Croatian Bureau of Statistics

Living Conditions Statistics Unit

Branimirova 19, 10000 Zagreb, Croatia

25 April 2025

25 April 2025

25 April 2025

The European Union Statistics on Income and Living Conditions (EU-SILC) is a survey-based instrument aiming at collecting timely and comparable cross-sectional and longitudinal multidimensional microdata on income, poverty, social exclusion and living conditions. In addition, it collects module variables every three years, six years or ad-hoc new policy needs modules.

The EU-SILC instrument provides two types of data:

- Cross-sectional data pertaining to a given time or a certain time period with variables on income, poverty, social exclusion and other living conditions;

- Longitudinal data pertaining to individual-level changes over time, observed periodically over four‐or more year rotation scheme (Annex III (2) of 2019/1700).
Social exclusion and housing condition information is collected mainly at household level while labour, education and health information is obtained for persons aged 16 and over. The core of the instrument is income information at very detailed component level and mainly collected at personal level.

• International Standard Classification of Education (ISCED'2011);

• International Standard Classification of Occupations (ISCO-08);

• Classification of Economic Activities (NACE Rev.2);

• 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 Eurostat webpage.

Data refer to all private households and individuals living in the private households in the national territory at the time of data collection.

The EU-SILC survey is a key instrument for the European Semester and the European Pillar of Social Rights, providing information on income distribution, poverty and social exclusion, as well as various related living conditions and poverty EU policies, such as on child poverty, access to health care and other services, housing, over indebtedness and quality of life. It is also the main source of data for microsimulation purposes and flash estimates of income distribution and poverty rates.

Statistical concepts and definitions for EU-SILC are specified in Regulation (EU) 2019/1700, Commission Implementing Regulation (EU) 2019/2181, and Commission Implementing Regulation (EU) 2019/2242. Additional information is available in the EU statistics on income and living conditions (EU-SILC) methodology and in the methodological guidelines and description of EU-SILC target variables.

Further details are provided in items 5, 15.1.1.1, 15.2.2 and 18.3.

Statistical units are private households and all persons living in these households who have usual residence in the Member State. Annex II of the Commission implementing regulation (EU) 2019/2242 defines specific statistical units per variable and specifies the, content of the quality reports on the organization of a sample survey in the income and living conditions domain pursuant to Regulation (EU) 2019/1700 of the European Parliament and of the Council.

The target population is private households and all persons composing these households having their usual residence in the Member State. Private household means a person living alone or a group of persons who live together, providing oneself or themselves with the essentials of living.

Definitions of reference population, household and household membership

The sub-populations that are not covered by the data collection includes: those who moved out of the country’s territory; or those with no usual residence; or those living in institutions or who have moved to an institution compared to the previous year.

Republic of Croatia

EU-SILC survey data for Republic of Croatia are available from 2010 onwards.

Not applicable.

The data involves several units of measure depending upon the variables. Income variables are transmitted to Eurostat in national currency. For more information, see methodological guidelines and description of EU-SILC target variables.

Description of reference period used for incomes

Regulation (EU) 2019/1700 was publish in OJ on 10 October 2019, establishing a common framework for European statistics relating to persons and households, based on data at individual level collected from samples (IESS). The Annex to the Commission implementing regulation (EU) 2019/2180 of 16 December 2019 specifies the detailed arrangements and content for the quality reports pursuant to Regulation (EU) 2019/1700 of the European Parliament and of the Council and Regulation (EU) 2019/2242.

Confidential microdata are not disclosed by Eurostat. Access to confidential microdata for scientific purposes may be granted on the  basis of Commission Regulation 557/2013 and Regulation 223/2009 of  the European Parliament and the Council on European statistics.

For more information see Eurostat data protection policy.

For more information see Eurostat statistical confidentiality and data protection policy.

The released calendar can be found in HR statistical website.

Please refer to the Release calendar - Eurostat (europa.eu) publicly available on the Eurostat’s website.

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.

Annual

Not applicable.

For more information please see the HR website.

Data and access avaliable in the HR website.

(Optional)

Microdata is available (e.g. for researchers) on the basis of a request specifying the method or persons who will access the data.

For more information please see the HR website.

(Optional)

Državni zavod za statistiku - Metodologija subnacionalnih statistika

Državni zavod za statistiku - Subnational statistics methodology

All requested concepts are provided, 100%.

Quality reports available in the HR website.

Principles of the Code of Practice of European Statistics is available online.

Quality assessment is mainly carried out by self-assessment and revision by colleagues (e.g. a superior colleague). They take place during the Quality report production itself and after the completion of the production (assessment and control).

Improvement in quality assurance procedures is continuously being worked on through various courses (e.g. SAS program education), education and self-education. Also, through the improvement of the questionnaire itself.

Chapters 12., 13., 14., 15., in this Quality report are chapters where the results are presented in more detail.

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

Eurostat carried out an online general User Satisfaction Survey (USS) in the period between April and July 2019 (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.

All necessary statistics are available and all requirements are met in accordance with laws, regulations and guidelines. Nucleus and module variables are collected in accordance with methodological guidelines and descriptions of EU-SILC variables.

Optional variables we did not collect:

RL080: Remote education

HI130G: Interest expenses [not including interest expenses for purchasing the main dwelling]

HI140G: Household debts

Information provided in the point 12.3.

According to Reg. (EU) 2019/1700 Annex II, precision requirements for all data sets are expressed in standard errors and are defined as continuous functions of the actual estimates and of the size of the statistical population in a country or in a NUTS 2 region. For the income and living conditions domain, the estimated standard errors of the following indicators are examined according to certain parameters set:

•  Ratio at‐risk‐of‐poverty or social exclusion to population;
•  Ratio of at‐persistent‐risk‐of‐poverty over four years to population;
•  Ratio at‐risk‐of‐poverty or social exclusion to population in each NUTS 2 region.

Further information is provided in section 13.2 Sampling error.

EU-SILC is a complex survey involving different sampling designs in different countries. In order to harmonize and make sampling errors comparable among countries, Eurostat (with the substantial methodological support of Net-SILC2) has chosen to apply the "linearization" technique coupled with the “ultimate cluster” approach for variance estimation.

Linearization is a technique based on the use of linear approximation to reduce non-linear statistics to a linear form, justified by asymptotic properties of the estimator. This technique can encompass a wide variety of indicators, including EU-SILC indicators. The "ultimate cluster" approach is a simplification consisting in calculating the variance taking into account only variation among Primary Sampling Unit (PSU) totals. This method requires first stage sampling fractions to be small which is nearly always the case. This method allows a great flexibility and simplifies the calculations of variances. It can also be generalized to calculate variance of the differences of one year to another.

The main hypothesis on which the calculations are based is that the "at risk of poverty" threshold is fixed. According to the characteristics and availability of data for different countries, we have used different variables to specify strata and cluster information. 

In particular, countries have been split into 3 groups:

1) BE, BG, CZ, IE, EL, ES, FR, HR, IT, LV, HU, PL, PT, RO, SI, UK and AL, whose sampling design could be assimilated to a two-stage stratified type we used DB050 (primary strata) for strata specification and DB060 (Primary Sampling Unit) for cluster specification;

2) DK, DE, EE, CY, LT, LU, NL, AT, SK, FI, CH whose sampling design could be assimilated to a one stage stratified type we used DB050 for strata specification and DB030 (household ID) for cluster specification;

3) MT, SE, IS, NO, whose sampling design could be assimilated to a simple random sampling, we used DB030 for cluster specification and no strata.

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.

Indicators and standard errors are filled in the tables in Annex A (tab 13.2.1.).

Persistent-risk-of-poverty is calculated and filled in (third table in the tab 13.2.1. of HR_2024_Annex A EU-SILC - content tables).

Non-sampling errors are basically of 4 types:

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

Coverage errors include over-coverage, under-coverage and misclassification:

• Over-coverage: relates either to wrongly classified units that are in fact out of scope, or to units that do not exist in practice.
• Under-coverage: refers to units not included in the sampling frame.
• Misclassification: refers to incorrect classification of units that belong to the target population

Coverage error

Not applicable.

 Measurement error for cross-sectional data

Cross-sectional information year 2024

Number and percentage of Proxy interview

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.

Cross-sectional information year 2024

Response and Non response rate

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.

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.

Annex 2 – Item non-response attached.

 Description of data entry, coding controls and the editing system

Not applicable.

The number of months from the last day of the reference period to the day of publication of complete and final results: T+3 (March 2025)

First results from SILC 2024 are final.

The number of months from the last day of the reference period to the day of publication of complete and final results: T+3 (March 2025).

Time lag between the actual delivery of the data and the target date when it should have been delivered. No time lag.

Punctuality is the time lag between the delivery/release date of data and the target date for delivery/release as agreed for delivery or announced in an official release calendar, laid down by Regulations or previously agreed among partners.

Final delivery date according to Regulation 2019/1700 is set for 28th February of the year N+1.

_P3=dact-dsch

P₃=11 March - 28 February

P₃=11 days

According to the Regulation (EU) 2019/1700 of the European Parliament and of the Council concerning EU-SILC: "Reliable statistics are needed at national as well as at regional level where better comparability is required. It is important that aggregated data be made available for comparable territorial units such as NUTS 2, while taking costs into account and providing Member States with the appropriate financial resources. In accordance with Regulation (EC) No 1059/2003 of the European Parliament and of the Council (9), all Member States’ statistics that are transmitted to the Commission (Eurostat) and that are to be broken down by territorial units should use the NUTS classification. Consequently, in order to establish comparable regional statistics, data on the territorial units should be provided in accordance with the NUTS classification."

Although the best way for keeping the comparability of data is to apply the same methods and definitions of variables, small departures of the definitions given by Eurostat are allowed in EU-SILC.

Not applicable.

Information provided in corresponding Annex.

15 reference periods.

F= Fully comparable; L= Largely comparable; P= Partly comparable and NC= Not collected.

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.

Not applicable.

Information provided in the Annex 7 - Coherence.

For each year, any lack of coherence in the EU-SILC data set shall be reported together with explanations for such inconsistencies.

Mean (average) interview duration per household =  48,1 minutes.

Mean (average) interview duration per person = 16,4 minutes.

Mean (average) interview duration for selected respondents (if applicable) =  (.) minutes (not applicable).

Cross-sectional information year 2024

Average Interview duration

The average interview duration is improved and accurate thanks to the following improvements:
- Increased number of CATI interviews
- Reduced number of questions in the questionnaire due to the switch to administrative sources
- Application upgrade and improvements with the aim of accurately calculating the average interview duration

No data revision.

No data revision.

No data revisions.

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.

The sampling frame for both stages was the 2021 Census data. The sources of EU-SILC data used: fully interview, interview and registers, registers, full record imputation, interview not completed, administrative sources.

Administrative sources were used for largest part of income data. 

Income variables affected: HY010, HY020, HY022, HY023, HY050G/N, HY060G/N, HY070G/N, PY010G/N, PY090G/N, PY100G/N, PY110G/N, PY130G/N, PY140G/N.

The sample design for EU-SILC 2024 was two-stage stratified design. In each stratum primary sampling units (PSUs) were systematically selected with probability proportional to size, and in the second stage 7 addresses (occupied dwellings) were randomly selected in each PSU. The sample was divided into 4 subsamples (rotational groups), each by itself representative of the whole population and similar in structure to the whole sample.

Stratification and sub stratification criteria

The sampling frame of segments for the first stage is divided into 8 strata. 6 strata are defined by 3 groups of counties (NUTS-3) and the types of municipality. The type of municipality can be city or municipality. Separate 2 strata are Grad Zagreb county and city of Rijeka and city of Split together.

In Croatia there are four statistical regions, according to The National Classification of Territorial Units for Statistics 2021 (HR_NUTS 2021. - NUTS-2):

1. Pannonian Croatia

2. Adriatic Croatia

3. City of Zagreb

4. North Croatia

Beside explicit stratification, implicit stratification according to counties (NUTS-3) and municipalities was applied. It means that the list of segments within strata was sorted according to counties and municipalities before selection.

Sample selection schemes

The sampling frame was divided into 8 strata. In the first stage we have selected segments as PSUs. The PSUs were systematically selected with probability proportional to size in each stratum (the measure of size was the number of private households according to the 2021 Census). Before selection of PSUs, segments were sorted according to counties and municipalities. In the second stage 7 addresses (occupied dwellings) were randomly selected from each PSU.

Described selection scheme is not self-weighting because of different sampling fractions between strata and unequal selection probabilities of addresses in the same stratum.  Unequal selection probabilities of addresses were a consequence of differences between the number of households and the number of occupied dwellings in selected segments and other discrepancies in the 2021 Census data. 

Sample distribution over time - Renewal of sample: rotational groups

EU-SILC 2024 sample was divided into 4 rotational groups. One rotational group was selected in 2021, one rotational group was selected in 2022, one rotational group was selected in 2023 and a new rotational group was selected in 2024. All rotational groups are similar in structure.

Cross-sectional information year 2024

Actual and achieved sample size

Longitudinal information years 2021 - 2024

 Achieved household sample size

Longitudinal information years 2021 - 2024

 Achieved individual sample size

The sampling frame for the new rotational group selected in 2024, for the both stages, was the 2021 Census data. Islands connected with land by bridges were included in the sampling population (the island of Krk, the island of Pag, the island of Murter, the island of Ciovo and the island of Vir). Other islands were excluded from the sampling population. The PSUs are called segments. Segments are area units which consist of 120 to 250 private households according to the Census 2021. They were derived from one or more enumeration districts used in the 2021 Census and belonging in the same municipality.

In each stratum PSUs were systematically selected with propability proportional to size (the mesure of size was the munber of private households in PSU), and in the second stage 7 adresses (occupied dwellings) were selected in each selected PSU. All private households in selected adresses construct a sample of households.

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

- the achieved sample size which is the number of observed sampling units (household or individual) with an accepted interview

- the effective sample size which is defined as the achieved sample size divided by the design effect with regards to the at-risk-of poverty rate indicator

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.

The Eurostat’s precision requirements for EU-SILC are expressed in terms of minimum effective sample size required for the measurement of variable ‘the risk of poverty rate’. The cross sectional effective sample size for Croatia calculated by Eurostat is 4,250 households and 9,250 persons aged 16+. The longitudinal effective sample size is 3,250 households and 7,000 persons aged 16+.

The actual sample size has to be larger than the effective sample size to the extent that the design effect for the variable ‘the risk of poverty rate’ exceeds 1.0 and to compensate for ineligible selected units and non-response.

In 2024, in a new rotational group, we selected 788 PSUs and 7 addresses from each PSU. (5,516 new addresses were selected). The regions in Croatia differ greatly in size and we used sample allocation between regions in proportion to square-root of the region size (in terms of the number of private households).  Within regions sample allocation is proportional to the number of private households.

Frequency of data collection is annualy.

Mode of data collection

Description of collecting income variables

Cross-sectional information year 2024

Fieldwork duration

There is a comprehensive validation procedure applied prior to finalisation of the EU-SILC database.

Source data is initially reviewed and controlled at national level. Control of the collected data is carried out in several iterations. First numerical-logical controls (ie. Small RLK) are carried out after the end of the first of the three month period of data collection, where potential errors and discrepancies in the collected survey data are controled and analyzed. The interviewers are contacted if necessary if there is need for some clarification or some additional checking of entered survey data. This procedure is carried out after each month of the three month period during collection of survey data.
After completion of the field work and after the implementation of basic arithmetical-logical controls, it is accessed to extensive and comprehensive numerical-logical controls of collected survey data (ie. Large RLK). In addition, controls of the collected data include control of data consistency, frequency control, ie. an analysis of outliers etc.

National EU-SILC database is subsequently submitted to Eurostat for validation and bilateral contacts are pursued as necessary.

According to regulation, weighting procedure and imputation were carried out. Details in Annex 6 - Estimation and Imputation and Annex 5 - Weighting procedure.

Imputation rates are part of Annex 6 - Estimation and imputation.

The weighting for the SILC 2024 data was based on the 2021 Census data.

Annex 6 attached.

Not applicable.

Not applicable.

Information (percentages) of missing values (flag=-1) of variables that are part of the Rolling module are filled in the Annex 9 - Rolling module.