Presentation on theme: "Centre for Market and Public Organisation An application of geographical data: inequalities in school access Paul Gregg, and Neil Davies, University of."— Presentation transcript:
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
An example, Clifton, Bristol:
Clifton, Bristol: Postcodes:
Clifton, Bristol: Postcodes
Clifton, Bristol: One window
Clifton, Bristol: Two windows
Number of peers within 100m for each child:
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 cohorts 1, 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.
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 1: Raw gradients
Within neighbourhood estimate: Differencing variables: Where = the mean of child is 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. Method 2:Spatial weighting
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: 1)Average GCSE points 2)Average KS2 of intake 3)Whether the school was oversubscribed 4)Further independent variables
Results (1) – Avg GSCE
Results (2) – Avg KS2
Results (3) - Oversubscription
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.