Poverty and Social Inclusion Indicators - National Level

Time series : SILC_1_en.xls

Poverty and Social Inclusion Indicators (Open Method of Coordination)

[ilc_li01] At-risk-of-poverty threshold (illustrative values)
Survey year		2023
Income reference year		2022
Single person	EUR	3 914
	NAC	7 655
	PPS	7 038
Two adults with two children younger than 14 years	EUR	8 219
	NAC	16 075
	PPS	14 780

[ilc_li02] At-risk-of-poverty rate (by age and gender)
age	gender	Survey year	2023
age	gender	Income reference year	2022
Total	total	1000 pers	1 325.9
	total	PC_POP	20.6
	male	1000 pers	612.8
	male	PC_POP	19.8
	female	1000 pers	713.1
	female	PC_POP	21.3
Less than 18 years	total	1000 pers	299.3
Less than 18 years	total	PC_POP	26.9
Between 18 and 64 years	total	1000 pers	689.9
	total	PC_POP	18.0
	male	1000 pers	358.0
	male	PC_POP	18.5
	female	1000 pers	331.9
	female	PC_POP	17.5
65 years and over	total	1000 pers	336.8
	total	PC_POP	22.2
	male	1000 pers	105.8
	male	PC_POP	17.7
	female	1000 pers	231.0
	female	PC_POP	25.2

[ilc_li09] At-risk-of-poverty rate before social transfers (by age and gender)
Survey year		2023
Income reference year		2022
age	gender
Total	total	45.3
	male	42.3
	female	48.1
Less than 18 years	total	40.5
Between 18 and 64 years	total	32.5
	male	32
	female	33.1
65 years and over	total	80.9
	male	77.7
	female	83

[ilc_li10] At-risk-of-poverty rate before social transfers, by gender and selected age groups (except pensions)
Survey year		2023
Income reference year		2022
age	gender
Total	total	28.5
	male	27.2
	female	29.8
Less than 18 years	total	37.0
Between 18 and 64 years	total	25.6
	male	25.8
	female	25.2
65 years and over	total	29.8
	male	22.9
	female	34.3

[ilc_li03] At-risk-of-poverty rate, by household type
Survey year	2023
Income reference year	2022
Households type
Total	20.6
Households without dependent children	17.5
One adult younger than 64 years	24.6
One adult older than 65 years	36.4
Single female	32.5
Single male	30.5
Two adults younger than 65 years	11.4
Two adults, at least one aged 65 years and over	10.7
Three or more adults	7.8
Households with dependent children	23.6
Single parent with dependent children	42.8
Two adults with one dependent child	14.6
Two adults with two dependent children	21.2
Two adults with three or more dependent children	55.2
Three or more adults with dependent children	22.4

[ilc_li04] At-risk-of-poverty rate, by most frequent activity status and by gender (18 years and over)
Survey year		2023
Income reference year		2022
Activity status	gender
Employment	total	11.4
	male	12.5
	female	10.1
Non employment	total	30.1
	male	29.2
	female	30.7
Unemployment	total	56.8
	male	62.3
	female	50.3
Retired	total	23.2
	male	18.2
	female	26.3
Inactive population - Other	total	32.7
	male	31.3
	female	33.5

[ilc_iw01\iw07] ] In-work at-risk-of-poverty rate (for persons aged 18-64)

(by full-time/part-time work)
Survey year	2023
Income reference year	2022
Employed
Total	11.7
male	12.9
Female	10.4
Type of employment
Full-time	10.3
Part-time	30.1

[ilc_pns1] At-risk-of-poverty rate of older people
Survey year		2023
Income reference year		2022
age	gender
60 years and over	total	20.9
	male	18.0
	female	23.0
75 years and over	total	25.1
	male	17.2
	female	29.4
Less than 60 years	total	20.4
	male	20.4
	female	20.4
Less than 75 years	total	20.1
	male	20.0
	female	20.1

[ilc_di11] Inequality of income distribution S80/S20 income quintile share ratio
Survey year		2023
Income reference year		2022
total	S80_S20	6.6
65 years and over	S80_S20	4.2
Less than 65 years	S80_S20	7.5

[ilc_di12] Inequality of income distribution Gini coefficient
Survey year	2023
Income reference year	2022
Gini coefficient	37.2

[ilc_mdsd11] Severe material and social deprivation rate by age and sex - new indicator
Survey year		2023
Income reference year		2022
age	sex
Total	total	18.0
	male	16.7
	female	19.2
Less than 18 years	total	19.0
Between 18 and 64 years	total	16.0
	male	15.8
	female	16.2
65 years and over	total	22.4
	male	17.6
	female	25.6

[ilc_lvhl11n] People living in households with very low work intensity by age and gender - new definition
Survey year			2023
Income reference year			2022
age	sex	unit
Between 18 and 64 years	total	1000PERS	231.0
	total	PC_POP	6.6
	male	1000PERS	119.7
	male	PC_POP	6.6
	female	1000PERS	111.3
	female	PC_POP	6.5
Less than 18 years	total	1000PERS	91.9
Less than 18 years	total	PC_POP	8.3
Less than 65 years	total	1000PERS	322.9
	total	PC_POP	7.0
	male	1000PERS	163.6
	male	PC_POP	6.9
	female	1000PERS	159.3
	female	PC_POP	7.1
1000 pers - 1000 persons
PC_POP - Percent of population

[ilc_peps01n] Population at risk of poverty or social exclusion by age and gender - new definition
Survey year			2023
Income reference year			2022
age	sex	unit
Total	total	1000PERS	1 933.1
	total	PC_POP	30.0
	male	1000PERS	875.5
	male	PC_POP	28.2
	female	1000PERS	1 057.6
	female	PC_POP	31.6
Less than 18 years	total	1000PERS	376.6
Less than 18 years	total	PC_POP	33.9
Between 18 and 64 years	total	1000PERS	1 021.8
	total	PC_POP	26.7
	male	1000PERS	518.3
	male	PC_POP	26.8
	female	1000PERS	503.5
	female	PC_POP	26.6
65 years and over	total	1000PERS	534.7
	total	PC_POP	35.3
	male	1000PERS	168.9
	male	PC_POP	28.3
	female	1000PERS	365.8
	female	PC_POP	39.9
1000 pers - 1000 persons
PC_POP - Percent of population

26.04.2024

Euro-SDMX Metadata Structure (ESMS)

SURVEY ON INCOME AND LIVING CONDITIONS (SILC) - Poverty and Social Inclusion Indicators

Contact

Contact organisation

National Statistical Institute

Contact organisation unit

Statistics on Living Conditions Department,

Demographic and Social Statistics Directorate

Contact name

Desislava Dimitrova, PhD

Contact person function

head of department

Contact mail address

2 P.Volov street, 1038 Sofia

Contact email address

[email protected]

Contact phone number

+359 2 9857 183

Contact fax number

Metadata update

Metadata last certified

05 September 2023

Metadata last posted

05 September 2023

Metadata last update

05 September 2023

Statistical presentation

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)

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.

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.

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.

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.

Statistical unit

Units of observation are households and household members.

Statistical population

The EU-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.

Reference area

Entire territory of Republic of Bulgaria

Time coverage

2006 - 2022

Base period

Not applicable.

Unit of measure

BGN, euro, percent (%), number of persons

Reference period

EU-SILC uses the following reference periods for the different variables included in the survey:

Constant;
Current;
Income reference period

The income reference period is the previous calendar year;

Working life: period of time between the time that person started his/her labor activity and now;
The reference period for the questions concerning “childcare for children up to 12 years old” is a typical (normal) week between January and June of the survey year. “The typical week” should be understood as one which is representative of the period as a whole;
Other periods of time associated with the data for the current economic activity, employment and unemployment for persons aged 16 and more:
reference week – refers to the period “from Monday to Sunday” of the week before the interview date.
previous 4 weeks – refers to the previous 4 weeks ending with the reference week.
last 12 months.

Institutional mandate

Legal acts and other agreements

Basic regulations

Regulation (EU) 2019/1700 of the European Parliament and the Council of 10 October 2019 establishing a common framework for European statistics relating to persons and households, based on data at individual level collected from samples, amending Regulations (EC) No 808/2004, (EC) No 452/2008 and (EC) No 1338/2008 of the European Parliament and of the Council, and repealing Regulation (EC) No 1177/2003 of the European Parliament and of the Council and Council Regulation (EC) No 577/98

Implementing regulations

COMMISSION IMPLEMENTING REGULATION (EU) 2019/2180 of 16 December 2019 specifying the detailed arrangements and content for the quality reports pursuant to Regulation (EU) 2019/1700 of the European Parliament and of the Council"
COMMISSION IMPLEMENTING REGULATION (EU) 2019/2181 of 16 December 2019 specifying technical characteristics as regards items common to several datasets pursuant to Regulation (EU) 2019/1700 of the European Parliament and of the Council"
COMMISSION IMPLEMENTING REGULATION (EU) 2019/2242 of 16 December 2019 specifying the technical items of data sets, establishing the technical formats and specifying the detailed arrangements and content of the quality reports on the organisation 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"
COMMISSION DELEGATED REGULATION (EU) 2020/256 of 16 December 2019 supplementing Regulation (EU) 2019/1700 of the European Parliament and of the Council by establishing a multiannual rolling planning"
COMMISSION DELEGATED REGULATION (EU) 2020/258 of 16 December 2019 supplementing Regulation (EU) 2019/1700 of the European Parliament and of the Council by specifying the number and the titles of the variables for the income and living conditions domain"

Data sharing

Not applicable.

Confidentiality

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.

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.

Release policy

Release calendar

Statistical information is published according to the Release Calendar presenting the results of the statistical surveys carried out by the National Statistical Institute.

Release calendar access

The Calendar is available on the NSI's website: https://www.nsi.bg/en/node/480

User access

The data for Income and living conditions (EU-SILC) are published on NSI website, section Social Inclusion and Living Conditions in accordance with the Law on Statistics (Chapter 5) and the European Statistics Code of Practice, respecting professional independence and in an objective, professional and transparent manner in which all users are treated equitably.

Frequency of dissemination

Annual

Accessibility and clarity

News release

Poverty and Social Inclusion Indicators.

Publications

Not applicable.

On-line 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: https://www.nsi.bg/en/node/8292 and INFOSTAT

Micro-data 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.

Other

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

Documentation on methodology

Regulations of the European Parliament and of the Council;
Methodological Guidelines and Description of EU-SILC target variables - Doc065;
Survey methodology: https://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

Detailed information about the list of social inclusion indicators, definitions and algorithm for their calculation on european level can be found on the following site:

https://ec.europa.eu/eurostat/statistics-explained/index.php/EU_statistics_on_income_and_living_conditions_(EU-SILC)_methodology

Quality documentation

National Quality Report.

Quality management

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.

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

Relevance

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.

User satisfaction

Not applicable.

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.

Accuracy and reliability

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. The allocation of the effective sample size is done according to the size of the country and ensuring minimum precision criteria for the key indicator at national level (absolute precision of the at-risk-of-poverty rate of 1%).

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.

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

Timeliness and punctuality

Timeliness

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

Punctuality

Not applicable.

Coherence and comparability

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.

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;
- an increase in the social disability pension;
- a supplement to all pensions was being paid through the whole year as a measure to relieve households of the effects of Covid-19 pandemic;
- 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%.

Coherence - cross domain

The cross-sectional data for the EU-SILC2022 were compared to the Labor force survey 2022 and HBS 2022.

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.

Coherence - internal

	SILC2022	Other source	Source
Population	6 838 937	6 838 937	Population as of 31.12.2021
· male	3 311 414	3 311 311	Population as of 31.12.2021
· female	3 527 523	3 527 626	Population as of 31.12.2021
Number of pensioners PL031=5	1 480 200	2 056 289	NSSI as of 31.12.2020
Number of persons received income from pension	1 546 591	2 080 454	NSSI average annual number of pensioners for 2021
Number of Households	2 943 017	2 943 017	LFS 2021
Employed	3 162 411	3 150 425	LFS 2021
Working full time	2 966 387	3 093 638	LFS 2021
Working part-time	196 024	56 787	LFS 2021
Unemployed, ILO definition	230 100	140 398	LFS 2021
Economically inactive	2 154 981	2 457 889	LFS 2021

Cost and burden

The total length of interviewing household in average 70 minutes.

Data revision

Data revision - policy

Not applicable.

Data revision - practice

No revisions to report.

Statistical processing

Source data

The sample for BG-SILC 2022 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 R2, R3, R4, R5 and R1.

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-SILC2022 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 2022 the sample size of the panel is 9239 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 2022 a new rotational group with 2770 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 (R1). 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 2021 the data collection took place in the period March-June 2022 with reference period of data the previous calendar year (2021).

Sample distribution (household questionnaire) over time

Month	Data	Number	%
March	1 – 10	11	0.1
	11 – 20	20	0.3
	21 – 31	25	0.3
April	1 – 10	447	5.8
	11 – 20	1098	14.3
	21 – 30	878	11.4
May	1 – 10	1476	19.2
	11 – 20	2491	32.4
	21 – 31	1005	13.1
June	1 – 10	219	2.9
	11 – 20	11	0.1
	21 – 30	3	0.0
	Total	7684	100

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 2021 is 9239 households:

6469 “old” (2017, 2018, 2019, 2020 and 2021),
2770 “new” households (drawn in 2022).

Number of households for which an interview is accepted for the database.

Rotational group breakdown and total

Number of households for which an interview is accepted for the database.
DB135=1	Rotational group breakdown and total
Rotational group	First wave	Households	%
1	2021	1506	19.6
2	2018	1004	13.1
3	2019	1124	14.6
4	2020	1267	16.5
5	2017	1065	13.9
6	2022	1718	14.5
Total		7684	100.0

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

Rotational group	First wave	Households’ members	%
1	2021	3131	20.3
2	2018	2053	13.3
3	2019	2178	14.1
4	2020	2496	16.2
5	2017	2131	13.8
6	2022	3445	22.3
Total		15434	100.0

The sample size for longitudinal component was 27740 households and 52781 persons aged 16 and over.

Number of households in longitudinal component for which an interview is accepted for the database

Number of households in longitudinal component
DB135 = 1

DB135 = 1		Rotational group					Total
DB135 = 1		1	2	3	4	5	Total
Year of the survey	2017	0	0	0	0	1590	1590
	2018	0	1359	0	0	1464	2823
	2019	0	1260	1461	0	1353	4074
	2020	0	1149	1315	1507	1231	5202
	2021	1648	1069	1226	1381	1136	6460
	2022	1506	1004	1124	1267	1065	5966
Total		3154	5841	5126	4155	7839	26115

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

Number of persons 16 years and older
RB250 = 11,14
RB250 = 11,14		Rotational group					Total
RB250 = 11,14		1	2	3	4	5	Total
Year of the survey	2017	0	0	0	0	3298	3298
	2018	0	2736	0	0	3050	5786
	2019	0	2548	2879	0	2814	8241
	2020	0	2327	2585	2940	2517	10369
	2021	3417	2164	2400	2712	2287	12980
	2022	3131	2053	2178	2496	2131	11989
Total		6548	11828	10042	8148	16097	52663

Frequency of data collection

Yearly

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	78.6%	21.3%

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 about 23 minutes, while the average individual interview duration was about 23 minutes.

Average interview duration = 69.7 minutes

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

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.

Non-response adjustment

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 31.12.2021. For the calibration the following variables at individual and at household level were used:

Individual level	Household level
AgeGender: 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 31.12.2021 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 2022 were not included in the calibration as they were not part of the population as of the end of 2021. 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.

Non-response adjustment

To adjust for non-response first all persons from the 2021 register (DB135 = 1 & RB110 in (1,2,3,4)) who were followed up in 2022 were marked as responding (current members of the household). Persons who have left the household between the two survey waves (2021 and 2022) were marked as non-responding. A logistic regression was used to calculate the probability for each individual to be enumerated between 2021 and 2022. 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 2022 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 31.12.2021 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 31.12.2021. The following variables on individual and household level were used for calibration:

Individual level	Household level
AgeGenderLocation 56 groups (14 age groups)(male female)(town, village)	Number of households: 2 groups (town, village)
DistrictsGenderLocation : 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.

Adjustment

Not aplicable

Comment

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Download in SDMX 2.0 file format: SURVEY ON INCOME AND LIVING CONDITIONS (SILC) - Poverty and Social Inclusion Indicators

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SURVEY ON INCOME AND LIVING CONDITIONS (SILC) - Poverty and Social Inclusion Indicators

Contact

Contact organisation

National Statistical Institute

Contact organisation unit

Statistics on Living Conditions Department,

Demographic and Social Statistics Directorate

Contact name

Desislava Dimitrova, PhD

Contact person function

head of department

Contact mail address

2 P.Volov street, 1038 Sofia

Contact email address

[email protected]

Contact phone number

+359 2 9857 183

Contact fax number

Statistical presentation

Data description

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.

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.

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 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.

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:

Statistical unit

Units of observation are households and household members.

Statistical population

Reference area

Entire territory of Republic of Bulgaria

Time coverage

2006 - 2022

Base period

Not applicable.

Statistical processing

Source data

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 R2, R3, R4, R5 and R1.

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-SILC2022 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 2022 a new rotational group with 2770 households was introduced

Stratification and sub stratification criteria

Sample selection schemes

The number of census enumeration units (PSU) is calculated for each strata included in the sample.

Sample distribution over time

In 2021 the data collection took place in the period March-June 2022 with reference period of data the previous calendar year (2021).

Sample distribution (household questionnaire) over time

Month	Data	Number	%
March	1 – 10	11	0.1
	11 – 20	20	0.3
	21 – 31	25	0.3
April	1 – 10	447	5.8
	11 – 20	1098	14.3
	21 – 30	878	11.4
May	1 – 10	1476	19.2
	11 – 20	2491	32.4
	21 – 31	1005	13.1
June	1 – 10	219	2.9
	11 – 20	11	0.1
	21 – 30	3	0.0
	Total	7684	100

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 2021 is 9239 households:

6469 “old” (2017, 2018, 2019, 2020 and 2021),
2770 “new” households (drawn in 2022).

Number of households for which an interview is accepted for the database.

Rotational group breakdown and total

Number of households for which an interview is accepted for the database.
DB135=1	Rotational group breakdown and total
Rotational group	First wave	Households	%
1	2021	1506	19.6
2	2018	1004	13.1
3	2019	1124	14.6
4	2020	1267	16.5
5	2017	1065	13.9
6	2022	1718	14.5
Total		7684	100.0

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

Rotational group	First wave	Households’ members	%
1	2021	3131	20.3
2	2018	2053	13.3
3	2019	2178	14.1
4	2020	2496	16.2
5	2017	2131	13.8
6	2022	3445	22.3
Total		15434	100.0

The sample size for longitudinal component was 27740 households and 52781 persons aged 16 and over.

Number of households in longitudinal component for which an interview is accepted for the database

Number of households in longitudinal component
DB135 = 1

DB135 = 1		Rotational group					Total
DB135 = 1		1	2	3	4	5	Total
Year of the survey	2017	0	0	0	0	1590	1590
	2018	0	1359	0	0	1464	2823
	2019	0	1260	1461	0	1353	4074
	2020	0	1149	1315	1507	1231	5202
	2021	1648	1069	1226	1381	1136	6460
	2022	1506	1004	1124	1267	1065	5966
Total		3154	5841	5126	4155	7839	26115

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

Number of persons 16 years and older
RB250 = 11,14
RB250 = 11,14		Rotational group					Total
RB250 = 11,14		1	2	3	4	5	Total
Year of the survey	2017	0	0	0	0	3298	3298
	2018	0	2736	0	0	3050	5786
	2019	0	2548	2879	0	2814	8241
	2020	0	2327	2585	2940	2517	10369
	2021	3417	2164	2400	2712	2287	12980
	2022	3131	2053	2178	2496	2131	11989
Total		6548	11828	10042	8148	16097	52663

Frequency of data collection

Yearly

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	78.6%	21.3%

The mean interview duration

The average household interview duration was about 23 minutes, while the average individual interview duration was about 23 minutes.

Average interview duration = 69.7 minutes

Data validation

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;

Design factor

Non-response adjustment

Adjustment to external data (calibration)

Individual level	Household level
AgeGender: 6 groups (0-17, 18-64, 65+)(male, female)	Number of households: 2 groups (town, village)
Number of persons by district: 28 groups

Weighting procedure for rotation groups from previous survey waves.

Non-response adjustment

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

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 31.12.2021 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

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 31.12.2021. The following variables on individual and household level were used for calibration:

Individual level	Household level
AgeGenderLocation 56 groups (14 age groups)(male female)(town, village)	Number of households: 2 groups (town, village)
DistrictsGenderLocation : 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)

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.

Adjustment

Not aplicable

Quality management

Quality assurance

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.

Quality assessment

Data are accompanied with quality reports analysing the accuracy, coherence and comparability of the data.

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

Relevance

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.

User satisfaction

Not applicable.

Completeness

Data completeness - rate

Not applicable

Accuracy and reliability

Overall accuracy

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.

Sampling errors - indicators

Sampling error - indicators

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	32.2	0.9	32.2	22.9	0.8	23.0	18.7	0.8	18.8	8.6	0.7	8.6
Male	29.4	1.0	29.4	19.9	0.9	19.9	17.6	0.9	17.7	8.2	0.6	8.3
Female	34.8	1.0	34.9	25.8	0.9	25.8	19.8	0.8	19.8	8.9	0.8	9.0
Age0-17	33.9	1.9	34.0	25.9	1.8	26.1	19.2	1.7	19.4	10.6	1.3	10.7
Age18-64	26.9	1.0	27.0	17.5	0.9	17.6	16.6	0.9	16.7	7.9	0.5	8.0
Age 65+	45.5	0.9	45.5	35.6	0.8	35.7	24.2	0.8	24.2

Estimation for main indicators by ethnic groups in 2022

Indicators	Percent	Standard error	Confidence interval
Indicators	Percent	Standard error	95% lower limit, in %	95% lower limit, in %
Population at-risk-of-poverty and social exclusion - new definition by ethnic group
Bulgarian ethnic group	25.0	0.8	23.6	26.5
Turkish ethnic group	36.9	2.7	31.8	42.3
Roma ethnic group	81.6	2.7	75.6	86.4
Other ethnic group	41.7	7.6	27.9	56.9
At-risk-of-poverty and ethnic group
Bulgarian ethnic group	17.3	0.7	16.1	18.7
Turkish ethnic group	24.1	2.3	19.9	28.9
Roma ethnic group	63.2	3.3	56.6	69.3
Other ethnic group	29.5	7.8	16.7	46.7
Severe material and social deprivation and ethnic group
Bulgarian ethnic group	13.1	0.6	12.0	14.2
Turkish ethnic group	21.4	2.1	17.5	25.8
Roma ethnic group	59.3	3.7	51.8	66.4
Other ethnic group	27.7	6.9	16.4	42.9

Estimation for indicator ‘at-risk-of-poverty’ by districts in 2022

	Blagoevgrad		Burgas		Varna		Veliko Tarnovo
	Percent	Standard error	Percent	Standard error	Percent	Standard error	Percent	Standard error
Total	17.5	4.2	18.8	3.9	18.6	2.2	15.8	3.2
0 - 17 years	23.7	9.5	24.1	8.2	15.2	4.0	12.5	5.0
18 - 64 years	14.3	4.3	13.3	3.9	16.6	2.3	9.4	3.1
65+ years	22.0	3.2	30.9	3.2	33.7	3.5	32.0	4.9
male	15.2	4.3	17.6	4.5	16.0	2.3	13.9	3.9
female	19.6	4.5	19.9	3.7	21.1	2.7	17.6	3.2
	Vidin		Vratsa		Gabrovo		Dobrich
	Percent	Standard error	Percent	Standard error	Percent	Standard error	Percent	Standard error
Total	17.0	5.1	23.1	4.5	19.4	3.4	27.0	5.5
0 - 17 years	32.0	16.6	36.3	9.9	19.7	7.6	41.6	10.4
18 - 64 years	15.0	5.9	19.2	4.8	9.0	2.9	20.6	5.5
65+ years	12.9	3.8	23.1	5.0	38.8	6.1	32.1	4.1
male	21.9	7.9	19.5	4.3	18.2	4.3	24.2	5.8
female	12.2	3.2	26.5	5.3	20.4	3.7	29.5	5.7
	Kardzhali		Kyustendil		Lovech		Montana
	Percent	Standard error	Percent	Standard error	Percent	Standard error	Percent	Standard error
Total	21.4	3.8	21.3	3.7	26.8	5.8	24.7	6.5
0 - 17 years	26.2	9.1	20.6	9.9	31.7	11.7	48.0	12.9
18 - 64 years	17.1	3.9	11.9	3.6	20.3	5.8	21.1	6.0
65+ years	33.0	5.2	44.5	6.1	39.5	6.0	11.3	3.9
male	20.8	4.3	15.7	4.2	27.0	7.1	21.2	5.9
female	22.1	4.1	26.5	4.4	26.6	5.7	27.9	7.5
	Pazardzhik		Pernik		Pleven		Plovdiv
	Percent	Standard error	Percent	Standard error	Percent	Standard error	Percent	Standard error
Total	18.6	3.1	25.5	6.0	21.2	4.3	19.6	2.9
0 - 17 years	19.0	5.5	44.1	14.7	29.8	9.8	22.3	6.4
18 - 64 years	16.0	3.6	17.0	5.6	16.5	4.2	16.4	2.7
65+ years	29.3	3.5	33.4	5.1	25.8	3.7	26.4	2.4
male	17.5	3.8	21.7	6.3	16.1	4.1	15.1	2.7
female	19.8	2.9	29.1	6.3	26.0	4.7	23.7	3.4
	Razgrad		Ruse		Silistra		Sliven
	Percent	Standard error	Percent	Standard error	Percent	Standard error	Percent	Standard error
Total	18.5	4.3	19.5	2.5	16.1	5.2	22.8	5.5
0 - 17 years	13.5	6.3	5.8	3.1	16.6	11.9	9.0	4.4
18 - 64 years	17.9	5.2	14.9	3.0	15.2	5.8	27.5	9.3
65+ years	24.6	6.4	40.2	3.7	17.9	3.9	21.8	3.6
male	15.5	4.6	17.2	2.9	22.0	6.8	16.0	6.0
female	21.2	4.7	21.7	3.0	10.6	3.6	29.3	5.6
	Smolyan		Sofia (stolitsa)		Sofia		Stara Zagora
	Percent	Standard error	Percent	Standard error	Percent	Standard error	Percent	Standard error
Total	24.1	7.3	24.4	2.0	23.1	4.8	33.9	5.0
0 - 17 years	36.5	21.7	23.6	4.5	31.4	14.0	41.3	10.7
18 - 64 years	17.2	7.3	18.8	2.1	14.2	4.9	24.9	4.7
65+ years	30.7	5.0	44.0	2.6	35.0	3.9	51.5	3.9
male	22.1	9.0	19.9	1.9	17.4	5.1	29.7	5.5
female	25.8	6.3	28.6	2.4	28.6	5.1	37.8	5.0
	Targovishte		Haskovo		Shumen		Yambol
	Percent	Standard error	Percent	Standard error	Percent	Standard error	Percent	Standard error
Total	16.2	3.1	21.1	3.6	12.9	3.2	22.2	4.3
0 - 17 years	10.9	6.5	23.8	9.1	8.2	4.0	14.9	8.8
18 - 64 years	10.4	2.6	18.3	4.0	9.5	3.3	11.4	4.1
65+ years	36.1	5.6	25.4	3.2	29.5	5.6	48.9	4.6
male	11.3	3.3	17.3	3.5	9.6	3.7	15.3	4.6
female	20.7	4.0	24.8	4.0	16.0	3.6	28.7	5.0

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

		2022 Cross sectional data
INCOME GROSS VARIABLES		Mean	No. Of observations Before Imputation	No. Of observations After Imputation	Standard error
Total hh gross income	(HY010)	25514.99	1184	7684	407.54
Total disposable hh income	(HY020)	21193.56	1148	7684	328.86
Total disposable hh income before social transfers other than old-age and survivors benefits	(HY022)	19655.32	1803	7650	325.12
Total disposable hh income before all social transfers	(HY023)	16530.71	3202	7191	342.66
Income from rental of property or land	(HY040)	2244.86	637	840	151.67
Family/ Children related allowances	(HY050)	2454.54	85	1132	140.26
Social exclusion payments not elsewhere classified	(HY060)	694.19	935	1035	26.98
Housing allowances	(HY070)	356.24	1	5	46.87
Regular inter-hh cash transfers received	(HY080)	3594.77	683	978	141.36
Alimonies received	(HY081)	2588.12	57	57	372.19
Interest, dividends, profit from capital investments in incorporated businesses	(HY090)	26114.35	8	127	4161.02
Interest repayments on mortgage	(HY100)	18.86	75	75	2.24
Income received by people aged under 16	(HY110)	2886.40	41	62	325.12
Regular taxes on wealth	(HY120)	108.45	5667	5667	1.61
Taxes paid on ownership of household main dwelling	(HY121G)	99.14	5265	5265	1.51
Regular inter household cash transfer paid	(HY130)	1876.40	218	218	200.83
Alimonies paid	(HY131)	1517.72	19	19	154.88
Tax on income and social contributions	(HY140)	6150.60	1109	4770	111.76
Value of goods produced for own consumption	(HY170)	566.74	1396	1396	32.86
Cash or near-cash employee income	(PY010)	14930.90	3196	7983	277.18
Other non-cash employee income	(PY020)	1082.65	715	715	45.36
Income from private use of company car	(PY021)	1600.64	48	48	111.44
Employers social insurance contributions	(PY030)	2658.90	2711	7645	47.15
Contribution to individual private pension plans	(PY035)	10000.00	1	1	.00
Cash profits or losses from self-employment	(PY050)	8637.06	355	1167	369.64
Pension from individual private plans	(PY080)	6027.13	4	4	49.46
Unemployment benefits	(PY090)	1914.92	728	860	119.10
Old-age benefits	(PY100)	6335.14	2994	6145	56.86
Survivors benefits	(PY110)	3511.33	216	295	129.76
Sickness benefits	(PY120)	722.83	2158	3473	26.50
Disability benefits	(PY130)	4094.41	90	752	74.83
Education-related allowances	(PY140)	769.28	40	40	117.82

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

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

Over-coverage - rate

Coverage error

Main problems	Population (sub-population)	Size of error	Comments
Over-coverage	243962	10.3	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

Common units - proportion

Not requested by Reg. 2019/2180

Measurement error

As with any other statistical survey, EU-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.

EU-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.

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.

Unit non-response - rate

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)*

99.7

98.9

99.9

88.3

69.1

96.9

99.9

100.0

12.0

31.6

3.2

0.1

12.1

31.7

3.2

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

Response rate for households by wave

Response rate for household	wave 2_2018	wave 3_2019	wave 4_2020	wave 5_2021	wave 6_2022
Wave response rate	97.28	97.09	96.00	96.85	87.97
L follow-up rate	94.88	95.53	94.47	95.81	95.96
Follow-up ratio	92.08	92.42	90.62	92.33	92.31
Achieved sample size ratio	92.08	92.42	90.60	92.35	92.31

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.

		2022 Cross-sectional data
INCOME GROSS VARIABLES AT HOUSEHOLD LEVEL		% of households having received an amount	% of households with missing values (before imputation)	% of households with partial information (before imputation)
Total hh gross income	(HY010)	100.0	0.4	84.2
Total disposable hh income	(HY020)	100.0	0.2	84.8
Total disposable hh income before social transfers other than old-age and survivors benefits	(HY022)	99.6	0.3	76.2
Total disposable hh income before all social transfers	(HY023)	93.6	0.9	54.5
Income from rental of property or land	(HY040G)	10.9	23.1	1.1
Family/ Children related allowances	(HY050G)	14.7	56.8	35.7
Social exclusion payments not elsewhere classified	(HY060G)	13.5	1.2	8.5
Housing allowances	(HY070G)	0.1	40.0	40.0
Regular inter-hh cash transfers received	(HY080G)	12.7	27.7	2.5
Alimonies received	(HY081G)	0.7
Interest, dividends, profit from capital investments in incorporated businesses	(HY090G)	1.7	80.3	13.4
Interest repayments on mortgage	(HY100G)	1.0
Income received by people aged under 16	(HY110G)	0.8	8.1	25.8
Regular taxes on wealth	(HY120G)	73.8
Regular inter household cash transfer paid	(HY130G)	2.8
Alimonies paid	(HY131G)	0.2
Tax on income and social contributions	(HY140G)	62.1	6.6	70.1
Value of goods produced for own consumption	(HY170G)	18.2
INCOME GROSS VARIABLES AT PERSONAL LEVEL		% of persons 16+ having received an amount	% of persons 16+ with missing values (before imputation)	% of persons 16+ with partial information (before imputation)
Cash or near-cash employee income	(PY010G)	51.7	4.6	55.4
Other non-cash employee income	(PY020G)	4.6
Income from private use of company car	(PY021G)	0.3
Employers social insurance contributions	(PY030G)	49.5	5.2	59.3
Cash profits or losses from self-employment	(PY050G)	7.6	27.5	42.1
Pensions from individual private plans	(PY080G)	0.0
Unemployment benefits	(PY090G)	5.6	13.1	2.2
Old-age benefits	(PY100G)	39.8	1.2	50.1
Survivors benefits	(PY110G)	1.9	16.6	10.2
Sickness benefits	(PY120G)	22.5	18.5	19.3
Disability benefits	(PY130G)	4.9	10.0	78.1
Education-related allowances	(PY140G)	0.3

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).

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 (2022 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 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.

Model assumption error

Not applicable

Seasonal adjustment

Not aplicable

Data revision - policy

Not applicable.

Data revision - practice

No revisions to report.

Data revision - average size

Timeliness and punctuality

Timeliness

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

Time lag - first results

First data are available 6 months after data collection.

Time lag - final results

Final results are available 12 months after data collection.

Punctuality

Not applicable.

Punctuality - delivery and publication

Coherence and comparability

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.

Asymmetry for mirror flows statistics - coefficient

Not applicable

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;
- an increase in the social disability pension;
- a supplement to all pensions was being paid through the whole year as a measure to relieve households of the effects of Covid-19 pandemic;
- 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%.

Length of comparable time series

Not applicable.

Coherence - cross domain

The cross-sectional data for the EU-SILC2022 were compared to the Labor force survey 2022 and HBS 2022.

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.

Coherence - sub annual and annual statistics

PE041 Highest ISCED level attained	SILC 2022		LFS 2022
Weighted by PB040	%	total	%	total
000 Less than primary education	1.5	89.0	0.8	45.8
100 Primary education	3.4	197.0	2.7	156.4
200 Lower secondary education	17.5	1005.3	18.7	1068.1
300 Upper secondary education (not further specified)	52.3	3003.5	53.2	3043.3
400 Post-secondary non-tertiary education (not further specified)	0.5	30.5	0.3	17.5
600 Bachelor or equivalent	7.7	442.5	7.5	430.4
700 Master or equivalent	16.6	955.3	16.4	940.3
800 Doctorate or equivalent	0.4	21.1	0.3	15.3

PL031 Self-defined current economic status	SILC 2022		LFS 2022
Weighted by PB040	%	total	%	total
employed (PL31 = 1, 2)	55.0	3 162.4	54.6	3 122.3
unemployed (PL31=3)	7.4	428.0	5.1	293.6
economically inactive (PL31=4, 5, 6, 8, 9)	37.5	2 155.0	40.3	2 301.2

PL040A Status in employment (PL31=1,2)	SILC 2022		LFS 2022
Weighted PB040	%	total	%	total
Employed (PL31 = 1,2)	100.0	3 162.4	100.0	3 150.4
employees (PL040A=3)	90.4	2 858.1	88.9	2 801.2
self-employed without employees (PL040A=2)	6.0	188.8	6.7	212.1
self-employed with employees (PL040A=1)	3.3	102.9	3.8	120.7
family worker (PL040A=4)	0.4	12.6	0.5	16.5

	HBS 2022	SILC 2022 (Unweighted)
Households type
One person household	38.2	35.3
Two persons household	35.2	32.8
Three persons household	13.9	15.1
Four and more person household	12.7	16.7
Structure of population by age, %
0-15	10.2	10.7
16-24	5.8	7.7
25-49	24.8	26.3
50-64	23.8	23.9
65+	35.4	31.4
Structure of population by level of education, %
Primary education	2.8	5.0
Lower secondary	16.6	19.3
Upper secondary	58	53.4
Tertiary education	22.6	22.2
Activity status, %
Employed	38.8	41.3
Unemployed	6.0	5.8
Economically inactive	55.2	52.9
Status in employment, %
Employer	1.3	3.3
Self-employed	6.3	6.1
Employee	92.2	90.3
Family worker	0.2	0.3
Dwelling type
Detached house	41.9	47.9
Semidetached house	7	9.9
Apartment or flat	50.6	41.8
Some other kind of accommodation	0.5	0.3

Structure of total household income by source, %	HBS 2021	SILC 2022 (Unweighted)
Income from work	62.6	67.9
Pensions	30.0	23.0
Family/children related allowances	0.9	1.5
Unemployment benefits	0.4	0.9
Other	6.1	6.7

Coherence - National Accounts

Not applicable

Coherence - internal

	SILC2022	Other source	Source
Population	6 838 937	6 838 937	Population as of 31.12.2021
· male	3 311 414	3 311 311	Population as of 31.12.2021
· female	3 527 523	3 527 626	Population as of 31.12.2021
Number of pensioners PL031=5	1 480 200	2 056 289	NSSI as of 31.12.2020
Number of persons received income from pension	1 546 591	2 080 454	NSSI average annual number of pensioners for 2021
Number of Households	2 943 017	2 943 017	LFS 2021
Employed	3 162 411	3 150 425	LFS 2021
Working full time	2 966 387	3 093 638	LFS 2021
Working part-time	196 024	56 787	LFS 2021
Unemployed, ILO definition	230 100	140 398	LFS 2021
Economically inactive	2 154 981	2 457 889	LFS 2021

Accessibility and clarity

News release

Poverty and Social Inclusion Indicators.

Publications

Not applicable.

On-line database

Data tables - consultations

Micro-data access

Other

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

Metadata - consultations

Documentation on methodology

Regulations of the European Parliament and of the Council;
Methodological Guidelines and Description of EU-SILC target variables - Doc065;
Survey methodology: https://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

Detailed information about the list of social inclusion indicators, definitions and algorithm for their calculation on european level can be found on the following site:

https://ec.europa.eu/eurostat/statistics-explained/index.php/EU_statistics_on_income_and_living_conditions_(EU-SILC)_methodology

Metadata completeness – rate

Quality documentation

National Quality Report.

Cost and burden

The total length of interviewing household in average 70 minutes.

Confidentiality

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.

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.

Comment

Download in SDMX 2.1 file format: SURVEY ON INCOME AND LIVING CONDITIONS (SILC) - Poverty and Social Inclusion Indicators

Metadata Structure Definition in SDMX 2.1: ESQRS_MSD+BNSI+2.0+SDMX.2.1.xml

Download in SDMX 2.0 file format: SURVEY ON INCOME AND LIVING CONDITIONS (SILC) - Poverty and Social Inclusion Indicators

Metadata Structure Definition in SDMX 2.0: ESQRS_MSD+BNSI+2.0+SDMX.2.0.xml

Poverty and Social Inclusion Indicators in 2023

Friday, 26 April 2024 - 11:00

In 2023, the average monthly poverty line for the country was 637.92 BGN per person. The number of persons who were below this line was 1 325.9 thousand, representing 20.6% of the population.

The general indicators of poverty assessment include subjective indicators related to material deprivation. They show the subjective assessment and personal attitude of the persons and households related to the possibility to meet individual needs. By 2020, nine questions related to the consumption of specific goods and services were used to assess the material deprivation of households. From...

SILC2023_en_08NKATC.pdf

Poverty and Social Inclusion Indicators in 2022

Wednesday, 26 April 2023 - 11:00

In 2022, the average monthly poverty line for the country is 525.92 BGN per person. The number of persons who are below this line is 1 571.9 thousand representing 22.9% of the population. From 2021, a new indicator of severe material and social deprivation is included in the poverty indicators which shows the lack of necessary and desirable items for living a worthy life. It is calculated as the share of persons in the population who cannot afford 7 out of 13 items - 6 at the individual level and 7 at the household level. This indicator is part of the combined indicator ‘Population at risk ...

SILC2022_en_4UPGZS4.pdf

Poverty and Social Inclusion Indicators in 2021

Friday, 29 April 2022 - 11:00

In 2021, the average monthly poverty line for the country is 504.33 BGN per person. The number of persons who are below this line is 1 532.4 thousand representing 22.1 % of the population. From 2021, a new indicator of severe material and social deprivation is included in the poverty indicators which shows the lack of necessary and desirable items for living a worthy life. It is calculated as the share of persons in the population who cannot afford 7 out of 13 items – 6 at the individual level and 7 at the household level. This indicator is part of the combined indicator "Population at risk...

SILC2021_en_6IY8TD4.pdf

Poverty and Social Inclusion Indicators in 2020

Wednesday, 28 April 2021 - 11:00

In 2020, the average monthly poverty line for the country is 451.00 BGN per person. The number of persons who are below this line is 1 659.9 thousand representing 23.8 % of the population.

...

SILC2020_en_FRTG25T.pdf

Poverty and Social Inclusion Indicators in 2019

Thursday, 30 April 2020 - 11:00

In 2019, the average monthly poverty line for the country is 413.04 BGN average per person. The number of persons who are below this line is 1 586.2 thousand representing 22.6 % of the population.

...

SILC2019_en_ARTRFBK.pdf

Poverty and Social Inclusion Indicators in 2018

Tuesday, 30 April 2019 - 11:00

In 2018, the average monthly poverty line for the country is 351.11 BGN average per person. The number of persons who are below this line is 1 550.8 thousand representing 22.0% of the population.

...

SILC2018_en_U9NYYPP.pdf

Poverty and Social Inclusion Indicators in 2017

Monday, 30 April 2018 - 11:00

In 2017, the average monthly poverty line for the country is 351.08 BGN per person. The number of persons who are below this line is 1 665.3 thousand representing 23.4% of the population.

...

SILC2017_en_89UE9ZC.pdf

Poverty and Social Inclusion Indicators in 2016

Thursday, 22 June 2017 - 11:00

According to the data from the NSI ‘Social Inclusion and Living Conditions’ (EU-SILC) survey in 2016 the average monthly poverty line for the country is 308.17 BGN per person. The number of persons who are below this line is 1 638.7 thousand representing 22.9% of the population in Bulgaria. In 2016 the share of children aged 0 - 17 years in at-risk-of-poverty is 31.9%, or 385.4 thousand.

...

SILC2016_en_VTLG9JX.pdf

Poverty and Social Inclusion Indicators in 2015

Friday, 16 September 2016 - 11:00

According to the data from the NSI ‘Social Inclusion and Living Conditions’ (EU-SILC) survey in 2015 the average monthly poverty line for the country is 325.83 BGN per person. The number of persons who are below this line is 1 585.8 thousand representing 22.0% of the population in Bulgaria. In 2015 the share of children aged 0 - 17 years in at-risk-of-poverty is 25.4%, or 305.6 thousand.

...

SILC2015_en_U9QJAY2.pdf

Poverty and Social Inclusion Indicators in 2014

Friday, 16 October 2015 - 11:00

According to the data from the NSI ‘Social Inclusion and Living Conditions’ (EU-SILC) survey in 2014 the average monthly poverty line for the country is 323.75 BGN per person. The number of persons who are below this line is 1 578.3 thousand representing 21.8% of the population in Bulgaria. In 2014 the share of children aged 0 - 17 years in at-risk-of-poverty is 31.7%, or approximately 377.3 thousand.

...

SILC2014_en_K3R26PA.pdf

Poverty and Social Inclusion Indicators in 2013

Monday, 15 December 2014 - 11:00

According to the data from the NSI ‘Social Inclusion and Living Conditions’ (EU-SILC) survey in 2013 the average monthly poverty line for the country is 285.92 BGN per person. The number of persons who are below this line is 1 527.5 thousand representing 21.0% of the population in Bulgaria. In 2013 the share of children aged 0 - 17 years in at-risk-of-poverty is 28.4%, or approximately 335.9 thousand.

...

SILC2013_en_AW1WN7M.pdf

Poverty and Social Inclusion Indicators (Data for 2011)

Monday, 16 December 2013 - 11:00

The poverty line for 2011 is BGN 279.67 average per month per household. At this amount of the poverty line 1 558.8 thousand persons or 21.2% of the population were below the poverty threshold. This is what the data from the 2012 survey on “Social Inclusion and Living Conditions” (EU-SILC) shows.

...

SILC2011_EN_3P9T7LO.pdf

Poverty and Social Inclusion Indicators (Final data for 2010)

Friday, 14 December 2012 - 11:00

Poverty and social inclusion indicators are part of the general EU indicators for tracing the progress in the field of poverty and social exclusion. Main source of statistical data, on which basis the general indicators are calculated is the annually conducted Survey on Income and Living Conditions (EU-SILC). NSI presents final results for 2010 based on the conducted in 2011 SILC....

Poverty and Social Inclusion Indicators (Preliminary results for 2010)

Friday, 28 September 2012 - 11:00

Poverty and social inclusion indicators are part of the general EU indicators for tracing the progress in the field of poverty and social exclusion. Main source of statistical data, on which basis the general indicators are calculated is the annually conducted Survey on Income and Living Conditions (EU-SILC). NSI presents preliminary results for 2010 based on the conducted in 2011 SILC. ...

Poverty and Social Inclusion Indicators based on the European Survey on Income and Living Conditions (EU-SILC) in the context of the Open Method for Coordination

Thursday, 15 December 2011 - 11:00

The European survey on income and living conditions (EU-SILC) is an instrument for providing comparative statistics for the income distribution, level and structure of poverty and social exclusion....

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Poverty and Social Inclusion Indicators - National Level

Poverty and Social Inclusion Indicators in 2023

Poverty and Social Inclusion Indicators in 2022

Poverty and Social Inclusion Indicators in 2021

Poverty and Social Inclusion Indicators in 2020

Poverty and Social Inclusion Indicators in 2019

Poverty and Social Inclusion Indicators in 2018

Poverty and Social Inclusion Indicators in 2017

Poverty and Social Inclusion Indicators in 2016

Poverty and Social Inclusion Indicators in 2015

Poverty and Social Inclusion Indicators in 2014

Poverty and Social Inclusion Indicators in 2013

Poverty and Social Inclusion Indicators (Data for 2011)

Poverty and Social Inclusion Indicators (Final data for 2010)

Poverty and Social Inclusion Indicators (Preliminary results for 2010)

Poverty and Social Inclusion Indicators based on the European Survey on Income and Living Conditions (EU-SILC) in the context of the Open Method for Coordination

Poverty mapping in the Republic of Bulgaria