The primary focus of the Survey on Income and Living Conditions (SILC) is the collection of information on the income and living conditions of different types of households in Ireland, in order to derive indicators on poverty, deprivation and social exclusion. It is a voluntary (for selected respondents) survey of private households. It is carried out under EU legislation (Council Regulation No 1177/2003) and commenced in Ireland in June 2003.
Information is collected continuously throughout the year with household interviews being conducted on a weekly basis. The income reference period for SILC is the 12 months immediately prior to the date of interview. Therefore, the income referenced spans the period from January 2017 to December 2018. In 2018, the achieved sample size was 4,382 households and 11,130 individuals.
For 2018, the results of the SILC survey were published eleven months after the end of the reference period and ten months after the end of the data collection period. It is important to take into account a number of factors when comparing the timeliness of the Irish results with those of other countries. These factors include; the timing and duration of the data collection fieldwork and the exact reference year of the data collected. For example, most EU member states use income data from the previous year (T-1) as a proxy for current (T) annual income. As noted above, the income referenced in Ireland’s 2018 SILC data spans the period from January 2017 to December 2018.
The SILC sample is a rotational sample. In 2018, a new sample was introduced. This means that wave 1 for the 2018 SILC comes from the 2018 sampling frame, while waves 2-4 come from the 2014 sampling frame.
There is both a cross-sectional and a longitudinal element to the SILC sample. Households interviewed for the first time are Wave 1 households. Households who are interviewed in subsequent years are Wave 2 households (2nd year in the sample), Wave 3 households (3rd year in the sample) or Wave 4 (4th and final year in the sample). The initial sample design attempts to seed the sample with 25% for each new wave. However, due to non-response and sample attrition the waves are not evenly balanced in the sample with Wave 1 households usually tending to dominate.
The CSO has strengthened its own rules and procedures around sample implementation. One of the key improvements in sample implementation over the past number of years is the ruling out of the substitution of households by interviewers.
The overall response rate for the SILC survey in 2018 was 46%. The response rate is heavily influenced by the Wave 1 response rate which was 33% in 2018. The response rates tend to be a lot higher for Wave 2-4 households and in 2018 the response rate for Wave 2-4 households was 69%.
In 2014, a new sampling methodology was introduced to improve the robustness of the SILC Sample. The sample methodology takes into account response rates and attrition rates to ensure the CSO achieves the required effective sample size required by Eurostat. In 2018, a new sample was introduced. This means that wave 1 for the 2018 SILC comes from the 2018 sampling frame, while waves 2-4 come from the 2014 sampling frame. The following is a brief overview of the revised SILC sample methodology:
A design weight is assigned to each household which is calculated as the inverse proportion to the probability with which the household was sampled. For SILC, the probability of the selection of a household is based on two elements; the probability of the selection of a block and the probability of selection of a household within that block. The design weights were calculated separately for each wave.
For Wave 1 households, the design weights were calculated as outlined above and adjusted so as to be proportional to the 2018 sample as a whole. For Wave 2-4 households, base weights were calculated by firstly adjusting the personal weights from the previous year for non-response. The Weight Share Method was then applied to calculate a base weight for the household. These design weights were then adjusted so as to be proportional to the original sample as a whole.
In accordance with Eurostat recommendation, CALMAR was used to calculate the household cross-sectional weights. Benchmark information was used to gross up the data to population estimates. The benchmark estimates were based on:
¨ One adult, no children
¨ Two adults, no children
¨ Three or more adults, no children
¨ One adult, one or more children
¨ Two adults, one to three children
¨ Other households with children
Due to the “integrative” calibration method, the personal weight generated in CALMAR is equal to the household weight. Because there is no individual non-response within a household, the weights for personal cross-sectional respondents aged 16 and over are the same as the overall personal weight.
Precision estimates and statistical significance
Estimates were calculated in SAS using the Jackknife and the Taylor Linearisation methodology. For the mean equivalised net disposable income, the ‘At Risk of Poverty’ rate, the ‘Deprivation’ rate and the ‘Consistent Poverty’ rate, the Jackknife Method in PROC SURVEYMEANS was used. The Taylor Linearisation Method in PROC SURVEYMEANS was used to measure the precision of the quantiles.
SAS routines and macros were developed to calculate the precision of the more complex statistics, i.e. the Gini Coefficient and the Quintile Share Ratio (QSR), using the Jackknife Method. The variance of the Gini and the QSR was estimated using the methodology outlined in Lohr1 Ch. 9 (Variance Estimation in Complex Surveys). The calculations of the precision estimates took into account the weighting, the complex structure of the sample, (i.e. the fact that the sample was a cluster sample as opposed to a simple random sample) and other complications arising from the methods adopted.
When measuring the year on year change of a statistic, we take into account both the variance of the statistic in each year (sample) and the covariance of the statistic between samples.
1Sampling: Design and Analysis, 2nd Edition, Sharon L. Lohr (2010).
The annual SILC survey is the main data source for SILC. Information is collected from all household members on laptop computers by trained interviewers, using Computer-Assisted Personal Interview (CAPI) software.
In addition, the CSO has two primary micro data sources. These are the Department of Social Protection (DSP) social welfare data and Revenue Commissioners’ employee income data. The CSO continues to work with DSP and Revenue to ensure good quality data is available on a timely basis.
Income details are collected at both a household and individual level in SILC. In analysis, each individual’s income is summed up to household level and in turn added to household level income components to calculate gross household income. The components of gross household income are:
Gross employee cash or near cash income
Gross non-cash employee income
Employer’s social insurance contributions
Gross cash benefits or losses from self-employment
Other direct income
Jobseekers related payments
Old-age payments (note that this includes all occupational pensions and other such social welfare payments to those aged 65 and over)
Family/children related allowances:
Other Social transfers:
Tax and social insurance contributions are also summed to household level and subtracted from the gross household income to calculate the total disposable household income. The components of disposable household income are gross household income less:
Employer’s social insurance contributions
Regular inter-household cash transfer paid
Tax (including USC) on income and social insurance contributions
Tax deducted at source from individual private pension plans
Real/Nominal income figures
Both nominal and real income figures are included in this release. Real income figures have been adjusted for inflation by applying a deflator to the nominal income figures. The deflator is derived from the monthly CPI and takes into account the rolling nature of the income data collected by SILC.
Equivalence scales are used to calculate the equivalised household size in a household. Although there are numerous scales, we focus on the national scale in this release. The national scale attributes a weight of 1 to the first adult, 0.66 to each subsequent adult (aged 14+ living in the household) and 0.33 to each child aged less than 14. The weights for each household are then summed to calculate the equivalised household size.
Equivalised disposable household Income
Disposable household income is divided by the equivalised household size to calculate equivalised disposable income for each person, which essentially is an approximate measure of how much of the income can be attributed to each member of the household. This equivalised income is then applied to each member of the household.
For the purposes of deriving household composition, a child was defined as any member of the household aged 17 or under. Households were analysed as a whole, regardless of the number of family units within the household. The categories of household composition are:
Tenure status refers to the nature of the accommodation in which the household resides. The status is provided by the respondent during the interview and responses are classified into the following three categories:
From 2014 onwards due to the new sampling methodology, areas are now classified as Urban or Rural based on the following population densities derived from Census of Population 2016:
Prior to 2014, areas were classified as Urban or Rural based on the following population densities:
The regional classifications in this release are based on the NUTS (Nomenclature of Territorial Units) classification used by Eurostat. The NUTS boundaries were amended on 21st November 2016 under Regulation (EC) No.2066/2016 and took effect from 1st January 2018. As a result, new NUTS (regional classification) groupings have been introduced for Ireland, see groupings below.
As the CSO weights results in the SILC using NUTS3 groups, survey estimates have been revised for SILC years 2012-2016 to take account of these changes. This reweighted data from 2012 to 2016 inclusive was published with the SILC 2017 results and users should note that there is a break in the regional data series from 2012 as the results for the period 2004 to 2011 are published using the old NUTS groupings.
|Northern & Western NUTS2 Region||Southern NUTS2 Region||Eastern & Midland NUTS2 Region|
At risk of poverty rate
This is the share of persons with an equivalised income below a given percentage (usually 60%) of the national median income. It is also calculated at 40%, 50% and 70% for comparison. The rate is calculated by ranking persons by equivalised income from smallest to largest and then extracting the median or middle value. Anyone with an equivalised income of less than 60% of the median is considered at risk of poverty at a 60% level.
Households that are excluded and marginalised from consuming goods and services which are considered the norm for other people in society, due to an inability to afford them, are considered to be deprived. The identification of the marginalised or deprived is currently achieved on the basis of a set of eleven basic deprivation indicators:
Individuals who experience two or more of the eleven listed items are considered to be experiencing enforced deprivation. This is the basis for calculating the deprivation rate.
The consistent poverty measure looks at those persons who are defined as being at risk of poverty and experiencing enforced deprivation (experiencing two or more types of deprivation).
An individual is defined as being in ‘consistent poverty’ if they are
This is the difference between the median equivalised income of persons below the at-risk-of-poverty threshold and the at-risk-of-poverty threshold, expressed as a percentage of the at-risk-of-poverty threshold. The purpose of the indicator is to measure how far below the poverty threshold the median income of people at risk of poverty is. The closer the median income of those at risk of poverty is to the at risk of poverty threshold the smaller the percentage will be.
This indicator is calculated based on two alternative measures of equivalised income. The first calculates equivalised income as the total disposable household income including old-age and survivors’ benefits but excluding all other social transfers. The second excludes all social transfers. Any person with an equivalised income before social transfers of less than 60% of the median after social transfers is considered at risk of poverty before social transfers (i.e. the same threshold is used for calculating the rate before and after social transfers).
For a given year, the “at risk of poverty rate anchored at a moment in time” is the share of the population whose income in a given year is below the at risk of poverty threshold calculated in the standard way for a previous base year and then adjusted for inflation. The purpose of this indicator is to get some indication of the changes in ‘absolute poverty’ over time. The deflator is derived from the monthly CPI and takes into account the rolling nature of the income data collected by SILC.
This is the relationship between cumulative shares of the population (ranked according to the level of income from lowest to highest) and the cumulative share of total income received by them, i.e. the Lorenz Curve. If there was perfect equality (i.e. each person receives the same income) the Gini coefficient would be 0%. A Gini coefficient of 100% would indicate there was total inequality and the entire national income was in the hands of one person.
Calculation of the Gini Coefficient
Wgti = Final calibrated weight per individual
Eq_Inci= Equivalised disposable income
This is the ratio of the average equivalised income received by the 20% of persons with the highest income (top quintile) to that received by the 20% of persons with the lowest income (lowest quintile).
The Central Statistics Office wishes to thank the participating households for their co-operation in agreeing to take part in the SILC survey and for facilitating the collection of the relevant data.
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