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The Administrative Data Research Network is an ESRC-funded project that ran from October 2013 - July 2018. It is currently at the end of its funding cycle and is no longer taking applications. Administrative data research will be taken forward in a new project, to be launched later in 2018.

Undermatch in higher education: prevalence, drivers and outcomes

Research overview

Evidence suggests that as many as 40% of students in the US are undermatched and that this is more common among students from ethnic minorities and lower socio-economic backgrounds. But students are more likely to complete their degree if they are academically well matched with their institution. Undermatch could therefore be a big factor in drop-out rates. Moreover, if employers value the reputation of university attended, graduates of less selective universities may have less success in the labour market regardless of their university experience. Undermatch therefore has important implications for equality, social mobility and the life chances of those from disadvantaged backgrounds.

This project would produce the first UK-based research on this phenomenon, examining the extent of undermatch in the UK, the characteristics of undermatched students and what are the main drivers of undermatch, whether undermatch affects students’ outcomes at university, and finally whether it affects their job prospects.


This research will shed new light on an un-researched issue, and present new evidence on this potential driver of the socio-economic gap in degree and labour market success. It will therefore contribute to policy on widening participation, social mobility and potentially on the design of the higher education application system. It will also have implications for information, advice and guidance strategies aimed at disadvantaged students.

Government departments

Department for Education

More about the research

This project would produce the first UK-based research on the so-called phenomenon of ‘undermatch’ – where a student’s academic credentials would permit them to access a university course that is more selective than the one that they attend – in other words, where their A-level grades would allow them to access a similar course at a more highly ranked university than those required by the university course that they are attending. While the evidence suggests that this is a significant issue in the US, with over 40% of students undermatching in their postsecondary choice, we have little idea of the scale of the issue in the UK. In addition, this project would advance the international literature on the topic by introducing a novel new approach to estimating undermatch, highlighting the likely biases in the existing methods used in the literature.

Taken together this will make a substantial contribution to our understanding of access and entry to Higher Education.

The research aims to address the following questions:

  1. What is the extent of undermatch in the UK system?
  2. What are the characteristics of undermatched students (such as gender, ethnicity, age, socio-economic status, prior attainment, predicted grades, subject choice at A-level, school type)? Do certain types of students – e.g. disadvantaged students – fail to apply to well-matched courses? Or do they apply to university but are rejected from well-matched courses in higher rates than advantaged students? Do predicted grades play a role in undermatch; i.e. are undermatched students more likely to have received pessimistic grade predictions relative to their realised achievement?
  3. Do students who undermatch have worse outcomes at university (drop out, and less likely to achieve a good degree)?
  4. Do undermatched students have worse early labour market outcomes (be unemployed, in lower status jobs or earn less after graduating)?

In order to answer our first three research questions, we can use linked NPD-HESA data, which enables us to track individual students from school into higher education, observing their academic attainment at school (GCSE, and A-level or equivalent subjects and grades), as well as the university and course they go on to attend and their outcomes including degree completion and degree classification.

In order to more fully investigate the drivers of undermatch (question 2), we would like to use linked NPD-UCAS-HESA data, via the ADRN. UCAS applications data would allow us to i) separate out whether undermatch arises because students do not apply to well matched colleges, or because they apply and are rejected by universities, and ii) understand whether the system of predicted grades is a driver of undermatch, i.e. whether undermatched students may be more likely to have had their grades underpredicted.

Our fourth question involves obtaining access to HESA’s Longitudinal Destinations of Leavers of Higher Education (long-DLHE) data linked to HESA data, which will enable us to follow a sample of university students into the labour market (3.5 years after graduation, aged around 25).

We will also complement our analysis in questions 1-4 using data from the Next Steps survey. This dataset is a longitudinal survey of a sample of pupils who attended school in England in 2004-2010. It captures the labour market experiences of the sample at age 25 in 2015, along with information on university attended, degree subject studied, A-level subjects and grades. This data also contains detailed information on the background of survey members including their family circumstances and their expectations and aspirations for higher education.

Date approved

April 2017

Lead researcher

Dr Gill Wyness, UCL Institute of Education

Page last updated: 24/10/2017