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The Government has rightly identified ‘levelling up’ opportunity and prosperity as a key aim. Ensuring young people across the country have access to relevant, good quality post-16 education and training options has to be part of how this is achieved.
New research shows that there is substantial geographic variation in post-16 provision. This has an impact on the opportunities that young people can access, particularly those who don’t achieve key attainment benchmarks such as grade 4 in English and maths.
Yet these spatial variations are not well-understood due to the lack of easily accessible data on young people’s post-16 trajectories and outcomes and the limited amount of research into the workings of the post-16 system at local and national level.
Through analysing data from the National Pupil Database (NPD) and Individualised Learner Record (ILR), our recently-completed project on post-16 transitions funded by the Nuffield Foundation shows how, in some places, young people enter the post-16 phase at a lower level of learning than in other places, despite having similar prior attainment.
Source: National Pupil Database and Individualised Learner Record. Data refers to the cohort of young people completing Key Stage 4 in 2015. City regions defined as combined authority areas except Nottingham, which is defined as the Local Authorities of Nottingham and Nottinghamshire, and Newcastle City Region, which refers to the original North East Combined Authority before Newcastle, North Tyneside and Northumberland split off to create the North of Tyne Combined Authority.
Differences in ‘starting level’ appear to be related to structural differences between areas in terms of the mix of post-16 providers – FE colleges, sixth form colleges, school sixth forms, and other providers. Locations also differ in terms of the types of vocational and academic courses offered to school leavers and in what is needed to get onto these. These differences have repercussions for young people’s achievements during the 16-19 phase.
These findings highlight the need to better understand the ways in which the decisions made by schools and colleges about what courses to offer, with what entry requirements and for which learners, are affecting education outcomes.
The focus of much data analysis tends to be on educational outcomes, rather than on the pathways leading to those outcomes, or on the systems and processes that shape them. Understanding these systems and processes requires detailed data on local provision and on young people’s transitions through the 16-19 system. But, unlike published data on specific attainment benchmarks – like the percentage of learners achieving a grade 4 in English and maths, or the percentage achieving Level 3 by 19 – this type of data is hard to come by. This is partly because some data are not routinely collected. For instance, no data is collected on the vocational and academic qualifications offered in different places, or the accessibility of these qualifications to learners with different levels of prior attainment.
In other cases, the data is collected, but not made readily accessible by central government. The NPD and ILR have a wealth of information about young people’s education and training activities and achievements. Moreover, they allow you to track young people all the way from secondary school and through the post-16 phase, and identify which specific groups, in which places, are not progressing in the way you might expect. This is vital information that could be used by local and city-regional government to improve post-16 outcomes. But accessing the full datasets requires an extremely lengthy application procedure. Local government analysts may not have any way to access the full data and rely on partial information supplied through ‘data cubes’ or other formats by the Department for Education (DfE). To be able to analyse the trajectories of all learners, it is necessary to combine the NDP and the ILR. This isn’t easy due to the lack of alignment between the two datasets.
Data accessibility also needs to be improved when it comes to the labour market outcomes associated with different post-16 pathways. These have become a greater focus of analysis (e.g. here and here) – facilitated through the Longitudinal Education Outcomes (LEO) dataset. This dataset is the first in the UK to systematically link administrative data on young people’s educational activities and attainment with HMRC data on employment and earnings, and offers the potential to understand which young people, in which places, are struggling to make a successful transition to the labour market.
Yet at the moment, LEO data can only be accessed by a select few researchers at approved institutions, and the amount of published data based on LEO is limited, focusing mostly on graduate outcomes. The DfE should increase their efforts to make this data available – in a safe way – to a wider range of analysts.
To meet the aim of ‘levelling up’, as well as to guide national reforms to the post-16 system, we need better insights into the variety of different education and training routes undertaken by young people, as well as about spatial variations in post-16 transitions and outcomes and what shapes these.
This requires more and better data, as well as analysis that explores the links between local opportunities and outcomes. This can be achieved though the following steps:
Sanne Velthuis is a Research Associate at the University of Manchester.
This blog is published as part of an occasional series by guest experts to provide a platform for new ideas in urban policy. While they do not always reflect our views, we consider them an important contribution to the debate.
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