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Widening Participation in Higher Education: Analysis using Linked Admin Data

This research study delves into the phenomenon of widening participation in higher education in the UK, examining the socio-economic factors influencing HE access. The analysis encompasses data from multiple institutes and research teams, exploring the impact of policy changes and prior achievements on HE participation. By utilizing a linear probability regression model and longitudinal admin data, the study scrutinizes the disparities in HE access among different socio-economic groups and institution types. The conclusions emphasize the importance of addressing low prior achievement to enhance HE participation from deprived backgrounds. However, the study also points out the role of universities in promoting inclusivity. While focusing on young participants, future research directions include considering mature students and enhancing data on private school attendees.

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Widening Participation in Higher Education: Analysis using Linked Admin Data

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  1. Widening Participation in Higher Education: Analysis using Linked Admin Data Institute of Education Institute for Fiscal Studies Centre for Economic Performance

  2. Research Team Haroon Chowdry Claire Crawford Lorraine Dearden Alissa Goodman Anna Vignoles

  3. Background and Motivation • Rapid expansion of HE in the UK • 43% of 17-30 year olds participated in 2005-06 • But widening participation still cause for concern • Socio-economic gap in HE participation appeared to widen in mid to late 1990s • Blanden & Machin (2004); Galindo-Rueda et al (2004); Glennerster (2001); Machin & Vignoles (2004) • Although may have narrowed somewhat since then • Raffe et al. (2006)

  4. Background and Motivation • Concerns increased following introduction of tuition fees in 1998 • But did not deter low income students (who were protected by increased loan availability) (Dearden, Fitzsimons & Wyness, 2008) • Recent policy developments may affect future participation • e.g. 2006-07 reforms (top-up fees) • Will soon be evaluated using this data

  5. Research Questions • How does the likelihood of HE participation vary by socio-economic background? • How much of this gap can be explained by prior achievement? • How does the type of HE participation vary across socio-economic groups?

  6. Methodology • Linear probability regression model • Easier to include school fixed effects • Two models: • HE participation (at age 19/20) • HE participation in a “high status” institution • Dependent variables are binary • 1 if participates, 0 otherwise

  7. New longitudinal admin data • Linked individual-level administrative data • School, FE and HE records • Data on participants AND non-participants • Consider two cohorts: • In Year 11 in 2001-02 or 2002-03 • Potential age 18/19 HE entry in 2004-05 or 2005-06 (age 19/20 entry 2005-06/2006-07) • State and private school students

  8. Data • Socio-economic background • State school analysis: • Free school meals status from PLASC • IMD quintiles based on home postcode (age 16) • State and private school analysis: • Assume FSM = 0 for all private school kids • IMD quintiles based on school postcode (age 16) • 47% of state school kids are in same quintile using home or school postcode • 81% are in same or adjacent quintile

  9. Data • Gender, MOB and school ID available for all • School fixed effects for state school analysis • School type dummies when include private school kids • Ethnicity, EAL, SEN from PLASC • Missing for private school kids • Neighbourhood measure of parental education based on 2001 Census • Based on home postcode for state school analysis • Based on school postcode when include private school kids

  10. Data • Prior attainment • State school analysis: • Quintiles (based on APS) at Key Stage 2, 3, 4 and 5 (plus indicators of reaching expected level at Key Stage 4 and 5) • Private school analysis: • Key Stage 4 and 5 results only • Exclusion of Key Stage 2 and 3 results makes negligible difference • Use of school rather than home postcode reduces raw differences (but end result similar) • Essentially eliminating within-school differences

  11. Male HE participation, by deprivation quintile

  12. HE participation (state school males)

  13. HE participation (state and private school males)

  14. Type of Participation • Also consider type of HE participation, because: • Students at less prestigious institutions more likely to drop out and/or achieve lower degree classification • Graduates from more prestigious institutions earn higher returns in the labour market • Define “high status” university as: • Russell Group university (20 in total) • Any UK university with an average 2001 RAE score greater than lowest found amongst Russell Group • Adds Bath, Durham, Lancaster, York, etc (21 in total)

  15. Female “high status” participation, by deprivation quintile

  16. “High status” HE participation (state school females)

  17. “High status” HE participation (state and private school females)

  18. Conclusions • Widening participation in HE to students from deprived backgrounds is largely about tackling low prior achievement • Focusing policy interventions post compulsory schooling unlikely to eliminate raw socio-economic gap in HE participation • But does not absolve universities

  19. Limitations • Young participants only • But other work looks at mature students • Limited information on private school students

  20. HE participation (state school males without Key Stage 2 & Key Stage 3 results)

  21. HE participation (state school males without KS2 & KS3 and using school postcode)

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