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:

Clifton, Bristol: Postcodes:

Clifton, Bristol: Postcodes

Clifton, Bristol: One window

Clifton, Bristol: Two windows

Number of peers within 100m for each child:

0

500

1,000

1,500

2,000

2,500

3,000

0 1 2 3 4 5 6 7 8 9 <10

Number of peers within 100m

Fre

q.

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.

• 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

iii xS iS

ix

• 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.

Method 2:Spatial weighting

iii xS

iii xxx -ix

iS

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 GSCEComparison of raw and geographic gradients in secondary school quality as measured by average

GCSE. Dep. Var. (1) (2) (3) (4) (5) (6) Average GCSE FSM only Income & social housing Personal characteristics FSM -0.34*** -0.28*** (-12.06) (-7.42) Income Social housing KS1 Male Ethnic minority Neighbourhood controls No Yes Observations 5588 5588 Adjusted R2 0.246 0.026

Comparison of raw and geographic gradients in secondary school quality as measured by average GCSE.

Dep. Var. (1) (2) (3) (4) (5) (6) Average GCSE FSM only Income & social housing Personal characteristics FSM -0.34*** -0.28*** -0.17*** -0.23*** (-12.06) (-7.42) (-5.82) (-6.12) Income 0.26*** 0.13*** (8.89) (3.44) Social housing -0.24*** -0.19*** (-8.57) (-4.94) KS1 Male Ethnic minority Neighbourhood controls No Yes No Yes Observations 5588 5588 5588 5588 Adjusted R2 0.246 0.026 0.278 0.034

Results (1) – Avg GSCE

Comparison of raw and geographic gradients in secondary school quality as measured by average GCSE.

Dep. Var. (1) (2) (3) (4) (5) (6) Average GCSE FSM only Income & social housing Personal characteristics FSM -0.34*** -0.28*** -0.17*** -0.23*** -0.12*** -0.15*** (-12.06) (-7.42) (-5.82) (-6.12) (-4.22) (-4.21) Income 0.26*** 0.13*** 0.20*** 0.08* (8.89) (3.44) (7.22) (2.23) Social housing -0.24*** -0.19*** -0.20*** -0.15*** (-8.57) (-4.94) (-7.15) (-4.29) KS1 0.04*** 0.05*** (10.99) (9.66) Male -0.01 -0.05* (-0.28) (-2.04) Ethnic minority 0.34*** 0.21*** (6.44) (4.13) Neighbourhood controls No Yes No Yes No Yes Observations 5588 5588 5588 5588 5588 5588 Adjusted R2 0.246 0.026 0.278 0.034 0.361 0.142

Results (1) – Avg GSCE

Results (2) – Avg KS2

Comparison of raw and geographic gradients in secondary school quality as measured by average KS2 of intake

Dep. Var. (1) (2) (3) (4) (5) (6) Average KS2 of intake FSM only Income & social housing Personal characteristics FSM -0.70*** -0.57*** (-10.45) (-6.64) Income Social housing KS1 Male Ethnic minority Neighbourhood controls No Yes Observations 5592 5592 Adjusted R2 0.210 0.049

Results (2) – Avg KS2

Comparison of raw and geographic gradients in secondary school quality as measured by average KS2 of intake

Dep. Var. (1) (2) (3) (4) (5) (6) Average KS2 of intake FSM only Income & social housing Personal characteristics FSM -0.70*** -0.57*** -0.38*** -0.50*** (-10.45) (-6.64) (-5.45) (-5.71) Income 0.48*** 0.24** (7.82) (3.14) Social housing -0.48*** -0.28** (-7.37) (-2.98) KS1 Male Ethnic minority Neighbourhood controls No Yes No Yes Observations 5592 5592 5592 5592 Adjusted R2 0.210 0.049 0.237 0.052

Results (2) – Avg KS2

Comparison of raw and geographic gradients in secondary school quality as measured by average KS2 of intake

Dep. Var. (1) (2) (3) (4) (5) (6) Average KS2 of intake FSM only Income & social housing Personal characteristics FSM -0.70*** -0.57*** -0.38*** -0.50*** -0.24*** -0.26*** (-10.45) (-6.64) (-5.45) (-5.71) (-3.91) (-3.32) Income 0.48*** 0.24** 0.33*** 0.12 (7.82) (3.14) (5.91) (1.64) Social housing -0.48*** -0.28** -0.37*** -0.20** (-7.37) (-2.98) (-6.19) (-2.58) KS1 0.09*** 0.12*** (10.26) (11.09) Male -0.01 -0.06 (-0.26) (-1.13) Ethnic minority 0.67*** 0.36*** (6.06) (3.49) Neighbourhood controls No Yes No Yes No Yes Observations 5592 5592 5592 5592 5592 5592 Adjusted R2 0.210 0.049 0.237 0.052 0.374 0.217

Results (3) - Oversubscription

Comparison of raw and geographic gradients in secondary school quality as measured by oversubscription

Dep. Var. (1) (2) (3) (4) (5) (6) oversubscribed FSM only Income & social housing Personal characteristics FSM -0.09*** 0.02 (-5.82) (0.84) Income Social housing KS1 Male Ethnic minority Neighbourhood controls No Yes Observations 4911 4911 Adjusted R2 0.086 0.003

Results (3) - Oversubscription

Comparison of raw and geographic gradients in secondary school quality as measured by oversubscription

Dep. Var. (1) (2) (3) (4) (5) (6) oversubscribed FSM only Income & social housing Personal characteristics FSM -0.09*** 0.02 -0.04* 0.03 (-5.82) (0.84) (-2.32) (1.27) Income 0.04 -0.02 (1.96) (-0.72) Social housing -0.11*** -0.06* (-6.54) (-2.41) KS1 Male Ethnic minority Neighbourhood controls No Yes No Yes Observations 4911 4911 4911 4911 Adjusted R2 0.086 0.003 0.101 0.005

Results (3) - Oversubscription

Comparison of raw and geographic gradients in secondary school quality as measured by oversubscription

Dep. Var. (1) (2) (3) (4) (5) (6) oversubscribed FSM only Income & social housing Personal characteristics FSM -0.09*** 0.02 -0.04* 0.03 -0.03 0.04 (-5.82) (0.84) (-2.32) (1.27) (-1.81) (1.83) Income 0.04 -0.02 0.03 -0.02 (1.96) (-0.72) (1.56) (-0.89) Social housing -0.11*** -0.06* -0.10*** -0.05* (-6.54) (-2.41) (-6.11) (-2.12) KS1 0.00* 0.01** (2.11) (2.71) Male 0.03* 0.03 (2.21) (1.92) Ethnic minority 0.06 0.01 (1.76) (0.38) Neighbourhood controls No Yes No Yes No Yes Observations 4911 4911 4911 4911 4911 4911 Adjusted R2 0.086 0.003 0.101 0.005 0.104 0.007

Results (4)Regression of secondary school quality on religious background

(1) (5) Dep. Var.: School avg

GCSE School

oversubscription Sunday school 0.16** 0.09** (2.92) (2.58) Had faith min 5yr 0.15*** 0.09** (3.34) (2.74) Had faith between 3-5yr 0.10 0.06 (1.21) (1.01) Had faith 1-2yr -0.36*** 0.07 (-3.35) (0.85) Had faith max 1yr 0.15 -0.00 (0.92) (-0.00) Church of England 0.02 0.04 (0.59) (1.72) Catholic 0.29*** 0.32*** (5.87) (9.48) Attends church weekly 0.14* 0.13*** (2.55) (3.52) Attends church monthly 0.07 -0.06 (1.34) (-1.66) Observations 5588 4911 Adjusted R2 0.092 0.034

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.

Questions, Comments?