Central Statistical Bureau of Latvia

Social Statistics Department

Lāčplēša Street 1, Riga, Latvia, LV-1010

26 May 2025

27 May 2025

27 May 2025

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.

• 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.

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.

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.

Definitions of reference population, household and household membership

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.

Reference area is territory of Latvia.

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.

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

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.

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.

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.

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)

EU-SILC 2024 press release's calendar:

1. 17 December 2024 - Household income rose by 14.6 % in 2023 (statistic institute website)
2. 27  December2024 - 21.6 % of population were at risk of poverty in 2023 (statistic institute website)
3. 25 March 2025 - Material and social deprivation rates the lowest recorded so far (statistic institute website)

Publications about EU-SILC 2024 data:

1. 16 January 2025 People at risk of poverty and social exclusion in Latvia in 2023, in Latvian (statistic institute website)
2. 31 January 2025 Household disposable income in Latvia in 2023, in Latvian (statistic institute website)
3. 25 March 2025 Material deprivation in Latvia in 2024, in Latvian (statistic institute website)

EU-SILC data online database:

1. Disposable income (statistic institute website); last update - 17 December 2024
2. Poverty and inequality (statistic institute website); last update - 27 December 2024
3. Self-reported health and health related habits (statistic institute website); last update - 13 January 2025
4. Quality of life (statistic institute website); last update - 13 January 2025
5. Minimum income level (statistic institute website); last update - 27 january 2025
6. Housing (statistic institute website); last update of EU-SILC data tables - 03 March 2025
7. Private households and families (statistic institute website); last update of EU-SILC data tables - 11 March 2025
8. Material deprivation (statistic institute website); last update - 25 March 2025
9. Access to services (EU-SILC ad-hoc modules) (statistic institute website); last update - 26 May 2025
10. Health and children`s health (EU-SILC ad-hoc modules) (statistic institute website); last update - 26 May 2025

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. 

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

EU-SILC 2024 data:

1. 17 December 2024 - Household income rose by 14.6 % in 2023 (Official statistical website)
2. 27 December 2024 - 21.6 % of population were at risk of poverty in 2023 (Official statistical website)
3. 25 March 2025 - Material and social deprivation rates the lowest recorded so far (Official statistical website)

Publications about EU-SILC 2024 data:

1. 16 January 2025 People at risk of poverty and social exclusion in Latvia in 2023, in Latvian (Official statistical website)
2. 31 January 2025 Household disposable income in Latvia in 2023, in Latvian (Official statistical website)
3. 25 March 2025 Material deprivation in Latvia in 2024, in Latvian (Official statistical website)

1. Disposable income (Official statistical website); last update - 17 December 2024
2. Poverty and inequality (Official statistical website); last update - 27 December 2024
3. Self-reported health and health related habits (Official statistical website); last update - 13 January 2025
4. Quality of life (Official statistical website); last update - 13 January 2025
5. Minimum income level (Official statistical website); last update - 27 January 2025
6. Housing (Official statistical website); last update - 03 March 2025
7. Private households and families (Official statistical website); last update - 11 March 2025
8. Material deprivation (Official statistical website); last update - 25 March 2025
9. Access to services (EU-SILC ad-hoc modules) (Official statistical website); last update - 26 May 2025
10. Health and children`s health (EU-SILC ad-hoc modules) (Official statistical website); last update - 26 May 2025

Information is not available.

1. Datasets for research and study
2. EU-SILC Public use files (information in Latvian only).

Not available.

Information is not available.

EU-SILC surveys methodology in Latvia is based on METHODOLOGICAL GUIDELINES AND DESCRIPTION OF EU-SILC TARGET VARIABLES (docSILC065).

Information is not available.

Information is not available.

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.

Information about Quality management systems in CSB of Latvia. 

The main users of EU-SILC statistical data are policy makers, research institutes, media, and students.

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.

All EU-SILC 2024 variables were transmitted to Eurostat.

100%

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.

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. 

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. 

Non-sampling errors are basically of 4 types:

• Coverage errors: errors due to divergences existing between the target population and the sampling frame.
• Measurement errors: errors that occur at the time of data collection. There are a number of sources for these errors such as the survey instrument, the information system, the interviewer and the mode of collection.
• Processing errors: errors in post-data-collection processes such as data entry, keying, editing and weighting.
• Non-response errors: errors due to an unsuccessful attempt to obtain the desired information from an eligible unit. Two main types of non-response errors are considered:
  • Unit non-response: refers to absence of information of the whole units (households and/or persons) selected into the sample.
  • Item non-response: refers to the situation where a sample unit has been successfully enumerated, but not all required information has been obtained.

Coverage 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

Coverage error

Not requested by Reg. 2019/2180 - This item can be filled in on voluntary basis

Measurement error for cross-sectional data

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.

Unit non-response rate for cross-sectional

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

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.

 See Annex 2 - Item non-response rate

 Description of data entry, coding controls and the editing system

 See Annex A, sheet 13.3.4 - Re-interview rates by wave

Not applicable

The data is prepared within the deadline set up by the Regulation (EU) No 2019/1700

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.

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.

CSB of Latvia publish EU-SILC data according to calendar of publication.

Data release calendar can be found in the official statistical Latvian website.

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.

Latvia in one region ar NUTS2 level.

Not applicable.

See Annex 8 - Break in series.

See Annex 8 - Break in series.

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.

Not applicable.

See Annex 7 - Coherence_15.3-15.3.2.

See Annex 7 - Coherence.

Mean (average) interview duration per household =  27.6 minutes.

Mean (average) interview duration per person = 9.9 minutes.

Not available

Not available

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.

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.

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)

1

Distribution of 16+ household members (RB245=1) by DB075 (rotational group)

A non-proportional allocation was used to select SSUs.

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.

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.

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)

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. 

Mode of data collection

Description of collecting income variables

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

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.

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.

For data imputation see Annex 6 - Estimation and Imputation.

For weighting procedure see Annex 5 - Weighting procedure.

For data estimation and imputation see Annex 6 - Estimation and Imputation.

Not applicable.

Not applicable.

Information on the quality of the rolling modules is available in the Annex 9 - Rolling module.