Sue Easton Town & Regional Planning University of Sheffield

 Intro to the project  Background ◦ The value of schools ◦ Accessibility and socioeconomic status  Research questions  Methods  Findings 

 ESRC Secondary Data Analysis Initiative (18 months)  Pupil census and parental preference data shared by local authority under strict data- sharing contract 

 Leech and Campos (2001) average additional increase for good secondary school catchment in Coventry: £10,000 - £20,000 (16-20%) on the average house in July Leech and Campos (2001)  Gibbons and Machin (2003) 1% increase in primary school performance (Key Stage 2) associated with a £90 increase in mortgage fees per child. Gibbons and Machin (2003)  Cheshire and Sheppard (2004) relationship non-linear, differed between primary & secondary schools. £42,541 (33.5%) between best and worst primary schools in Reading (2000) Cheshire and Sheppard (2004)

 Burgess et al. (2011) – only 37% of schools within 3km of a child’s house actually accessible to that child. Lowest Socioeconomic Status quintile in metropolitan areas effectively excluded from over 70% of schools within 3km of their home. Burgess et al. (2011)  Hamnett and Butler (2013) showed that the most popular schools in East London had the shortest distances to school within the tightest de facto catchment areas. They argue that places at the best state schools now rationed through geography via “distance to school” criteria. Hamnett and Butler (2013

 How do local education markets in Sheffield interact with local housing markets?  What are the socioeconomic and spatial differences in de facto catchment areas for different types of schools (faith/secular, high and low-performing)?  Do the best (highest-performing) state schools have tighter, more homogeneous, higher socioeconimic local catchment areas?

 Property Price index – HPI-adjusted, weighted ◦ Based on Land Registry data (4 years)  “De facto” catchment areas for schools ◦ Based on network analysis of drive and walk times ◦ From point data on pupil residential location  Geodemographic classification of Sheffield using census output areas ◦ Based on census 2011 data, property price index and urban form variables (building density etc)  Parental Preference Data – too “fluid” for analysis (administrative data).

Property Sales (£)

 Correlation of 0.51 between mean 65% catchment property price and school performance on Key Stage 2 results (at age 11) for best state primary schools (p=0.000, N = 52)  Correlation of 0.88 mean 65% catchment property price ( data) and school performance (using Key Stage 4 – GCSEs) excluding the Catholic High School at top-performing secondary schools (p = 0.01, N=7).  Positive correlation between “core” standard distance and best-performing secondary schools on key stage results (0.75 at 5% significance). Weaker for primaries.  Inverse relationship between property price and residential density?

 Endogeneity – children of high-performing professionals advantaged from birth >> perform better academically >> cluster together and attend local schools in/near wealthier residential areas.  Definitely a relationship between property price and school performance on KS results – strongest at secondary level.  Distance-based over-subscription criteria exclude children from other residential areas from accessing the best state schools and associated peer group (thinking social capital). Also religious selection criteria.  Inverse relationship between residential population density and school performance based on Key Stage results.  Next steps – to test relationships between pupils in residential areas and schools in a cross-classified multilevel model.

Tinsley Junior 91.5% BME -12 below Average for England KS2

-26 below mean for England (KS2); 34% FSM, 88% BME pupils

Department of Town & Regional Planning University of Sheffield

Correlation = 0.88 (p=0.01)