A still classroom, a stack of marked scripts and a teacher tracing patterns across school lists — that is the picture unfolding behind the statistics on the leaving certificate. A new ESRI study places the school a young person attends and the educational background of their mother at the centre of why some pupils score far higher than others, and it raises questions about whether current supports are enough for the most disadvantaged schools.
How does the school a pupil attends shape Leaving Certificate outcomes?
The ESRI finds that the school a student attends is more likely to influence performance than the neighbourhood where they live. The research notes the level of choice among second-level schools makes it possible to separate school effects from neighbourhood effects more precisely than in areas where children all attend a single local school. It also finds that the social mix of the school matters: students in Deis-designated schools record much lower grades than those in socially mixed schools, while students in fee-paying schools record higher grades.
Report authors Emer Smyth and Merike Darmody write that “effects are found to be cumulative, with both the primary and second-level school attended affecting grades. ” The finding reframes discussions about educational disadvantage from place-based explanations alone to include the composition and resources of the schools themselves.
What family and neighbourhood factors affect leaving certificate performance?
The study highlights multiple family and neighbourhood dimensions that independently shape exam results. Maternal education stands out: there is a gap of over 100 Leaving Certificate points between children of graduate mothers and those whose mothers have Junior Cycle education or less. The authors stress the importance of a multidimensional view of background, noting that “maternal education, social class, financial strain and family type all have independent effects on exam performance. ”
Neighbourhood characteristics also influence outcomes. The ESRI finds lower grades in areas marked by greater socioeconomic disadvantage and higher levels of neighbourhood disorder. Taken together, family background, school social mix and neighbourhood context operate cumulatively to widen or narrow a pupil’s chances of reaching their potential.
What is being done and what do the authors recommend?
Schools serving disadvantaged communities currently receive extra supports through the Deis programme. The ESRI authors observe, however, that “these do not appear sufficient to bridge the gap in outcomes, at least for this cohort of young people. ” They add that the findings contribute to evidence supporting additional interventions for the most deprived schools and point toward a proposed Deis plus designation as a next step.
Research Professor Emer Smyth, one of the report authors, emphasises the range of influences at work, saying family background, social class, financial strain and family type all affect outcomes. The report’s call is pragmatic: strengthen supports where the evidence shows gaps remain, and recognise that addressing disadvantage requires action at the level of school resources and composition as well as family supports.
Practically, the study steers attention to targeted measures in schools with concentrated disadvantage and to policies that recognise cumulative effects across primary and second-level education. It also reframes the policy debate: boosting only neighbourhood-level investment will not be enough where school context and family background remain decisive.
Back in the quiet classroom that opened this story, the figures on a results sheet now look less like individual fate and more like the product of layered contexts — the school attended, a mother’s level of education, and the strain a family lives under. The ESRI’s analysis ends not with a single prescription but with a clear direction: if the goal is to close the gaps in Leaving Certificate performance, supports must follow the evidence and focus on the schools and families where they are needed most.
