Poverty and Social Inclusion Indicators by District


Time series: SILC_3_obl_en.xls

Poverty and Social Inclusion Indicators (Open Method of Coordination)*
*From 2016 onward the participation of the household in the survey is extended which is the reason of the increase in the number of rotational groups in the panel – from 4 to 6.

[OV-1] At-risk-of-poverty threshold (illustrative values of the region)
Survey year 2017 2017
Income reference year 2016 2016
  Single person Two adults with two children younger than 14 years
Bulgaria 4 213 8 848
Blagoevgrad 3 669 7 706
Burgas 4 387 9 212
Varna 4 543 9 540
Veliko Tarnovo 3 717 7 805
Vidin 2 812 5 905
Vratsa 3 223 6 769
Gabrovo 4 783 10 045
Dobrich 4 084 8 576
Kardzhali 3 456 7 258
Kyustendil 4 147 8 708
Lovech 3 679 7 726
Montana 2 885 6 059
Pazardzhik 2 812 5 906
Pernik 4 401 9 241
Pleven 3 775 7 927
Plovdiv 4 108 8 628
Razgrad 3 527 7 407
Ruse 4 527 9 508
Silistra 3 568 7 492
Sliven 2 852 5 989
Smolyan 3 719 7 811
Sofia cap. 6 126 12 864
Sofia 4 162 8 741
Stara Zagora 4 756 9 987
Targovishte 3 406 7 153
Haskovo 3 140 6 594
Shumen 3 559 7 474
Yambol 4 458 9 363
[OV-1a] At-risk-of-poverty rate by poverty threshold of the region (by gender)
Survey year 2017
Income reference year 2016
  total male female
Bulgaria 23.4 21.8 24.9
Blagoevgrad 13.5 11.7 15.2
Burgas 25.7 23.8 27.5
Varna 25.6 25.2 26.0
Veliko Tarnovo 24.1 26.0 22.3
Vidin 17.0 16.9 17.1
Vratsa 22.5 22.4 22.5
Gabrovo 22.5 16.3 28.3
Dobrich 19.0 19.5 18.6
Kardzhali 29.1 27.7 30.5
Kyustendil 17.3 17.0 17.7
Lovech 25.3 25.1 25.5
Montana 23.0 24.0 22.1
Pazardzhik 18.7 16.8 20.6
Pernik 17.2 15.1 19.3
Pleven 14.8 13.3 16.3
Plovdiv 21.9 20.1 23.6
Razgrad 15.7 16.4 14.9
Ruse 21.6 16.9 26.2
Silistra 17.2 17.9 16.5
Sliven 28.2 30.1 26.4
Smolyan 20.9 17.5 24.1
Sofia cap. 20.6 19.4 21.6
Sofia 20.8 16.3 25.1
Stara Zagora 23.6 20.2 26.8
Targovishte 19.2 18.0 20.3
Haskovo 17.0 17.8 16.2
Shumen 25.8 24.8 26.8
Yambol 20.2 12.4 27.7
[OV-2] Inequality of income distribution S80/S20 income quintile share ratio
Survey year 2017
Income reference year 2016
  S80_S20
Bulgaria 8.2
Blagoevgrad 5.2
Burgas 8.8
Varna 7.3
Veliko Tarnovo 7.4
Vidin 9.5u
Vratsa 6.2
Gabrovo 5.7u
Dobrich 8.4
Kardzhali 6.6
Kyustendil 4.7
Lovech 8.6
Montana 8.9
Pazardzhik 10.2
Pernik 4.7
Pleven 5.8
Plovdiv 6.8
Razgrad 6.2
Ruse 5.4
Silistra 6.2
Sliven 13.0
Smolyan 7.1
Sofia cap. 8.9
Sofia 5.7
Stara Zagora 8.1
Targovishte 4.8
Haskovo 9.0
Shumen 7.9
Yambol 7.7

u - unreliable/uncertain data
[SI-C2] Inequality of income distribution Gini coefficient
Survey year 2017
Income reference year 2016
  Gini coefficient
Bulgaria 40.2
Blagoevgrad 32.3
Burgas 38.2
Varna 38.1
Veliko Tarnovo 34.7
Vidin 42.4
Vratsa 33.3
Gabrovo 34.4
Dobrich 41.6
Kardzhali 33.9
Kyustendil 28.9
Lovech 38.4
Montana 37.4
Pazardzhik 41.7
Pernik 29.1
Pleven 33.3
Plovdiv 35.0
Razgrad 32.4
Ruse 30.1
Silistra 33.4
Sliven 43.9
Smolyan 36.3
Sofia cap. 42.3
Sofia 33.9
Stara Zagora 36.3
Targovishte 27.7
Haskovo 42.8
Shumen 35.9
Yambol 39.0
[OV-C11] At-risk-of-poverty rate before social transfers (by gender)
Survey year 2017
Income reference year 2016
  total male female
Bulgaria 44.8 42.6 46.9
Blagoevgrad 41.1 40.1 42.1
Burgas 48.0 46.9 49.0
Varna 46.6 44.1 49.1
Veliko Tarnovo 49.5 49.5 49.5
Vidin 54.8 53.6 55.9
Vratsa 54.2 52.0 56.3
Gabrovo 39.7 34.0 45.1
Dobrich 45.7 43.5 47.8
Kardzhali 46.2 44.7 47.7
Kyustendil 46.9 43.7 50.0
Lovech 52.3 49.7 54.6
Montana 54.2 56.4 52.0
Pazardzhik 47.0 45.2 48.8
Pernik 42.5 39.6 45.2
Pleven 45.0 39.9 50.0
Plovdiv 42.7 39.1 46.0
Razgrad 37.0 33.7 40.3
Ruse 38.5 32.5 44.2
Silistra 41.0 38.6 43.4
Sliven 53.3 52.4 54.2
Smolyan 48.6 48.7 48.6
Sofia cap. 36.9 35.6 38.1
Sofia 47.1 43.5 50.7
Stara Zagora 42.4 39.7 45.1
Targovishte 43.4 39.6 47.0
Haskovo 53.5 50.8 56.1
Shumen 44.4 42.4 46.4
Yambol 42.9 38.2 47.4
[SI-C6] At-risk-of-poverty rate before social transfers, by gender (except pensions)
Survey year 2017
Income reference year 2016
  total male female
Bulgaria 29.2 27.5 30.8
Blagoevgrad 26.5 25.2 27.9
Burgas 31.6 29.1 34.0
Varna 29.4 27.5 31.3
Veliko Tarnovo 27.3 29.0 25.8
Vidin 27.0 28.6 25.5
Vratsa 33.7 34.3 33.1
Gabrovo 24.7 18.2 30.8
Dobrich 22.8 22.4 23.1
Kardzhali 33.6 32.3 35.0
Kyustendil 27.8 25.4 30.2
Lovech 32.8 31.8 33.8
Montana 35.2 35.9 34.6
Pazardzhik 34.6 35.3 33.9
Pernik 24.8 22.6 27.0
Pleven 20.8 18.8 22.8
Plovdiv 27.6 25.6 29.4
Razgrad 23.6 24.0 23.2
Ruse 27.1 21.6 32.4
Silistra 28.4 25.7 30.9
Sliven 41.2 41.7 40.6
Smolyan 31.1 29.7 32.5
Sofia cap. 23.8 22.8 24.7
Sofia 25.2 20.9 29.4
Stara Zagora 28.7 25.0 32.2
Targovishte 24.9 23.3 26.4
Haskovo 24.4 25.2 23.5
Shumen 33.5 32.4 34.5
Yambol 22.9 15.6 30.0
[SI-P8] Persent of population lacking at least 4 items in the economic strain and durables dimension by gender
Survey year 2017
Income reference year 2016
  total male female
Bulgaria 30.0 28.8 31.1
Blagoevgrad 22.6 23.8 21.4
Burgas 32.9 31.7 34.0
Varna 43.4 41.6 45.2
Veliko Tarnovo 39.0 41.9 36.2
Vidin 29.4 31.3 27.5
Vratsa 38.9 39.0 38.8
Gabrovo 15.9 11.7 19.8
Dobrich 26.4 24.7 28.0
Kardzhali 35.2 35.5 35.0
Kyustendil 26.3 21.5 30.8
Lovech 38.6 37.7 39.4
Montana 24.6 23.5 25.7
Pazardzhik 45.8 45.8 45.7
Pernik 27.3 25.3 29.3
Pleven 29.0 26.2 31.6
Plovdiv 29.5 28.0 30.8
Razgrad 30.7 32.0 29.4
Ruse 34.1 31.5 36.7
Silistra 27.2 27.4 27.0
Sliven 41.0 41.9 40.1
Smolyan 43.1 41.6 44.4
Sofia cap. 20.0 18.3 21.5
Sofia 30.7 30.0 31.3
Stara Zagora 31.1 27.0 34.9
Targovishte 21.2 16.2 25.9
Haskovo 28.3 27.0 29.6
Shumen 37.3 36.6 38.0
Yambol 15.3 13.5 17.0
[LVHL11] People living in households with very low work intensity
Survey year 2017
Income reference year 2016
  18-59 years
  1000 pers PC_POP
Bulgaria 409.7 10.5
Blagoevgrad 13.7 7.6
Burgas 21.7 9.4
Varna 25.4 10.7
Veliko Tarnovo 19.8 17.5
Vidin 10.1 22.1
Vratsa 11.7 14.2
Gabrovo 3.1 5.4
Dobrich 2.7 2.7
Kardzhali 8.5 9.4
Kyustendil 4.4 7.1
Lovech 11.5 17.3
Montana 21.2 30.9
Pazardzhik 18.5 12.1
Pernik 9.1 12.2
Pleven 10.9 9.2
Plovdiv 37.7 10.2
Razgrad 10.9 16.4
Ruse 13.9 11.1
Silistra 5.7 9.1
Sliven 17.3 17.7
Smolyan 11.8 20.3
Sofia cap. 33.7 4.2
Sofia 14.9 11.2
Stara Zagora 17.0 9.4
Targovishte 10.1 16.5
Haskovo 19.6 17.7
Shumen 21.4 21.1
Yambol 3.2 4.6
[PEPS01] Population at risk of poverty or social exclusion by gender
Survey year
2017
Income reference year 2016
  total male female
1000 pers PC_POP 1000 pers PC_POP 1000 pers PC_POP
Bulgaria 2 766.6 38.9 1 286.8 37.2 1 479.8 40.4
Blagoevgrad 91.2 29.3 45.7 30.0 45.5 28.6
Burgas 172.2 41.7 80.6 40.2 91.6 43.1
Varna 225.2 47.7 106.5 46.2 118.7 49.1
Veliko Tarnovo 109.1 44.8 56.9 48.6 52.2 41.3
Vidin 30.9 34.5 15.7 35.9 15.2 33.1
Vratsa 81.5 47.8 41.4 48.7 40.1 46.8
Gabrovo 32.9 29.3 12.2 22.4 20.7 35.7
Dobrich 54.3 30.4 24.8 28.5 29.5 32.2
Kardzhali 66.7 44.1 31.8 42.4 34.9 45.7
Kyustendil 42.0 34.1 18.1 30.2 23.9 37.7
Lovech 61.0 47.2 28.1 44.5 32.9 49.8
Montana 48.2 35.6 24.7 37.2 23.4 34.1
Pazardzhik 126.4 48.5 62.5 48.9 64.0 48.1
Pernik 43.0 34.7 19.8 32.7 23.2 36.6
Pleven 91.8 36.8 43.3 35.4 48.5 38.1
Plovdiv 247.1 36.8 114.4 35.4 132.7 38.1
Razgrad 43.4 37.3 21.8 38.0 21.6 36.6
Ruse 100.5 45.0 42.7 39.1 57.8 50.5
Silistra 36.0 31.7 17.5 31.3 18.5 32.2
Sliven 92.2 48.5 45.2 49.0 47.0 48.1
Smolyan 53.1 48.6 25.0 47.0 28.1 50.0
Sofia cap. 412.2 31.0 183.2 28.9 228.9 33.0
Sofia 89.3 38.1 40.3 35.0 48.9 41.1
Stara Zagora 124.0 38.5 51.8 33.2 72.2 43.5
Targovishte 41.4 36.4 17.5 31.5 23.8 41.1
Haskovo 79.0 33.8 37.1 32.3 41.9 35.1
Shumen 73.0 41.7 33.9 39.4 39.1 43.9
Yambol 34.8 28.5 12.9 21.5 21.9 35.2

1000 pers - Per 1000 persons
PC_POP - Percent of population
30.04.2018


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

desislavadimitrova@nsi.bg

Contact phone number

+359 2 9857 183

Contact fax number
Metadata update
Metadata last certified11 September 2018
Metadata last posted11 September 2018
Metadata last update11 September 2018
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 - 2017

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

· Regulation (EC) No 1177/2003 concerning Community statistics on income and living conditions (EU-SILC);

· Regulation (EC) No 1553/2005 amending Regulation (EC) No 1177/2003;

· Regulation (EC) No 1980/2003 implementing Regulation (EC) No 1177/2003 of the European Parliament and of the Council concerning Community statistics on income and living conditions (EU-SILC) as regards definitions and updated definitions;

· Regulation (EC) No 1981/2003 implementing Regulation (EC) No 1177/2003 of the European Parliament and of the Council concerning Community statistics on income and living conditions (EU-SILC) as regards the fieldwork aspects and the imputation procedures;

· Regulation (EC) No 1982/2003 of 21 October 2003 implementing Regulation (EC) No 1177/2003 of the European Parliament and of the Council concerning Community statistics on income and living conditions (EU-SILC) as regards the sampling and tracing rules;

· Regulation (EC) No 1983/2003 of 7 November 2003 implementing Regulation (EC) No 1177/2003 of the European Parliament and of the Council concerning Community statistics on income and living conditions (EU-SILC) as regards the list of target primary variables;

- Regulation (EC) No 2256/2015 of the Commission of 4 Dec 2015 amending Regulation (EC) No 1983/2003 implementing Regulation (EC) No 1177/2003 of the European Parliament and the Council concerning Community statistics on income and living conditions (EU-SILC) as regards the list of target primary variables;

· Regulation (EC) No 28/2004 of 5 January 2004 implementing Regulation (EC) No 1177/2003 of the European Parliament and of the Council concerning Community statistics on income and living conditions (EU-SILC) as regards the detailed content of intermediate and final quality reports.

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: http://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: http://www.nsi.bg/en/node/8292

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
Detailed information about the list of social inclusion indicators, definitions and algorithm for their calculation can be found on the following site:  
http://ec.europa.eu/eurostat/statistics-explained/index.php/EU_statistics_on_income_and_living_conditions_(EU-SILC)_methodology
 
 
Quality documentation

National Quality Report according to Regulation (EC)  28/2004.

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.

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.

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

Punctuality

Not applicable.

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.

Comparability - over time

Not applicable.

Coherence - cross domain

The cross-sectional data for the EU-SILC2017 were compared to the Labor force survey 2017 and HBS 2017.

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

 

SILC 2017

Other source

Source

Population

7 101 859

7 101 859

Population as of 31.12.2016

·         male

3 449 978

3 449 978

·         female

3 651 881

3 651 881

Number of pensioners PL031=7

1 716 649

2 181 356

NSSI as of 31.12.2016

Number of persons received income from pension

2 359 833

2 181 356

NSSI as of 31.12.2016

Number of Households

2 918 370

2 999 841

LFS 2017

Employed

3 099 037

3 150 174

LFS 2017

Working full time

2 983 684

3 073 780

LFS 2017

Working part-time

115 353

76 394

LFS 2017

Unemployed

313 675

206 887

LFS 2017

Economically inactive

2 372 575

2 649 931

LFS 2017

 

Cost and burden

The total length of interviewing household in average below 71 minutes.

Data revision
Data revision - policy

Not applicable.

Data revision - practice

Not applicable.

Statistical processing
Source data

The sample for EU-SILC 2017 are selected from the sampling frame based on the Population Census 2011. The data base includes all private households and their current members residing in the country. Persons living in collective households and in institutions are excluded from the target population. Student’s and worker’s hostels are excluded at the first stage of selection of PSU, because student’s and worker’s households rarely stay on the same addresses and are difficult to trace.

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

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

The longitudinal component consists of the sub-samples R1, R2, R3, R4 and R6.

All personal/household income variables were collected by interview.

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-SILC2017 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.

Up to 2015, the survey "Statistics on income and living conditions" is carried out on a four-year rotating panel of private households. The sample size every year is around 7300 addresses/ private households which are distributed in all areas of the country. All members of a sampled household that are at the age of 16 or more are also surveyed. Each year one rotational group is dropped and replaced by another. 

Since 2015, with the financial support of the European Commission, households from the 9th and 10th rotational groups are followed for the fifth (5) and sixth (6) consecutive year respectively.

In 2017 a new rotational group with 2405 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 last rotational group (R5). The sampling frame is annually updated with  data from the Information System “Demography” data (ISD). Information about new born and died persons is used for actualization of sampling frame.

The sample is stratified by administrative-territorial districts in the country (NUTS3) and the household’s location. As a result 56 strata are formed (28 of urban and 28 of rural population). Municipalities and settlements are ranged according to the number of their population within each stratum.

Sample selection schemes

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

The clusters on the first stage are chosen with probability proportion to population size (number of households) in the PSUs. Systematic sampling of secondary units (households) in each primary unit selected is applied. Each PSU contains 5 households

Sample distribution over time

The survey is carried from March to May of the year 2017 with reference period of data the previous calendar year (2016).


Month

Data

Number

%

March

 

 1 – 10

5

0.1

11 – 20

161

2.2

21 – 31

757

10.3

April

 

 

1 – 10

829

11.3

11 – 20

888

12.1

21 – 30

942

12.8

May

 

 

1 – 10

1161

15.8

11 – 20

1442

19.6

21 – 31

1166

15.9

 

Total

7351

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 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 2017 is 8653 households:

- 6248 “old” (2012, 2013, 2014, 2015 and 2016),

- 2504 “new” households (drawn in 2017).

 

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

1237

16.8

2

2012

974

13.2

3

2013

967

13.2

4

2014

1121

15.2

5

2017

1590

21.6

6

2016

1462

19.9

Total

 

7351

100

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

 RB250 = 11,14

 

Rotational group

First wave

Households’ members

%

1

2015

2567

16.7

2

2012

2082

13.5

3

2013

2016

13.1

4

2014

2345

15.2

5

2017

3298

21.4

6

2016

3074

20.0

Total

 

15382

100


The sample size for longitudinal component was 31146 households and 71249 persons aged 16 and over. 

 

Number of households in longitudinal component

 

DB135 = 1

Rotational group

Total

1

2

3

4

6

Year of the survey

2012

0

1328

0

0

0

1328

2013

0

1224

1264

0

0

2488

2014

0

1140

1185

1462

0

3787

2015

1451

1091

1108

1315

0

4965

2016

1333

1044

1037

1208

438

5060

2017

1237

974

967

1121

1462

5761

Total

4021

6801

5561

5106

1900

23389

 

Number of persons 16 years and older

 

RB250 = 11,14

Rotational group

Total

1

2

3

4

6

Year of the survey

2012

0

2871

0

0

0

2871

2013

0

2646

2623

0

0

5269

2014

0

2447

2469

3040

0

7956

2015

3004

2319

2312

2767

0

10402

2016

2784

2233

2188

2550

3370

13125

2017

2567

2082

2016

2345

3074

12084

Total

8355

14598

11608

10702

6444

51707

 

 

Frequency of data collection

Yearly

Data collection

SILC2017 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.

1-Face to face PAPI 
(% of total)
2- Face to face CAPI 
(% of total)
6-PAPI with proxy 
(% of total)
7-CAPI with proxy 
(% of total)
17.3%60.4% 4.4% 17.9%

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

Average interview duration = 71.2

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 31.12.2016. For the calibration the following variables at individual and at household level were used:

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

The information on individuals as of 31.12.2016 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 2017 were not included in the calibration as they were not part of the population as of the end of 2016. 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 2017 was 2.5 and the lower – 0.2.

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 (10, 11, 12, 13, 14) 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 2016 register (DB135 = 1 & RB110 in (1,2,3,4)) who were followed up in 2017 were marked as responding (current members of the household). Persons who have left the household between the two survey waves (2016 and 2017) were marked as non-responding. A logistic regression was used to calculate the probability for each individual to be enumerated between 2016 and 2017. 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 2017 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 31.12.2016 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 2017 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 31.12.2016. The following variables on individual and household level were used for calibration:

Individual levelHousehold level
Age*Gender*Location 56 groups (14 age groups)*(male female)*(town, village)Number of households: 2 groups (town, village)
Districts*Gender*Location : 112 groups 
Number of pensioners:  3 groups 

 

Age groups:

 (0-15) (16-19) (20-24) (25-29) (30-34) (35-39) (40-44) (45-49) (50-54) (55-59) (60-64) (65-69) (70-74) (75+)

 

In 2016 the number of pensioners was used as calibration variable for first time.

This variable had 3 levels:

1 - old-age pensions

2 - social pensions

3 - all others(rest of population)

To allocate each person to the correct sub-population data from NSSI was used- number of personal pensions as of 31.12. There were two reasons to use this variable as a calibration variable. First, get better estimation of pensioners and second, to reduce the standard error of the AROPE indicator.

After calibration the final cross-sectional weight DB090 of the household was obtained. The individual cross-section weight RB050 was equal to the corresponding household weight DB090 (RB050=DB090).

The newborn in 2017 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
Metadata Structure Definition in SDMX 2.1: ESMS_MSD+BNSI+2.0+SDMX.2.1.xml
Metadata Structure Definition in SDMX 2.0: ESMS_MSD+BNSI+2.0+SDMX.2.0.xml


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

desislavadimitrova@nsi.bg

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 - 2017

Base period

Not applicable.

Statistical processing
Source data

The sample for EU-SILC 2017 are selected from the sampling frame based on the Population Census 2011. The data base includes all private households and their current members residing in the country. Persons living in collective households and in institutions are excluded from the target population. Student’s and worker’s hostels are excluded at the first stage of selection of PSU, because student’s and worker’s households rarely stay on the same addresses and are difficult to trace.

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

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

The longitudinal component consists of the sub-samples R1, R2, R3, R4 and R6.

All personal/household income variables were collected by interview.

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-SILC2017 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.

Up to 2015, the survey "Statistics on income and living conditions" is carried out on a four-year rotating panel of private households. The sample size every year is around 7300 addresses/ private households which are distributed in all areas of the country. All members of a sampled household that are at the age of 16 or more are also surveyed. Each year one rotational group is dropped and replaced by another. 

Since 2015, with the financial support of the European Commission, households from the 9th and 10th rotational groups are followed for the fifth (5) and sixth (6) consecutive year respectively.

In 2017 a new rotational group with 2405 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 last rotational group (R5). The sampling frame is annually updated with  data from the Information System “Demography” data (ISD). Information about new born and died persons is used for actualization of sampling frame.

The sample is stratified by administrative-territorial districts in the country (NUTS3) and the household’s location. As a result 56 strata are formed (28 of urban and 28 of rural population). Municipalities and settlements are ranged according to the number of their population within each stratum.

Sample selection schemes

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

The clusters on the first stage are chosen with probability proportion to population size (number of households) in the PSUs. Systematic sampling of secondary units (households) in each primary unit selected is applied. Each PSU contains 5 households

Sample distribution over time

The survey is carried from March to May of the year 2017 with reference period of data the previous calendar year (2016).


Month

Data

Number

%

March

 

 1 – 10

5

0.1

11 – 20

161

2.2

21 – 31

757

10.3

April

 

 

1 – 10

829

11.3

11 – 20

888

12.1

21 – 30

942

12.8

May

 

 

1 – 10

1161

15.8

11 – 20

1442

19.6

21 – 31

1166

15.9

 

Total

7351

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 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 2017 is 8653 households:

- 6248 “old” (2012, 2013, 2014, 2015 and 2016),

- 2504 “new” households (drawn in 2017).

 

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

1237

16.8

2

2012

974

13.2

3

2013

967

13.2

4

2014

1121

15.2

5

2017

1590

21.6

6

2016

1462

19.9

Total

 

7351

100

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

 RB250 = 11,14

 

Rotational group

First wave

Households’ members

%

1

2015

2567

16.7

2

2012

2082

13.5

3

2013

2016

13.1

4

2014

2345

15.2

5

2017

3298

21.4

6

2016

3074

20.0

Total

 

15382

100


The sample size for longitudinal component was 31146 households and 71249 persons aged 16 and over. 

 

Number of households in longitudinal component

 

DB135 = 1

Rotational group

Total

1

2

3

4

6

Year of the survey

2012

0

1328

0

0

0

1328

2013

0

1224

1264

0

0

2488

2014

0

1140

1185

1462

0

3787

2015

1451

1091

1108

1315

0

4965

2016

1333

1044

1037

1208

438

5060

2017

1237

974

967

1121

1462

5761

Total

4021

6801

5561

5106

1900

23389

 

Number of persons 16 years and older

 

RB250 = 11,14

Rotational group

Total

1

2

3

4

6

Year of the survey

2012

0

2871

0

0

0

2871

2013

0

2646

2623

0

0

5269

2014

0

2447

2469

3040

0

7956

2015

3004

2319

2312

2767

0

10402

2016

2784

2233

2188

2550

3370

13125

2017

2567

2082

2016

2345

3074

12084

Total

8355

14598

11608

10702

6444

51707

 

 

Frequency of data collection

Yearly

Data collection

SILC2017 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.

1-Face to face PAPI 
(% of total)
2- Face to face CAPI 
(% of total)
6-PAPI with proxy 
(% of total)
7-CAPI with proxy 
(% of total)
17.3%60.4% 4.4% 17.9%

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

Average interview duration = 71.2

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 31.12.2016. For the calibration the following variables at individual and at household level were used:

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

The information on individuals as of 31.12.2016 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 2017 were not included in the calibration as they were not part of the population as of the end of 2016. 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 2017 was 2.5 and the lower – 0.2.

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 (10, 11, 12, 13, 14) 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 2016 register (DB135 = 1 & RB110 in (1,2,3,4)) who were followed up in 2017 were marked as responding (current members of the household). Persons who have left the household between the two survey waves (2016 and 2017) were marked as non-responding. A logistic regression was used to calculate the probability for each individual to be enumerated between 2016 and 2017. 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 2017 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 31.12.2016 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 2017 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 31.12.2016. The following variables on individual and household level were used for calibration:

Individual levelHousehold level
Age*Gender*Location 56 groups (14 age groups)*(male female)*(town, village)Number of households: 2 groups (town, village)
Districts*Gender*Location : 112 groups 
Number of pensioners:  3 groups 

 

Age groups:

 (0-15) (16-19) (20-24) (25-29) (30-34) (35-39) (40-44) (45-49) (50-54) (55-59) (60-64) (65-69) (70-74) (75+)

 

In 2016 the number of pensioners was used as calibration variable for first time.

This variable had 3 levels:

1 - old-age pensions

2 - social pensions

3 - all others(rest of population)

To allocate each person to the correct sub-population data from NSSI was used- number of personal pensions as of 31.12. There were two reasons to use this variable as a calibration variable. First, get better estimation of pensioners and second, to reduce the standard error of the AROPE indicator.

After calibration the final cross-sectional weight DB090 of the household was obtained. The individual cross-section weight RB050 was equal to the corresponding household weight DB090 (RB050=DB090).

The newborn in 2017 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

Estimation for main indicators in 2017

 

 AROPEAt risk of poverty -60%Severe Material DeprivationVery low work intensity
Ind.valueStand. errorsHalf CI (95%)Ind.valueStand. errorsHalf CI (95%)Ind.valueStand. errorsHalf CI (95%)Ind.valueStand. errorsHalf CI (95%)
Total38.90.838.923.40.723.430.00.830.010.40.710.5
Male37.20.939.521.80.821.828.80.928.811.00.811.0
Female40.40.943.124.90.824.931.10.831.29.80.79.8
Age0-1741.61.743.729.21.629.233.11.733.213.21.513.2
Age18-6434.80.937.418.90.818.927.00.927.111.10.711.1
Age 65+48.91.051.832.00.932.036.31.036.3 -  - -

 

Estimation for main indicators by ethnic groups in 2017

Indicators

Percent

Standard error

Confidence interval

95% lower limit, in %

95% upper limit, in %

Population at-risk-of-poverty and social exclusion by ethnic group

Bulgarian ethnic group

31.4

0.9

29.7

33.1

Turkish ethnic group

54.9

3.2

48.6

61.1

Roma ethnic group

90.8

2.1

85.7

94.1

Other ethnic group

30

6.7

18.6

44.5

At-risk-of-poverty and ethnic group

 

 

 

Bulgarian ethnic group

15.7

0.6

14.6

17

Turkish ethnic group

37.7

2.9

32.1

43.5

Roma ethnic group

77.2

2.9

71

82.5

Other ethnic group

20.4

6.6

10.4

36.3

Severe material deprivation and ethnic group

Bulgarian ethnic group

23.7

0.8

22.1

25.3

Turkish ethnic group

36.9

3.2

30.9

43.3

Roma ethnic group

81

2.8

74.9

85.9

Other ethnic group

17.4

3.5

11.5

25.3

Low work intensity and ethnic group

Bulgarian ethnic group

6.1

0.5

5.2

7.2

Turkish ethnic group

18.4

2.8

13.6

24.5

Roma ethnic group

38.8

4.1

31.2

47

Other ethnic group

10.2

5.4

3.5

26.4

 

Estimation for indicator ‘at-risk-of-poverty’ by districts in 2017 

 

 

Blagoevgrad

Burgas

Varna

Veliko Tarnovo

 

Percent

Standard error

Percent

Standard error

Percent

Standard error

Percent

Standard error

Total

13.5

2.6

25.7

4.8

25.6

3.1

24.1

4.2

0 - 17 years

3.2

1.4

6.7

2.7

3.5

0.9

6.9

2

18 - 64 years

6.6

1.4

13.1

2.5

12

2.1

10.2

2.2

65+ years

3.7

0.7

5.9

0.9

10.1

1.5

7

1.2

Male

11.7

2.7

23.8

5

25.2

3.6

26

4.8

Female

15.2

2.7

27.5

4.8

26

3.4

22.3

4.2

 

Vidin

Vratsa

Gabrovo

Dobrich

 

Percent

Standard error

Percent

Standard error

Percent

Standard error

Percent

Standard error

Total

17

6.1

22.5

5.5

22.5

4.3

19

4.3

0 - 17 years

1.1

1.1

7.7

2.7

1.7

1.1

4.2

1.8

18 - 64 years

10.9

4.3

11.2

3

8.5

2.4

10.2

2.8

65+ years

5

1.6

3.6

0.9

12.4

2.7

4.5

1.2

Male

16.9

7.5

22.4

5.9

16.3

4.9

19.5

5

Female

17.1

5.4

22.5

5.5

28.3

4.4

18.6

4.3

 

Kardzhali

Kyustendil

Lovech

Montana

 

Percent

Standard error

Percent

Standard error

Percent

Standard error

Percent

Standard error

Total

29.1

5.1

17.3

4.1

25.3

6.4

23

8.3

0 - 17 years

3.9

1.3

2.1

1.7

4.1

1.5

7.6

3.8

18 - 64 years

19.1

3.7

6.6

2

15.1

4.9

13.5

4.9

65+ years

6.1

1.4

8.6

2

6

1.2

2

0.7

Male

27.7

5.2

17

4.3

25.1

7.2

24

7.8

Female

30.5

5.6

17.7

4.7

25.5

6.1

22.1

9.2

 

Pazardzhik

Pernik

Pleven

Plovdiv

 

Percent

Standard error

Percent

Standard error

Percent

Standard error

Percent

Standard error

Total

18.7

4.3

17.2

4

14.8

3.1

21.9

3

0 - 17 years

4.6

1.9

3.8

1.8

3.7

1.5

5.2

1.3

18 - 64 years

12.3

2.8

9.8

2.5

6.3

1.6

10.9

1.9

65+ years

1.8

0.4

3.6

1

4.8

0.9

5.9

0.7

Male

16.8

3.8

15.1

4.4

13.3

3.4

20.1

3.4

Female

20.6

5.3

19.3

4

16.3

3.4

23.6

3

 

Razgrad

Ruse

Silistra

Sliven

 

Percent

Standard error

Percent

Standard error

Percent

Standard error

Percent

Standard error

Total

15.7

3.8

21.6

3.1

17.2

4.4

28.2

6.6

0 - 17 years

1.2

0.6

2.7

0.9

2.7

1.4

11.7

3.6

18 - 64 years

9.8

3.2

11.9

2.3

11.3

3.3

15

4.1

65+ years

4.6

1.1

7.1

1.3

3.2

0.8

1.5

0.5

Male

16.4

4.9

16.9

3

17.9

4.5

30.1

7.2

Female

14.9

3.4

26.2

4.1

16.5

4.7

26.4

6.3

 

Smolyan

Sofia (stolitsa)

Sofia

Stara Zagora

 

Percent

Standard error

Percent

Standard error

Percent

Standard error

Percent

Standard error

Total

20.9

5.9

20.6

2.1

20.8

3.6

23.6

3.8

0 - 17 years

2.3

1.3

4.3

0.8

1.7

0.8

5.1

2.1

18 - 64 years

11

5.4

9.6

1.4

10.6

2.9

9.9

2

65+ years

7.6

1.6

6.6

0.7

8.5

1.6

8.6

1.3

Male

17.5

5.6

19.4

2.5

16.3

3.8

20.2

3.7

Female

24.1

6.5

21.6

2

25.1

4.3

26.8

4.2

 

Targovishte

Haskovo

Shumen

Yambol

 

Percent

Standard error

Percent

Standard error

Percent

Standard error

Percent

Standard error

Total

19.2

3.9

17

4

25.8

7.8

20.2

4.5

0 - 17 years

3.7

1.5

4

1.3

6.4

2.9

0.9

0.8

18 - 64 years

11

2.5

9.9

2.6

15.8

5.3

9.5

3.9

65+ years

4.5

1.2

3.2

0.8

3.6

0.8

9.7

2

Male

18

4.1

17.8

4.6

24.8

8.2

12.4

4.1

Female

20.3

4.3

16.2

3.9

26.8

7.5

27.7

5.4

 

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:
  1. – Unit non-response: refers to absence of information of the whole units (households and/or persons) selected into the sample
  2. – Item non-response: refers to the situation where a sample unit has been successfully enumerated, but not all required information has been obtained
Coverage 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 - 1.5%
  • address unable to access - 1.1%
  • address does not exist or is non-residential address or is unoccupied or not principal residence - 6.9% 
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 and PAPI 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*

 0.972 0.985 0.895 0.721 0.996 0.996 12.92 28.95 0.400.3613.2629.21

* 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*

BC

 0.987 0.992 0.854 0.767 0.992 0.993 15.79 23.900.84 0.73 16.5024.45

 A* = Total sample; C = * Longitudinal 1 wave 2012 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.

 

 

2017 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)

99.9

15.4

0.7

83.9

Total disposable hh income

(HY020)

99.9

17.9

0.3

81.8

Total disposable hh income before social transfers other than old-age and survivors benefits

(HY022)

98.9

26.5

0.2

73.3

Total disposable hh income before all social transfers

(HY023)

92.3

57.3

1.2

41.5

Imputed rent

(HY030)

100.0

100.0

 

 

Income from rental of property or land

(HY040)

14.8

84.7

14.2

1.1

Family/ Children related allowances

(HY050)

16.9

20.9

46.1

33.0

Social exclusion payments not elsewhere classified

(HY060)

10.7

26.8

45.3

27.9

Housing allowances

(HY070)

0.0

100.0

 

 

Regular inter-hh cash transfers received

(HY080)

9.6

93.6

6.4

 

Alimonies received 

(HY081)

0.8

100.0

 

 

Interest, dividends, profit from capital investments in incorporated businesses

(HY090)

0.8

51.8

48.2

 

Interest repayments on mortgage

(HY100)

1.5

100.0

 

 

Income received by people aged under 16

(HY110)

0.8

59.6

8.8

31.6

Regular taxes on wealth

(HY120)

70.4

100.0

 

0.0

Regular inter household cash transfer paid

(HY130)

3.3

88.6

10.6

0.8

Alimonies paid 

(HY131)

0.4

100.0

 

 

Tax on income and social contributions

(HY140)

61.4

37.2

7.7

55.0

Value of goods produced by own-consumption 

(HY170)

28.0

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)

50.9

60.3

10.3

29.4

Other non-cash employee income

(PY020)

7.2

100.0

 

 

Income from private use of company car

(PY021)

0.4

 

100.0

 

Employers social insurance contributions

(PY030)

45.7

100.0

 

 

Contribution to individual private pension plans 

(PY035)

0.3

100.0

 

 

Cash profits or losses from self-employment

(PY050)

11.3

10.3

67.2

22.6

Pension from individual private plans 

(PY080)

0.0

100.0

 

 

Unemployment benefits

(PY090)

4.5

45.3

54.7

 

Old-age benefits

(PY100)

36.7

54.9

1.5

43.6

Survivors benefits

(PY110)

1.3

43.0

30.0

27.1

Sickness benefits

(PY120)

11.5

39.9

57.1

3.0

Disability benefits

(PY130)

6.2

8.6

36.9

54.5

Education-related allowances

(PY140)

0.1

100.0

 

 

Gross monthly earnings for employees 

(PY200)

40.1

100.0

 

 

 

Processing error
Data-entry phase
EU-SILC data were collected with two kinds of questionnaires – household and individual questionnaire.  Households and individuals from previous waves of observation are interviewed by electronic devices (CAPI).  The data entry program was developed in LimeSurvey. MySQL  has been used as database.  Households and individuals from new rotational group are interviewed by paper questionnares (PAPI).  The data entry program was developed in Visual Basic.Net. MS Access has been used as database.
 
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).
 
The rates of failed edits for income variables are not available.

 

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 (2017 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-SILC2017 were compared to the Labor force survey 2017 and HBS 2017.

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

Coherence of number of persons with external sources

PE040 Highest ISCED level attained

SILC 2017

LFS 2017

Weighted by PB040

%

Number of persons

%

Number of persons

000 Less than primary education

1.8

108.2

0.9

54.7

100 Primary education

4.5

268.9

3.6

219.0

200 Lower secondary education

21.6

1307.1

20.8

1248.7

300 Upper secondary education (not further specified)

49.1

2965.6

51.5

3095.5

400 Post-secondary non-tertiary education (not further specified)

0.7

42.7

0.3

19.6

600 Bachelor or equivalent

3.8

228.3

6.7

401.0

700 Master or equivalent

18.3

1102.7

15.8

951.3

800 Doctorate or equivalent

0.3

15.6

0.3

17.1

Total

 

6039.2

 

6007.0

 

PL031 Self-defined current economic status 

SILC 2017

LFS 2017

Weighted by PB040

%

Number of persons

%

Number of persons

employed (PL031 = 1,2,3,4) 

51.3

3 099.0

52.1

3 129.9

unemployed (PL031=5)

9.4

567.5

5.7

342.4

economically inactive (PL031=6,7,8,10,11) 

39.3

2 372.6

42.2

2 537.4

 

PL040 Status in employment (PL031=1,2,3,4)

SILC 2017

LFS 2017

Weighted PB040

%

Number of persons

%

Number of persons

Employed (PL031 = 1,2,3,4) 

100.0

3 099.0

100.0

3 150.2

Employees (PL040=3)

89.3

2 768.7

88.1

2 775.3

Self-employed without employees (PL040=2)

6.3

194.5

7.5

235.3

Self-employed with employees (PL040=1)

3.9

120.1

3.6

114.8

Family worker (PL040=4)

0.5

15.8

0.8

24.7

 

Households type

HBS 2017

SILC 2017

One person household

30.0

36.6

Two persons household

35.6

29.3

Three persons household

17.6

15.3

Four and more person household

16.7

18.8

   

 Structure of population by age,  %

HBS 2017

SILC 2017

0-15

12.1

11.5

16-24

7.4

8.5

25-49

29.1

29.5

50-64

22.9

22.7

65+

28.5

27.9

   

Activity status, %

HBS 2017

SILC 2017

Employed

38.8

35.0

Unemployed

8.2

6.8

Economically inactive

53.0

58.2

   

Status in employment, %

HBS 2017

SILC 2017

Employer

1.2

3.7

Self-employed

7.6

6.6

Employee

90.8

89.1

Family worker

0.4

0.6

   

 Structure of population by level of education, %

HBS 2017

SILC 2017

Primary education

4.5

6.5

Lower secondary

20.6

23.5

Upper secondary

52.9

50.2

Tertiary education

22.0

19.7

   

Dwelling type

HBS 2017

SILC 2017

Detached house

39.2

47.0

Semidetached house

10.3

12.1

Apartment or flat

50.0

40.5

Some other kind of accommodation

0.5

0.4

   

Non monetary household deprivation

HBS 2017

SILC 2017

Telephone

3.9

3.1

Color TV

0.4

1.5

Computer

6.3

14.7

Washing machine

4.8

9.7

Car

14.7

20.0

 

Coherence - National Accounts

Not applicable

Coherence - internal

 

SILC 2017

Other source

Source

Population

7 101 859

7 101 859

Population as of 31.12.2016

·         male

3 449 978

3 449 978

·         female

3 651 881

3 651 881

Number of pensioners PL031=7

1 716 649

2 181 356

NSSI as of 31.12.2016

Number of persons received income from pension

2 359 833

2 181 356

NSSI as of 31.12.2016

Number of Households

2 918 370

2 999 841

LFS 2017

Employed

3 099 037

3 150 174

LFS 2017

Working full time

2 983 684

3 073 780

LFS 2017

Working part-time

115 353

76 394

LFS 2017

Unemployed

313 675

206 887

LFS 2017

Economically inactive

2 372 575

2 649 931

LFS 2017

 

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: http://www.nsi.bg/en/node/8292

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
Detailed information about the list of social inclusion indicators, definitions and algorithm for their calculation can be found on the following site:  
http://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 below 71 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
Metadata Structure Definition in SDMX 2.1: ESQRS_MSD+BNSI+2.0+SDMX.2.1.xml
Metadata Structure Definition in SDMX 2.0: ESQRS_MSD+BNSI+2.0+SDMX.2.0.xml
  • 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.

  • 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.

  • 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.

  • 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. 

  • 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. 
     
  • 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.

  • 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.

  • 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.

  • 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.

  • Poverty mapping in the Republic of Bulgaria
    The National Statistical Institute presents to users of statistical information the electronic bilingual (Bulgarian/English) publication Poverty mapping in the Republic of Bulgaria.