Reference metadata describe statistical concepts and methodologies used for the collection and generation of data. They provide information on data quality and, since they are strongly content-oriented, assist users in interpreting the data. Reference metadata, unlike structural metadata, can be decoupled from the data.
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). The Finnish EU-SILC longitudinal data is observed over the four-year rotation scheme.
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 (2011) in the Canberra Group Handbook on Household Income Statistics and by OECD (2013) in OECD Framework for Statistics on the Distribution of Household Income, Consumption and Wealthshould 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.
Further details are provided in concepts 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
Private households and all the persons composing the private household and who are permanently resident (=usual residence) in Finland. Persons permanently institutionalised, living in collective households, or in residential homes are not included in the concept.
Those persons who according to the Population Information System had a legal domicile in Finland on 31 December belong to the permanent resident population (Municipality of Residence Act 201/1994). Persons are domiciled if their stay is intended to last or has lasted at least one year.
Private household refers to the common housekeeping unit. Private household includes a person residing alone (=one person household), or all the persons, related or not, who reside and have their meals together or otherwise use their income together (=multiperson household).
See the private household definition. Persons who are temporarily absent from the household's main dwelling and from home are counted in household members if they have close family ties to the household and they do not form a household of their own. Such persons are as follows:
Persons conducting conscript service
Persons residing and working in another locality or abroad if they are involved in the acquisition and use of household income
Persons residing and studying in another locality if they use income received mostly from their parents
Persons temporarily in institution, on holiday or travelling
The following persons form a household of their own:
Subtenants
Domestic staff
Students living on their own if they live mostly on their own income or on a student loan
Students residing in dormitories, unless they are married or officially cohabiting
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 for at least 12 months.
According to the round 2021 census the number of population permanently living in Finland was 5,533,793, of that 82,552 was person not living in private households (incl. homeless persons). The reference year of the figures is 2020.
3.7. Reference area
The country as a whole and NUTS -regions (2-digits). The NUTS -regions are as follows: FI19: Länsi-Suomi, FI1B: Helsinki-Uusimaa, FI1C: Etelä-Suomi, FI1D: Pohjois- ja Itä-Suomi, FI20: Åland.
With regard to the estimated ratio at-risk-of-poverty or social exclusion to population and their precision requirements Åland is an exempt at the NUTS regions (1-digit, 2-digit levels) due to its population size which is less than 100 000 habitants ((EU2019/1700, Annex II). The NUTS 1-digit regions are FI1: MANNER-SUOMI, FI2: ÅLAND.
3.8. Coverage - Time
Annual data, reference year 2024. Data are available for the survey years 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 income
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
2023
2023
2023
0 - 5 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.
The compilation of the Finnish EU-SILC statistics is guided nationally by the Statistics Act. The Statistics Act contains provisions on the collection and processing of data and on the obligation to supply data. Besides the Statistics Act, the processing of data for statistical purposes is subject to the provisions of the Personal Data Act and the Act on the Openness of Government Activities. Further information: Statistical legislationExternal link
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.
According to the Statistics Act, Statistics Finland has the right to release data for statistics compilation to another statistical authority, and data collected for statistical purposes to release for scientific research.
The statistics are compiled within the Statistics Act (280/2004), the EU's General Data Protection Regulation (EU) 2016/679 and the national Data Protection Act (1050/2018). Confidentiality of data collected for statistical purposes is steered in the Act on the Openness of Government activities (621/1999). In addition, the EU Regulation on European statistics and especially for EU-SILC the EU regulation 2019/1700 (See concept 6.1) steer the statistics compilation. The data materials are protected at all stages of processing with the necessary physical and technical solutions, by following the guidelines by Statistics Finland. Employees have access only to the data essential for their duties. The use of data is restricted by usage rights. Members of the personnel have signed a pledge of secrecy upon entering the service. Violation of data protection is punishable.
The processing of the data is restricted by usage rights. All persons employed by Statistics Finland have signed a pledge of secrecy, where they have obliged to keep secret the data prescribed as confidential. The main legislation which steers the statistics compilation with regarding confidentiality is stated in the concept 7.1. Confidentiality – Policy.
The statistical data on which the statistics on living conditions are based are released to Eurostat, the Statistical Office of the European Union, for the comparative EU-SILC statistics. The statistical data do not contain direct identifiers. Protection measures common to the countries and, where necessary, nation-specific measures, are applied to the micro data which Eurostat releases from the EU-SILC statistics for scientific research use upon application. Researchers treating the data sign an individual confidentiality declaration.
The Finnish EU-SILC data is combined with the service set of Statistics Finland's income distribution statistics. The data is released as confidential by Statistics Finland for scientific research purposes upon application, including Statistics Finland’s remote access system.
At the national level the statistical data compiled for EU-SILC are released in the statistics on living conditions of Statistics Finland annually. Further information: Statistics on living conditions - Statistics Finland
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.
Statistics Finland publishes new statistical data at 8 am on weekdays in its web service. The release times of statistics are given in advance in the release calendar available in the web service. The data are public after they have been updated in the web service.
Over the past year there has been 3 news releases and 3 reviews in the statistics on living conditions and 1 news release in the income distribution statistics:
Every 10th child experienced material deprivation in 2024 (release, published 16 May 2025)
Lasten aineellinen puute yleistyi hieman Euroopassa vuonna 2024 – Suomessa puute yleistyi nopeimmin (review in Finnish, published 16 May 2025)
Aineellista puutetta koki yhä useampi lapsi, myös pienempiä puutteita oli aiempaa useammalla vuonna 2024 (review in Finnish, published 16 May 2025)
Suomalaislapsilla toimintarajoitteita toiseksi eniten Euroopassa vuonna 2024 (review in Finnish, published 16 May 2025)
Altogether 35.7 per cent of persons aged 16 or over used public transport in 2024 (release published 15 May 2025)
People at risk of poverty or social exclusion numbered 930,000 in 2023 (release published 6 March 2025)
The figures on persons burdened by housing costs are available from the income distribution statistics by Statistics Finland.
10.3. Dissemination format - online database
Data from the Finnish EU-SILC is disseminated as free-of-charge statistical database (open data) tables of the statistics on living conditions by Statistics Finland in the API interface in the following formats: XLSX, CSV, JSON, JSON-stat and PX (PcAxis).
National micro data or unit-level data are available from Statistics Finland for scientific studies and statistical surveys. The Research Services offer ready-made data and tailoring of data according to research need. Further information: Micro data | Statistics Finland.
The data follows the EU regulations for EU-SILC (framework regulation EU 2019/1700), common ESS recommendations and guidelines. Descriptions and analysis concerning methodology for the EU-SILC countries, for example Finland, are available in Eurostat’s methodological publications.
The quality and publication of Official Statistics of Finland are guided by the recommendations of the Advisory Board of Official Statistics of Finland. See Recommendation on Quality Description.
In addition, the general methodology and guideline for ensuring statistics quality of Statistics Finland is described in Quality Guidelines for Official Statistics, 2nd revised version by Statistics Finland handbooks. Quality Guidelines for Official Statistics | Statistics Finland.
Annex on the metadata of the income benefits: Annex 10 - Metadata on benefits.
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.
Data completeness rate is nearly 100 % for compulsory variables. Data files are complete except the variable PB120 on individual interview duration and HY170G/HY170N on value of goods produced for own consumption.
PB120 couldn’t be collected due to the selected-respondent mode (S-R) and electronic questionnaire design used for interviewing. The questionnaire has been modularized by topics consisting of both personal and household questions.
HY170G/HY170N isn’t significant for any household groups at the national level based on the statistics on household's consumption from the reference year 2022. The data of the statistics is in comparative household budget survey (HBS).
The optional variable RL080 was not collected.
12.3.1. Data completeness - rate
Data completeness rate is 100 % for compulsory variables.
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
In terms of precision requirements, the representativeness of the sample and the effective sample size is to be achieved. The effective sample size combines sample size and sampling design effects which depends on sampling design, population structure and non-response rate.
The overall accuracy and representativeness are good both at national and at the regional level (precision requirements are not compulsory for FI20) in the Finnish EU-SILC cross-sectional survey.
Instead for the ratio of at-persistent-risk-of-poverty Statistics Finland applied the derogation concerning precision requirement (EU 2019/1700) during the survey years 2021−2023. The subsequent derogation was extended to 2024−2025. Based on the recommendations from development actions (ESS Grant – 101016418 – 2020-FI-SILC) implemented for the 2022 longitudinal survey and updated for 2019 – 2021 the precision requirement is expected to be compliant provided that the first wave sample size is sufficient.
Accuracy and representativeness are not necessarily good for all detailed sub-domains of the headline indicators published by Eurostat. In particular, for non-EU-27 citizens, the sample number may be sufficient to meet the publication criteria. However, sampling errors (design-based standard errors) are wide, due to the heterogenous structure of the group in the target populations, and the small sample accepted. Other such groups that meet the publication criteria but with not so accurate estimates in the headline indicators are children’s detailed age groups by sex and the persons of 18-24-year-olds.
Among the error sources (sampling error and other error sources are coverage, measurement, non-response and processing errors) main errors of the Finnish EU-SILC sample are related to non-responses. Unit non-responses are corrected by weighting and item non-responses in objective type of variables by imputing. For specific modules, i.e. quality of life, additional weights are supplied. Coverage (the frame population differs from the basic target population), measurement (the measured value of the result variable differs from its actual value) and processing error sources are assessed to be negligible for estimates.
As an outcome of error sources, data may contain systematic errors (the measured value of the result variable differs from its correct value). By comparisons of the estimates from the Finnish EU-SILC with total data resources and statistics the overall bias has been verified to be rather small.
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.
Sampling errors for the income components of the Finnish data are calculated with the estimation technique by taking both the sampling design and weighting into account.
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.
Sampling errors for the indicators of the Finnish data are calculated using an estimation technique based on the rescaling bootstrap for the indicators, taking account of sampling design and weighting.
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
Private households and persons at least 16 years old in private households
1 %
The estimate is based on the sample.
Under-coverage
Private households and persons at least 16 years old in private households
<0.5 %
Estimate. Under-coverage is small due to exhaustive and up-to-date sampling frame as regards to the target population, see concept 18.1.3 Sampling frame
Misclassification
.
.
.
13.3.1.2. Common units - proportion
Common unit proportion is 100 %. Units are covered by data from interviews and from statistical registers of Statistics Finland (e.g. basic statistical register: population and dwelling data resource) dated to 31.12.
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
• Problems emerging in connection with integrating different data sources (administrative, interviews) o Missing linking codes (for few units) o Differences in time reference periods • CAWI was introduced into the 2022 survey on income and living conditions (SILC), first for the one-person, and in 2023 for the multi-person households (household-dwelling units) of later waves provided that the sample person (S-R) is responding. The first wave interviews of multi-person households were collected by using CATI for measuring household composition properly. • Selection of informant o Selected respondents (S-R) informativity on the household questions o Use of proxy respondents instead of S-R and for personal interviews in CATI
ο CAWI allows only S-R for responding
o High item non-response rates for variables for which a proxy respondent is not allowed
• Household composition is validated in each wave
• General fieldwork problems o Lack of telephone numbers and CATI non-contacts in the fieldwork (addresses are known) o Language problems for foreigners (questionnaires are in FI, SWE, EN) o Variable specific problems § HH060: Detailed items were introduced into the 2022 questionnaire being equal with HBS § PL211A—PL211L: Use of new registers as a primary data source since 2022, updating collection and data validation programs to provide the validated data for processing jointly with interviewed data § PL070—PL090: Deriving the variables on the basis of PL211A—PL211L since 2022 § PL060, PL100: High unit non-response rates for working hours, missing items are imputed § PE021, PE041: The variables on education are collected from different registers § RB031: The data coverage is deficient in immigrations that took place before 1990
• Fieldwork tools o The CATI and CAWI mode questionnaires in Finnish, Swedish and English, interviewers’ instructions, contact letters, brochures on the SILC survey, pocket statistics on the SILC data, list of questions on housing, websites, motivational videos for participating in the survey § New tools for the CAWI mode, re-designing questionnaire for 2022 • Technical specificities of the questionnaire o Questionnaire composing of blocks of questions is updated from the previous year, feedback regarding with the survey is utilised for the questionnaire form, the changes are mostly due to the new modularisation § Filters and checks have been built-in the questionnaire § For the sample of 2.-4. waves, relevant data of the previous year are pre-filled into the questionnaire o A new version of Blaise programmed for 2022 o Modes are changeable during the interview o Many testing procedures of the questionnaire is used, focus is on the parts which undergo changes
• General description of the interviewer training routines o Basic training on interviewing and questionnaire standards and codes of practices o Interviewer’s SILC instructions, written guidelines and training in the teams-meeting o During the whole SILC fieldwork period, interviewers' information desk • Fieldwork management and data reception o Interview data collections group: § Transmits the fieldwork tools to the field and organises interviewer training at the beginning of the project § Controls, monitors and supervises the fieldwork, follows fieldwork progress and receives the output from the field § Collects feedback from the interviewers with a standardised questionnaire after the fieldwork § Checks that all the sampled units are adequately processed and transmits the data to the statistics team
• Quality control: selection of the informant o Use of proxy for selected respondent couldn’t be interviewed reduces non-responses. For all subjective variables proxy is not allowed. Choice of the proxy from the household members depends on the interviewer. • Quality control: general fieldwork problems o HB100, PB120 - Household and personal interview duration - In the selected respondent model, the duration of the interview is measured as the duration for both household and personal interview in variable HB100. Variable PB120 is empty. o Due to the increased response rate in 2020 the first wave sample size kept in 5,500 households to ensure high-enough sample size for both the cross-section of 2022 and longitudinal component for the coming years. • Quality control: variable-specific problems o Imputed values, see variables on Annex 6 - Estimation and imputation
For more information, please see Annex -Measurement errors
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
100.0
100.0
..
72.7
55.0
..
..
..
..
27.3
45.0
..
..
..
..
27.3
45.0
..
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.
Annex A EU-SILC content tables
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.
Imputation, information is provided in concept 18.5.3 Estimation and imputation.
Description of data entry, coding controls and the editing system
Data entry and coding
(if any used)
Editing controls
Database construction (incl. module files) for the survey year
Update of the interviewed data to the database and verifying correctness of the update
Checking completeness of interviews for sample by controls concerning verifying requirements set for responded data content, and acceptance of the sample persons
Completing missing personal information needed for sample persons from registers
Checking against registers that co-residents are in-scope of the statistical population. Excluding (or including) persons who are erroneous in-scope (out-scope) in the sample
Validating registers correctness which are combined to the sample
Updating changes of classifications and processing programs for the survey year
Design of the Blaise questionnaire for CATI and CAWI: checking and programming routing and editing controls (consistency, plausibility) for interviewed data with regards to changes
Training of interviewers and providing instructions for the fieldwork of interviewed data to reduce measurement errors
Detecting missing and erroneous information, outliers, illogical and inconsistent information: editing after the in-built Blaise checks and imputing
Validating data consistency at the unit level in the cross-sectional component and the longitudinal component of survey over the waves
Comparing frequencies between survey years
13.3.5. Model assumption error
Not applicable.
14.1. Timeliness
Concepts 14.1.1 and 14.1.2.
14.1.1. Time lag - first result
The time lag for the first results on 2024 published in Income distribution statistics was 9.5 months and in Statistics on living conditions by Statistics Finland was 10 months since the last day of the data reference period (data collection period).
14.1.2. Time lag - final result
The time lag for the final results on 2024 published in Statistics on living conditions by Statistics Finland was 10 months since the last day of the data reference period (data collection period).
14.2. Punctuality
The first 2024 data files were submitted to Eurostat within the date for the data trasmission in December of the survey year N, and the final data files were submitted with 2 months time lag for the date (December 31st).
14.2.1. Punctuality - delivery and publication
The first release from the Finnish EU-SILC on 2024 were published punctually in Income distribution statistics at the end of February in year N+1. The time lag between the end of survey year and publication was 2 months since the end of year N.
15.1. Comparability - geographical
The data are regionally comparable according to the regional classifications (NUTS2, LAU) used for the statistics, as an exception Åland (NUTS1: FI2 and NUTS2: FI20). Representativeness is not good, precision requirement is not compulsory due to it’s small population size (EU 2019/1700; Annex 2). Further information: Concept 3.7 Reference area.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not applicable.
15.2. Comparability - over time
In 2022, there were changes in the measurement of PL032, which affected low work intensity (LWI) and at-risk-of-poverty-and-social-exclusion (AROPE), as well as in longitudinal weighting of the 4-year panel (weights DB095, PB050, PB060, PB080, RB064) which in turn contributed to at-persistent-risk-of-poverty. The changes were updated for 2019 – 2020 (PL031) and 2021 (PL032) to be revised so that time series are comparable for the indicators (LWI, AROPE, at-persistent-risk-of-poverty) since 2019. The previous comparable time series are from the years 2003 to 2018.
For most other nucleus variables, comparable time series are available for the year from 2003 to 2024.
New administrative data sources were introduced on topics related to labour market participation / main activity status (PL070 – PL090, PL211A – PL211L) which have negligible or in some small modality classes moderate impact on comparability between 2022 and previous years. Overall, the impact is negligible, and the variables are comparable.
In 2024, administrative data sources were introduced to edit the interviewed data for the variable HH021 tenure status, for the control relationship of the categories: tenant, rent at market price and tenant, rent at reduced price. The impact is significant. The correction causes a break compared to previous survey years in terms of detailed modality (between the values 3 and 4).
In 2024, part of basic social assistance intended to cover housing costs was transferred from HY060G to HY070G in accordance with the methodological guidelines. The impact on households as a whole is negligible.
CAWI mode was started to be introduced into survey in 2022, first to collect interviews from one person households. In 2023, CAWI was extended to the multi-person households interviewed in the longitudinal component of survey (concept 18.3). The change in mode has impacts on subjective type of variables, which are mostly negligible for the comparability of time series. They are commented in Annex 8-Breaks in series in 2022 and 2023.
The lengths of time series for the cross-sectional (AROPE, LWI) and longitudinal (at persistent-risk-of-poverty) indicators are 6 years.
For most of the nucleus variables the lengths of time series are 20 years.
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
.
Value of goods produced for own consumption
(HY170)
NC
.
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.
In addition to National Accounts (concept 15.3.2), the Finnish EU-SILC 2024 was compared with the total data on Income Distribution Statistics (IDS) by Statistics Finland and the European System of Integrated Social Protection Statistics (ESSPROS) compiled by the National Institute for Health and Welfare (THL).
Comparisons with Household Budget Statistics (HBS) are not provided, the latest survey year refers to 2022.
15.3.1. Coherence - sub annual and annual statistics
Not applicable.
15.3.2. Coherence - National Accounts
Annex 7 - Coherence
15.4. Coherence - internal
Annex 7 - Coherence
Mean (average) interview duration per household = 36.4 minutes.
Mean (average) interview duration per household, CATI = 38.3 minutes.
Mean (average) interview duration per household, CAWI = 33.4 minutes.
Mean (average) interview duration per person = minutes. (Not applicable)
Mean (average) interview duration for selected respondents (if applicable) = minutes. (Not applicable)
17.1. Data revision - policy
Methodological changes to the survey year and revisions to time series data are principally planned in advance for the Finnish EU-SILC data sets. Unplanned revisions for the released estimates are made mostly due to changes in administrative data sources. The time series are always revised for sufficient cross-sectional years and for the whole longitudinal component, if the impact on the key result data of the statistics is statistically significant and the data sources for the revisions are available. Planned and implemented revisions, including the reasons and nature for revisions and the impact on variables, is clearly communicated to Eurostat and when approriate, guidance is asked.
Information on data revisions is delivered nationally along the release of Statistics on living conditions by Statistics Finland, and possibly in advance, especially then if there are forthcoming changes for the planned release calendar.
17.2. Data revision - practice
Necessary updates for the cross-sectional data after the December 31st of the survey year N + 1 and exceptionally concerning the income variables after the February 28th of the survey year N + 1 and for the longitudinal weights after the October 31st of the survey year N + 1 are conducted as revisions.
Statistical changes which have been implemented for data and have an impact for time series on variables and indicators, are reported to Eurostat annually in a separate annex on breaks for time series. These changes may be revised for previous years’ data if there are methodological improvements and impacts are statistically significant.
For the FI-SILC 2022 cross-sectional data on labour market participation / main activity status, the production of variable PL032 was corrected to be based solely on interviews by removing the register check in the definition of retired (7 in retirement) and permanently disabled or/and unfit to work (8 Permanently disabled). Variables PL032 for 2021 and PL031 for 2019 and 2020 were revised equivalently to be based on the Eurostat recommendations and common agreement on the procedure with Eurostat (The revisions of 2019 and 2020 data files).
The longitudinal weight RB064 was updated by using additional information for 4-year panel calibration. Consequently, the variables DB095, PB050, PB060, PB080 were checked. The revisions were carried for 2019, 2020 and 2021.
In 2023 and in 2024 there weren't any revisions.
17.2.1. Data revision - average size
0 % for AROPE and 0 % for at-persistent-risk-of-poverty rates (%) in 2004-2024.
Detailed information concerning sampling frame, sampling design, sampling units, sampling size, weightings and mode of data collection can be found in this section (please see below). Such information is mainly used for the computation of the accuracy measures.
18.1. Source data
The source data are collected with interviews and from administrative data sources. The compilation of Statistics Finland's statistics (e.g. Finnish EU-SILC) at the unit level is guided by the general act of the national statistical service, the Statistics Act (280/2004, amendment 361/2013). Only such necessary information that are not available from administrative data sources are collected from data suppliers by interviewing.
Basic statistical registers of Statistics Finland are as follows:
Population and dwelling data resource
Business information system
Real estate information system
The key administrative registers in the Finnish EU-SILC dataset production are as follows:
The Population Information System of Digital and the Population Data Services Agency (DVV) and the Statistics Finland's population and dwelling data resource
The Tax Administration's tax database
The Social Insurance Institution of Finland's database on social security schemes (pension insurance, health insurance compensation and rehabilitation, registers of child maintenance allowances, financial aid for students, housing allowances and social assistance)
The National Institute for health and Welfare's register of social assistance
The register of pension contingency of the Finnish Centre for Pensions
Statistics Finland's Register of Completed Education and Degrees
The State Treasury's database on the military injuries indemnity system
The Financial Supervisory Authority's data (earnings-related unemployment allowances)
Statistics Finland's Business Register
The Employment Fund's data files
Incomes Register
The quality of the data collected and compiled by administrative authorities for specific purposes is basically good. The administrative registers are exhaustive and reliable, and they are updated frequently. As regards population and dwelling data resource, the quality of the estimated data is examined, for example, in the quality description of Statistics Finland's statistics on dwellings and housing conditions.
The administrative data collection is based on close cooperation with the administrative authorities and Statistics Finland. This applies to the planned data content, data transmission and its validation. After the overall quality of the administrative registers has been verified, the correctness and congruence of the data from many sources linked at the unit level are checked with the derived classifications and variables in more detail in the statistics (SILC dataset) production system. The Finnish EU-SILC data compilation is integrated with production of Statistics Finland's income distribution statistics. Basic statistics registers based on administrative registers provides a crucial framework for the statistics. The common identifiers used unambiguously for direct records linking from registers are person ID, domicile ID and enterprise ID as being pseudonymised.
The basic statistical and administrative registers are used for many purposes in the Finnish EU-SILC dataset production: use of sampling frame, sampling, weighting and estimation, prefilling records for interview questionnaire, analysis of unit non-response, interviewed data checks and editing, deriving variables and controlling interviewed data collection quality. By topics of the datasets the registers are used to technical items, person and household characteristics, educational attainment and background, participation in education and training, and for income variables.
18.1.1. Sampling Design
The sampling design is stratified sampling. A random sample of persons (5,500) including their household-dwelling units is drawn with non-proportional quota from the strata formed in the overall frame. The frame covers the target population almost without errors (see Concept 8.1.3). Until the survey year 2021, the draw was two-phased and a person sample (5,500 or 5,000) was drawn from the so-called master sample. A master sample, which consisted of 50,000 persons (exceptionally 100,000 in 2017, 2020 and 2021), was drawn systematically in the overall frame in the first phase.
The strata used are 12 socio-economic groups. Socio-economic groups are usually formed on the basis of taxable income according to the household's (household-dwelling unit) highest earning income type and income level (for example, entrepreneurs are an exception). Tax information refers to the previous calendar year of the income reference year.
In 2020, a draw of an additional sample of 500 persons was included in the first survey round of the sample. Since 2020, the sample size of the first survey round has been 5,500 persons and their household-dwelling units.
As a result of the panel's design, an additional sample of 16-year-olds is selected for the second to fourth survey rounds following the sampling of the main sample.
Sample persons (and their household-dwelling units) refer to the population registered as permanently resident in Finland on 31 December. The sample unit is a person aged 16 and over.
The actual sample sizes of the cross-sectional FI-SILC survey by rotational groups were as follows: 12,492 (total); 5,500 (1. wave), 2,766 (2. wave), 2,197 (3. wave), 2,029 (4. wave). The achieved samples (S-R) were as follows: 8,982 (total); 2,983 (1. wave), 2,193 (2. wave), 1,934 (3. wave), 1,872 (4. wave). The number of the achieved sample including co-residents at least 16 years old was 17,173. By waves the achieved sample numbers were 5,638 (1. wave), 4,216 (2. wave), 3,729 (3. wave) and 3,590 (4. wave).
The achieved sample size, concept 18.3 Data collection: Annex 4-Data_collection
18.1.2. Sampling unit
The sampling unit is a person.
18.1.3. Sampling frame
The Finnish EU-SILC follows 4-year rotational panel design.
Sampling frame
The sampling frame is based on Statistics Finland's basic register on population and dwelling data resource which uses data from the Population Information System of the Digital and Population Data Services Agency (DVV) on a regular (weekly) basis. The sampling frame containing information on individuals and their attributes (incl. identification codes) is exhaustive and up-to-date as regards to the target population.
The sampling frame is used in sampling purposes of several statistics, one of which is the Finnish EU-SILC: construction of the private household-dwelling units, stratification, and sampling (Concept 18.1.1 Sampling Design). The quality of sampling frame is validated and the frame is expanded by an auxiliary data from other registers, such as the tax data from tax administration, by means of a direct record linkage.
The Population Information System of the Digital and Population Data Services Agency (DVV) is a compilation of the local registers maintained by the population register districts. It covers basic information on all Finnish citizens and foreign nationals permanently residing in Finland. Each person is registered in the municipality where he/she has a permanent place of residence. The data also includes persons living temporarily abroad or elsewhere in the country and persons without a postal address (i.e. homeless persons). Population groups, such as asylum seekers and refugees, are included in the resident population after the processing of residence permit. The registration of population data in the system is generally considered to be of a high quality.
The sampling frame is put into operation before the reference population date (31.12.), and therefore contains errors. For this reason, it is checked with the population and dwelling data resource, which is updated later, and a minor imperfection is corrected before the sample selection.
The statistical population for the reference time point (31.12.) is completed for the Statistics Finland's population and dwelling data by records updates during the next three months. The data are used in the preliminary population statistics and further in the Statistics Finland's household dwelling units and the total data of the income distributions statistics.
The estimated frame error is based on the difference between sampling frame and final data of Statistics Finland's population and dwelling data resource. See concept 6.3.1 Coverage error on error components.
Actions for the interviewed data
In interviews, when a sample person did not belong to the reference population (31.12), this so-called over-coverage is removed. A small number of sample persons belonging to the under-coverage left outside the sampling frame, which synchronizes with registers updating with a delay. They remain outside the sample.
The household composition, the common housekeeping unit is checked apart from the household-dwelling unit in the interviews. Common sources of errors, which are corrected, are persons who have recently changed a place of residence and/or household in private household population, new-borns, died, recently moved to institutions/abroad or co-residents moved from institutions/abroad.
The coverage error has been described as more detailed in concept 6.3.1 Coverage error.
18.2. Frequency of data collection
Data is collected annually. Fieldwork for interviewed data was taken place from January 2nd to May 14th in 2024. Data collection for administrative sources ended in October of the survey year.
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
0.0
0.3
51.5
38.3
0.0
0.0
9.4
0.4
0.0
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
Almost all income are collected from administrative sources.
Only some items are interviewed from households. The items are as follows:
Employee income and pensions from abroad, not taxed in Finland (PY010G, PY100G)
Some monetary compensation items from benevolent funds (PY130G)
Maternity grant (HY050G)
Untaxed part of strike assistance (HY060G)
Interest income taxed at source (HY090G)
Regular inter-household transfers received and paid (excl. maintenance support) (HY080G, HY130G)
Gross, net
The use of gross income is strongly recommended because the estimates are more accurate than the estimates of the net income variables.
After ensuring the overall quality of registers from administrative authorities, the correctness and consistency of data from many sources linked at the unit level are checked with the derived classifications and variables in more detail in the statistics (SILC dataset) production system. The data compilation has been integrated with the production of income distribution statistics of Statistics Finland.
Gross income variables are converted for net by effective tax-rate of type of taxation income.
The source data are collected from statistical and administrative data sources and by interviews.
Statistical and administrative registers are exhaustive and of a high standard. Changes in the data content are validated in more detail during the data entry and processing phases.
With regard to the interviewed data, many quality controls have been built-up into the electronic questionnaires. They are the plausibility and logical checks of values, as well as the validation of the routing of the questions in the questionnaire. The questionnaire is designed and tested annually to consider changes implemented, such as those due to modularisation, and to minimize the risk of response errors.
After the interviewed data collection the data are processed at the unit level with the necessary checks and edits following standard rules, almost all the data are automatically edited in electronic procedures. Quality is ensured by checking the correctness and consistency of the data on classifications and variables derived from many sources (incl. registers) taking into account of reference times
The main source of error in the sample data is unit non-response, which is corrected using a weighting method based on the sampling design. Item non-responses of objective type of variables are imputed and flagged. Besides non-response and random variation, the quality of output is slightly affected by coverage errors (the frame population differs from the basic target population) and measurement errors (the measured value of the result variable differs from its actual value). See concept 13.3.1 on coverage error and concept 13.3.2 on measurement error.
The coverage and systematic errors of the compiled dataset are examined by comparing estimated figures with other statistical sources, such as the Statistics Finland's income distribution statistics on total data, the statistics of the Tax Administration, the Social Insurance Institution, the Finnish Centre for Pensions and the National Institute for Health and Welfare, and Statistics Finland's national accounts on household sector.
18.5. Data compilation
Data compilation concerning estimation and imputation, and weighting are described in Annexes.
18.5.1. Imputation - rate
Annex 6 - Estimation and Imputation
18.5.2. Calculation of weighting factors and weight adjustments
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). The Finnish EU-SILC longitudinal data is observed over the four-year rotation scheme.
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.
Further details are provided in concepts 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.
The country as a whole and NUTS -regions (2-digits). The NUTS -regions are as follows: FI19: Länsi-Suomi, FI1B: Helsinki-Uusimaa, FI1C: Etelä-Suomi, FI1D: Pohjois- ja Itä-Suomi, FI20: Åland.
With regard to the estimated ratio at-risk-of-poverty or social exclusion to population and their precision requirements Åland is an exempt at the NUTS regions (1-digit, 2-digit levels) due to its population size which is less than 100 000 habitants ((EU2019/1700, Annex II). The NUTS 1-digit regions are FI1: MANNER-SUOMI, FI2: ÅLAND.
Description of reference period used for income
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
2023
2023
2023
0 - 5 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
In terms of precision requirements, the representativeness of the sample and the effective sample size is to be achieved. The effective sample size combines sample size and sampling design effects which depends on sampling design, population structure and non-response rate.
The overall accuracy and representativeness are good both at national and at the regional level (precision requirements are not compulsory for FI20) in the Finnish EU-SILC cross-sectional survey.
Instead for the ratio of at-persistent-risk-of-poverty Statistics Finland applied the derogation concerning precision requirement (EU 2019/1700) during the survey years 2021−2023. The subsequent derogation was extended to 2024−2025. Based on the recommendations from development actions (ESS Grant – 101016418 – 2020-FI-SILC) implemented for the 2022 longitudinal survey and updated for 2019 – 2021 the precision requirement is expected to be compliant provided that the first wave sample size is sufficient.
Accuracy and representativeness are not necessarily good for all detailed sub-domains of the headline indicators published by Eurostat. In particular, for non-EU-27 citizens, the sample number may be sufficient to meet the publication criteria. However, sampling errors (design-based standard errors) are wide, due to the heterogenous structure of the group in the target populations, and the small sample accepted. Other such groups that meet the publication criteria but with not so accurate estimates in the headline indicators are children’s detailed age groups by sex and the persons of 18-24-year-olds.
Among the error sources (sampling error and other error sources are coverage, measurement, non-response and processing errors) main errors of the Finnish EU-SILC sample are related to non-responses. Unit non-responses are corrected by weighting and item non-responses in objective type of variables by imputing. For specific modules, i.e. quality of life, additional weights are supplied. Coverage (the frame population differs from the basic target population), measurement (the measured value of the result variable differs from its actual value) and processing error sources are assessed to be negligible for estimates.
As an outcome of error sources, data may contain systematic errors (the measured value of the result variable differs from its correct value). By comparisons of the estimates from the Finnish EU-SILC with total data resources and statistics the overall bias has been verified to be rather small.
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
Data compilation concerning estimation and imputation, and weighting are described in Annexes.
The source data are collected with interviews and from administrative data sources. The compilation of Statistics Finland's statistics (e.g. Finnish EU-SILC) at the unit level is guided by the general act of the national statistical service, the Statistics Act (280/2004, amendment 361/2013). Only such necessary information that are not available from administrative data sources are collected from data suppliers by interviewing.
Basic statistical registers of Statistics Finland are as follows:
Population and dwelling data resource
Business information system
Real estate information system
The key administrative registers in the Finnish EU-SILC dataset production are as follows:
The Population Information System of Digital and the Population Data Services Agency (DVV) and the Statistics Finland's population and dwelling data resource
The Tax Administration's tax database
The Social Insurance Institution of Finland's database on social security schemes (pension insurance, health insurance compensation and rehabilitation, registers of child maintenance allowances, financial aid for students, housing allowances and social assistance)
The National Institute for health and Welfare's register of social assistance
The register of pension contingency of the Finnish Centre for Pensions
Statistics Finland's Register of Completed Education and Degrees
The State Treasury's database on the military injuries indemnity system
The Financial Supervisory Authority's data (earnings-related unemployment allowances)
Statistics Finland's Business Register
The Employment Fund's data files
Incomes Register
The quality of the data collected and compiled by administrative authorities for specific purposes is basically good. The administrative registers are exhaustive and reliable, and they are updated frequently. As regards population and dwelling data resource, the quality of the estimated data is examined, for example, in the quality description of Statistics Finland's statistics on dwellings and housing conditions.
The administrative data collection is based on close cooperation with the administrative authorities and Statistics Finland. This applies to the planned data content, data transmission and its validation. After the overall quality of the administrative registers has been verified, the correctness and congruence of the data from many sources linked at the unit level are checked with the derived classifications and variables in more detail in the statistics (SILC dataset) production system. The Finnish EU-SILC data compilation is integrated with production of Statistics Finland's income distribution statistics. Basic statistics registers based on administrative registers provides a crucial framework for the statistics. The common identifiers used unambiguously for direct records linking from registers are person ID, domicile ID and enterprise ID as being pseudonymised.
The basic statistical and administrative registers are used for many purposes in the Finnish EU-SILC dataset production: use of sampling frame, sampling, weighting and estimation, prefilling records for interview questionnaire, analysis of unit non-response, interviewed data checks and editing, deriving variables and controlling interviewed data collection quality. By topics of the datasets the registers are used to technical items, person and household characteristics, educational attainment and background, participation in education and training, and for income variables.
Annual
Concepts 14.1.1 and 14.1.2.
The data are regionally comparable according to the regional classifications (NUTS2, LAU) used for the statistics, as an exception Åland (NUTS1: FI2 and NUTS2: FI20). Representativeness is not good, precision requirement is not compulsory due to it’s small population size (EU 2019/1700; Annex 2). Further information: Concept 3.7 Reference area.
In 2022, there were changes in the measurement of PL032, which affected low work intensity (LWI) and at-risk-of-poverty-and-social-exclusion (AROPE), as well as in longitudinal weighting of the 4-year panel (weights DB095, PB050, PB060, PB080, RB064) which in turn contributed to at-persistent-risk-of-poverty. The changes were updated for 2019 – 2020 (PL031) and 2021 (PL032) to be revised so that time series are comparable for the indicators (LWI, AROPE, at-persistent-risk-of-poverty) since 2019. The previous comparable time series are from the years 2003 to 2018.
For most other nucleus variables, comparable time series are available for the year from 2003 to 2024.
New administrative data sources were introduced on topics related to labour market participation / main activity status (PL070 – PL090, PL211A – PL211L) which have negligible or in some small modality classes moderate impact on comparability between 2022 and previous years. Overall, the impact is negligible, and the variables are comparable.
In 2024, administrative data sources were introduced to edit the interviewed data for the variable HH021 tenure status, for the control relationship of the categories: tenant, rent at market price and tenant, rent at reduced price. The impact is significant. The correction causes a break compared to previous survey years in terms of detailed modality (between the values 3 and 4).
In 2024, part of basic social assistance intended to cover housing costs was transferred from HY060G to HY070G in accordance with the methodological guidelines. The impact on households as a whole is negligible.
CAWI mode was started to be introduced into survey in 2022, first to collect interviews from one person households. In 2023, CAWI was extended to the multi-person households interviewed in the longitudinal component of survey (concept 18.3). The change in mode has impacts on subjective type of variables, which are mostly negligible for the comparability of time series. They are commented in Annex 8-Breaks in series in 2022 and 2023.
