Lower levels of economic, social and cultural capital have been widely shown to be associated with poorer educational outcomes; e.g., across countries/economies participating in the Programme for International Assessment (PISA) 2018, ‘advantaged’ students scored on average 89 points higher in reading achievement than ‘disadvantaged’ students (OECD, 2020). Equity in education is therefore a major international policy priority (OECD, 2018) with various approaches in place to provide additional supports to students from deprived backgrounds. There is variation in how countries identify beneficiaries and deliver interventions and the merits or otherwise of using various indicators as proxies for disadvantage have received attention in the literature (e.g., Domina et al., 2018; Driessen, 2017; Taylor, 2018). This paper contributes to the field by presenting data from Ireland.

Ireland has a long history of supporting disadvantaged students (ERC, 1998) and has been recognised as being effective at limiting the impact of socio-economic background on achievement (European Commission, 2019). The 1998 Education Act defines educational disadvantage as: “the impediments to education arising from social or economic disadvantage which prevent students from deriving appropriate benefit from education in schools” (Ireland, 1998, Section 32). Since 2005, additional resources have been delivered at school-level through the Delivering Equality of Opportunity in Schools (DEIS) programme (DES, 2017). Supports include reduced class sizes, additional grant aid, access to school meals, and priority access to teacher professional development for schools with the highest concentrations of students from disadvantaged backgrounds.

Primary schools were originally identified for inclusion in the programme based on socio-economic data gathered through a survey of school principals. At second-level, centrally held data on achievement in state examinations, retention levels, and proxy indicators of socio-economic status were used to identify schools (Weir, 2006). There has been little change in the original group of DEIS schools and the need for review was acknowledged in the 2017 DEIS plan (DES, 2017). A small number of additional schools were added to DEIS in 2017 using an alternative approach to assessing disadvantage.

The identification approach used in 2017 was based on the Haase Pratschke (HP) index of deprivation, derived from census data (Haase & Pratschke, 2017). Advantages include moving away from survey-based methods and avoiding the use of examination data in the identification process (in line with current national policy to avoid using achievement outcomes for this purpose). The stated intention in the DEIS plan is to review the socio-economic context of all primary and second-level schools, using the HP index, and to identify schools that merit support under the DEIS programme. Work is ongoing in finalising the precise method of applying the HP index.

Advantages of the HP index are that it is based on routinely collected census data and it is almost universally available for all students. However, it is less clear whether or not it is a strong predictor of educational outcomes. Relatively little is known about how HP compares to other disadvantage and socio-economic status indicators, e.g., the index of economic, social and cultural status (ESCS) in PISA (OECD, 2019). In this paper, we examine the associations between the HP index, two other indicators of socio-economic status and PISA reading achievement. Specifically:

1. How is school-average HP related to other measures of socio-economic disadvantage (average ESCS and percent fee-waiver)?
2. How is school-average HP related to school-average reading achievement and is this comparable to the association between reading achievement and other school-level measures of disadvantage?
3. Do school characteristics, such as enrolment size or location, help identify when average HP is likely to be a less effective predictor of reading achievement?

This analysis uses data from: PISA 2018; the State Examinations Commission (SEC; a statutory public body with responsibility for the operation of State Certificate examinations in Ireland); and, Department of Education (DE). The variables used are: • PISA 2018: reading achievement and school-average ESCS. Derived using Principal Component Analysis, ESCS is a composite score based on parental education, highest parental occupation, and home possessions. • Percentage of students with an examination fee-waiver (SEC): A student is entitled to an examination fee-waiver if the student’s family has a medical card. A medical card is awarded on the basis of low family income and has been used as a proxy indicator of socio-economic status. This variable was previously used in PISA sampling in Ireland as an indicator of socio-economic status and has been used extensively in previous national research. It was one of the variables used in the 2005 DEIS identification of schools. • School-mean HP (DE): The HP index shows the relative affluence or deprivation of 18,488 small areas in Ireland. Small areas are areas of population generally comprising between 80 and 120 dwellings. The HP index is based on three dimensions: demographic profile, social class composition, and labour market situation in each small area (Haase & Pratsche, 2017). For every student in Ireland, the postcode (Eircode) associated with the student’s home address was matched to their small area’s HP score. A school’s mean HP score was calculated by computing the average HP score of all students in a school. • Other school characteristics including enrolment size and location are drawn from SEC and PISA. Software: IEA’s IDB Analyzer (version 4.0.39, for the analysis of large scale assessment data); IBM SPSS Statistics (version 26); MlwiN (version 3.05). Methods: Bivariate correlations between variables are examined. Level 2 residuals are saved from a multilevel model, using student reading achievement as the outcome and HP as predictor. Consideration is given to school characteristics of schools where 95% confidence intervals for level 2 residuals for the fitted model do not overlap zero.

Research Question 1: Statistically significant, large correlations are shown between school-average HP and the other two measures of socio-economic disadvantage (Cohen, 1988). Specifically, school-average HP correlates strongly and positively with school-average ESCS (r = 0.75, p < .001) and negatively with percent medical card (r = -0.82, p < .001). Research Question 2: There is a strong positive association between school-average HP and school-average reading achievement (r = 0.66, p < .001). This correlation is somewhat weaker than the association between reading and school-average ESCS (r = 0.81) and between reading and percent medical card (r = -0.76) indicating that school-average HP is somewhat less effective at explaining variance in reading achievement. Research Question 3: Level 2 residuals will be used from a multilevel model to consider the characteristics of schools where HP less effectively predicts reading achievement. There are challenges in using any of the three socio-economic indicators for the purposes of identifying schools for DEIS. ESCS is a rich measure, but is expensive and impractical to collect at a population level. Percent fee-waiver will become unavailable if the current medical card system is phased out as planned. An alternative is therefore required which is robust to changes in the health system and practical to gather. HP is available for all schools and represents a viable option. This analysis (a) validates HP as a school-average indicator of disadvantage, (b) shows that HP has a similar, albeit weaker, association with achievement compared with other similar measures, and (c) explores the stability of HP as a predictor across school locations and enrolment sizes.

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