Poverty and Social Inclusion Indicators | National statistical institute

Statistical domain

Poverty and Social Inclusion Indicators

ESS Standard for Quality Reports Structure (ESQRS)

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

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)

Sector coverage

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 - 2020

Base period

Not applicable.

Statistical processing

Source data

The sample for EU-SILC 2020 are selected from the sampling frame based on the Population Census 2011. The data base includes all private households and their current members residing in the country. Persons living in collective households and in institutions are excluded from the target population. Student’s and worker’s hostels are excluded at the first stage of selection of PSU, because student’s and worker’s households rarely stay on the same addresses and are difficult to trace.

The frame is regularly updated according to the administrative changes made.

Household data within the selected PSUs are updated according to the Information System “Demography” data (ISD).

The longitudinal component consists of the sub-samples R1, R2, R3, R5 and R6.

All personal/household income variables were collected by interview.

In some cases, where the information on income component is unavailable a register to obtain missing value information is used. The National Social Security Institute keeps a register of all persons for whom employers pay social insurance contributions and of all self-insured persons. This register contains some data on personal income but it is generated by a labour activity of the persons and moreover, this is only the income on which the person was insured. From Social Assistance Agency obtained on income from social benefits

Type of sampling design

Six-year rotation panel is used for EU-SILC2020 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 2020 a new rotational group with 2680 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 (R4). 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

The survey is carried from April to June of the year 2020 with reference period of data the previous calendar year (2019).

Sample distribution (household questionnaire) over time

Month	Data	Number	%
april	01 – 10	6	0.1
	11 – 20	14	0.2
	21 – 31	306	4.2
may	01 – 10	321	4.4
	11 – 20	1040	14.2
	21 – 30	1371	18.7
june	01 – 10	2128	29.1
	11 – 20	1445	19.8
	21 – 31	682	9.3
	Total	7313	100.0

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 necessary sample size for Bulgaria is determined in the Annex II of the Framework Regulation (1177/2003) to guarantee an effective sample size with regard to the at-risk-of-poverty indicator of 4500 households. The longitudinal sample for two successive waves should comprise at least 3500 households.

The total gross sample size (number of households) has been made analyzing the non-response rates and design effects of the previous EU-SILC surveys.

The total sample size in 2020 is 9052 households:

- 6372 “old” (2015, 2016, 2017, 2018 and 2019),

- 2680 “new” households (drawn in 2020).

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

Rotational group breakdown and total

DB135=1	Rotational group breakdown and total
Rotational group	First wave	Households	%
1	2015	952	13.0
2	2018	1149	15.7
3	2019	1315	18.0
4	2020	1507	20.6
5	2017	1231	16.8
6	2016	1159	15.8
Total		7313	100

Rotational group breakdown and total
RB250 = 11,14 Number of persons of 16 years or older who are members of the households for which the interview is accepted for the database, and who completed a personal interview.

Rotational group	First wave	Households’ members	%
1	2015	1931	13.2
2	2018	2327	15.9
3	2019	2585	17.6
4	2020	2940	20.1
5	2017	2517	17.2
6	2016	2361	16.1
Total		14661	100.0

The sample size for longitudinal component was 32374 households and 53938 persons aged 16 and over.

Number of households in longitudinal component

DB135 = 1		Rotational group					Total
DB135 = 1		1	2	3	5	6	Total
Year of the survey	2015	1451	0	0	0	0	1451
	2016	1335	0	0	0	1598	2933
	2017	1236	0	0	1590	1462	4288
	2018	1132	1359	0	1464	1345	5300
	2019	1034	1260	1461	1353	1262	6370
	2020	952	1149	1315	1231	1159	5806
Total		7140	3768	2776	5638	6826	26148

Number of persons 16 years and older

RB250 = 11,14		Rotational group					Total
RB250 = 11,14		1	2	3	5	6	Total
Year of the survey	2015	2545	0	0	0	0	2545
	2016	2445	0	0	0	2950	5395
	2017	2376	0	0	3006	2815	8197
	2018	2252	2609	0	2925	2703	10489
	2019	2139	2548	2879	2814	2618	12998
	2020	1931	2327	2585	2517	2361	11721
Total		13688	7484	5464	11262	13447	51345

Frequency of data collection

Yearly

Data collection

SILC2018 data are collected with questionnaires (CAPI and PAPI) through personal interview with household, including in the sample and all household members aged 16 and more.

2- Face to face CAPI (% of total)	7-CAPI with proxy (% of total)
76.4%	23.6%

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 21 minutes.

Average interview duration = 66

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 31.12.2019. 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 31.12.2019 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 2020 were not included in the calibration as they were not part of the population as of the end of 2019. 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 upper limit in 2020 was 2.5 and the lower – 0.25.

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 (13, 14, 15, 16, 17) 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 2019 register (DB135 = 1 & RB110 in (1,2,3,4)) who were followed up in 2020 were marked as responding (current members of the household). Persons who have left the household between the two survey waves (2019 and 2020) were marked as non-responding. A logistic regression was used to calculate the probability for each individual to be enumerated between 2019 and 2020. 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 2020 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 31.12.2019 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 for 2020 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 31.12.2019. 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 2020 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

Quality management

Quality assurance

The Survey on Income and Living Conditions (SILC) is an annual survey implemented in the framework of Regulation (EC) No 1177/2003, which defines Scope, Definitions, Time coverage, Characteristics of the data, Sample size, Publication and Access to data.

Quality assessment

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

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.

Data completeness - rate

Not applicable

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.

Regulation 1177/2003 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 SPSS ver.20.

Sampling errors - indicators

Sampling error - indicators

	AROPE			At risk of poverty -60%			Severe Material 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	32.1	0.9	32.1	23.8	0.9	23.8	19.4	0.8	19.4	7.9	0.6	7.9
Male	29.8	1.0	29.8	21.7	0.9	21.7	18.4	0.9	18.4	8.1	0.6	8.1
Female	34.2	1.0	34.2	25.8	0.9	25.8	20.2	0.8	20.2	7.7	0.7	7.7
Age0-17	33.3	1.9	33.3	28.3	1.9	28.3	20.0	1.7	20.0	10.6	1.4	10.6
Age18-64	25.9	1.0	25.9	17.5	0.9	17.5	16.2	0.8	16.2	-	-	-
Age 0+59	-	-	-	-	-	-	-	-	-	8.5	0.8	8.5
Age 65+	48.3	0.9	48.3	38.3	0.9	38.3	27.8	0.9	27.8	-	-	-

Estimation for main indicators by ethnic groups in 2020

Indicators	Percent	Standard error	Confidence interval
Indicators	Percent	Standard error	95% lower limit, in %	95% upper limit, in%
Population at-risk-of-poverty and social exclusion by ethnic group
Bulgarian ethnic group	25.1	0.8	23.7	26.6
Turkish ethnic group	39.9	2.9	34.3	45.7
Roma ethnic group	79.4	3.2	72.5	84.9
Other ethnic group	20.7	9.4	7.9	44.4
At-risk-of-poverty and ethnic group
Bulgarian ethnic group	17.8	0.7	16.5	19.1
Turkish ethnic group	29.5	2.6	24.7	34.7
Roma ethnic group	66.2	3.5	59.0	72.8
Other ethnic group	12.7	6.0	4.8	29.5
Severe material deprivation and ethnic group
Bulgarian ethnic group	13.8	0.6	12.7	15.0
Turkish ethnic group	20.4	2.3	16.3	25.3
Roma ethnic group	62.0	4.3	53.4	69.9
Other ethnic group	14.2	7.3	4.8	35.0
Low work intensity and ethnic group
Bulgarian ethnic group	5.4	0.5	4.5	6.3
Turkish ethnic group	8.7	1.6	6.1	12.3
Roma ethnic group	25.5	3.9	18.7	33.7
Other ethnic group	2.4	2.1	0.4	12.3

Estimation for indicator ‘at-risk-of-poverty’ by districts in 2020

	Blagoevgrad		Burgas		Varna		Veliko Tarnovo
	Percent	Standard error	Percent	Standard error	Percent	Standard error	Percent	Standard error
Total	19.6	3.5	29.0	4.1	24.0	2.8	25.0	4.3
0 - 17 years	23.3	7.4	40.9	8.8	23.7	6.0	26.9	8.6
18 - 64 years	15.6	4.0	21.7	3.9	17.9	2.8	15.8	4.5
65+ years	29.3	3.5	39.1	3.2	37.8	3.9	42.6	5.2
male	19.5	3.9	27.3	4.5	20.2	3.0	23.6	4.7
female	19.6	3.4	30.6	4.4	27.5	3.4	26.4	4.5
	Vidin		Vratsa		Gabrovo		Dobrich
	Percent	Standard error	Percent	Standard error	Percent	Standard error	Percent	Standard error
Total	23.0	6.1	24.5	6.2	12.3	2.7	20.0	3.8
0 - 17 years	46.5	12.3	37.8	13.2	8.0	4.6	32.0	10.3
18 - 64 years	19.9	6.6	18.4	5.9	4.2	2.1	13.2	3.5
65+ years	16.3	4.6	29.9	5.9	31.9	5.2	30.6	5.0
male	23.6	6.8	22.6	6.4	7.6	2.7	21.2	4.4
female	22.4	6.0	26.2	6.2	16.7	3.9	18.8	3.8
	Kardzhali		Kyustendil		Lovech		Montana
	Percent	Standard error	Percent	Standard error	Percent	Standard error	Percent	Standard error
Total	21.1	3.6	24.8	5.6	20.1	4.3	29.4	10.5
0 - 17 years	17.0	8.1	33.0	14.7	24.7	11.1	43.5	16.1
18 - 64 years	15.7	3.7	16.8	5.2	15.5	4.9	31.1	10.9
65+ years	40.2	5.0	37.7	5.4	29.3	5.0	8.9	3.2
male	17.0	3.3	19.3	4.4	21.0	6.3	29.2	9.9
female	25.0	4.3	29.9	7.2	19.3	3.5	29.5	11.2
	Pazardzhik		Pernik		Pleven		Plovdiv
	Percent	Standard error	Percent	Standard error	Percent	Standard error	Percent	Standard error
Total	21.7	4.8	16.9	3.9	20.6	5.3	25.3	3.7
0 - 17 years	35.6	10.2	18.4	9.8	32.2	11.7	29.2	7.2
18 - 64 years	18.6	4.7	10.1	3.1	14.9	5.0	20.1	3.6
65+ years	21.6	3.6	40.8	6.3	24.9	3.7	37.9	3.0
male	22.7	5.7	13.5	3.9	16.9	5.5	25.2	3.7
female	20.7	4.3	20.1	4.6	24.2	5.3	25.4	3.9
	Razgrad		Ruse		Silistra		Sliven
	Percent	Standard error	Percent	Standard error	Percent	Standard error	Percent	Standard error
Total	12.6	2.6	20.8	3.3	29.1	6.0	31.3	6.7
0 - 17 years	2.8	2.1	28.1	8.3	31.9	11.0	47.5	13.2
18 - 64 years	9.7	2.7	14.2	3.2	27.1	6.8	29.9	7.0
65+ years	32.8	6.8	34.4	3.9	33.1	5.7	19.1	4.3
male	8.8	3.0	16.7	3.2	33.0	6.7	29.6	7.2
female	16.1	3.2	24.6	3.9	25.4	5.7	32.9	6.6
	Smolyan		Sofia (stolitsa)		Sofia		Stara Zagora
	Percent	Standard error	Percent	Standard error	Percent	Standard error	Percent	Standard error
Total	26.2	7.0	22.5	2.0	20.6	3.6	26.9	4.5
0 - 17 years	30.0	14.8	21.2	3.8	8.3	8.1	32.7	10.2
18 - 64 years	14.9	7.2	16.5	2.1	10.3	3.9	18.7	4.4
65+ years	50.9	7.1	46.7	2.6	45.5	4.6	44.6	4.0
male	21.9	8.4	19.5	2.0	16.8	3.9	24.0	5.5
female	30.1	6.3	25.4	2.3	24.4	4.0	29.7	3.9
	Targovishte		Haskovo		Shumen		Yambol
	Percent	Standard error	Percent	Standard error	Percent	Standard error	Percent	Standard error
Total	18.9	3.8	17.1	3.4	16.4	5.0	22.2	4.4
0 - 17 years	29.4	12.3	13.0	5.7	16.1	6.9	42.4	13.0
18 - 64 years	9.4	2.7	17.3	4.2	13.2	5.5	12.8	3.8
65+ years	36.4	5.9	18.6	3.5	29.4	5.7	31.4	5.3
male	16.2	4.0	18.1	4.1	12.8	5.5	18.9	5.6
female	21.4	5.2	16.0	3.3	19.9	5.0	25.3	4.2

		2020 Cross sectional data
INCOME GROSS VARIABLES		Mean	No. Of observations Before Imputation	No. Of observations After Imputation	Standard error
Total hh gross income	(HY010)	22401.13	1995	7312	462.36
Total disposable hh income	(HY020)	18539.51	1963	7312	390.68
Total disposable hh income before social transfers other than old-age and survivors benefits	(HY022)	17342.66	3075	7266	388.25
Total disposable hh income before all social transfers	(HY023)	15117.88	3463	6714	418.07
Imputed rent	(HY030)	2181.97	7313	7313	19.40
Income from rental of property or land	(HY040)	2364.79	757	991	165.71
Family/ Children related allowances	(HY050)	2338.20	95	1152	144.87
Social exclusion payments not elsewhere classified	(HY060)	629.95	746	880	23.81
Housing allowances	(HY070)	240.00	2	2	0.00
Regular inter-hh cash transfers received	(HY080)	2249.29	689	727	100.29
Alimonies received	(HY081)	2171.14	44	44	328.00
Interest, dividends, profit from capital investments in incorporated businesses	(HY090)	18822.45	15	117	3429.71
Interest repayments on mortgage	(HY100)	19.20	79	79	1.94
Income received by people aged under 16	(HY110)	1693.95	28	58	167.23
Regular taxes on wealth	(HY120)	101.32	5001	5002	1.78
Regular inter household cash transfer paid	(HY130)	1861.84	224	224	138.31
Alimonies paid	(HY131)	1523.45	21	21	335.15
Tax on income and social contributions	(HY140)	5451.55	1230	4539	109.02
Value of goods produced for own consumption	(HY170)	537.74	1435	1435	36.23
Cash or near-cash employee income	(PY010)	12624.84	3417	7644	261.46
Other non-cash employee income	(PY020)	1012.95	654	654	46.53
Income from private use of company car	(PY021)	1971.97	0	46	237.97
Employers social insurance contributions	(PY030)	2203.58	7173	7173	35.26
Contribution to individual private pension plans	(PY035)	560.34	8	8	370.26
Cash profits or losses from self-employment	(PY050)	7950.91	341	1680	484.18
Pension from individual private plans	(PY080)	3133.43	10	10	217.10
Unemployment benefits	(PY090)	1581.53	144	523	109.26
Old-age benefits	(PY100)	4960.31	461	5921	41.64
Survivors benefits	(PY110)	2630.65	19	266	104.97
Sickness benefits	(PY120)	660.75	877	2146	34.10
Disability benefits	(PY130)	1728.08	106	2285	46.26
Education-related allowances	(PY140)	772.24	23	23	68.66
Gross monthly earnings for employees	(PY200)	1194.16	5163	5963	16.31

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

Percentage of non-contacted addresses by reasons:

address can not be located - 0.6%
address unable to access - 1.3%
address does not exist or is non-residential address or is unoccupied or not principal residence - 7.3%

Common units - proportion

not requested by Reg.28/2004

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 methods. 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 contact rate (Ra)*		Complete household interviews (Rh)*		Complete personal interviews (Rp)*		Household Non-response rate (NRh)*		Individual non-response rate (NRp)*		Overall individual non-response rate (NRp)*
A*	B*	A*	B*	A*	B*	A*	B*	A*	B*	A*	B*
0.994	0.979	0.855	0.620	0.977	0.992	15.02	39.31	0.29	0.84	15.27	39.82

* All the formulas are defined in the Commission Regulation 28/2004, Annex II

A* = Total sample; B = * New sub-sample

Longitudinal data
Address contact rate (Ra)*		Complete household interviews (Rh)*		Complete personal interviews (Rp)*		Household Non-response rate (NRh)*		Individual non-response rate (NRp)*		Overall individual non-response rate (NRp)*
A*	C*	A*	C*	A*	C*	A*	C*	A*	C*	A*	C*
0.980	0.991	0.843	0.683	0.994	0.991	17.31	32.39	0.560	0.891	17.77	32.99

A* = Total sample; C = * Longitudinal 1 wave 2015 year

Item non-response - rate

The computation of item non-response is essential to fullfil the precision requirements concerning publication as stated in the Commission Regulation No 1982/2003. Item non-response rate is provided for the main income variables both at household and personal level.

		2020 Cross sectional data
INCOME GROSS VARIABLES AT HOUSEHOLD LEVEL		% of households having received an amount	% 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	27.3	0.3	72.4
Total disposable hh income	(HY020)	100.0	26.8	0.2	73.0
Total disposable hh income before social transfers other than old-age and survivors benefits	(HY022)	99.4	42.3	0.2	57.5
Total disposable hh income before all social transfers	(HY023)	91.8	51.6	0.4	48.0
Imputed rent	(HY030)	100.0
Income from rental of property or land	(HY040)	13.6	76.4	22.9	0.7
Family/ Children related allowances	(HY050)	15.8	8.2	72.5	19.3
Social exclusion payments not elsewhere classified	(HY060)	12.0	84.8	3.1	12.2
Housing allowances	(HY070)	100.0
Regular inter-hh cash transfers received	(HY080)	9.9	94.8	5.2
Alimonies received	(HY081)	0.6	100.0
Interest, dividends, profit from capital investments in incorporated businesses	(HY090)	1.6	12.8	82.9	4.3
Interest repayments on mortgage	(HY100)	1.1	100.0
Income received by people aged under 16	(HY110)	0.8	48.3	22.4	29.3
Regular taxes on wealth	(HY120)	68.4	100.0	0.0
Regular inter household cash transfer paid	(HY130)	3.1	100.0
Alimonies paid	(HY131)	0.3	100.0
Tax on income and social contributions	(HY140)	62.1	27.1	7.3	65.6
Value of goods produced by own-consumption	(HY170)	19.6	100.0
INCOME GROSS VARIABLES AT PERSONAL LEVEL		% of persons 16+ having received an amount	% 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	(PY010)	52.1	44.7	7.4	47.9
Other non-cash employee income	(PY020)	4.5	100.0
Income from private use of company car	(PY021)	0.3	100.0
Employers social insurance contributions	(PY030)	48.9	100.0
Contribution to individual private pension plans	(PY035)	0.1	100.0
Cash profits or losses from self-employment	(PY050)	11.5	20.3	55.0	24.7
Pension from individual private plans	(PY080)	0.1	100.0
Unemployment benefits	(PY090)	3.6	27.5	17.6	54.9
Old-age benefits	(PY100)	40.4	7.8	0.8	91.4
Survivors benefits	(PY110)	1.8	7.1	19.5	73.3
Sickness benefits	(PY120)	14.6	40.9	19.1	40.1
Disability benefits	(PY130)	15.6	4.6	11.4	84.0
Education-related allowances	(PY140)	0.2	100.0
Gross monthly earnings for employees	(PY200)	40.7	86.6	1.0	12.5

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 (2020 operation). All personal/household income variables were collected by interview. For persons interviewed with electronic devices and 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) 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 requested by Reg.28/2004

Seasonal adjustment

Not aplicable

Data revision - policy

Not applicable.

Data revision - practice

Not applicable.

Data revision - average size

Timeliness and punctuality

Timeliness

SILC cross-sectional and longitudinal data are available in the form of tables 12 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

According to the Regulation (EC) No 1177/2003 of the European Parliament and of the Council concerning EU-SILC: "Comparability of data between Member States shall be a fundamental objective and shall be pursued through the development of methodological studies from the outset of EU-SILC data collection, carried out in close collaboration between the Member States and Eurostat".

Although the best way for keeping the comparability of data is to apply the same methods and definitions of variables, small departures of the definitions given by Eurostat are allowed in EU-SILC. In this way, the mentioned Regulation in its article 16th says: "Small departures from common definitions, such as those relating to private household definition and income reference period, shall be allowed, provided they affect comparability only marginally. The impact of comparability shall be reported in the quality reports."

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.

Asymmetry for mirror flows statistics - coefficient

Not requested by Reg. 28/2004.

Comparability - over time

Not applicable.

Length of comparable time series

Not applicable.

Coherence - cross domain

The cross-sectional data for the EU-SILC2020 were compared to the Labor force survey 2020 and HBS 2020.

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 - sub annual and annual statistics

Highest ISCED level attained

PE040 Highest ISCED level attained	SILC2020		LFS 2020
Weighted PB040	%	persons	%	persons
000 Less than primary education	1.6	95.1	0.8	47.5
100 Primary education	4.0	234.6	3.5	204.6
200 Lower secondary education	20.6	1209.7	20.0	1172.3
300 Upper secondary education (not further specified)	49.1	2887.6	51.4	3012.9
400 Post-secondary non-tertiary education (not further specified)	0.8	48.6	0.4	20.8
600 Bachelor or equivalent	4.6	268.8	6.9	403.0
700 Master or equivalent	19.1	1123.8	16.8	981.8
800 Doctorate or equivalent	0.3	16.8	0.3	18.2

Self-defined current economic status

PL031 Self-defined current economic status	SILC2020		LFS 2020
Weighted PB040	%	total	%	total
Еmployed (PL031 = 1,2,3,4)	53.2	3 129.2	52.8	3089.0
Unemployed (PL031=5)	7.9	463.8	4.6	269.6
Economically inactive (PL031=6,7,8,10,11)	38.9	2 292.0	42.6	2495.8

Status in employment weighted

PL040 Status in employment (PL031=1,2,3,4)	SILC2020		LFS 2020
Weighted PB040	%	total	%	total
Employed (PL031 = 1,2,3,4)	100.0	3 129.2	100.0	3121.7
Employees (PL040=3)	90.2	2 822.8	88.9	2776.1
Self-employed without employees (PL040=2)	5.8	181.1	6.9	214.7
Self-employed with employees (PL040=1)	3.6	112.9	3.6	112.0
Family worker (PL040=4)	0.4	12.3	0.6	18.9

	HBS 2020	SILC 2020 (Unweighted)
Households type
One person household	35.0	34.1
Two persons household	35.8	33.7
Three persons household	14.9	15.6
Four and more person household	14.3	16.6
Structure of population by age, %
0-15 years	11.6	10.7
16-24 years	5.4	7.2
25-49 years	27.2	26.7
50-64 years	22.5	23.9
65+ years	33.3	31.5
Structure of population by level of education, %
Primary education	4.5	5.3
Lower secondary	18.7	21.7
Upper secondary	53.1	51.1
Tertiary education	23.7	21.9
Activity status, %
Employed	38.0	40.1
Unemployed	7.9	6.2
Economically inactive	54.1	53.8
Status in employment, %
Employer	1.5	3.6
Self-employed	7.5	6.3
Employee	90.8	89.6
Family worker	0.2	0.5
Dwelling type
Detached house	41.8	46.5
Semidetached house	7.8	11.3
Apartment or flat	49.2	41.9
Some other kind of accommodation	1.2	0.3
Non monetary household deprivation
Telephone	1.5	2.4
Color TV	0.2	1.3
Computer	4.3	11.6
Washing machine	2.4	7.4
Car	9.1	14.8

Structure of total household income by source, %	HBS 2019	SILC 2020 (Unweighted)
Income from work	63.9	70.8
Pensions	27.3	21.9
Family/children related allowances	1.2	1.6
Unemployment benefits	1.7	0.6
Other	5.9	5.1

Mean equivalised disposable income by deciles, BGN	HBS 2019	SILC 2020 (Unweighted)
I	3116	2652
II	4246	4062
III	4885	4898
IV	5507	5820
V	6284	6953
VI	7195	8350
VII	8346	9983
VIII	9634	12148
IX	11433	15682
X	17620	30958

Coherence - National Accounts

Not applicable

Coherence - internal

	SILC2020	Other source	Source
Population	6 951 482	6 951 482	Population as of 31.12.2019
· male	3 369 646	3 369 646	Population as of 31.12.2019
· female	3 581 836	3 581 836	Population as of 31.12.2019
Number of self-defined pensioners PL031=7	1 594 121	2 135 836	NSSI as of 31.12.2019
Number of persons received income from pension	2 142 149	2 145 271	NSSI average annual number of pensioners for 2019
Number of Households	2 931 327	2 912 736	LFS 2020
Employed	3 129 158	3 121 717	LFS 2020
Working full time	2 969 246	3 057 269	LFS 2020
Working part-time	159 912	64 448	LFS 2020
Unemployed, ILO definition	235 048	168 494	LFS 2020
Economically inactive	2 291 981	2 570 879	LFS 2020

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

Data tables - consultations

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.

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_2020.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 according to Regulation (EC) 28/2004.

Cost and burden

The total length of interviewing household in average 65 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

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