1.1. Contact organisation
State Data Agency. Statistics Lithuania
1.2. Contact organisation unit
Living Standard and Employment Statistics Division
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
29 Gedimino Ave., LT-01500, Vilnius, Lithuania
1.6. Contact email address
Confidential because of GDPR
1.7. Contact phone number
Confidential because of GDPR
1.8. Contact fax number
2.1. Metadata last certified
30 May 2025
2.2. Metadata last posted
30 May 2025
2.3. Metadata last update
30 May 2025
3.1. Data description
The European Union Statistics on Income and Living Conditions (EU-SILC) is a survey-based instrument aiming at collecting timely and comparable cross-sectional and longitudinal multidimensional microdata on income, poverty, social exclusion and living conditions. In addition, it collects module variables every three years, six years or ad-hoc new policy needs modules.
The EU-SILC instrument provides two types of data:
- Cross-sectional data pertaining to a given time or a certain time period with variables on income, poverty, social exclusion and other living conditions;
- Longitudinal data pertaining to individual-level changes over time, observed periodically over four year rotation scheme (Annex III (2) of 2019/1700).
Social exclusion and housing condition information is collected mainly at household level while labour, education and health information is obtained for persons aged 16 and over. The core of the instrument is income information at very detailed component level and mainly collected at personal level.
3.2. Classification system
- International Standard Classification of Education (ISCED'2011);
- International Standard Classification of Occupations (ISCO-08);
- Classification of Economic Activities (NACE Rev.2-2008);
- Common classification of territorial units for statistics (NUTS 2);
- SCL - Geographical code list;
- The recommendations made by the United Nations in the Canberra Group Handbook on Household Income Statistics should also be taken into account.
For more details on the classification used please, see EU Vocabularies, Eurostat's metadata server or CIRCABC
3.3. Coverage - sector
Data refer to all private households and individuals living in the private households in the national territory at the time of data collection.
The EU-SILC survey is a key instrument for the European Semester and the European Pillar of Social Rights, providing information on income distribution, poverty and social exclusion, as well as various related living conditions and poverty EU policies, such as on child poverty, access to health care and other services, housing, over indebtedness and quality of life. It is also the main source of data for microsimulation purposes and flash estimates of income distribution and poverty rates.
3.4. Statistical concepts and definitions
Statistical concepts and definitions for EU-SILC are specified in Regulation (EU) 2019/1700, Commission Implementing Regulation (EU) 2019/2181, and Commission Implementing Regulation (EU) 2019/2242. Additional information is available in the EU statistics on income and living conditions (EU-SILC) methodology and in the methodological guidelines and description of EU-SILC target variables (see CIRCABC).
Further details are provided in items 5, 15.1.1.1, 15.2.2 and 18.3.
3.5. Statistical unit
Statistical units are private households and all persons living in these households who have usual residence in Lithuania. Annex II of the Commission implementing regulation (EU) 2019/2242 defines specific statistical units per variable and specifies the content of the quality reports on the organization of a sample survey in the income and living conditions domain pursuant to Regulation (EU) 2019/1700 of the European Parliament and of the Council.
3.6. Statistical population
The target population is private households and all persons composing these households having their usual residence in Lithuania. Private household means a person living alone or a group of persons who live together, providing oneself or themselves with the essentials of living.
3.6.1. Reference population
Definitions of reference population, household and household membership
| Reference population |
Private household definition |
Household membership |
|---|---|---|
| No difference to the common definition. |
No difference to the common definition. |
No difference to the common definition. |
3.6.2. Population not covered by the data collection
The sub-populations that are not covered by the data collection includes: those who moved out of the country’s territory; or those with no usual residence; or those living in institutions or who have moved to an institution compared to the previous year.
3.7. Reference area
Regions, country.
3.8. Coverage - Time
Yearly. Since 2005
3.9. Base period
Not applicable.
The data involves several units of measure depending upon the variables. Income variables are transmitted to Eurostat in national currency. For more information, see methodological guidelines and description of EU-SILC target variables available on CIRCABC
Description of reference period used for incomes
| Period for taxes on income and social insurance contributions |
Income reference periods used |
Reference period for taxes on wealth |
Lag between the income ref period and current variables |
|---|---|---|---|
| last calendar year |
last calendar year |
last calendar year |
0-4 months |
6.1. Institutional Mandate - legal acts and other agreements
Regulation (EU) 2019/1700 was publish in OJ on 10 October 2019, establishing a common framework for European statistics relating to persons and households, based on data at individual level collected from samples (IESS). The Annex to the Commission implementing regulation (EU) 2019/2180 of 16 December 2019 specifies the detailed arrangements and content for the quality reports pursuant to Regulation (EU) 2019/1700 of the European Parliament and of the Council and Regulation (EU) 2019/2242.
6.2. Institutional Mandate - data sharing
Confidential microdata are not disclosed by Eurostat. Access to confidential microdata for scientific purposes may be granted on the basis of Commission Regulation 557/2013 and Regulation 223/2009 of the European Parliament and the Council on European statistics.
7.1. Confidentiality - policy
In the process of statistical data collection, processing and analysis and dissemination of statistical information, Statistics Lithuania fully guarantees confidentiality of the data submitted by respondents (households, enterprises, institutions, organisations and other statistical units), as defined in the Confidentiality policy guidelines of the State Data Agency.
7.2. Confidentiality - data treatment
Statistical Disclosure Control Manual, approved by Order No DĮ-29 of 19 January 2024 of the Director General of Statistics Lithuania;
The State Data Governance Information System Data Security Regulations and Rules for the Secure Management of Electronic Information in the State Data Governance Information System, approved by Order No DĮ-163 of 20 August 2024 of the Director General of the State Data Agency.
8.1. Release calendar
Data release calendar can be found in the official statistical website.
8.2. Release calendar access
Please refer to the Release calendar - Eurostat (europa.eu) publicly available on the Eurostat’s website.
8.3. Release policy - user access
In line with the Community legal framework and the European Statistics Code of Practice, Eurostat disseminates European statistics on Eurostat's website (see section 10 - 'Accessibility and clarity'), respecting professional independence and in an objective, professional and transparent manner in which all users are treated equitably. The detailed arrangements are governed by the Eurostat protocol on impartial access to Eurostat data for users. Additional information about microdata access is available in Statistics on Income and Living Conditions - Access to microdata - Eurostat (europa.eu).
Annual
10.1. Dissemination format - News release
Data release calendar can be found in the official statistical website.
10.2. Dissemination format - Publications
More information can be found in the official statistical website.
10.3. Dissemination format - online database
More information can be found in the official statistical website.
10.3.1. Data tables - consultations
Optional.
10.4. Dissemination format - microdata access
More information can be found in the open dataset dedicated website.
10.5. Dissemination format - other
More information can be found in the official statistical website.
10.5.1. Metadata - consultations
Optional
10.6. Documentation on methodology
More information can be found in the official statistical website.
10.6.1. Metadata completeness - rate
Optional
10.7. Quality management - documentation
More information can be found in the official statistical website.
11.1. Quality assurance
The quality of statistical information and its production process is ensured by the provisions of the European Statistics Code of Practice and ESS Quality Assurance Framework.
In 2007, a quality management system, conforming to the requirements of the international quality management system standard ISO 9001, was introduced at Statistics Lithuania. The main trends in activity of Statistics Lithuania aimed at quality management and continuous development in the institution are established in the Quality Policy. Monitoring of the quality indicators of statistical processes and their results and self-evaluation of statistical survey managers is regularly carried out in order to identify the areas which need improvement and to promptly eliminate the shortcomings.
11.2. Quality management - assessment
Quality of data is in compliance with the requirements of accuracy, timeliness and punctuality, coherence and comparability.
According to the yearly updated Plan for Measuring the Indicators of Activities of The State Data Agency, results of the quality indicators of the statistical Income and Living Conditions Survey are presented in fields 13–15 of this metainformation inventory.
The quality of the information obtained is analysed. Additional statistical quality checks are performed at the macro level. The estimates of statistical indicators are compared with the previous period, other statistical information and data from administrative sources.
12.1. Relevance - User Needs
The main users of EU-SILC statistical data are policy makers, research institutes, media, and students.
12.2. Relevance - User Satisfaction
Eurostat carried out an online general User Satisfaction Survey (USS) in the period between April and July 2019 to obtain a better knowledge about users, considering their needs and satisfaction with the services provided by Eurostat. The survey has shown that EU-SILC is of very high relevance for users. For the majority, both aggregates and micro-data were important or essential in their work irrespective of the purpose of their use. The use of the ad-hoc modules was less widespread than the use of the nucleus variables. Nevertheless, there was high interest to repeat these modules in order to have the possibility of comparing data over time. Users emphasized their strong need for more detailed micro-data, which is currently not possible. Under the new legal framework implemented from 2021, the NUTS 2 division will be available for the main indicators. Finally, users were satisfied with overall quality of the service delivered by Eurostat, which encompasses data quality and the supporting service provided to them.
For more information, please consult the User Satisfaction Survey.
12.3. Completeness
All required variables are transmitted to Eurostat according to the Regulation.
HY145N is collected under HY140(G,N) seeing the most of the income components are reported gross.
HY121(G,N) collected under HY120(G,N).
Voluntary module on impact of covid-19 has been not collected.
12.3.1. Data completeness - rate
optional
13.1. Accuracy - overall
According to Reg. (EU) 2019/1700 Annex II, precision requirements for all data sets are expressed in standard errors and are defined as continuous functions of the actual estimates and of the size of the statistical population in a country or in a NUTS 2 region. For the income and living conditions domain, the estimated standard errors of the following indicators are examined according to certain parameters set:
- · Ratio at‐risk‐of‐poverty or social exclusion to population;
- · Ratio of at‐persistent‐risk‐of‐poverty over four years to population;
- · Ratio at‐risk‐of‐poverty or social exclusion to population in each NUTS 2 region.
Further information is provided in section 13.2 Sampling error.
13.2. Sampling error
EU-SILC is a complex survey involving different sampling designs in different countries. In order to harmonize and make sampling errors comparable among countries, Eurostat (with the substantial methodological support of Net-SILC2) has chosen to apply the "linearization" technique coupled with the “ultimate cluster” approach for variance estimation.
Linearization is a technique based on the use of linear approximation to reduce non-linear statistics to a linear form, justified by asymptotic properties of the estimator. This technique can encompass a wide variety of indicators, including EU-SILC indicators. The "ultimate cluster" approach is a simplification consisting in calculating the variance taking into account only variation among Primary Sampling Unit (PSU) totals. This method requires first stage sampling fractions to be small which is nearly always the case. This method allows a great flexibility and simplifies the calculations of variances. It can also be generalized to calculate variance of the differences of one year to another.
The main hypothesis on which the calculations are based is that the "at risk of poverty" threshold is fixed. According to the characteristics and availability of data for different countries, we have used different variables to specify strata and cluster information.
In particular, countries have been split into 3 groups:
1) BE, BG, CZ, IE, EL, ES, FR, HR, IT, LV, HU, PL, PT, RO, SI, UK and AL, whose sampling design could be assimilated to a two-stage stratified type we used DB050 (primary strata) for strata specification and DB060 (Primary Sampling Unit) for cluster specification;
2) DK, DE, EE, CY, LT, LU, NL, AT, SK, FI, CH whose sampling design could be assimilated to a one stage stratified type we used DB050 for strata specification and DB030 (household ID) for cluster specification;
3) MT, SE, IS, NO, whose sampling design could be assimilated to a simple random sampling, we used DB030 for cluster specification and no strata.
13.2.1. Sampling error - indicators
The concept of accuracy refers to the precision of estimates computed from a sample rather than from the entire population. Accuracy depends on sample size, sampling design effects and structure of the population under study. In addition to that, sampling errors and non-sampling errors need to be taken into account. Sampling error refers to the variability that occurs at random because of the use of a sample rather than a census and non-sampling errors are errors that occur in all phases of the data collection and production process.
13.3. Non-sampling error
Non-sampling errors are basically of 4 types:
- Coverage errors: errors due to divergences existing between the target population and the sampling frame.
- Measurement errors: errors that occur at the time of data collection. There are a number of sources for these errors such as the survey instrument, the information system, the interviewer and the mode of collection.
- Processing errors: errors in post-data-collection processes such as data entry, keying, editing and weighting.
- Non-response errors: errors due to an unsuccessful attempt to obtain the desired information from an eligible unit. Two main types of non-response errors are considered:
- Unit non-response: refers to absence of information of the whole units (households and/or persons) selected into the sample.
- Item non-response: refers to the situation where a sample unit has been successfully enumerated, but not all required information has been obtained.
13.3.1. 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
13.3.1.1. Over-coverage - rate
No coverage errors were detected
13.3.1.2. Common units - proportion
optional
13.3.2. Measurement error
Proxy interview rate = 24.9 per cent
Measurement error for cross-sectional data
Cross-sectional data
| Source of measurement errors |
Building process of questionnaire |
Interview training |
Quality control |
|---|---|---|---|
| The measurement errors originate from the questionnaire (its wording, design), the data collection method, the interviewers and the respondents. While it is impossible to avoid this type of errors completely, procedures were taken to reduce them as much as possible. |
The questionnaires were developed according to the EU-SILC regulations and methodological guidelines and description of EU-SILC target variables DocSILC065. The questionnaires were tested during the first wave of pilot survey conducted in 2004. Designing questionnaires for main operation errors and interviewers feedbacks from the pilot survey were considered. Also the experience from the different waves of the survey was used to improve the questionnaire. |
The interviewer's training was carried-out by specialists from Living standard statistics and Interviewers management divisions in Statistics Lithuania in the middle of January. Interviewers’ manual presenting instructions on filling in the questionnaires and detailed explanations for all income components, particularly benefits, were prepared. Special emphasis was placed on tracing rules and specifics of assigning household and person numbers in the longitudinal survey. Methodical explanations were combined with practical tests using laptops. Fieldwork has started immediately after interviewers training. Fieldwork was carried out by permanent interviewers. In total 7 supervisers and 71 interviewers were involved. One interviewer had an average 95 selected addresses.
|
The interviewer's were consulted and checked by supervisors and specialists from Living standard statistics and Interviewers management divisions during the fieldwork |
13.3.3. 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 to Annex VI of the Reg.(EU) 2019/2242
- 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/selected person (including phone, mail if applicable) successfully contacted/Number of valid addresses/selected person (including phone, mail if applicable) 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 (including phone, mail if applicable)
• Individual non-response rates (NRp) is 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) is 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, phones, mails etc.) was selected, the individual non-response rates will be calculated for ‘the 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.
13.3.3.1. Unit non-response - rate
Unit non-response rate for cross-sectional
| Cross sectional data | |||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Address (including phone, mail if applicable) contact rate | Complete household interviews | Complete personal interviews | Household Non-response rate | Individual non-response rate | Overall individual non-response rate | ||||||||||||
| (Ra)* | (Rh)* | (Rp)* | (NRh)* | (NRp)* | (NRp)* | ||||||||||||
| A* | B* | C* | A* | B* | C* | A* | B* | C* | A* | B* | C* | A* | B* | C* | A* | B* | C* |
| 99.5 | 98.66 | 99.94 | 84.13 | 66.56 | 95.78 | 100.0 | 100.0 | 100.0 | 16.29 | 34.33 | 4.28 | 0.00 | 0.00 | 0.00 | 16.29 | 34.33 | 4.28 |
where
A=total (cross-sectional) sample,
B =New sub-sample (new rotational group) introduced for first time in the survey this year,
C= Sub-sample (rotational group) surveyed for last time in the survey this year.
13.3.3.2. Item non-response - rate
The computation of item non-response is essential to fulfil the precision requirements. Item non-response rate is provided for the main income variables both at household and personal level.
Item non-response which refers to the situation where a sample unit has been successfully enumerated, but not all the required information has been obtained.
13.3.3.2.1. Item non-response rate by indicator
Annex
13.3.4. Processing error
Description of data entry, coding controls and the editing system
| Data entry and coding (if any used) |
Editing controls |
|---|---|
| Data were entered by interviewers or respondents. Orbeon software was used for data entry. | Completed questionnaires were checked by supervisors. Necessary call-backs were made.The computer program included the possible logical checks between questions and questionnaires, also a package of alerts (warning and error ones) related to ranges of admissible values and logical connections between questions. Coding controls were implemented in post-data-collection. After the data entry was finished the data were checked for consistency by specialists of the Living Standard and Employment Statistics Division of Statistics Lithuania |
13.3.5. Model assumption error
Not applicable
14.1. Timeliness
See the information provided in 14.1.1 and 14.1.2
14.1.1. Time lag - first result
Statistical information is published in 12 months after the end of the reporting period (fieldwork)
14.1.2. Time lag - final result
Statistical information is published in 13 months after the end of the reporting period (fieldwork)
14.2. Punctuality
See the information provided in 14.2.1
14.2.1. Punctuality - delivery and publication
Statistical information is published in accordance with an Official Statistics Calendar. In case of delay, users are notified in advance by indicating the reason and a new date of publication.
15.1. Comparability - geographical
Statistical information is comparable between EU countries.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not applicable.
15.2. Comparability - over time
Since 2005
15.2.1. Length of comparable time series
Since 2005
15.2.2. Comparability and deviation from definition for each income variable
Comparability and deviation from definition for each income variable
| Income |
Identifier |
Comparability |
Deviation from definition if any |
|---|---|---|---|
| Total hh gross income |
(HY010) |
F |
|
| Total disposable hh income |
(HY020) |
F |
|
| Total disposable hh income before social transfers other than old-age and survivors' benefits |
(HY022) |
F |
|
| Total disposable hh income before all social transfers |
(HY023) |
F |
|
| Income from rental of property or land |
(HY040) |
F |
|
| Family/ Children related allowances |
(HY050) |
F |
|
| Social exclusion payments not elsewhere classified |
(HY060) |
F |
|
| Housing allowances |
(HY070) |
F |
|
| Regular inter-hh cash transfers received |
(HY080) |
F |
|
| Alimonies received |
(HY081) |
F |
|
| Interest, dividends, profit from capital investments in incorporated businesses |
(HY090) |
F |
|
| Interest paid on mortgage |
(HY100) |
F |
|
| Income received by people aged under 16 |
(HY110) |
F |
|
| Regular taxes on wealth |
(HY120) |
F |
|
| Taxes paid on ownership of household main dwelling |
(HY121) |
F |
|
| Regular inter-hh transfers paid |
(HY130) |
F |
|
| Alimonies paid |
(HY131) |
F |
|
| Tax on income and social contributions |
(HY140) |
F |
|
| Repayments/receipts for tax adjustment |
(HY145) |
F |
|
| Value of goods produced for own consumption |
(HY170) |
F |
|
| Cash or near-cash employee income |
(PY010) |
F |
|
| Other non-cash employee income |
(PY020) |
F |
|
| Income from private use of company car |
(PY021) |
F |
|
| Employers social insurance contributions |
(PY030) |
F |
|
| Contributions to individual private pension plans |
(PY035) |
F |
|
| Cash profits or losses from self-employment |
(PY050) |
F |
|
| Pension from individual private plans |
(PY080) |
F |
|
| Unemployment benefits |
(PY090) |
F |
|
| Old-age benefits |
(PY100) |
F |
|
| Survivors benefits |
(PY110) |
F |
|
| Sickness benefits |
(PY120) |
F |
|
| Disability benefits |
(PY130) |
F |
|
| Education-related allowances |
(PY140) |
F |
|
F= Fully comparable; L= Largely comparable; P= Partly comparable and NC= Not collected.
15.3. Coherence - cross domain
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.
15.3.1. Coherence - sub annual and annual statistics
Not applicable.
15.3.2. Coherence - National Accounts
Annex
15.4. Coherence - internal
no inconsistencies
Mean (average) interview duration per household = 41 minutes.
17.1. Data revision - policy
The revision policy applied by Statistics Lithuania is described in the Description of Procedure for Performance, Analysis and Publication of Revisions of Statistical Information.
17.2. Data revision - practice
The final results are published, no scheduled revisions are performed.
17.2.1. Data revision - average size
The average size of revisions is not calculated.
Detailed information concerning sampling frame, sampling design, sampling units, sampling size, weightings and mode of data collection can be found in this section (please see below). Such information is mainly used for the computation of the accuracy measures.
18.1. Source data
The sampling frame of EU-SILC is the Population Register updated regularly.
The sources of income are the statistical survey and the following administrative data sources: the State Social Insurance Fund Board (Sodra), the State Tax Inspectorate (STI), the Ministry of Social Security and Labour (MSSL).
18.1.1. Sampling Design
For the first time households which were selected for the survey in 2005 divided into 4 rational groups. One of these groups was dropped out after 2005 operation and not included to the survey of 2006 according to the original integrated design. A new sub-sample of households was selected to the sample of year 2006. For new sample stratified sample design was used. Population register was used as a sampling frame. Simple random sample of persons was used in each stratum. The second group was dropped out after 2006 operation and not included to the survey of year 2007. A new sub-sample of households was selected to the sample of year 2007 according the same rules as selected a new sub-sample before and so was in every following year. And so on.
While selecting the new rotational group of the sample the country were grouped into 25 strata: 5 largest cities, other cities and rural area by county (a total of 10 counties). Simple random sample of non–institutional persons aged 16 and over was selected from the Population Register in each stratum. Household which lives in the selected person’s address was surveyed.
Within each of 25 strata simple random sample was used to select the person’s address.
18.1.2. Sampling unit
Persistent resident aged 18 and over with related household members.
18.1.3. Sampling frame
Population Register
18.2. Frequency of data collection
Fixed income reference period was used and therefore the sample was not principally divided into months or weeks. Fieldwork period was from the January till the April.
18.3. Data collection
| Mode of data collection
Description of collecting income variables
|
18.4. Data validation
Completed questionnaires were checked by supervisors. Necessary call-backs were made.The computer program included the possible logical checks between questions and questionnaires, also a package of alerts (warning and error ones) related to ranges of admissible values and logical connections between questions. Coding controls were implemented in post-data-collection. After the data entry was finished the data were checked for consistency by specialists of the Living Standard and Employment Statistics Division.
18.5. Data compilation
Non-income data are mainly collected as interview, some personal data are obtained from population register.
Income data are collected from interview, linked to an administrative data, compared and edited if needed.
All wave data are pieced together by topic, compared and edited if necessary.
18.5.1. Imputation - rate
no additional information
18.5.2. Calculation of weighting factors and weight adjustments
annex
18.5.3. Estimation and imputation
Item non-response is mostly related employee cash or near cash income (PY010), cash benefits or losses from self-employment (PY050) and tax on Income and Social Contributions (HY140). Also few cases are related disability benefits (PY130), family/child related allowances (HY050) and interest, dividends, etc (HY090).
Deterministic methods (median imputation) were used for PY010G, PY050G.PY030G, HY090G.
Deductive methods were used for HY050G, HY140G (deductive imputation).
The data on the private use of the company car is collected in the individual questionnaire. The questions about car mode, type, year and other are asked. The amount which person has gained is estimated using Straight Line Method.
18.6. Adjustment
Not applicable.
18.6.1. Seasonal adjustment
Not applicable.
LT_2024_Annex 3-Sampling_errors_13.2
LT_2024_Annex 4-Data_collection_18.3
LT_2024_Annex 5-Weighting procedure
LT_2024_Annex 5 -Weighting procedure
LT_2024_Annex 7-Coherence_15.3-15.3.2
LT_2024_Annex 8-Breaks in series_15.2-updated
LT_2024_Annex 9-Rolling module
LT_2024_Annex A EU-SILC - content tables
LT 2024 questionnaires
The European Union Statistics on Income and Living Conditions (EU-SILC) is a survey-based instrument aiming at collecting timely and comparable cross-sectional and longitudinal multidimensional microdata on income, poverty, social exclusion and living conditions. In addition, it collects module variables every three years, six years or ad-hoc new policy needs modules.
The EU-SILC instrument provides two types of data:
- Cross-sectional data pertaining to a given time or a certain time period with variables on income, poverty, social exclusion and other living conditions;
- Longitudinal data pertaining to individual-level changes over time, observed periodically over four year rotation scheme (Annex III (2) of 2019/1700).
Social exclusion and housing condition information is collected mainly at household level while labour, education and health information is obtained for persons aged 16 and over. The core of the instrument is income information at very detailed component level and mainly collected at personal level.
30 May 2025
Statistical concepts and definitions for EU-SILC are specified in Regulation (EU) 2019/1700, Commission Implementing Regulation (EU) 2019/2181, and Commission Implementing Regulation (EU) 2019/2242. Additional information is available in the EU statistics on income and living conditions (EU-SILC) methodology and in the methodological guidelines and description of EU-SILC target variables (see CIRCABC).
Further details are provided in items 5, 15.1.1.1, 15.2.2 and 18.3.
Statistical units are private households and all persons living in these households who have usual residence in Lithuania. Annex II of the Commission implementing regulation (EU) 2019/2242 defines specific statistical units per variable and specifies the content of the quality reports on the organization of a sample survey in the income and living conditions domain pursuant to Regulation (EU) 2019/1700 of the European Parliament and of the Council.
The target population is private households and all persons composing these households having their usual residence in Lithuania. Private household means a person living alone or a group of persons who live together, providing oneself or themselves with the essentials of living.
Regions, country.
Description of reference period used for incomes
| Period for taxes on income and social insurance contributions |
Income reference periods used |
Reference period for taxes on wealth |
Lag between the income ref period and current variables |
|---|---|---|---|
| last calendar year |
last calendar year |
last calendar year |
0-4 months |
According to Reg. (EU) 2019/1700 Annex II, precision requirements for all data sets are expressed in standard errors and are defined as continuous functions of the actual estimates and of the size of the statistical population in a country or in a NUTS 2 region. For the income and living conditions domain, the estimated standard errors of the following indicators are examined according to certain parameters set:
- · Ratio at‐risk‐of‐poverty or social exclusion to population;
- · Ratio of at‐persistent‐risk‐of‐poverty over four years to population;
- · Ratio at‐risk‐of‐poverty or social exclusion to population in each NUTS 2 region.
Further information is provided in section 13.2 Sampling error.
The data involves several units of measure depending upon the variables. Income variables are transmitted to Eurostat in national currency. For more information, see methodological guidelines and description of EU-SILC target variables available on CIRCABC
Non-income data are mainly collected as interview, some personal data are obtained from population register.
Income data are collected from interview, linked to an administrative data, compared and edited if needed.
All wave data are pieced together by topic, compared and edited if necessary.
The sampling frame of EU-SILC is the Population Register updated regularly.
The sources of income are the statistical survey and the following administrative data sources: the State Social Insurance Fund Board (Sodra), the State Tax Inspectorate (STI), the Ministry of Social Security and Labour (MSSL).
Annual
See the information provided in 14.1.1 and 14.1.2
Statistical information is comparable between EU countries.
Since 2005
