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Centre for Market and Public Organisation. An application of geographical data: inequalities in school access Paul Gregg, and Neil Davies, University of Bristol, CMPO. Overview. What are the uses of geographic data? Geographic proximity: Unique to ALSPAC How can it be applied?
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Centre for Market and Public Organisation An application of geographical data: inequalities in school access Paul Gregg, and Neil Davies, University of Bristol, CMPO
Overview • What are the uses of geographic data? • Geographic proximity: Unique to ALSPAC • How can it be applied? • Description of the data • Method • Application – SES gradients in school access • Results • Conclusion
Uses of geographic data • Location has an effect on many processes, e.g.: • Access to services • Exposure to pollutants • Peer group effects • Segregation • It is useful to include neighbourhood in our models. • Postcode fixed effects • Spatial estimators
Constructing geographic data • Postcodes • are available • ALSPAC records postcodes when sending out questionnaires • Date of change is recorded • Can be matched to longitude and latitude • Problem - confidentiality • Possible to identify individuals using postcodes
Constructing geographic data Solution: • Release postcodes attached to scrambled IDs • Match IDs to a ‘window’ of their peers within 100m • Remove postcodes • Unscramble IDs to leave a dataset of linked IDs • We have matched at 100m 200m and 500m • For the years 1991-2005
An example, Clifton, Bristol:
Application: School Access • Work in progress! • Questions/comments welcome
Motivation: • Schools matter: • Peer effects • Teacher effects • Previous studies have shown that access to good schools is not evenly distributed across neighbourhoods. • Individuals sort across neighbourhoods to gain access. • Individual students within a neighbourhood attend schools of differing quality, • What individual level factors are these differences in school quality correlated with? • What are the mechanisms are used to obtain high quality schooling? • This paper seeks to describe these differences in school quality. • Do these individuals have different preferences or is the assignment mechanism biased? • Is there greater sorting across variables observable to schools?
Background: School access (1) • Allocation to schools by: • Location • Academic Ability • Prices • Preferences • Religion • The English system is a hybrid of all them. • Once we control for location how much of the variation in gradients of school quality remain?
Background: School access (2) Location: • Large socio-economic gradients in access to school quality • Individuals sort across neighbourhoods to gain access • Largest determinant of school quality gradient is location, • Poor children are 14 pp less likely to attend a good school than non-poor. • Controlling for postcodes this difference falls to 2 pp. • see Burgess and Briggs (2006)
Background: School access (3) • Individual students within a neighbourhood attend schools of differing quality, • Why? • What individual level factors are these differences correlated with? • What are the outcomes of these allocation mechanisms? • This paper uses the richness and geographic proximity of the ALSPAC observations to describe these differences. • Conditional on location what determines the quality of school a child attends?
Defining school quality: • Our dependent variable is school quality, specifically: • Exam results of prior cohorts1, • KS1, KS2, KS3, and KS4 (GCSE) • % of students who have: • Free school meals • Statement of special educational needs • Whether the school is oversubscribed 1 School quality Variables are lagged in time to obtain quality of school when child applied to school.
Method 1: Raw gradients • Raw gradients: • This regression links the quality of school, an individual attends to there individual characteristics, • One of the variables commonly used is whether the child takes free school meals, • We wish to control for location:
Method 2:Spatial weighting • Within neighbourhood estimate: • Differencing variables: • Where = the mean of child i’s neighbours within 100m who attend a different school. • is the difference in school quality • We want to know how differences in the X variables are correlated with differences in school quality.
Spatial weighting (3) • Bandwidth: the window • Postcodes, 100m, 200m, 500m • Allows within neighbourhood estimates • Sample selection • Who is included? • Same school? • State/Private schools? • Sample splits?
Results Results for secondary schools: • Average GCSE points • Average KS2 of intake • Whether the school was oversubscribed • Further independent variables
Overview of complete results: • Secondary • Variables observable to schools variables highly significant • KS1, FSM, and location. • Primary school quality • Similar in magnitude to previous results • Strongest sorting by religion, particularly through Catholic schools • Primary • Much smaller coefficients • Evidence of sorting by FSM and KS1 • Evidence of school choosing? • Some evidence of sorting by religion, again due to Catholic schools.
Conclusions for school markets • There are socio-economic gradients in access to school quality • These remains when controlling for location. • Even within neighbourhoods school quality is correlated with measures of income. • Most strongly with FSM, also KS1 evidence of schools choosing? • Strongest correlations with religion • School quality is highly persistent, • primary school quality significant determinant of secondary school quality • Some evidence that ethnic minorities attend better schools • Would lotteries be fairer?
Uses of geographic data • Location has an effect on many processes, e.g.: • Access to services • Exposure to pollutants • Peer group effects • Segregation • It is useful to control for neighbourhood.
