SURVEY ON INCOME AND LIVING CONDITIONS (SILC) - Poverty and Social Inclusion Indicators| Contact |
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| Contact organisation | National Statistical Institute |
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| Contact organisation unit | Statistics on Living Conditions Department, Demographic and Social Statistics Directorate |
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| Contact name | Desislava Dimitrova, PhD |
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| Contact person function | head of department |
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| Contact mail address | 2 P.Volov street, 1038 Sofia |
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| Contact email address | [email protected] |
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| Contact phone number | +359 2 9857 183 |
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| Contact fax number | |
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| Statistical presentation |
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| Data description | Survey on income and living conditions (SILC) is a tool for providing timely and comparable data on income distribution, level and structure of poverty and social exclusion. The survey is carried out in a European methodology and provides information about the current state (cross-sectional data) and longitudinal (longitudinal data) changes in income level and structure of poverty and social exclusion. 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) • 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. |
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| 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.
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| Sector coverage | Data refers 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 providing information required by the European Semester and the European Pillar of Social Rights, in particular for 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 incomes 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.
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| Statistical concepts and definitions | Total household income: Two main concepts for total household income are applied: - Total household gross income (HY010);
- Total disposable household income (HY020).
Total household gross income (HY010) is computed as the sum for all household members of gross personal income components: - Gross employee cash or near cash income (PY010G);
- Gross non-cash employee income - company car (PY021G);
- Gross cash benefits or losses from self-employment (including royalties) (PY050G);
- Pensions received from individual private plans (PY080G);
- Unemployment benefits (PY090G), old-age benefits (PY100G), survivor' benefits (PY110G), sickness benefits (PY120G), disability benefits (PY130G) and education-related allowances (PY140G) plus gross income components at household level:
- Income from rental of a property or land (HY040G);
- Family/children related allowances (HY050G), housing allowances (HY070G), social exclusion not elsewhere classified (HY060G);
- Regular inter-household cash transfers received (HY080G);
- Interests, dividends, profit from capital investments in unincorporated business (HY090G);
- Income received by people aged under 16 (HY110G).
Total disposable household income (HY020) can be computed as total household gross income (HY010) is reduced to: - Regular taxes on wealth (HY120G);
- Regular inter-household cash transfer paid (HY130G);
- Tax on income and social insurance contributions (HY140G).
Household definition: Household is two or more persons, living in one dwelling or part of dwelling, sharing common budget and eating together. Household is a person, living in one dwelling, room or part of it to a dwelling, has a separate budget for the cost of meals and expenses to satisfy other needs. Equivalence scale: For the calculation of indicators of poverty and social inclusion using the total disposable household income is "equalised". Due to the different composition and number of persons in the household equivalent scales apply. Use the modified OECD scale, which gives a weight of 1.0 to the first person aged 14 or more, a weight of 0.5 to other persons aged 14 or more and a weight of 0.3 to persons aged 0-13. The weights are given to each member of the household and summed to obtain an equivalent household size. Total disposable net income for each household is divided by its equivalent size and form the total disposable net income per equivalent unit. |
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| Statistical unit | Units of observation are households and household members. |
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| Statistical population | The BG-SILC target population consists of all private households and their current members residing in the country. Persons living in collective households and in institutions are generally excluded from the target population. |
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| Reference area | Entire territory of Republic of Bulgaria |
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| Time coverage | 2006 - 2023 |
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| Base period | Not applicable. |
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| Statistical processing |
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| Source data | The sample for BG-SILC 2023 are selected from the sampling frame based on the Population Census 2011. 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 student’s and worker’s 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, R2, R3, R4 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. Type of sampling design | | Six-year rotation panel is used for BG-SILC2023 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 2023 the sample size of the panel is 9389 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 2023 a new rotational group with 2760 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 2011 was used as sampling frame for selection new rotational group (R5). 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 | Sample distribution over time | In 2023 the data collection took place in the period March-June 2023 with reference period of data the previous calendar year (2022). Sample distribution (household questionnaire) over time 
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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 identified. Sampling rate and sampling size 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 EU-SILC surveys. The total sample size in 2023 is 9389 households: - 6629 “old” (2018, 2019, 2020, 2021 and 2022),
- 2760 “new” households (drawn in 2023).
Number of households for which an interview is accepted for the database. Rotational group breakdown and total  RB250 = 11,14 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  The sample size for longitudinal component was 27712 households and 52731 persons aged 16 and over. Number of households in longitudinal component for which an interview is accepted for the database  Number of persons 16 years and older who are members of the households for which the interview is accepted for the database, and who completed a personal interview  |
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| Frequency of data collection | Yearly |
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| Data collection | SILC2021 data are collected with CAPI questionnaires through personal interview with households included in the sample as well as all household members aged 16 and more. | CAPI | CAPI-proxy | % of total | 84.3% | 15.7% |
The mean interview duration 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. The average household interview duration was 65.9 minutes, while the average individual interview duration was about 21.4 minutes. |
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| 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 |
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| Data compilation | The database of each country contains a 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). Design factor For the first year of the panel each household from the new rotation group got a sampling weight inversely proportional to the probability of selection of the household. These were the household’s design weights DB080. To adjust for non-responding households the procedure “weighting classes” was used. The households were divided into classes where the probability to respond was assumed to be homogenous within the classes. Due to lack of information (demographic characteristics) for the non-responding households these classes were the sampling strata. The ratio of the weights of the responding households to the weights of all households in the given class was calculated. - Adjustment to external data (calibration)
After reflecting the non-responding households the base weights for the new rotation group were calibrated to the population as of 31.12.2022. For the calibration the following variables at individual and at household level were used: | Individual level | Household level | | Age*Gender: 6 groups (0-17, 18-64, 65+)*(male, female) | Number of households: 2 groups (town, village) | | Number of persons by district: 28 groups | |
The information on individuals as of 31.12.2022 was available from the ISD. The information on the households was an estimation made on the basis of the updated file on Census 2011 and data on the split-off households from the SILC survey. Persons born in 2023 were not included in the calibration as they were not part of the population as of the end of 2022. For the calibration of weights the SAS Macro Calmar 2 was used. The logit method (M=3 in Calmar) was used for the calibration by setting upper and lower limits of the g-weights. The G-weights were the ratio of the assigned weights and the final calibrated weights. The calibrated weights with reflected non-responding households were the base weights (RB060) for the new rotation group and will be used in the weighting procedure in the following years. These weights were also the longitudinal weights (DB095) of the households from the new rotation group. Weighting procedure for rotation groups from previous survey waves. To get the base weights for the current year, the base weights (RB060) for each rotation group from the previous year were adjusted taking into account the non-response. The adjustment procedure was made on an individual and not on household level. To adjust for non-response first all persons from the 2022 register (DB135 = 1 & RB110 in (1,2,3,4)) who were followed up in 2023 were marked as responding (current members of the household). Persons who have left the household between the two survey waves were marked as non-responding. A logistic regression was used to calculate the probability for each individual to be enumerated between 2022 and 2023. The weights of the enumerated persons were adjusted with the probability of following up (result of logistic regression) and thus the base weights (RB060) for 2023 were get. The model was applied for each rotation group separately. The independent variables used in the model were: poverty indicators, education, economic activity, age, sex, household size, household type, income, dwelling type. The dependent variable was the one showing if the individual was enumerated or not. New members of the household after first year who were not part of the sample got base weights for the current year as follows: - Children born to a sample mother got the weight of the mother;
- Persons who have come into the sample household outside the target population got base weight which was equal to the average base weight of the household members;
- Persons who have come into the sample household from other non-sample household within the target population got base weight equal to 0.
Weight sharing Each person in the household should receive equal weight within the household (RB050 cross-sectional weight). For this reason, each household member whit zero and non-zero base weight received average base weight within the household. - Adjustment to external data (calibration)
After the non-response adjustment procedures, each of the 5 rotation groups was calibrated separately to the population as of 31.12.2022 according to the method described above. The same variables and levels as for the new rotation group were used for calibration. Combining all (6) sub-samples After applying all procedures for non-response adjustment and calibration, all sub-samples (rotation groups) were combined together. Each sub-sample separately represented all population of the country. To combine all sub-samples all weights were multiplied an appropriate scaling factor. The scaling factor used was 1/6 as there were 6 rotation groups in the panel. Final cross-sectional weights Calibration of all rotation groups to current population. After successfully applying all the procedures the weights were calibrated to the population as of 31.12.2022. The following variables on individual and household level were used for calibration: Individual level | Household level | Age*Gender*Location 56 groups (14 age groups)*(male female)*(town, village) | Number of households: 2 groups (town, village) | Districts*Gender*Location : 112 groups | | Number of pensioners: 3 groups | |
Age groups: (0-15) (16-19) (20-24) (25-29) (30-34) (35-39) (40-44) (45-49) (50-54) (55-59) (60-64) (65-69) (70-74) (75+) In 2016 the number of pensioners was used as calibration variable for first time. This variable had 3 levels: 1 - old-age pensions 2 - social pensions 3 - all others(rest of population) To allocate each person to the correct sub-population data from NSSI was used- number of personal pensions as of 31.12. There were two reasons to use this variable as a calibration variable. First, get better estimation of pensioners and second, to reduce the standard error of the AROPE indicator. After calibration, the final cross-sectional weight DB090 of the household was obtained. The individual cross-section weight RB050 was equal to the corresponding household weight DB090 (RB050=DB090). The newborn in 2022 were not included in the calibration. They received the corresponding household weight after calibration. The personal cross-section weight for all individuals aged 16 and more (PB040) was calculated after the age group (0-15) was removed. Only the individuals who have responded (or were imputed) to the individual questionnaire (RB250 in (11,14)) were used. After one more calibration, the weight PB040 (personal cross-sectional weight for all household members aged 16 and more) was obtained. |
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| Adjustment | Not aplicable |
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| Quality management |
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| Quality assurance | The Survey on Income and Living Conditions (SILC) is an annual survey implemented in the framework of Regulation (EC) No 1700/2019, which defines Scope, Definitions, Time coverage, Characteristics of the data, Sample size, Publication and Access to data. National statistical Institute is certified according to ISO 9001. In practical terms for the EU-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.
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| Quality 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 departm |
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| Relevance |
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| User needs | 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.
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| User satisfaction | Not applicable. |
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| Completeness | SILC covers only people living in private households (all persons aged 16 and over within the household are eligible for the operation), i.e. persons living in collective households and in institutions are generally excluded from the target population. |
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| Data completeness - rate | Not applicable |
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| Accuracy and reliability |
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| Overall accuracy | As with any other statistical survey, SILC may be burdened with errors due to sampling and other relating to the inability to be interviewed some of the units in the sample, as well as the errors taking place at the stage of data recording, data processing, etc. In terms of precision requirements, the representativeness of the sample and the effective sample size is to be achieved. The effective sample size combines sample size and sampling design effect which depends on sampling design, population structure and non-response rate. Regulation 1700/2019 defines the minimum effective sample sizes to be achieved to compensate for all kinds of non-response. 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. The estimated standard error of a particular estimate shall not be bigger than the following amount: 
| N | a | b | Income and living conditions domain | Ratio at‐risk‐of‐poverty or social exclusion to population | Number of private households in the country in millions and rounded to 3 decimal digits | 900 | 2600 | Ratio of at‐persistent‐risk‐of‐poverty over four years to population | Number of private households in the country in millions and rounded to 3 decimal digits | 350 | 1000 | Ratio at‐risk‐of‐poverty or social exclusion to population in each NUTS 2 region | Number of private households in the NUTS 2 region in millions and rounded to 3 decimal digits | 600 | 0 |
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| Sampling error | Computations of standard errors were carried out using SAS programs for the SILC Quality Reports and Complex Sample analysis in IBM SPSS ver.27. |
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| Sampling errors - indicators | Sampling error - indicators Main indicators, standard error and CI at country level 
Estimation for main indicators by ethnic groups in 2023 
Estimation for indicator ‘at-risk-of-poverty’ by districts in 2023 
Sampling errors for the income components by mean, total number of observations (before and after imputation) and standard errors 
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| 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
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| 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
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| Over-coverage - rate | Coverage error | Main problems | Population (sub-population) | Size of error | Comments | | Over-coverage | 276333 | 10.7 | Non-contacted addresses for the following reasons: - Address cannot be located; -Address unable to access; - Address does not exist or is non-residential address or is unoccupied or not principal residence | | Under coverage | NA | NA | | | Misclassification | NA | NA | |
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| Common units - proportion | Not requested by Reg. 2019/2180 |
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| 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. |
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| 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 the Commission Regulation 28/2004: - 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 successfully contacted/Number of valid addresses 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 - Individual non-response rates (NRp) will be 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) will be 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) was selected, the individual non-response rates will be calculated for ‘the selected respondent’, for all individuals aged 16 years or older and for the non-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.
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| Unit non-response - rate | 
where A=total (cross-sectional) sample, B =New sub-sample (new rotational group) introduced for first time in the survey this year, C= Sub-sample (rotational group) surveyed for last time in the survey this year The figures for the above table are calculated by the updated SAS code. Longitudinal Data  |
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| 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. 
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| Processing error | Data-entry phase 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 2017). The program is currently running on Windows 10 based tablet PCs. We used the following components when installing the program: - ASP.NET v4.0 as an application server
- MS SQL Server 2008 R2 as database server (for NSI)
- Access as database server (for tablet PCs)
- Internet Information Services as a web server
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. Data processing phase 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. As well as this, there were values that exceeded the maximum possible sizes of unemployment, old-age, survivor`, sickness and disability benefits. 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). |
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| Imputation - rate | Imputation procedure Data processing is performed with statistical software SPSS. Total gross income and disposable household income were calculated according to Document 065 (20223operation). 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 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. If either the net or the gross value was missing for PY010 or PY050, the missing value was calculated on the basis of a net-gross conversion and vice versa. In case of missing information on income components, the data of the National Revenue Agency, the National Social Security Institute and Social Assistance Agency are used. When data from administrative registers are not available, the regression deterministic imputation method is applied. For imputation of income variables in personal data file the following groups were created: - Region (NUTS 2)
- Age
- Sex
- Status in employment
- Occupation
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 according to labour and social insurance legislations. In some cases where only net income amounts were available these had to be converted to gross values using all necessary information. Extreme income values and missing 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. Imputed rent Imputed rents are estimated for dwellings used as main residence by the households. The imputation is applied for those households that did not report paying rent: -owners-occupiers -rent-free tenants The market rent is the rent due for the right to use an unfurnished dwelling on the private market, excluding charges for heating, water, electricity, etc. Stratification method based on actual rents is used (the same used by National Accounts – the same stratification variables and the same market rents). The method is in line with ESA’95 and requirements of Commission Decision 95/309 and Commission Regulation 1722/2005 on the principle of estimating dwelling services. Stratification variables: -location (district centre with university, other district centre, smaller town, rural area) -size of the dwelling -number of rooms (1, 2, 3, 4+) -amenities – availability of central heating Actual market rents – main data sources: -current price statistics -household budget survey -real estate agencies 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. |
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| Model assumption error | Not applicable |
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| Seasonal adjustment | Not aplicable |
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| Data revision - policy | Not applicable. |
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| Data revision - practice | No revisions to report. |
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| Data revision - average size | |
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| Timeliness and punctuality |
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| Timeliness | SILC cross-sectional and longitudinal data are available in the form of tables 10 months after the end of the data collection period. |
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| Time lag - first results | First data are available 6 months after data collection. |
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| Time lag - final results | Final results are available 12 months after data collection. |
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| Punctuality | Not applicable. |
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| Punctuality - delivery and publication | |
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| Coherence and comparability |
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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 - geographical | 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. |
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| Asymmetry for mirror flows statistics - coefficient | Not applicable |
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| Comparability - over time | In Bulgaria no breaks in series/significant changes in year 2022. A number of income measures were implemented during the year which could be explained by taking into consideration the following: - There was an increase in the pensions in 2022:
- the minimum amount of the old-age pension increased during the year (from BGN 370 to BGN 467 or by 26.22% in the end of the year);
- an increase of 10% for all pensions as of 01.07.2022;
- from July 1, 2022, the amount of the old-age social pension increased from BGN 170.00 to BGN 247.00 (an increase of 45.3 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;
- twice in 2022 an additional amount was paid in the amount of BGN 70 for each pensioner in April and December 2022;
- the maximum amount of one or more pensions received without the supplements to them is increased from BGN 1,500 to BGN 3,400 in the end of year.
- The minimum wage was increased from BGN 610 to BGN 710 or by 16.4%).
- Compared to the fourth quarter of 2021, the average monthly wages and salaries in the fourth quarter of 2022 rose by 16.6%.
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| Length of comparable time series | Not applicable. |
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| Coherence - cross domain | The cross-sectional data for the BG-SILC2023 were compared to the Labor force survey 2023 and HBS 2023. 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.
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| Coherence - sub annual and annual statistics | |
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