Conference Program

Attainment gaps are a key indicator of educational and societal inequalities. The analysis and reporting of educational attainment are often based on cross-sectional data and take the form of simple breakdowns of students’ achievement by selected characteristics. These descriptive statistics, although relevant, are potentially misleading. They fail to account for the interplay between students’ characteristics and, crucially, for information that is only available when longitudinal data is used.

The aim of this presentation is to show the advantage of using longitudinal data for the systematic reporting of attainment gaps. From a methodological perspective, we will demonstrate that, despite their complexity, educational inequalities can be systematically measured and reported. More specifically, we will outline an analytical research strategy that makes use of micro-data gathered through schooling paths to account for the interplay between students’ characteristics and other factors affecting their academic performance, most notably attainment at prior stages of education. From a more substantive perspective, we will present evidence on attainment gaps across different groups of secondary school students in a way that facilitates the identification of over-time trends in educational inequalities.

We used the National Pupil Database, a rich administrative linked micro-dataset, maintained by the UK Department for Education. The dataset covers eight cohorts of students in England completing their lower- and upper- secondary education between 2018 and 2025. For all students, it features a broad set of characteristics at individual- and school-level recorded during their primary and secondary education. At student level, the dataset includes standardised measures of educational attainment, specifically at the end of primary, lower secondary and upper secondary education. It also includes socio-demographic characteristics (eg, gender, ethnicity, first language spoken at home, special educational needs), and socio-economic background. At school level, we used information on the type of schools attended, their geographical location, and a measure of deprivation. A multivariate analysis based on a regression approach was employed to measure the impact of each characteristic, once other factors were held fixed. To account for school-level effects, a multi-level regression approach was used. Each year’s data was analysed separately, using the longitudinal component of the data as a crucial aspect of modelling academic performance. To interpret findings, a set of criteria was considered to flag changes over time in attainment gaps that were considered ‘notable’.

We will present evidence on attainment gaps for students taking different education pathways in England. We will focus on the dynamics of educational inequalities between 2018 and 2025, drawing conclusions on the impact of the assessment arrangement put in place in 2020 and 2021 as a response to the pandemic and the return to a ‘new normal’. We will show the mitigating (or sometimes exacerbating) effects that the longitudinal information on prior attainment and the school-level characteristics can have on the estimation of attainment gaps. In this way, it will be possible to argue that collecting and making available rich longitudinal micro-data is key to producing evidence on the true dynamics of educational attainment.