Persons heating their dwelling with district heating by used energy source, household composition and degree of urbanisation ilc_lvhe04

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The data involves several units of measure, depending on the variable. For more information, see the methodological guidelines and description of EU-SILC target variables available in CIRCABC. Most indicators are reported as shares. Some are reported in other units (e.g., percent, thousands of persons, monetary units, etc.). More information is available in Eurobase, living condition database section.

The reference period is the survey year. The nucleus or annual variables are collected yearly using the reference period as specified in Annex II, Article 7(1) of EU regulation 2019/2242 and as well as in the Methodological guidelines.

For all countries, the reference period for income variables in EU-SILC is the previous calendar year. Ireland, until 2019, collected income information for the 12-month period immediately preceding the sample household's interview date. For most of the countries, the fieldwork was carried out from January until July 2023. The lag between the income variables and the other variables varies across countries (Figure 2).

Figure 2: Lag between the income reference period and current variables by countries, 2023

 Figure 2

Source: EU-SILC Micro-database 2023 (July 2024)

Annual

EU-SILC was designed to keep respondent burden controlled, to avoid a high non-response rate and to ensure the quality of the information collected. The method of interview significantly impacts the interview duration. The interview duration in 2023 is more than 90 minutes for Croatia, Romania and Germany, while for some countries the interview duration per household is less than 30 minutes (Netherlands, Italy, Latvia, and Denmark).

Annex 2 - Mode of data collection and fieldwork, provides more information about the length of interview by country.

The metadata is issued in the SIMS format for European Union Statistics on Income and Living Conditions.

EU comparative quality reports and national quality reports can be found in CIRCABC or on the Eurostat website.

 

19.1 Some specific description from 2023 national quality reports

 

Reference population and household membership

Belgium: Tertiary students often residence at a private address in their university town, while coming back home during the weekend. They remain officially registered at their parents’ address. In BE-SILC, they belong to their parents’ household.

Estonia: Persons living in collective households are included in the reference population. The share of persons who are living in collective households and who are not at the same time members of some other private household is likely to be very low. Additionally, there is no feasible way to estimate their share in the total population. Thus, the exclusion of these persons is unlikely to affect the comparability and reliability of the estimates.

Malta: A person is a household member if s/he is usually resident in that particular dwelling and shares in household expenses. Persons who are temporarily absent for reasons of holiday, travel, work, health, education or similar are included as long as the persons do not intend to stay away for more than 6 months.

Portugal: Anyone living in the household who participates in the common budget and has no other address, even if they are away for less than 6 months.

 

Methods to calculate the variance, std error and CI for the main indicators (AROPE, AROP, SMDS, LWI)

There are countries that use their national methods to estimate precision requirements for the main indicators.

Belgium: Standard errors are estimated by jackknife repeated replication (JRR) method. The clusters are the groups, the strata made by two (or three) groups, using sampling order.

The design effect for the Median equivalised disposable income = 1.02.  

France: For the variance at national level: Estimates with a linearisation of the AROPE and persistence in poverty were tested with no significant gain. In a conservative approach, more traditional precision estimators (Horvitz-Thompson-Narain estimators or calibrated estimators) were used.

For variance at the level of NUTS 2 regions: The small area method used is equivalent to regressing interest variables on the auxiliary variables used to construct the small area weights. This equivalence between the synthetic estimator and a regression is exploited when calculating the variance by calculating the variance of the estimate using the linearisation approach on regression coefficients (this allows to calculate a variance on the small area weights, which can be negative).

Croatia: The Sampling, Statistical Methods and Analyses Department calculates the indicators and the variance in the R programming language.  

Italy: In calculating the standard errors, we take into account all the characteristics of the sample design.

Latvia: CSB of Latvia use own methodology for calculation of sampling errors using R (package vardpoor).

Lithuania: Variance for the main indicators (AROPE, etc.) is calculated using a different method than Eurostat (for reference Annex A in concept 13.2.1). Standard errors as well as calibrated weights are calculated using SAS macro CLAN (CLAN97.sas) developed by Statistics Sweden. CLAN computes estimate of a parameter q and an estimate - based on Taylor linearization - of the standard error . The generalised regression (GREG) estimator is used with auxiliary information.

Luxembourg: internal program mainly based on proc surveymeans.

Hungary: The standard error calculation is based on the linearization method, which takes into account the characteristics of the sampling plan and the effects of calibration.

Malta uses national method to calculate the standard error for the main indicators.

Netherlands uses national method to calculate variance for the main indicators (AROPE, etc.). Variances for the SILC indicators have been estimated taking into account the sampling design, various stages of non-response attrition, as well as the weighting to known population control totals.

Austria uses the calibrated bootstrap procedure using the R Packagesurveysd” developed by the Methods Department of statistics Austria.

Romania: The variance for main indicators of EUSILC was computed using Taylor linearization method implemented in ReGenesees package; it was computed by considering the sampling plan and the calibration information.

Finland: Sampling errors are calculated with the estimation technique based on rescaling bootstrap for the indicators taking account of sampling design and weighting.

Sweden: Standard errors in Annex 3 were calculated using PROC SURVEYMEANS SAS procedure where DB030 was not used for cluster specification. Design was assimilated to a one stage stratified type. A variable available in the national dataset was taken for strata specification, DB090 and PB040 were chosen as weights. Note that all the standard errors published by Statistics Sweden as well as standard errors of the main indicators used with the quality assessment and presented in Annex A 13.2.1 were calculated in line with the national framework with stratification, clustering as well as household size taken into account. Calculations were made using SAS macro-ETOS (Estimation of Totals and Order Statistics) which was designed to compute point and standard error estimates of totals and order statistics (parameters) from sample surveys as well as rational functions of these parameters. According to ETOS 2.0 User’s guide (2012) the Estimating Equations (EE) technique was used for estimation of the variance of the order statistics, and the Taylor linearization method was used for the variance estimation of non-linear functions like ratios and products.

 

Break in time series by years

Indicators: “For Portugal, the methodology to calculate household type differ from the one used by INE (available on the website of Portugal Institute National of Statistics).”

Coverage: France: Since 2022, the survey also covers 4 overseas departments: Guadeloupe, Martinique, French Guiana and Réunion.

Methodological changes: Luxembourg: Design adjustments impacted by covid-19 crisis, as well as the introduction of a mixed mode of data collection resulted on break in series for 2020 and impacted the lack of four-year longitudinal data for 2020-2023.

For detailed information about significant changes and breaks in time series, please see the overview of breaks in series.