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Geographic Inequalities and Access to Higher Education: Is the Proximity to Higher Education Institution Associated with the Probability of Attendance in England?

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Listed:
  • Peter M. White

    (Manchester Metropolitan University)

  • David M. Lee

    (Manchester Metropolitan University)

Abstract

The relationship between geography and the selection of university is well documented and suggests that a student’s location substantially limits their choice of institution. However, there are few studies investigating the interplay between geography and the decision to attend university, particularly in the UK. This study aims to establish if distance to university is associated with the probability of enrolling in higher education in England and forms the quantitative half of a mixed-methods study on the subject. An ordinal logistic regression analysis of HESA enrollment data, which controls for deprivation, is employed. The investigation uses average direct measures of distance between each census area and university campus in England to determine the relationship between distance and enrollment. The findings suggest that geographical distance to university has a negative association with university enrollment. Students within the 10 km measure had a significantly increased odds of enrolling at university than students in the 40 km and above category (odds ratio 10.89; 95% CI 1–2), when controlling for deprivation and population density. It is hoped that the findings of this research will help to raise awareness of geographical inequalities and inform policy on university access.

Suggested Citation

  • Peter M. White & David M. Lee, 2020. "Geographic Inequalities and Access to Higher Education: Is the Proximity to Higher Education Institution Associated with the Probability of Attendance in England?," Research in Higher Education, Springer;Association for Institutional Research, vol. 61(7), pages 825-848, November.
    • Handle: RePEc:spr:reihed:v:61:y:2020:i:7:d:10.1007_s11162-019-09563-x
    DOI: 10.1007/s11162-019-09563-x
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    References listed on IDEAS

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    Cited by:

    1. Xianbo Li, 2022. "Sequence Model and Prediction for Sustainable Enrollments in Chinese Universities," Sustainability, MDPI, vol. 15(1), pages 1-25, December.

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