1.1. Contact organisation
Central Statistical Bureau of Latvia
1.2. Contact organisation unit
Social Statistics Department
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
Lāčplēša Street 1, Riga, Latvia, LV-1010
1.6. Contact email address
Confidential because of GDPR
1.7. Contact phone number
Confidential because of GDPR
1.8. Contact fax number
Confidential because of GDPR
2.1. Metadata last certified
26 May 2025
2.2. Metadata last posted
27 May 2025
2.3. Metadata last update
27 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‐or more 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.
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.
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 the Member State. 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 the Member State. 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 |
|---|---|---|
| There were no divergences from the common definition. Persons living in private households within national territory were the reference population of the EU-SILC survey. |
There were no divergences from the common definition. |
There were no divergences from the common definition. Due to the complexity of household membership several practical and comprehensive explanations based on specific cases (examples) were given to interviewers. |
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
Reference area is territory of Latvia.
3.8. Coverage - Time
Latest data is 2024 data and 2023 data (for income, poverty, income inequality, low work intensity, activity status during 2023).
Data are available for the survey years 2005-2024.
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.
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 |
|---|---|---|---|
| In Latvia taxes and social insurance contributions refer to the income received during the income reference period (2023). The only exception is repayments or receipts for tax adjustment. These are taxes and social insurance contributions, which have been received/paid during the income reference period, but may refer to previous years. Those repayments/receipts are included in variable HY140 (tax on income and social contributions). |
There were no divergences from the common definition. In Latvia the income reference period is the previous calendar year (2023). |
In Latvia the reference period for taxes on wealth refer to the income reference period (2023). |
The lag between the end of the income reference period and current variables is from 2 to 7 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
For EU-SILC microdata CSB of Latvia uses anonymisation rules prepared by Eurostat. In case of non-standard data request the data have to be revised by staff responsible for confidentiality issues in CSB of Latvia.
7.2. Confidentiality - data treatment
For EU-SILC microdata CSB of Latvia uses anonymisation rules prepared by Eurostat. In case of non-standard data request the data have to be revised by staff responsible for confidentiality issues in CSB of Latvia.
In publications CSB of Latvia use several symbols informing data users about the data:
- Magnitude zero
0.0 Magnitude less than 0.05 of the unit employed
0.00 Magnitude less than 0.005 of the unit employed
( ) Data based on small number of respondent answers (20–49 observations)
... Data not available or too uncertain for presentation (for example in case of less than 20 observations)
8.1. Release calendar
EU-SILC 2024 press release's calendar:
- 17 December 2024 - Household income rose by 14.6 % in 2023 (statistic institute website)
- 27 December2024 - 21.6 % of population were at risk of poverty in 2023 (statistic institute website)
- 25 March 2025 - Material and social deprivation rates the lowest recorded so far (statistic institute website)
Publications about EU-SILC 2024 data:
- 16 January 2025 People at risk of poverty and social exclusion in Latvia in 2023, in Latvian (statistic institute website)
- 31 January 2025 Household disposable income in Latvia in 2023, in Latvian (statistic institute website)
- 25 March 2025 Material deprivation in Latvia in 2024, in Latvian (statistic institute website)
EU-SILC data online database:
- Disposable income (statistic institute website); last update - 17 December 2024
- Poverty and inequality (statistic institute website); last update - 27 December 2024
- Self-reported health and health related habits (statistic institute website); last update - 13 January 2025
- Quality of life (statistic institute website); last update - 13 January 2025
- Minimum income level (statistic institute website); last update - 27 january 2025
- Housing (statistic institute website); last update of EU-SILC data tables - 03 March 2025
- Private households and families (statistic institute website); last update of EU-SILC data tables - 11 March 2025
- Material deprivation (statistic institute website); last update - 25 March 2025
- Access to services (EU-SILC ad-hoc modules) (statistic institute website); last update - 26 May 2025
- Health and children`s health (EU-SILC ad-hoc modules) (statistic institute website); last update - 26 May 2025
8.2. Release calendar access
Please refer to the Release calendar - Eurostat (europa.eu) publicly available on the Eurostat’s website.
Advance dissemination calendar of Official statistics of Latvia available on the Official statistics portal.
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 EU statistics on income and living conditions - Microdata - Eurostat.
EU-SILC data of CSB of Latvia:
1) Datasets for research and study
2) EU-SILC Public use files (information in Latvian only).
Annual
10.1. Dissemination format - News release
EU-SILC 2024 data:
- 17 December 2024 - Household income rose by 14.6 % in 2023 (Official statistical website)
- 27 December 2024 - 21.6 % of population were at risk of poverty in 2023 (Official statistical website)
- 25 March 2025 - Material and social deprivation rates the lowest recorded so far (Official statistical website)
10.2. Dissemination format - Publications
Publications about EU-SILC 2024 data:
- 16 January 2025 People at risk of poverty and social exclusion in Latvia in 2023, in Latvian (Official statistical website)
- 31 January 2025 Household disposable income in Latvia in 2023, in Latvian (Official statistical website)
- 25 March 2025 Material deprivation in Latvia in 2024, in Latvian (Official statistical website)
10.3. Dissemination format - online database
- Disposable income (Official statistical website); last update - 17 December 2024
- Poverty and inequality (Official statistical website); last update - 27 December 2024
- Self-reported health and health related habits (Official statistical website); last update - 13 January 2025
- Quality of life (Official statistical website); last update - 13 January 2025
- Minimum income level (Official statistical website); last update - 27 January 2025
- Housing (Official statistical website); last update - 03 March 2025
- Private households and families (Official statistical website); last update - 11 March 2025
- Material deprivation (Official statistical website); last update - 25 March 2025
- Access to services (EU-SILC ad-hoc modules) (Official statistical website); last update - 26 May 2025
- Health and children`s health (EU-SILC ad-hoc modules) (Official statistical website); last update - 26 May 2025
10.3.1. Data tables - consultations
Information is not available.
10.4. Dissemination format - microdata access
- Datasets for research and study
- EU-SILC Public use files (information in Latvian only).
10.5. Dissemination format - other
Not available.
10.5.1. Metadata - consultations
Information is not available.
10.6. Documentation on methodology
EU-SILC surveys methodology in Latvia is based on METHODOLOGICAL GUIDELINES AND DESCRIPTION OF EU-SILC TARGET VARIABLES (docSILC065).
10.6.1. Metadata completeness - rate
Information is not available.
10.7. Quality management - documentation
Information is not available.
11.1. Quality assurance
Management Systems of the Central Statistical Bureau (CSB) are certified according to requirements of ISO 9001:2015 standard "Quality management systems – Requirements" and information security management system standard ISO 27001:2013.
On November 29, 2018 the CSB gained a certificate of ISO 9001:2015 standard “Quality Management Systems – Requirements”, which is already the second international certificate gained by the CSB. The certification refers to development, production and dissemination of official statistics.
On September 21, 2017 the CSB certified information security management system, by checking its compliance with the international standard ISO 27001:2013 “Information technology. Security techniques. Information security management systems”. This standard reflects the best practice in information security management. It has been developed to identify and prevent any possible threats in the maintenance of information.
More detailed information about Quality management systems in CSB of Latvia can be found in the official website.
11.2. Quality management - assessment
Information about Quality management systems in CSB of Latvia.
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 EU-SILC 2024 variables were transmitted to Eurostat.
12.3.1. Data completeness - rate
100%
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.
CSB of Latvia use own methodology for calculation of sampling errors R-software and vardpoor packages.
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.
See Annex A, sheet 13.2.1. - Main indicators, standard error and CI at country level & Persistent-risk-of-poverty ratio over four years to the population, standart error and CI*
* CSB of Latvia use own methodology for calculation of sampling errors R-software and vardpoor packages.
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
Coverage error
| Main problems |
Population (sub-population) |
Size of error |
Comments |
|---|---|---|---|
| Over-coverage |
The target population is private households and all persons composing these households having their usual residence in Latvia |
3.8% |
|
| Under-coverage |
- |
- |
Is not estimated |
| Misclassification |
- |
- |
Is not estimated |
13.3.1.2. Common units - proportion
Not requested by Reg. 2019/2180 - This item can be filled in on voluntary basis
13.3.2. Measurement error
Measurement error for cross-sectional data
|
|
Cross-sectional data |
||
|---|---|---|---|
| Source of measurement errors |
Building process of questionnaire |
Interview training |
Quality control |
| Measurement errors were detected by logical checks and verification of received data, including verification online during the fieldwork. |
In SILC 2024 operation 4 types of questionnaires were utilized (3 types of questionnaires are the same as in the previous SILC operations and 1 separate type of questionnaire to collect secondary variables [3-year's and 6-year's modules]: the Household Register (to collect demographic information about all household members), the Household Questionnaire (to collect all information related to household – dwelling costs, housing conditions, income components received at the household level etc.), Personal Questionnaire (to collect all needed information for each household member aged 16 and over in previous calendar year), Questionnaire for secondary variables and the Sample List (additional document to record all necessary information about household members). The household members’ first, second names, contact addresses and phone numbers (fixed and mobile phone numbers) were recorded in the Sample List (before entering of the information in the CAPI/CATI application). The new CAPI and CATI applications, instead of Blaise, were carried out since EU-SILC 2013 using new program ISDMS-CASIS (Metadata Driven Integrated Statistical Data Management System - Computer Assisted Survey Information System). The new CAWI applications was carried out since EU-SILC 2017 also using program ISDMS-CASIS. The CAWI, CAPI and CATI applications as well as the paper questionnaires (to be applied in specific circumstances) of the EU-SILC survey were available in Latvian and in Russian (the language of the largest ethnic minority in Latvia). Only households that were participating in the EU-SILC survey for the second, third or fourth time and if person, who answered to Household Questionnaire during EU-SILC 2023, had internet connection for personal use at home (PD070=1), were used for CAWI. Households, which was inactive during CAWI fieldwork (did not participated in CAWI), were used for CATI or CAPI. Households (addresses) that were participating in the EU-SILC survey for the first, second, third, or fourth time and had have specified phone numbers in the previous waves, were used for CATI. |
The CSB interviewer’s service carried out the fieldwork of the EU-SILC survey. For the field staff was organised a 1 day intensive training session. The aims of the training were to introduce the fieldwork staff with methodology of the EU-SILC survey, to instruct interviewers for accurate fieldwork execution of the survey and give them information to motivate respondents for participation in the survey. |
Measurement errors were detected by logical checks and verification of received data, including verification online during the fieldwork. During the fieldwork distribution of the main variables has been compared between all interviewers. In case of significant difference of results, the interviewer was asked to peruse the methodology of the variable again. Compliance of the database with Eurostat requirements was checked with the SAS program. |
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
| 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 |
| 98.15 | 95.47 |
100.00 |
74.85 |
60.72 |
89.10 |
100.00 |
100.00 |
100.00 |
26.53 |
42.03 |
10.90 |
0.00 |
0.00 |
0.00 |
26.53 |
42.03 |
10.90 |
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.
See Annex A, sheet 13.3.3.1 - Unit non-response rate for longitudinal data
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
See Annex 2 - Item non-response rate
13.3.4. Processing error
Description of data entry, coding controls and the editing system
| Data entry and coding (if any used) |
Editing controls |
|---|---|
| 2024 was the 12 year when the ISDMS-CASIS (Metadata Driven Integrated Statistical Data Management System - Computer Assisted Survey Information System)data entry application was applied. Compared with 2012 Blaise based data entry application, the data entry program was not significantly changed. However, some specific processes (mainly connected with filling of Household register) in ISDMS-CASIS (in comparison with Blaise) were introduced. The quantity of personal data from the previous year of the survey introduced into the program had remained the same compared with 2023. The new CAWI applications was carried out since EU-SILC 2017 also using program ISDMS-CASIS. Compared with CAPI or CATI data entry application, the CAWI data entry program was not significantly changed. However, some specific processes (mainly connected with filling of Household register and the number and type of checks) in CAWI was introduced. It was done to make easier entry process for CAWI respondents (as much as possible). Data were transformed from ISDMS-CASIS to IBM SPSS or MS Excel, where the initial database had been scrutinized and corrected. Data from the EU-SILC 2024 operation were compared with data from the previous EU-SILC operations, when it was possible. Compliance of the database with Eurostat requirements was checked with the SAS data checking program. |
See description in the previous column |
See Annex A, sheet 13.3.4 - Re-interview rates by wave
13.3.5. Model assumption error
Not applicable
14.1. Timeliness
The data is prepared within the deadline set up by the Regulation (EU) No 2019/1700
14.1.1. Time lag - first result
First results are published 12 months after income reference period (year of 2023).
Data release calendar can be found in the official statistical Latvian website.
14.1.2. Time lag - final result
Final results are published 13 months after income reference period (year of 2023).
Data release calendar can be found in the official statistical Latvian website.
14.2. Punctuality
CSB of Latvia publish EU-SILC data according to calendar of publication.
Data release calendar can be found in the official statistical Latvian website.
14.2.1. Punctuality - delivery and publication
CSB of Latvia publish EU-SILC data according to calendar of publication.
Data release calendar can be found in the official statistical Latvian website.
First results at national level are published 12 months after income reference period (year of 2023).
100% of data release delivered on time.
15.1. Comparability - geographical
Latvia in one region ar NUTS2 level.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not applicable.
15.2. Comparability - over time
See Annex 8 - Break in series.
15.2.1. Length of comparable time series
See Annex 8 - Break in series.
15.2.2. Comparability and deviation from definition for each income variable
|
Comparability and deviation from definition for each income variable
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.
See Annex 7 - Coherence.
15.3.1. Coherence - sub annual and annual statistics
Not applicable.
15.3.2. Coherence - National Accounts
See Annex 7 - Coherence_15.3-15.3.2.
15.4. Coherence - internal
See Annex 7 - Coherence.
Mean (average) interview duration per household = 27.6 minutes.
Mean (average) interview duration per person = 9.9 minutes.
17.1. Data revision - policy
Not available
17.2. Data revision - practice
Not available
17.2.1. Data revision - average size
Not available
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
Stratified, two stage sampling design with rotating panels is used for EU-SILC survey at CSB Latvia. At the first stage, primary sampling units (PSUs) are selected from the list of survey polygons with inclusion probabilities proportional to their size. Survey polygons are artificially created GIS polygons that divide the whole population of dwellings into approximately homogeneous areas by their size (approx. 300 dwellings for each survey polygon in cities and towns and approx. 150 dwellings otherwise).
At the second stage, dwellings are used as secondary sampling units (SSUs). Since 2021 CSB Latvia produces and stores frames of dwellings and persons on monthly basis (beginning of corresponding month) in Social statistics database register (SSDN). New sample panel for the next year’ s EU-SILC sample is selected during November using information from corresponding month. Hence, the time lag between the last update of frames and beginning of the EU-SILC survey interviewing process is three months.
Important note abour PSUs: until EU-SILC 2020 (first wave households) CSB of Latvia used PSUs numbers which deffers form PSUs numbers, which will be used for first wave households from EU-SILC 2021 onwards.
18.1.1. Sampling Design
Type of sampling design
A stratified two-stage sampling was used for the EU-SILC survey in Latvia. A systematic sampling with inclusion probabilities proportional to the unit size was carried out at the first stage and a simple random sampling was carried out at the second stage.
Stratification and sub-stratification criteria
The stratification was made depending on the type of municipality (Riga, cities, towns, rural areas). The Classification of Administrative Territories and Territorial Units (CATTU) of Latvia was used for stratification.
Sample size and allocation criteria
Actual sample size of EU-SILC 2023 - 8742
Achieved sample size of EU-SILC 2023 - 6091
Individual interviews (16+ persons) - 10496
Distribution of Achieved sample by DB075 (rotational group)
| Total | DB075=1 | DB075=2 | DB075=3 | DB075=4 |
|---|---|---|---|---|
| 6091 | 1382 | 1465 | 1960 | 1284 |
1
Distribution of 16+ household members (RB245=1) by DB075 (rotational group)
| Total | DB075=1 | DB075=2 | DB075=3 | DB075=4 |
|---|---|---|---|---|
| 10496 | 2401 | 2510 | 3319 | 2266 |
A non-proportional allocation was used to select SSUs.
18.1.2. Sampling unit
The Population Census counting areas were used as primary sampling units (PSUs) at the first stage. In general, the entire territory of Latvia is covered in lists of Population Census counting areas. PSUs were selected by a systematic sampling with inclusion probabilities proportional to the population size (number of households) of PSUs.
Dwellings were used as secondary sampling units (SSUs). A simple random sampling was used to select SSUs from the PSUs selected at the first sampling stage. In Latvia several households can be registered in one dwelling. All households and individuals living in the selected dwelling were included in the EU-SILC survey in the first wave. If none of persons enumerated in the Household List lived in the selected dwelling in the first wave, then it was possible to interview all households and individuals living in the selected dwelling.
18.1.3. Sampling frame
Stratified, two stage sampling design with rotating panels is used for EU-SILC survey at CSB Latvia. At the first stage, primary sampling units (PSUs) are selected from the list of survey polygons with inclusion probabilities proportional to their size. Survey polygons are artificially created GIS polygons that divide the whole population of dwellings into approximately homogeneous areas by their size (approx. 300 dwellings for each survey polygon in cities and towns and approx. 150 dwellings otherwise).
At the second stage, dwellings are used as secondary sampling units (SSUs). Since 2021 CSB Latvia produces and stores frames of dwellings and persons on monthly basis (beginning of corresponding month) in Social statistics database register (SSDN). New sample panel for the next year’ s EU-SILC sample is selected during November using information from corresponding month. Hence, the time lag between the last update of frames and beginning of the EU-SILC survey interviewing process is three months.
To compensate the non-response and taking into account the design effect it was decided to select 8672 dwellings. In Latvia more than one household can live in one dwelling. Therefore, there were 8742 households living in the selected dwellings. In case if it was not possible to contact the selected dwelling (the dwelling cannot be located, it was not possible to contact any person living in the dwelling or the dwelling was inaccessible, etc.) it was assumed that one household lived in the selected dwelling.
The response rates differ very much in each stratum. For this reason dwellings were not included with probabilities proportional to stratum size, but the initial sample size was proportional to population size of each stratum. The initial sample size was adjusted according to response rates in each stratum to get the final sample size in each stratum.
Renewal of sample: rotational groups
Latvia applies a rotational panel where the sample is divided into four sub-samples. Each of them represents the whole population. Every year one rotation group rotates out (is dropped) and a new one is added to the sample.
18.2. Frequency of data collection
A sample distribution over time was not used because the EU-SILC survey is organized on an annual basis. The number of households successfully interviewed in each month of fieldwork is shown below in table below.
Sample distribution over time (Longitudinal 2021 - 2023, Cross-sectional 2024)
| Month |
2021 |
2022 |
2023 |
2024 |
Total |
|||||
|---|---|---|---|---|---|---|---|---|---|---|
| number |
% |
number |
% |
number |
% |
number |
% |
number |
% |
|
| January | 0 |
0.0 | 25 | 0.7 | 40 | 0.8 | 79 | 1.3 | 144 | 0.9 |
| February |
0 |
0.0 | 110 | 3.0 | 269 | 5.7 | 1750 | 28.7 | 2129 | 12.9 |
| March |
10 |
0.5 | 524 | 14.4 | 455 | 9.6 | 768 | 12.6 | 1757 | 10.7 |
| April |
310 |
15.8 | 975 | 26.8 | 994 | 20.9 | 1247 | 20.5 | 3526 | 21.4 |
| May |
610 | 31.1 | 1208 | 33.2 | 1382 | 29.1 | 1049 | 17.2 | 4249 | 25.8 |
| June |
888 |
45.2 | 654 | 18.0 | 1181 | 24.9 | 1198 | 19.7 | 3921 | 23.8 |
| July |
146 |
7.4 | 140 | 3.9 | 430 | 9.1 | 0 | 0.0 | 716 | 4.4 |
| TOTAL |
1964 |
100.0 | 3636 | 100.0 | 4751 | 100.0 | 6091 | 100.0 | 16442 | 100.0 |
Fieldwork
First time CAWI data collection started in the 31th of January 2024 and lasted till the 20st of February 2024.
CAPI/CATI data collection started in the 15th of March 2024 and lasted till the 30th of June 2024.
18.3. Data collection
Mode of data collection
|
|
1-PAPI |
2-CAPI |
3-CATI |
4-CAWI |
5-PAPI proxy |
6-CAPI-proxy |
7-CATI-proxy |
8-CAWI proxy |
9-other (missing) |
|---|---|---|---|---|---|---|---|---|---|
| % of total |
3.1 |
8.8 |
46.3 |
5.2 |
0.7 |
4.1 |
24.3 |
1.9 |
5.7 |
Description of collecting income variables
| The source or procedure used for the collection of income variables |
The form (gross, net) in which income variables at component level have been obtained |
The method used for obtaining target variables in the required form |
|---|---|---|
|
According to the agreement signed between the CSB and the SSIA micro-data files regarding pensions and state social benefits paid to the EU-SILC 2024 respondents (during 2023) were received from the SSIA and used to prepare corresponding income variables. Additionally CSB received from SSIA data about new benefits which SSIA paid to respondents because of Covid-19 crysis. In 2013 bilateral agreements between CSB and almost all municipalities were signed and starting from EU-SILC 2013 data on municipal benefits are obtained from the registers of municipalities (most of municipalities store this data in centralized system – SOPA). According to the bilateral agreements municipalities have to prepare aggregated data on municipal benefits (in EU-SILC 2015 – 7 types of aggregated municipal benefits). The data is aggregated by 5 EU-SILC income variables, except Guaranteed minimum income benefit and Municipal child birth benefit, which CSB collects from municipal registers separately. Only information about some minor benefits administrated by few local municipalities or pensions paid by foreign countries (excluding Russia, Belarus and Ukraine) and service pensions, which were not administrated by SSIA, was collected via questionnaires in EU-SILC 2023. The exception was the net employee cash or near cash income (PY010N), which was available from the State Revenue Service (SRS) and from questionnaire. The gross employee cash or near cash income (PY010G) was obtained counting up the net employee cash or near cash income from questionnaire or SRS with paid taxes from the SRS. Information from the SRS is also used for imputation purposes if the amount of the net employee cash or near cash income was missing in the questionnaire and in those cases when the SRS information showed higher income than reported in the questionnaire. Household income variables (such as imputed rent, income from rental property and land etc.) were collected from the household respondent, which was responsible for issues related to dwelling and the household as a whole. An exception was income from dividends/ profit from capital investment. This variable together with personal income variables (such as employee income, self-employment income, education related allowances, etc.) was collected from each household member eligible for the personal interview. Since EU-SILC 2013 income from interest was collected from SRS. Since EU-SILC 2018 INCOME FROM RENTAL OF A PROPERTY OR LAND (HY040) partly was collected from SRS. |
See description in the previous column |
See description in the previous column |
Checking of an administrative data sources:
- additional data about respondents was included as preprint and respondent was able to correct it during interview;
- income data of pensions and benefits from administrative registers was compared with same data of previous years (number of recipients and totals)
- income data of employee cash or near cash income was compared with two different data sources - annual data and monthly data
18.4. Data validation
Data validation was detected by logical checks and verification of received data, including verification online during the fieldwork. During the fieldwork distribution of the main variables has been compared between all interviewers. In case of significant difference of results, the interviewer was asked to peruse the methodology of the variable again.
Compliance of the database with Eurostat requirements was checked with the SAS program.
18.5. Data compilation
Data editing
Data from the EU-SILC 2024 operation were compared with data from the previous EU-SILC operations, when it was possible. During the fieldwork distribution of the main variables has been compared between all interviewers. In case of significant difference of results, the interviewer was asked to peruse the methodology of the variable again. Compliance of the database with Eurostat requirements was checked with the SAS data checking program.
Weighting procedure
For weighting procedure see Annex 5 - Weighting procedure.
Data estimation and imputation
For data estimation and imputation see Annex 6 - Estimation and Imputation.
18.5.1. Imputation - rate
For data imputation see Annex 6 - Estimation and Imputation.
18.5.2. Calculation of weighting factors and weight adjustments
For weighting procedure see Annex 5 - Weighting procedure.
18.5.3. Estimation and imputation
For data estimation and imputation see Annex 6 - Estimation and Imputation.
18.6. Adjustment
Not applicable.
18.6.1. Seasonal adjustment
Not applicable.
Information on the quality of the rolling modules is available in the Annex 9 - Rolling module.
Annex 1-National Questionnaire LV (EN)
Annex 1-National Questionnaire LV (RU)
Annex 2-Item_non_response
Annex 3-Sampling_errors
Annex 4-Data_collection
Annex 5-Weighting procedure
Annex 6-Estimation and Imputation
Annex 7-Coherence
Annex 7-Coherence_15.3-15.3.2
Annex 8-Breaks in series
Annex 9-Rolling module
Annex A-Content tables
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‐or more 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.
27 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.
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 the Member State. 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 the Member State. Private household means a person living alone or a group of persons who live together, providing oneself or themselves with the essentials of living.
Reference area is territory of Latvia.
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 |
|---|---|---|---|
| In Latvia taxes and social insurance contributions refer to the income received during the income reference period (2023). The only exception is repayments or receipts for tax adjustment. These are taxes and social insurance contributions, which have been received/paid during the income reference period, but may refer to previous years. Those repayments/receipts are included in variable HY140 (tax on income and social contributions). |
There were no divergences from the common definition. In Latvia the income reference period is the previous calendar year (2023). |
In Latvia the reference period for taxes on wealth refer to the income reference period (2023). |
The lag between the end of the income reference period and current variables is from 2 to 7 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.
Data editing
Data from the EU-SILC 2024 operation were compared with data from the previous EU-SILC operations, when it was possible. During the fieldwork distribution of the main variables has been compared between all interviewers. In case of significant difference of results, the interviewer was asked to peruse the methodology of the variable again. Compliance of the database with Eurostat requirements was checked with the SAS data checking program.
Weighting procedure
For weighting procedure see Annex 5 - Weighting procedure.
Data estimation and imputation
For data estimation and imputation see Annex 6 - Estimation and Imputation.
Stratified, two stage sampling design with rotating panels is used for EU-SILC survey at CSB Latvia. At the first stage, primary sampling units (PSUs) are selected from the list of survey polygons with inclusion probabilities proportional to their size. Survey polygons are artificially created GIS polygons that divide the whole population of dwellings into approximately homogeneous areas by their size (approx. 300 dwellings for each survey polygon in cities and towns and approx. 150 dwellings otherwise).
At the second stage, dwellings are used as secondary sampling units (SSUs). Since 2021 CSB Latvia produces and stores frames of dwellings and persons on monthly basis (beginning of corresponding month) in Social statistics database register (SSDN). New sample panel for the next year’ s EU-SILC sample is selected during November using information from corresponding month. Hence, the time lag between the last update of frames and beginning of the EU-SILC survey interviewing process is three months.
Important note abour PSUs: until EU-SILC 2020 (first wave households) CSB of Latvia used PSUs numbers which deffers form PSUs numbers, which will be used for first wave households from EU-SILC 2021 onwards.
Annual
The data is prepared within the deadline set up by the Regulation (EU) No 2019/1700
Latvia in one region ar NUTS2 level.
See Annex 8 - Break in series.
