1.1. Contact organisation
Statistics Estonia
1.2. Contact organisation unit
Population and 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
Tatari 51, 10134 Tallinn, Estonia
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
25 March 2024
2.2. Metadata last posted
25 March 2024
2.3. Metadata last update
25 March 2024
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, 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 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 |
|---|---|---|
| Persons living in collective households are included in the reference population. The share of persons who are living in collective households and who are not at the same time members of some other private household is likely to be very low. Additionally, there is no feasible way to estimate their share in the total population. Thus, the exclusion of these persons is unlikely to affect the comparability and reliability of the estimates. |
There were no divergences from the common definition. |
There were no divergences from 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
Estonia as a whole.
3.8. Coverage - Time
2004 - 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 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 |
|---|---|---|---|
| There were no divergences from the common definition. Tax on income and social insurance contributions, as well as tax repayments and receipts refer to the income received during the income reference period (previous calendar year). |
There were no divergences from the common definition. Taxes on wealth paid during the income reference periood (previous calendar year) were recorded. |
There were no divergences from the common definition. Taxes on wealth paid during the income reference periood (previous calendar year) were recorded. |
Fieldwork period was 15 January 2024 - 19 Mai 2024. The lag between the income reference period and current variables ranges from 1 to 5 months, thus not exceeding 8 months stipulated in the regulation. |
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
The dissemination of data collected for the purpose of producing official statistics is guided by the requirements provided for in § 32, § 34, § 35, § 38 of the Official Statistics Act.
7.2. Confidentiality - data treatment
The treatment of confidential data is regulated by the Procedure for Protection of Data Collected and Processed by Statistics Estonia (in Estonian).
See more details on the website of Statistics Estonia in the section Õigusaktid.
8.1. Release calendar
Notifications about the dissemination of statistics are published in the release calendar, which is available on the website. Every year on 1 October, the release times of the statistical database, news releases, main indicators by IMF SDDS and publications for the following year are announced in the release calendar (in the case of publications – the release month).
Please refer to the Release calendar - Statistic Estonia (stat.ee)
Preliminary data are published 160 days after the field-work.
Final data and microdata files are published 240 days after the end of the reference year (T + 240).
The data have been published at the time announced in the release calendar.
8.2. Release calendar access
Please refer to the Release calendar - Eurostat (europa.eu) publicly available on the Eurostat’s website.
Please refer to the Release calendar - Statistic Estonia (stat.ee).
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.
All users have been granted equal access to official statistics: dissemination dates of official statistics are announced in advance and no user category (incl. Eurostat, state authorities and mass media) is provided access to official statistics before other users. Official statistics are first published in the statistical database. If there is also a news release, it is published simultaneously with data in the statistical database. Official statistics are available on the website at 8:00 a.m. on the date announced in the release calendar.
Annual
10.1. Dissemination format - News release
The news release “Relative poverty” once a year - 2024 edition
The news release “Life expectancy and healthy life years” once a year - 2024 edition
10.2. Dissemination format - Publications
2024 year publications on Stat EE:
- 84 000 Eesti inimest tunneb ilmajäetust. Mida see tähendab?
- Balti riikide kõige õnnelikumad inimesed elavad Eestis
- Eesti elanike oodatav eluiga oli mullu läbi aegade kõrgeim, tervena elatud aastate arv aga vähenes
- Parem kui lätlastel, kehvem kui soomlastel. Millise hinnangu annab Eesti inimene oma tervisele?
10.3. Dissemination format - online database
Data are published in the statistical database on Statistics Estonia Statistical database in the tables LES87 and LEV13 of the subject area “Social life / Well-being of children”,
in the tables LEM01–LEM05 of the subject area “Social life / Households / General data of households”,
in the tables ST01–ST24 of the subject area “Social life / Income”,
in the tables LES81–LES88 of the subject area “Social life / Social exclusion and poverty / Poverty and coping of children”,
in the tables LES01–LES25, LES290 and LES51 of the subject area “Social life / Social exclusion and poverty / Poverty and inequality”,
in the tables LES61–LES67 of the subject area “Social life / Social exclusion and poverty / Poverty, coping and social relationships of elderly people”.
in the tables TH51–TH54 of the subject area “Social life / Health/Access to healthcare
in the tables TH75–TH79 of the subject area “Social life / Health/ Health status
in the tables THV41–THV54 of the subject area “Social life / Health/ Disabled persons/ Contriving of disabled persons
10.3.1. Data tables - consultations
Not applicable
10.4. Dissemination format - microdata access
The dissemination of data collected for the purpose of producing official statistics is guided by the requirements provided for in § 33, § 34, § 35, § 36, § 38 of the Official Statistics Act. Access to microdata and anonymisation of microdata are regulated by Statistics Estonia’s procedure for dissemination of confidential data for scientific purposes.
10.5. Dissemination format - other
Data serve as input for statistical activities 40009 “Income, poverty and material deprivation”, 40205 “Living conditions”, 40611 “Integration of disabled persons”, 40612 “Health” and 41001 “Social exclusion – Laeken indicators”.
10.5.1. Metadata - consultations
Not applicable
10.6. Documentation on methodology
The Estonian Social Survey. Methodological Report (2010).
See also annex on the metadata of the income benefits (Annex 10 -Metadata on benefits).
10.6.1. Metadata completeness - rate
All requested concepts are provided, 100%
10.7. Quality management - documentation
Not applicable
11.1. Quality assurance
To assure the quality of processes and products, Statistics Estonia applies the EFQM Excellence Model, the European Statistics Code of Practice and the Quality Assurance Framework of the European Statistical System (ESS QAF). Statistics Estonia is also guided by the requirements in § 7. “Principles and quality criteria of producing official statistics” of the Official Statistics Act.
11.2. Quality management - assessment
Statistics Estonia performs all statistical activities according to an international model (Generic Statistical Business Process Model – GSBPM). According to the GSBPM, the final phase of statistical activities is overall evaluation using information gathered in each phase or sub-process; this information can take many forms, including feedback from users, process metadata, system metrics and suggestions from employees. This information is used to prepare the evaluation report which outlines all the quality problems related to the specific statistical activity and serves as input for improvement actions.
Standardised output has been achieved through the definition of specific formats (list and description of output variables; data formats) and the determination of fixed deadlines for data transmission.
12.1. Relevance - User Needs
The main users of EU-SILC statistical data are policy makers, research institutes, media, and students.
Ministry of Social Affairs
The main users of ESS (EU-SILC) are:
- European Union institutions, government authorities (in Estonia, mostly the Ministry of Social Affairs – social protection and social inclusion issues), international organisations (e.g. OECD, UNICEF);
- Annual yearbooks and other publications (incl. analytical compilations) of Statistics Estonia and Eurostat;
- Researchers who have access to microdata;
- End users (consumers), incl. the media, who are interested in the statistics on living conditions, incomes and social inclusion in Estonia and the European Union.
12.2. Relevance - User Satisfaction
Eurostat carried out an online general User Satisfaction Survey (USS) in the period between April and July 2019 (repeated in June-July 2022) to obtain better understanding about users’ 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 microdata 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. Users emphasized their strong need for more detailed microdata.
For more information, please consult the User Satisfaction Survey.
12.3. Completeness
The variables HY145, HY121, HY081, HY131 are missing.
If the income at component level is reported gross or some of the components are reported gross and some net of tax,
adjustments (HY145) will be recorded in the variable HY140G.
Amount of HY121 of included in HY120.
Amount of HY081 of included in HY080.
Amount of HY081 of included in HY131.
Variables PH040-PH070
National EU_SILC did not take into account the change (adding the -2 flag) of the PH040-PH070 variables since 2016 antil 2023.
Therefore, not possible to separate those respondents who did not need to see a doctor and those who did, but had no problems.
All optional variables in national data are empty because they not asked.
12.3.1. Data completeness - rate
The data are complete and in compliance with the data composition requirements of EU-SILC regulation of the European Commission.
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:
- 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;
- 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;
- MT, SE, IS, NO, whose sampling design could be assimilated to a simple random sampling, we used DB030 for cluster specification and no strata.
See also “Annex 3 – Sampling errors”.
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
Coverage error
| Main problems |
Population (sub-population) |
Size of error |
Comments |
|---|---|---|---|
| Over-coverage |
134/7550 |
1,77% |
|
| Under-coverage |
- |
- |
|
| Misclassification |
- |
- |
|
13.3.1.2. Common units - proportion
Not applicable
13.3.2. Measurement error
Measurement error for cross-sectional data
Cross-sectional data
|
|
Building process of questionnaire |
Interview training |
Quality control |
|---|---|---|---|
| The measurement errors can stem from the questionnaire (its wording, design etc), the interviewers and the data collection method. While it is impossible to avoid this type of errors completely, steps were taken to reduce them as much as possible. |
The ESS questionnaire has been drafted following international experience in collecting income data. Where possible questions and wordings from Statistics Estonia’s previous surveys, the reliability and validity of which had been checked in practise, were used. In 2007 the questionnaire was supplemented using the experience from the past three waves. The main corrections in the household questionnaire were adding in questions about production of foodstuffs for own consumption and questions allowing the calculation of savings from imputed rent. In the personal questionnaire the main developments in 2007 were adding questions about education obtained since the previous interview for the longitudinal panel, allowing the choice to report wage income as yearly or monthly and net or gross, adding questions about non-monetary income from wage labour and a separate block of income questions for entrepreneurs. The social benefit questions were also updated and additional checkpoints created to ask respondents the questions that concern their situation specifically. The questions on child-care, familybenefits and unemployment benefits were also improved. In 2008 questions about managerial duties for current and last job were added and socio-economic statuses were prefilled for respondents who had answered the personal questionnaire the previous year for the months they had already provided answers for. An additional question was added regarding pensions paid by the local government and the conscript allowance paid to young men serving time in the armed forces. In 2009, the questions used to determine a respondent’s level of education were improved. Previously a person had to choose their level of education from a long list of official names, resulting in considerable errors. In 2009 these questions were redesigned for more accuracy and less respondent-induced errors. In 2010, the questions used to determine using childcare services reformulated to better meet Eurostat’s guidelines. In 2011 during the first month questions about current costs were asked in two currencies (euros, kroons). Respondent was asked to choose which currency he would like to answer. Other notable modifications in 2011 concerned the following variables. The question about the number of rooms available to the household was reformulated according to the Eurostat’s guidelines. The questions about intra-household sharing of resources were excluded and the questions about intergenerational transmission of disadvantages were added. 1) The module of Over-indebtedness, consumption and wealth as well as labour has been deleted in 2022: new modules have been added (3-year rolling module – Health and 6-year rolling module - Quality of life) and previous year modules have been removed. in 2023: new modules have been added (3-year rolling module - Labour Market and 6-year rolling module - Intergenerational transmission of advantages and disadvantages, housing difficulties) and previous year modules have been removed. in 2024: new modules have been added (3-year rolling module - Children and 6-year rolling module - Access to services) and previous year modules have been removed. |
All interviewers attended a day long training session. During the training, the EU-SILC survey manager briefed the interviewers on all updates in the questionnaires, discussed previous years’ errors, tracing rules. 60 interviewers partisipated in the training. |
The data is checked in three stages: the initial input check on the laptop during the interview, the secondary check on the newly received data in the office, and finally the data cleaning. |
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 |
| 97,38 | 93,75 |
100 |
78,05 |
64,41 |
89,68 |
92,08 |
88,99 |
94,34 |
23,99 |
39,62 |
10,32 |
7,92 |
11,01 |
5,66 |
30,01 |
46,27 |
15,4 |
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.
See EE_2024_Annex A EU-SILC - content tables
13.3.3.2.1. Item non-response rate by indicator
Item non-response rates are given in the Annex 2.
13.3.4. Processing error
Description of data entry, coding controls and the editing system
| Data entry and coding (if any used) |
Editing controls |
|---|---|
| Checking the data was done in three stages: data-entry checks during the interview, additional in-office checks during fieldwork and lastly data cleaning. The data for 2021 operation was collected using CAPI, CAWI and CATI. Data of studies in Estonia were transferred from Estonian Education Information System, data of studies abroad were collected via questionnaire using CAPI, CAWI and CATI. The data-entry program was written in VVIS (Data entry environment VVIS -- KR: Program for CAPI; KDM – qestionnaire designing/scripts) and contained most of the consistency checks. In Statistics Estonia, interviewers are required to react in some form to all error messages that occur during interviewing. The solution is either to correct an erroneous situation or if the situation is unusual but correct, add a remark to the data entry-program explaining this error. These logical checks allow to correct most of the errors already during an interview. The primary data-entry consistency controls were of 5 major types: 1) Checks of consistency between different answers. These included, but were not limited to following instances: 1. Whether an educational level attained was possible below a certain age, or educational levels were possible in said combinations for given years; 2. whether answers provided to different non-monetary deprivation tems agreed with each other; 3. whether the relationships in the household matrix were consistent with each other as well as with the age and sex of the household members; 4. whether the difference between the starting and finishing time of the interview was too short or too long and so on. 5. membership in pension plans checked by year of birth to see if legally bound to have joined pension pillar. 6. checks for correct survey area, interviewer code and personal number matching household numbers. 2) Tracing checks. These controls were implemented to ensure that all split-off households and new household members were assigned correct split numbers and person numbers respectively. 3) Checks not allowing for occupations to be written on too general a scale for coding. (e.g. salesperson, cleaner) 4) Checks for goods produced for own consumption, for instance quantities; 5) Checks with information from the previous year. These controls concerned demographic data, information on educational level and labour status as well as the calendar of activities. The in-office staff promptly checked the questionnaires that were electronically transmitted to the central office. This stage included the following controls: 1) All the errors suppressed by interviewers were activated and checked; 2) All remarks made by interviewers in the data entry-program were read through and where necessary, relevant corrections were made. 3) All split-off households as well as all households from which at least one member had left were scrutinized one by one. 4) All category ‘other’ answers were gone through to see if they could be classified under one of the given options. 5) Errors in coding were gone through. 6) Study benefits were checked by possibility of obtaining them in the school the respondent attended and legally set amounts. 7) Consistency between time reporter working under socio-economic status and months that salary was received. 8) Demographic information in the interviewers’ reports was compared to the data recorded in the electronic questionnaires. | All mistakes found during the secondary in-office data editing were put up in a shared excel table, and had to be clarified with the interviewer or interviewee by the end of the fieldwork period. This was done in co-operation of the EU-SILC team and the interviewers’ supervisors. The third and final stage of data checks involved later in-office data cleaning. The controls implemented at this stage involved further checks of data consistency, consistency across time, and of extreme income values and as a final step the Eurostat data-checks. Extreme values for all income components as well as total income were checked and handled on a case-by-case basis. |
13.3.5. Model assumption error
Not applicable
14.1. Timeliness
For national data.
Preliminary data are published 160 days after the field-work.
Final data and microdata files are published 240 days after the end of the reference year (T + 240).
14.1.1. Time lag - first result
Preliminary national data are published 160 days after the field-work.
14.1.2. Time lag - final result
Final national data and microdata files are published 160 days after the end of the reference year (T + 160).
14.2. Punctuality
The data have been published at the time announced in the release calendar.
14.2.1. Punctuality - delivery and publication
Not applicable
15.1. Comparability - geographical
The Estonian social survey is part of the European Union Statistics on Income and Living Conditions (EU-SILC), which is coordinated by Eurostat.
An EU-SILC survey is conducted in all EU member states and in Ireland, Norway, Switzerland and Turkey based on a harmonized methodology that allows publication of internationally comparable statistics on poverty, inequality and income.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not applicable.
15.2. Comparability - over time
EU-SILC in Estonia collects the respondent’s annual income from the previous calendar year. Income data from survey-year 2014 onwards are partially registry data (Tax and Customs Board, Unemployment Insurance Fund, Health Insurance Fund, Social Insurance Board) -- PY010, PY050, PY080, PY090, PY100, PY110, PY120, PY130, HY050, HY070, HY090, HY120, HY145.
Estonia records part of its income components as net values, but for some components ‘gross’ values are collected. According to the Eurostat guidelines, if the income at component level is reported gross or some of the components is reported gross and some net of tax (exampl, HY040), adjustments will be included under the variable HY140 not in HY145 (correction was make in EU-SILC 2019).
Also see “Annex 8 – Breaks in series”
15.2.1. Length of comparable time series
Not available.
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) |
NC |
he income components (HY040 INCOME FROM RENTAL) are reported gross and some net, in this case HY145N must be empty. HY145 is included in HY140G |
| 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.
See also "Annex 7 -Coherence"
15.3.1. Coherence - sub annual and annual statistics
Not applicable.
15.3.2. Coherence - National Accounts
See EE_2024_Annex 7-Coherence_15.3-15.3.2
15.4. Coherence - internal
The internal consistency of the data is ensured by the use of a common methodology for data collection and data aggregation.
Before the beginning of the Estonian social survey, incomes, and based on that poverty and inequality indicators were calculated on the basis of Household budget survey data.
Mean (average) interview duration per household = 39 minutes.
Mean (average) interview duration per person = 16 minutes.
Mean (average) interview duration for selected respondents (if applicable) = minutes. (Not applicable)
17.1. Data revision - policy
The data revision policy and notification of corrections are described in the section Principles of dissemination of official statistics of the website of Statistics Estonia.
17.2. Data revision - practice
Not applicable
17.2.1. Data revision - average size
Not applicable.
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
Statistical Population Register of Estonia (residents of Estonia).
18.1.1. Sampling Design
Statistical Population Register of Estonia (residents of Estonia). The sampling frame consists of people 14 years old and older.
The sample includes 8,200 households. Systematic stratified random sampling is used. The stratification is based on place of residence.
The sampling of persons is carried out by geographically stratified systematic sampling procedure, i.e. independent sub-samples are drawn separately from the non-overlapping subpopulations called strata. Each person is included with his or her household and all members of this household aged 15 or more are interviewed.
The 15 counties and Tallinn city are divided into four strata by population size: I – Tallinn; II – four bigger counties (Harju (excl. Tallinn), Ida-Viru, Pärnu and Tartu counties); III – ten smaller counties (Jõgeva, Järva, Lääne, Lääne-Viru, Põlva, Rapla, Saare, Valga, Viljandi and Võru counties); IV – Hiiu county.
18.1.2. Sampling unit
Households
Each household is to be interviewed four times, the rotation period is 12 months, whereas every year part of the sample is replaced.
Thus, during the year the survey is cross-sectional which guarantees higher accuracy of estimates while using the given sample size.
The interviews carried out with households in four consecutive years will allow getting more precise estimates of changes occurred over the years.
All households living permanently in Estonia are considered the survey population. Persons living in institutional households (children’s homes, care homes, convents, etc.) are excluded.
18.1.3. Sampling frame
Concerning the SILC instrument, three different sample size definitions can be applied:
- the actual sample size which is the number of sampling units selected in the sample - 7470 households.
- the achieved sample size which is the number of observed sampling units (household or individual) with an accepted interview - 5564 households and 10367 persons.
Minimum requirements are thus satisfied (3500 households and 7750 persons).
Given that the effective sample size has been already treated in the section dealing with sampling errors, in this section the attention focuses mainly on the achieved sample size.
| Sample | n |
|---|---|
| Actual sample size | 7470 |
| Total number of hhs with interviews | 5515 |
| Total number of 16+ persons with interviews | 9562 |
| Total number of 16+ persons with full-record imputation | 822 |
18.2. Frequency of data collection
Data is collected every year.
Fieldwork
Fieldwork period was 15 January 2024 - 19 May 2024
Renewal of sample: rotational groups
The sample consists of 4 rotational groups (renewal is on households):
rotational group from 2021 (DB075= 1);
rotational group from 2022 (DB075= 2);
rotational group from 2023 (DB075= 4);
new sub-sample (DB075=4).
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 |
|---|---|---|---|---|---|---|---|---|---|
| % of total |
|
|
58,03 |
18,26 |
|
|
11,81 |
3,99 |
7,92 |
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 |
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
Most of income variables (PY010, PY050, PY090, PY100, PY110, PY120, PY130, HY050) came from the registers (Tax and Customs Board, Unemployment Insurance Fund, Health Insurance Fund, Social Insurance Board) from survey-year 2014. Some income components were collected via telephone or web. |
Table summarizes mode in which different income variables were collected. Income data from survey-year 2014 onwards are basically registry data – gross; net is calculated (Tax and Customs Board, Unemployment Insurance Fund, Health Insurance Fund, Social Insurance Board) -- PY010, PY050, PY080, PY090, PY100, PY110, PY120, PY130, HY040, HY050, HY090.
|
Where only net values were collected or only net or gross value was recorded, the corresponding net and gross values were calculated on the basis of recorded values. Conversion algorithms were created on the basis of the local tax system. Information as to which taxes were paid on income components were also collected and taken into account in conversions. |
18.4. Data validation
Collected data is edited and checked against the previous values and against register data and other sources where possible.
Large set of logical and plausibility checks are applied and manual and automatic editing is carried out.
18.5. Data compilation
Weighting and imputation procedure are described in the added documents.
Annexes:
Annex 5 - Weighting procedure
Annex 6 - Estimation and Imputation
18.5.1. Imputation - rate
Please see the information provided in the Annex 6 - Estimation and Imputation.
18.5.2. Calculation of weighting factors and weight adjustments
Please see the information provided in the Annex 5 - Weighting procedure
18.5.3. Estimation and imputation
In the case of missing or unreliable data, estimate imputation based on established regulations will be used. According to the regulation of the European Commission, all missing values of income variables should be imputed. As a rule, statistical imputation is the method used for that.
In case of within-household non-response, the persons who have not responded are imputed by using a person, characterized by similar variables, who is selected from among the responded persons by using the nearest neighbour method.
Variables and statistical units which were not collected but which are necessary for producing the output are calculated. New variables are calculated by applying arithmetic conversion to already existing variables. This may be done repeatedly, the derived variable may, in turn, be based on previously derived new variables.
For statistical units weights are calculated, which are used to expand the data of the sample survey to the total population.
The weights are calculated on the basis of design weights derived from inclusion probabilities. The weights, which are first adjusted to compensate for the bias caused by non-response and then calibrated to the population data, are used in calculating the final data. The basis of the calibration is the distribution of the population of Estonia by sex and age group and county on the 1st of January according to demographic data.
Microdata are aggregated to the level necessary for analysis. This includes summation of data according to the classification and calculating various statistical measures, e.g. average, median, dispersion, etc. The collected data are converted into statistical output. This includes calculating additional variables.
See also Annex 6 - Estimation and Imputation
18.6. Adjustment
Not applicable.
18.6.1. Seasonal adjustment
Not applicable.
In 2023, the conversion of data processing scripts/programs from SAS to R or VAIS (VAIS is a proprietary data processing system/environment) was completed.
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.
25 March 2024
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 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.
Estonia as a whole.
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 |
|---|---|---|---|
| There were no divergences from the common definition. Tax on income and social insurance contributions, as well as tax repayments and receipts refer to the income received during the income reference period (previous calendar year). |
There were no divergences from the common definition. Taxes on wealth paid during the income reference periood (previous calendar year) were recorded. |
There were no divergences from the common definition. Taxes on wealth paid during the income reference periood (previous calendar year) were recorded. |
Fieldwork period was 15 January 2024 - 19 Mai 2024. The lag between the income reference period and current variables ranges from 1 to 5 months, thus not exceeding 8 months stipulated in the regulation. |
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
Weighting and imputation procedure are described in the added documents.
Annexes:
Annex 5 - Weighting procedure
Annex 6 - Estimation and Imputation
Statistical Population Register of Estonia (residents of Estonia).
Annual
For national data.
Preliminary data are published 160 days after the field-work.
Final data and microdata files are published 240 days after the end of the reference year (T + 240).
The Estonian social survey is part of the European Union Statistics on Income and Living Conditions (EU-SILC), which is coordinated by Eurostat.
An EU-SILC survey is conducted in all EU member states and in Ireland, Norway, Switzerland and Turkey based on a harmonized methodology that allows publication of internationally comparable statistics on poverty, inequality and income.
EU-SILC in Estonia collects the respondent’s annual income from the previous calendar year. Income data from survey-year 2014 onwards are partially registry data (Tax and Customs Board, Unemployment Insurance Fund, Health Insurance Fund, Social Insurance Board) -- PY010, PY050, PY080, PY090, PY100, PY110, PY120, PY130, HY050, HY070, HY090, HY120, HY145.
Estonia records part of its income components as net values, but for some components ‘gross’ values are collected. According to the Eurostat guidelines, if the income at component level is reported gross or some of the components is reported gross and some net of tax (exampl, HY040), adjustments will be included under the variable HY140 not in HY145 (correction was make in EU-SILC 2019).
Also see “Annex 8 – Breaks in series”
