As the Deputy is aware, my Department has introduced an objective, statistics based model for assessing which schools merit inclusion in the DEIS Programme, so that all stakeholders can have confidence that we are targeting extra resources at those schools with the highest levels of concentrated disadvantage.
The key data sources used in the DEIS identification process are the DES Primary Online Database (POD) and Post-Primary Online (PPOD) Databases, and CSO data from the National Census of Population as represented in the Pobal HP Index for Small Areas which is a method of measuring the relative affluence or disadvantage of a particular geographical area. Variables used in the compilation of the HP Index include not only single parent rate, but those related to demographic growth, dependency ratios, education levels, overcrowding, social class, occupation and unemployment rates. This data is combined with pupil data, anonymised and aggregated to a small area, to provide information on the relative level of concentrated disadvantage present in the pupil cohort of individual schools. This data is applied uniformly to all schools in the country in a fair and objective way, to identify the relative level of concentrated disadvantage present in each school.
The calculation of the level of disadvantage in each school is based on the socio-economic background of their pupil cohort using centrally held data as previously outlined. It is not based on the location of the school but on the geographical CSO Small Areas where the pupil cohort resides. Therefore it is important to understand that the demographic of neighbouring schools will not necessarily be the same and can indicate different levels of disadvantage based on the actual pupil cohort in each individual school.
A detailed document explaining the methodology used in the Identification process is available on the Department’s website at
DEIS Plan 2017 states that the improved data on the socio-demographic of schools resulting from the new identification model will have an impact not only on the assessment of schools for inclusion in the programme but also on the scaling of resources to allow for more graduated levels of support. This in turn allows for the ultimate objective of allocating resources to best meet the identified need of individual schools.
In order to achieve this, the current identification model needs to be as accurate as possible and this will be facilitated by the use of Eircode to ensure correct inputting of addresses. Further analysis is also required to examine other variables known to be strong predictors of educational disadvantage in the context of resource allocation.