There is increasing international attention paid to the role played by geography in shaping university progression. Recent research has considered important questions such as the impacts that the spatial distribution of universities within countries and the distance students are prepared to move away from home have upon university participation. In Australia, distance has been shown to impact upon both university aspirations and entry, especially as concerns lower socioeconomic groups (Parker, Jerrim, and Anders, 2009). In some countries, geography intersects in important ways with race and ethnicity, for example, Indigenous communities in Mexico are often located in rural areas with few universities nearby (Delgado, 2016). Similarly, in the USA, Hillman (2016) identifies ‘education deserts’, places with few college options, often home to large Hispanic communities with low educational attainment.

In the UK, policy debates around inequalities are increasingly taking a spatial turn too. The 2016 referendum decision to leave the European Union has led to a growing focus on the UK government’s so-called ‘levelling up’ agenda, aimed at decentring political and economic power away from London. Within these increasingly place-based policy narratives, universities have also been scrutinised, with government ministers frequently calling out elite universities on the spatial, as well as social, profiles of their student bodies (e.g. Lammy, 2017).     

My research is concerned with the role place plays in access to elite universities in the UK, after all other known determinants are accounted for (including the spatially uneven distribution of elite universities themselves). It aims firstly to explore how university participation varies according to where, geographically in England, young people live, and, secondly, to build a greater understanding of the reasons why some areas may be under/overrepresented and what can be done to address this. The research questions I focus on include: 

1.Taking into account individual characteristics, are there geographical differences in access to elite universities? 

1.1 Which localities send more/fewer students to elite universities than might be expected?  

1.2 What might explain these geographical patterns of participation? 

2. How might widening participation policy and practitioners best address geographical differences in access to elite universities? 

A critical realist framework is used, chosen for its emancipatory dimension (Baert, 2015) and perspective that unobservable structures underlie the observable inequalities in society (Matthews and Ross, 2010).  Moreover, the findings of related research within the UK context, such as Manley and Johnson (2014) and Wright (2014) who find both observable characteristics of areas of low/high elite university progression, as well as unexplained variance – suggesting the role played by underlying structures – demonstrate the relevance of adopting such a framework.  

In a changing world, with an increasing drive towards homogenisation across many areas of society, including education, the findings of my research evidence the importance of building in-depth understanding of local contexts for education policy. However, revealing as they do the uneven nature of widening participation provision and educational opportunity across England, they also highlight the need for increased coordination amongst policy makers and practitioners nationwide to ensure a fairer distribution of opportunities. My research also offers potentially important insights into unequal geographical patterns of university participation, particularly as concerns elite universities, in other country contexts too, especially countries with similar stratified university systems like the US and France. As such, it offers an important topic for discussion with the Research in Higher Education network and beyond at ECER 2022.    

A mixed-methods research approach was used. Phase one of the research involved quantitative analysis of specially requested data from the Higher Education Statistics Agency (HESA); the official agency for data collection on students enrolled on UK-based university courses. The extract analysed contained the data for all England-domiciled, first-degree entrants across five separate cohorts. Elite universities were defined as the 24 universities of the Russell Group (a self-selected group of UK research-intensive universities) and - recognising the subjective nature of this grouping - any university scoring within the top 20 when their rankings within the UK’s Complete University Guide for both entry standards and research scores were combined. This resulted in the addition of three further universities to create a ‘top27’ grouping. Local areas were defined by Middle Super Output Areas (MSOAs), a geographical hierarchy used by the UK’s Office for National Statistics. Due to the nested structure of the data (students within MSOAs), a multilevel modelling approach was adopted. A sequence of two-level (students at level 1, MSOAs at level 2) random-intercept logistic models with a binary outcome of progression (or not) to a university within the ‘top27’ grouping was used. The sequence of increasingly complex models included the testing of ‘control variables’, variables included to account for the impacts of observable individual and MSOA characteristics known to affect entry to elite universities (including attainment, socio-economic status, ethnicity, and distance). The random (MSOA-effects) of the first (null) and final models were listed and mapped, with the latter used to identify areas of higher and lower than expected progression once the impacts of all control variables had been accounted for. One area with higher-than-expected progression (an area of East London) and another with the converse (an area of Northwest Nottingham) were selected as case study areas for the second qualitative phase of research. The principal component of this research consisted of six in-depth semi-structured interviews in each location, comprising three students aged 16-18 with the academic potential to attend an elite university and three staff members. The interview guides focussed on students’ university choices and the key factors impacting their decisions. Once transcribed, thematic analysis (Braun and Clarke, 2006) was conducted to identify common themes across the data. Having identified important themes, this primary data set was augmented with relevant secondary data from a related study. This included interview data from Suffolk, Liverpool, Tyneside and a separate area of East London.

The study’s initial quantitative analyses enabled a granular look at elite university progression rates by local area across England and the identification of areas of lower and higher than expected participation. While they showed that ‘place’ in itself is not a highly significant factor in entry to elite universities overall, they evidenced a distinct urban–rural patterning to progression (Davies, Donnelly, and Sandoval‐Hernandez, 2021). Indeed, when raw progression rates by area alone were considered, rural areas typically had higher progression rates to elite universities. However, when the full range of individual characteristics were accounted for, including attainment, socio-economic status, ethnicity and accessibility to elite universities, the converse was true— localities within and surrounding major urban centres were those with the highest progression rates. This was especially noticeable within London. The East London case study research suggests that a convergence of opportunities that strongly privilege elite universities and careers may play an important role in helping explain local disadvantaged students’ higher than expected elite university progression rates. These include local schools’ prioritisation of financial resources and building of partnerships with external organisations for this aim, high engagement from elite universities, and students’ interactions with locally situated businesses overt in privileging elite university study. Such experiences do not appear to be shared to the same extent by working-class young people outside the capital. Case study data from disadvantaged areas within Nottingham, Liverpool, Suffolk, and Tyneside suggests that schools there are typically focussed on a wider range of priorities and have greater engagement with local post-1992 institutions, and that students’ work-related opportunities and outlooks often mirror their less economically prosperous local contexts.

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