | wave4_22 | wave5_23 | wave6_24 |
|---|---|---|---|---|---|---|
| Wave response rate | Sample persons | 99.48 | 99.66 | 99.93 | 99.84 | 99.81 |
| co-residents | 88.89 | 100 | 100 | 100 | 99.58 | |
| L follow-up rate | Sample persons | 99.84 | 99.8 | 99.95 | 99.82 | 99.87 |
| Achieved sample size ratio | All persons | 89.51 | 91.82 | 90.95 | 93.8 | 94.35 |
| Sample persons | 89.51 | 91.83 | 90.16 | 92.94 | 93.4 | |
| co-residents | . | 87.5 | 253.7 | 158.4 | 138.8 | |
| Response rate for non-sample persons | co-residents | 88.89 | 100 | 100 | 100 | 99.58 |
Sample and responce 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 = 2019 | 1461 | 3279 | 100.0 | 87.8 |
| Wave 2 = 2020 | 2969 | 6357 | 95.0 | 86.9 |
| Wave 3 = 2021 | 4472 | 9822 | 95.1 | 86.8 |
| Wave 4 = 2022 | 5976 | 13019 | 94.0 | 86.4 |
| Wave 5 = 2023 | 8383 | 16203 | 84.2 | 86.8 |
| Wave 6 = 2024 | 9728 | 19310 | 86.6 | 86.7 |
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 income variables the following information are provided in the Annex 2:
- percentages of households (per income components collected or compiled at household level)/persons (per income components collected or compiled at personal level) having received an amount for each income component,
- percentage of missing values for each income component collected or compiled at household/personal level,
- percentage of partial information for each income component collected or compiled at household/personal level.
13.3.4. Processing error
Description of data entry, coding controls and the editing system
| Data entry and coding (if any used) |
Editing controls |
|---|---|
| EU-SILC data were collected with two kinds of questionnaires – household and individual questionnaire. Households and individuals are interviewed by electronic devices (CAPI). The data entry program was developed on Visual Basic.NET (MS Visual Studio 2022). The program is currently running on Windows 10 based tablet PCs. We used the following components when installing the program:
A large number of edit checks (hard and soft) between questions in both questionnaires were implemented for ensuring data correctness and consistency. For example, two external files (at household and personal level) were used for verifying correctness of identifiers and for checking against previously collected information – household composition and questions such as day, month and year of birth, sex etc. for those individuals who are not observed for the first time. All gross income values were checked if they are equal or greater than net values (hard error) and if net values are greater or equal than gross values divided by two (soft error). In order to check the consistency of data on child allowances an additional check has been implemented – the program checks if the number and age of children in the household corresponds to the child allowances received in the household (hard error). Another check that has been added is between the salary of an individual, his/her profession and the minimum insurance income (soft error). According to national legislation the minimum insurance income is set to a certain level according to the profession type. For checking purposes, lower and upper boundaries, narrower than absolute, were set for most of the questions on income (e.g. social benefits, pensions) based upon national legislation. Internal files (implemented in the database) that hold valid ISCO-08 and NACE codes and descriptions were included. During data entry phase, data entry operators were enabled to generate progress report by using SQL queries. The report contained form IDs, form status, number of errors and number of suppressed signals. A report for the number of individuals and households been interviewed or not grouped by interviewee had been added. |
After data-entry phase, further data checking and editing was performed by SILC unit, using SPSS scripts. Initially, data were checked whether all questionnaires have been entered and completed. Special attention was paid to split-off households. Next, all suppressed signals and remarks made by data entry operators were checked up and relevant corrections were made. After that, data were converted to SPSS data sets. Extreme income values were compared with data provided by National Social Security Institute or administrative data sources and data from previous waves, where possible and corrected if necessary. All SILC target variables were computed after checking original variable(s). Finally, four transmission files were converted to .csv format and verified by Eurostat` SAS checking programs. The main errors detected in the post-data-collection process were related to double registration of child allowances and personal income from agriculture, property or land. Both of them were recorded in household` and individual` questionnaires. All gross income values were checked if they are equal or greater than net values (hard error) and if net values are greater or equal than gross values divided by two (soft error). |
13.3.5. Model assumption error
Not applicable.
14.1. Timeliness
SILC cross-sectional and longitudinal data are available in the form of tables 10 months after the end of the data collection period.
Date of dissemination of nation results: 25 April 2025.
14.1.1. Time lag - first result
First data are available 6 months after data collection.
14.1.2. Time lag - final result
Final results are available 10 months after data collection.
14.2. Punctuality
The first results were sent for validation on 17 December 2024 according to the IESS regulation.
14.2.1. Punctuality - delivery and publication
No time lag between delivery of data and deadline of legislation.
The final data were validated at the middle of February 2025, or in time according to the deadline of 28 February of year N+1.
15.1. Comparability - geographical
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.
Comparability across EU Member States is considered high due to use of harmonised concepts, variables, definitions and classifications.
Comparability between different regions of the country is considered high.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not applicable.
15.2. Comparability - over time
No break in series to be reported for 2024. Please see the annex Break in series for more information.
Regarding income, the following changes should be taken into account:
- There was an increase in the pensions in 2023:
- the minimum amount of the old-age pension increased during the year (from BGN 467 to BGN 523 or by 12.0% );
- an increase of 12.0% for all pensions as of 01 July 2023;
- from 1 July 2023, the amount of the old-age social pension increased from BGN 247.00 to BGN 276.64 (an increase of 12.0 percent). By the same percentage, the amounts of all related non-employment pensions, as well as the amounts of foreign aid supplements and war veterans supplements, increased
- an increase in the social disability pension;
- in 2023 an additional amount was paid in the amount of BGN 70 for each pensioner in April 2023;
- The minimum wage was increased from BGN 710 to BGN 780 or by 9.9%).
- The average annual gross wages and salaries of the employees under labour contract in 2023 increased by 15.3% compared to 2022.
15.2.1. Length of comparable time series
Not applicable.
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.
The cross-sectional data for the EU-SILC2024 were compared to the Labor force survey 2024 and HBS 2024.
When comparing SILC and HBS we must take into account the discrepancies. The differences are to great extent brought about by the methodological diversity. Here are the main methodological differences:
- Different reference periods for income variables – in HBS the main variables of income is estimated quarterly and yearly and presented in the form of average values. In EU-SILC the reference period is the previous calendar year;
- Different types of income are taken into account i.e. in HBS the information is collected both about the income in cash and in kind, while in EU-SILC – only about the income in cash (with a few exceptions), which may be important for the income from farming and social benefits other than retirement pay and pension;
- Different way of data collection – in HBS the respondents make records in the so called diary. They have to determine the data sources themselves and do not have them listed in the diary. In EU-SILC each respondent is asked detailed questions. In EU-SILC all the income missing data are imputed, while there is no imputation in HBS;
- HBS data are not weighted.
| SILC2024 | Other source | Source | |
|---|---|---|---|
| Population | 6 445 481 | 6 445 481 | Population as of 31 December 2023 |
|
3 097 698 | 3 097 698 | |
|
3 347 783 | 3 347 783 | |
| Number of pensioners PL31=5 | 1 478 192 | 2 037 336 | NSSI as of 31 December 2023 |
| Number of persons received income from pension | 1 524 595 | 2 031 191 | NSSI average annual number of pensioners for 2023 |
| Number of Households | 2 865 708 | 2 854 131 | LFS 2024 |
| Employed | 3 000 552 | 2 932 885 | LFS 2024 |
| Working full time | 2 826 006 | 2 880 297 | LFS 2024 |
| Working part-time | 174 546 | 52 589 | LFS 2024 |
| Unemployed | 159 822 | 127 449 | LFS 2024 |
| Economically inactive | 2 112 249 | 2 382 754 | LFS 2024 |
15.3.1. Coherence - sub annual and annual statistics
Not applicable.
15.3.2. Coherence - National Accounts
The cross-sectional data for the BG-SILC2024 were compared to the National Accounts (NA) related to preliminary measurement of household income in 2023.
When comparing SILC and NA we must take into account the following discrepancies:
- Population differences - According to the National Accounts the reference population for Household sector (S.14) consists of all persons, national or foreign, who are permanently settled in the economic territory of the country. In the EU-SILC the reference population includes all private households residing in the country that have registered place of residence therefore people living in the institutional households or without registered place of residence are excluded, but they are included in NA.
- Income reference period - In BG-SILC, the income reference period is the year prior to the data collection, while the household demographic information refers to the year of the survey. National Accounts for a specific year refer to the income generated in that specific year.
- Self-employed income - The household income component in the National Accounts - mixed income, refers to remuneration for work carried out by the owner of unincorporated enterprise classified in the household sector or members of the family. It has both characteristics, those of wages and salaries, and of profit curried out by entrepreneur. In the EU-SILC it corresponds to the cash benefits or losses from self-employment, although it includes also property income received in connection with financial and other assets belonging to the enterprise, whereas it is not included in mixed income in the National Accounts.
Coverage rates (CR) are calculated as the percentage of the BG-SILC value compared with the corresponding NA value, with the following formula for each income component:
CR = (BG-SILCincome_weighted _total / NAincome) * 100
The coverage rate for “Ïncome from self-employment” is 142.1% and the coverage rate for “Employee income” is 123.3%.
The following should be taken into account when making comparisons with national accounts:
- The difficulty of including in the sample the highest incomes, (resulting in underestimation of selfemployed income);
- The possibility for self-employed persons to declare income lower than the actual received (the presence of a minimum insurance income of self-employed persons close to the minimum wage for the country).
- In the SILC survey, some of the respondents could not indicate what taxes and fees were paid, and they indicated only gross or net remuneration. If the information on tax and insurance contributions was missing, the amounts were imputed according to labour and social insurance legislations, regardless of whether the person is actually insured or not. In some cases where only net income amounts were available these had to be converted to gross values using all necessary information.
15.4. Coherence - internal
No any lack of coherence in the BG-SILC data set that was coded/collected differently outside of the methodology.
Mean (average) interview duration per household = 67 minutes.
Mean (average) interview duration per person 16+ = 21.8 minutes.
The mean interview duration per household is calculated as the sum of the duration of all household interviews plus the sum of the duration of all personal interviews, divided by the number of household questionnaires completed. Only households accepted for the database have to be considered.
17.1. Data revision - policy
Not applicable.
17.2. Data revision - practice
No revisions to report.
17.2.1. Data revision - average size
Not applicable.
18.1. Source data
The sample for BG-SILC 2024 are selected from the sampling frame based on the Population Census 2021. The data base includes all private households and their current members residing in the country. Persons living in collective households and in institutions are excluded from the target population. Student’s and worker’s hostels are excluded at the first stage of selection of PSU, because these households rarely stay on the same addresses and are difficult to trace.
The frame is regularly updated according to the administrative changes made.
Household data within the selected PSUs are updated according to the Information System “Demography” data (ISD).
The longitudinal component consists of the sub-samples R1, R3, R4, R5 and R6.
All personal/household income variables were collected by interview. Where the information is available, the data from the administrative source is directly used.
The National Revenue Agency provides data from the register of insured persons. This register used for PY010, PY030, PY050 and HY090 variables.
The National Social Security Institute provides data on income from pensions and other social security payments. This register used for PY090, PY100, PY110, PY120, PY130, HY050 and HY110 variables.
The Social Assistance Agency provides data on income from social benefits. This register used for HY050, HY060 and HY070 variables.
18.1.1. Sampling Design
Type of sampling design
Six-year rotation panel is used for BG-SILC2024 in Bulgaria. It contains 6 independent sub-samples and follows stratified two-stage cluster sampling design.
Separated strata are formed based on the country administrative-territorial division. All private households in the country are covered.
In 2024 the sample size of the panel is 9722 private households from 6 rotational groups, distributed over all regions of the country. Except from the sampled household all its members aged 16 years or more are also surveyed. Households are participating in the survey for 6 consecutive years. Every year 1 rotational group is dropped and replaced by another.
In 2024 a new rotational group with 2660 households was introduced.
Stratification and sub stratification criteria
The general population and administrative-territorial division by statistical districts of the settlement, comprises all the households in the country. Register prepared for the Population Census 2021 was used as sampling frame for selection last rotational group (R2). The sampling frame is annually updated with data from the Information System “Demography” data (ISD). Information about new born and died persons is used for actualization of sampling frame.
The sample is stratified by administrative-territorial districts in the country (NUTS3) and the household’s location. As a result 56 strata are formed (28 of urban and 28 of rural population). Municipalities and settlements are ranged according to the number of their population within each stratum.
Sample selection schemes
The number of census enumeration units (PSU) is calculated for each strata included in the sample.
The clusters on the first stage are chosen with probability proportion to population size (number of households) in the PSUs. Systematic sampling of secondary units (households) in each primary unit selected is applied. Each PSU contains 5 households.
18.1.2. Sampling unit
Two stage sampling on a territorial principle is implemented as follows:
- on the first stage - the census enumeration units (PSU) are selected;
- on the second stage - the households are sampled.
18.1.3. Sampling frame
Concerning the SILC instrument, three different sample size definitions can be applied:
- the actual sample size which is the number of sampling units selected in the sample
- 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.
The total gross sample size (number of households) has been calculated analyzing the non-response rates and design effects of the previous BG-SILC surveys.
The total sample size in 2024 is 9722 households:
- 7062 “old” (2019, 2020, 2021, 2022 and 2023),
- 2660 “new” households (drawn in 2024).
Number of households for which an interview is accepted for the database.
Rotational group breakdown and total
| Rotational group | First wave | Households | % |
|---|---|---|---|
| 1 | 2021 | 1358 | 16.2 |
| 2 | 2024 | 1752 | 20.8 |
| 3 | 2019 | 1021 | 12.1 |
| 4 | 2020 | 1156 | 13.7 |
| 5 | 2023 | 1621 | 19.2 |
| 6 | 2022 | 1519 | 18.0 |
| Total | 8427 | 100.0 |
Number of persons of 16 years or older who are members of the households for which the interview is accepted for the database, and who completed a personal interview.
Rotational group breakdown and total
| Rotational group | First wave | Households’ members | % |
|---|---|---|---|
| 1 | 2021 | 2818 | 16.8 |
| 2 | 2024 | 3382 | 20.2 |
| 3 | 2019 | 1972 | 11.8 |
| 4 | 2020 | 2287 | 13.7 |
| 5 | 2023 | 3251 | 19.4 |
| 6 | 2022 | 3033 | 18.1 |
| Total | 16743 | 100.0 |
The sample size for longitudinal component was 30329 households and 55718 persons aged 16 and over.
Number of households in longitudinal component
| DB135 = 1 | Rotational group | Total | |||||
|---|---|---|---|---|---|---|---|
| 1 | 3 | 4 | 5 | 6 | |||
| Year of the survey | 2019 | 0 | 1461 | 0 | 0 | 0 | 1461 |
| 2020 | 0 | 1315 | 1507 | 0 | 0 | 2822 | |
| 2021 | 1648 | 1226 | 1381 | 0 | 0 | 4255 | |
| 2022 | 1506 | 1124 | 1267 | 0 | 1718 | 5615 | |
| 2023 | 1434 | 1067 | 1205 | 1724 | 1629 | 7059 | |
| 2024 | 1358 | 1021 | 1156 | 1621 | 1519 | 6675 | |
| Total | 5946 | 7214 | 6516 | 3345 | 4866 | 27887 | |
Number of persons 16 years and older
| RB250 = 11,14 | Rotational group | Total | |||||
|---|---|---|---|---|---|---|---|
| 1 | 3 | 4 | 5 | 6 | |||
| Year of the survey | 2019 | 0 | 2879 | 0 | 0 | 0 | 2879 |
| 2020 | 0 | 2585 | 2940 | 0 | 0 | 5525 | |
| 2021 | 3417 | 2400 | 2712 | 0 | 0 | 8529 | |
| 2022 | 3131 | 2178 | 2496 | 0 | 3445 | 11250 | |
| 2023 | 2939 | 2065 | 2376 | 3437 | 3240 | 14057 | |
| 2024 | 2818 | 1972 | 2287 | 3251 | 3033 | 13361 | |
| Total | 12305 | 14079 | 12811 | 6688 | 9718 | 55601 | |
18.2. Frequency of data collection
In 20243 the data collection took place in the period March-June 2024 with reference period of data the previous calendar year (2023).
Sample distribution (household questionnaire) over time
| Month | Data | Number | % |
|---|---|---|---|
| March | 1 – 10 | 0 | 0.0 |
| 11 – 20 | 0 | 0.0 | |
| 21 – 31 | 416 | 4.9 | |
| April | 1 – 10 | 1517 | 18.0 |
| 11 – 20 | 1678 | 19.9 | |
| 21 – 30 | 1304 | 15.5 | |
| May | 1 – 10 | 1021 | 12.1 |
| 11 – 20 | 1363 | 16.2 | |
| 21 – 31 | 981 | 11.6 | |
| June | 1 – 10 | 147 | 1.7 |
| 11 – 20 | 0 | 0.0 | |
| 21 – 30 | 0 | 0.0 | |
| Total | 8427 | 100.0 |
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 |
|
83.5% |
|
|
|
16.5% |
|
|
|
Description of collecting income variables
| The source or procedure used for the collection of income variables |
The form (gross, net) in which income variables at component level have been obtained |
The method used for obtaining target variables in the required form |
|---|---|---|
| All personal/household income variables are collected by interview. The data from the administrative source is directly used for persons interviewed with electronic devices and where the information is available. The National Revenue Agency provides data from the register of insured persons. The National Social Security Institute provides data on income from pensions and other social security payments. The Social Assistance Agency provides data on income from social benefits. |
The interviewers and the respondents have the option of reporting income gross and/or net at component level. From 2012 onward Emploee cash or near cash income (PY010) is collected only net. |
The gross income was obtained by summing up net value, income tax payments and compulsory social insurance contributions. If the information on tax and insurance contributions was missing, the amounts were imputed in accordance with the labour and social insurance legislations. |
18.4. Data validation
In the process Data-entry is a logical control of extreme values, filled-in information on all issues, data comparability checks, links between individual questionnaires and registers is carried out. After processing the primary data and receiving the target changes, a verification with the SAS program provided by Eurostat for verification and validation of the data is performed. Additional compatibility checks are performed before publishing the information.
18.5. Data compilation
The database contains different types of weights:
- Household cross-sectional weight (target variable DB090) to obtain the actual number of private households in the country;
- Personal cross-sectional weight (target variable RB050) to obtain the actual number of persons in the country;
- Personal cross-sectional weight for each household member aged 16 and more (target variable PB040) to obtain the number of persons aged 16 and more in the country;
Weighting factors were calculated as required to take into account the units’ probability of selection, non-response and to adjust the sample to external data relating to the distribution of households and persons in the target population, such as sex and age, residence or administrative-territorial districts (NUTS 3).
18.5.1. Imputation - rate
The information provided here is the same as the one in the concept 13.3.3.2.1.
18.5.2. Calculation of weighting factors and weight adjustments
The weighting procedure consists of the following steps: First, calculation of design weights which represent the inverse of the inclusion probabilities of the sample units. According to the country specifics the sample is stratified to 56 strata by NUTS 3 (28 regions) and location (town/village). Second, adjustment for non-response. The procedure used is “weighting classes”. The classes are defined by the sampling design strata because of the limited information available for non-responding households. The final step is calibration of the non-response weights to population totals for the new rotation group (the result is base weights RB060). Calibration is done using the SAS Macro Calmar 2. The main source for calibration totals is the Information System “Demography”. Another source of information is the 2021 Population Census database.
The weighting procedure for the previous 5 rotation groups consists of the following steps: The base weights RB060 were adjusted for non-response at individual level. On the basis of a logistic regression the weights of the enumerated persons were adjusted with the probability of following up to obtain RB060 for 2024. This was applied for each rotation group separately. After that RB060 for each of the 5 rotation groups were calibrated separately to the population as of 31 December 2023 the same way as for the new rotation group.
To combine all sub-samples (the 6 rotation groups) all weights were multiplied with a scaling factor of 1/6 and the weights were calibrated to the population as of 31 December 2023 using variables at individual and at household level.
Detailed information on the weighting procedures is included in Annex 5.
18.5.3. Estimation and imputation
Imputation procedure
Data processing is performed with statistical software SPSS. Total gross income and disposable household income were calculated according to Document 065 (2024 operation). All personal/household income variables were collected by interview. Where the information is available, the data from the administrative source is directly used. The National Revenue Agency provides data from the register of insured persons. The National Social Security Institute provides data on income from pensions and other social security payments. The Social Assistance Agency provides data on income from social benefits. The interviewers and the respondents have the option of reporting income gross and/or net at component level. From 2012 Emploee cash or near cash income (PY010) is collected only net. The form in which the net amounts are recorded in database are net of tax on income at source and of social contributions.
The education at pre-school data were checked with the administrative data from the Ministry of Education. Those data show if a child is registered or not in a childcare/school structure. If the data for number of hours of education during a typical week are missing and the children are registered to school on the basis of register information then imputed the number of hours according to the national standards to the children that appear enrolled (40 hours weekly).
List of variables where imputation is used and the instances of imputation as a percentage of the total number of observations per variable shall be reported in the annex.
Company car
The information on the private use of a company car is collected in the individual questionnaire. To evaluate the benefits of private use of company car we used the amount of kilometers driven, the number of months in which the car is used, the cost of fuel under statutory spending limits and the average price of fuel for the year. Take into account the amount that the employer provides of limit on fuel costs. In case of missing value imputation is applied with the use of hot-deck and regression imputation with simulated residuals methods.
18.6. Adjustment
Not applicable.
18.6.1. Seasonal adjustment
Not applicable.
No comments.
The European Union Statistics on Income and Living Conditions (EU-SILC) is a survey-based instrument aiming at collecting timely and comparable cross-sectional and longitudinal multidimensional microdata on income, poverty, social exclusion and living conditions. In addition, it collects module variables every three years, six years or ad-hoc new policy needs modules.
The EU-SILC instrument provides two types of data:
- Cross-sectional data pertaining to a given time or a certain time period with variables on income, poverty, social exclusion and other living conditions;
- Longitudinal data pertaining to individual-level changes over time, observed periodically over four‐or more year rotation scheme (Annex III (2) of 2019/1700).
Social exclusion and housing condition information is collected mainly at household level while labour, education and health information is obtained for persons aged 16 and over. The core of the instrument is income information at very detailed component level and mainly collected at personal level.
EU-SILC provides four basic files containing target variables based on common concepts and definitions.
Annual data for the countries contain the following components:
- Household register (D-file);
- Personal register (R-file)
- Basic data;
- Child care;
- Household data (Н-file)
- Basic data;
- Housing;
- Material deprivation;
- Income at household level;
- Personal data of people aged 16 and more (Р-file)
- Basic data;
- Education;
- Health status;
- Economic activity;
- Individual income.
Each year additional data on the household and household members on specific topics is collected, the so-called ad-hoc modules.
The indicators on poverty and social inclusion are calculated on the basis of the survey "Statistics on income and living conditions" and a common methodology for data collection, target variables obtaining and calculating of common indicators, approved by Eurostat. The poverty rate is the share of households that are below the poverty line which is defined as 60% of the median equivalised disposable income.
25 April 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 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 BG-SILC target population consists of all private households and their current members residing in the country.
Usual residence is a place where a person normally spends their daily period of rest, regardless of temporary absences for purposes of recreation, holidays, visits to friends and relatives, business, medical treatment or religious pilgrimage.
The following persons shall be considered to be the usual residents of Bulgaria:
- those who have lived in their place of usual residence for a continuous period of at least 12 months before the reference date (as defined for a specific data collection); or
- those who arrived in their place of usual residence during the 12 months before the reference date (as defined for a specific data collection) with the intention of staying there for at least 1 year.
Entire territory of Republic of Bulgaria
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 income tax repayment and compulsory social insurance contributions is the previous calendar year. |
The income reference period is the previous calendar year. |
The reference period for taxes on wealth is the previous calendar year. |
The lag between the income reference period and current variables is at minimum 3 months and at maximum 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 are transmitted to Eurostat in national currency.
For more information, see the methodological guidelines and description of EU-SILC target variables available in CIRCABC.
The database contains different types of weights:
- Household cross-sectional weight (target variable DB090) to obtain the actual number of private households in the country;
- Personal cross-sectional weight (target variable RB050) to obtain the actual number of persons in the country;
- Personal cross-sectional weight for each household member aged 16 and more (target variable PB040) to obtain the number of persons aged 16 and more in the country;
Weighting factors were calculated as required to take into account the units’ probability of selection, non-response and to adjust the sample to external data relating to the distribution of households and persons in the target population, such as sex and age, residence or administrative-territorial districts (NUTS 3).
The sample for BG-SILC 2024 are selected from the sampling frame based on the Population Census 2021. The data base includes all private households and their current members residing in the country. Persons living in collective households and in institutions are excluded from the target population. Student’s and worker’s hostels are excluded at the first stage of selection of PSU, because these households rarely stay on the same addresses and are difficult to trace.
The frame is regularly updated according to the administrative changes made.
Household data within the selected PSUs are updated according to the Information System “Demography” data (ISD).
The longitudinal component consists of the sub-samples R1, R3, R4, R5 and R6.
All personal/household income variables were collected by interview. Where the information is available, the data from the administrative source is directly used.
The National Revenue Agency provides data from the register of insured persons. This register used for PY010, PY030, PY050 and HY090 variables.
The National Social Security Institute provides data on income from pensions and other social security payments. This register used for PY090, PY100, PY110, PY120, PY130, HY050 and HY110 variables.
The Social Assistance Agency provides data on income from social benefits. This register used for HY050, HY060 and HY070 variables.
Annual
SILC cross-sectional and longitudinal data are available in the form of tables 10 months after the end of the data collection period.
Date of dissemination of nation results: 25 April 2025.
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.
Comparability across EU Member States is considered high due to use of harmonised concepts, variables, definitions and classifications.
Comparability between different regions of the country is considered high.
No break in series to be reported for 2024. Please see the annex Break in series for more information.
Regarding income, the following changes should be taken into account:
- There was an increase in the pensions in 2023:
- the minimum amount of the old-age pension increased during the year (from BGN 467 to BGN 523 or by 12.0% );
- an increase of 12.0% for all pensions as of 01 July 2023;
- from 1 July 2023, the amount of the old-age social pension increased from BGN 247.00 to BGN 276.64 (an increase of 12.0 percent). By the same percentage, the amounts of all related non-employment pensions, as well as the amounts of foreign aid supplements and war veterans supplements, increased
- an increase in the social disability pension;
- in 2023 an additional amount was paid in the amount of BGN 70 for each pensioner in April 2023;
- The minimum wage was increased from BGN 710 to BGN 780 or by 9.9%).
- The average annual gross wages and salaries of the employees under labour contract in 2023 increased by 15.3% compared to 2022.
