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 survey of private households. The SILC Anonymised Microdata File (AMF) contains both personal and household level data. Household data is at present duplicated for each member of the household. If performing household level analysis, please be aware of this and subset the data to include a single entry per household (interview_hh = 1). In 2021 the European legislative basis (Regulation No 1177/2003) for the production of statistics on income and living conditions has been repealed by Regulation 2019/1700. This new framework regulation establishes a common framework for European statistics relating to persons and households, based on data at individual level collected by samples. In order to meet the requirements of the new regulation, the Central Statistics Office (CSO) introduced changes to many SILC business processes. These changes have resulted in a break in the SILC time series for 2020.Data from 2020 onwards is not directly comparable with data from 2004-2019. To make the difference clear, national use variables from 2020 onwards have been re-named. Census Revision to SILC 2020-SILC 2022 The annual Survey of Income and Living Condition (SILC) results are weighted using population estimates which are generated on an ongoing basis. Census of Population 2022 results have been used to revise population estimates for 2020 to 2022, and consequently results for SILC survey years 2020, 2021 and 2022 are revised. Please note: SILC AMF data is cross-sectional microdata in which household and/or individuals cannot be tracked over time. The household id variables in each cross sectional file are randomly generated and cannot be linked between yearly datasets.
Probability: Simple random, Probability: Cluster, Probability: Multistage In 2022 a new sampling methodology was introduced to ensure SILC will be able to meet the precision requirements specified in the IESS regulation. Wave 1 was selected using this methodology in SILC 2022. Waves 2, 3, 4 and 5 comes from the 2018 sampling frame, while wave 6 comes from the 2014 sampling frame, using the previous sample selection methodology in place since 2014. The following is a brief overview of the revised SILC sample methodology, from which wave 1 of SILC 2022 was selected: The SILC sample is a Stratified Simple Random Sample (SSRS). The sample is stratified by county and 10 equivalised income bands. Households are selected using Neyman allocation. The sampling frame is the 2016 Census, excluding households previously sampled for other social surveys. Including longitudinal cases from the older sample selection methodology (waves 2-6), a target of 12,000 households are selected for interview. The following is a brief overview of the 2014 SILC sample methodology, from which waves 2-6 of SILC 2022 were selected: The SILC sample is a multi-stage cluster sample resulting in all households in Ireland having an equal probability of selection. The sample is stratified by NUTS4 and quintiles derived from the Pobal HP (Haase and Pratschke) Deprivation Index. In the 2018 sample the clusters are based on Census Enumeration Areas, rather than the Household Survey Collection Unit Small Areas used in the 2014 sample. A sample of 1,200 blocks (i.e. Census Enumeration Areas, Census 2016) from the total population of blocks is selected. Blocks are selected using probability proportional to size (PPS), where the size of the block is determined by the number of occupied households on Census night 2016. 100 households from each block are selected at random to be retained for selection within each block. All occupied households on Census night 2016 within each block are eligible for selection in the SILC sample. Households within blocks are selected using simple random sampling without replacement (SRS) for inclusion in the survey sample.
Telephone interview: CATI
