National Statistical Institute

Statistics on Living Conditions Department,

Demographic and Social Statistics Directorate

Desislava Dimitrova, PhD

head of department

2 P.Volov street, 1038 Sofia

[email protected]

+359 2 9857 183

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.

• Basic data;
• Child care;

• Basic data;
• Housing;
• Material deprivation;
• Income at household level;

• Basic data;
• Education;
• Health status;
• Economic activity;
• Individual income.

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. 

• 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.

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.

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.

Units of observation are households and household members.

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.

Entire territory of Republic of Bulgaria

2006 - 2024

Not applicable.

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 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, 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.

Sampling unit

Yearly

SILC2024 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.

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 67 minutes, while the average individual interview duration was about 22 minutes.

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

• 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;

Design factor

Not aplicable

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. 

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

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.

Not applicable.

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.

Not applicable

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:

Computations of standard errors were carried out using SAS programs for the SILC Quality Reports and Complex Sample analysis in IBM SPSS ver.27.

Sampling error - indicators

Main indicators, standard error and CI at country level 

Estimation for main indicators by ethnic groups in 2024

Estimation for indicator ‘at-risk-of-poverty’ by districts in 2024

Sampling errors for the income components by mean, total number of observations (before and after imputation) and standard errors

 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:

1. Unit non-response: refers to absence of information of the whole units (households and/or persons) selected into the sample
2. Item non-response: refers to the situation where a sample unit has been successfully enumerated, but not all required information has been obtained

Coverage errors include over-coverage, under-coverage and misclassification:

• Over-coverage: relates either to wrongly classified units that are in fact out of scope, or to units that do not exist in practice
• Under-coverage: refers to units not included in the sampling frame
• Misclassification: refers to incorrect classification of units that belong to the target population

Coverage error

Not requested by Reg. 2019/2180

• 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.

Not applicable

Not aplicable

Not applicable.

No revisions to report.

SILC cross-sectional and longitudinal data are available in the form of tables 10 months after the end of the data collection period.

First data are available 6 months after data collection.

Final results are available 12 months after data collection.

Not applicable.

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.

Not applicable

In Bulgaria no breaks in series/significant changes in year 2023. 

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 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 01.07.2023;
  • from July 1, 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.

Not applicable.

The cross-sectional data for the BG-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. 

Poverty and Social Inclusion Indicators.

Not applicable.

Detailed results are available to all users of the NSI website under the heading Social Inclusion and Living Conditions - Poverty and Social Inclusion Indicators: http://www.nsi.bg/en/node/8292 and INFOSTAT

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.

Information service on request, according to the Rules for the dissemination of statistical products and services to NSI.

• Regulations of the European Parliament and of the Council;
• Methodological Guidelines and Description of EU-SILC target variables - Doc065;
• Survey methodology: http://www.nsi.bg/sites/default/files/files/metadata/SILC_Methodology_EN.pdf
• Indicators - Methodological notes: https://www.nsi.bg/sites/default/files/files/metadata/SILC_description_indicators_EN.pdf

National Quality Report.

The total length of interviewing household in average 67 minutes.

• 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.

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.