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Evidence of Segregation? Using PLASC to model the core catchment areas of schools

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Title: School choice, school competition and ethnic segregation Author: Rich Harris Last modified by: ecern Created Date: 8/23/2007 1:40:25 PM Document presentation ... – PowerPoint PPT presentation

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Title: Evidence of Segregation? Using PLASC to model the core catchment areas of schools


1
Evidence of Segregation? Using PLASC to model
the core catchment areas of schools
  • Rich Harris
  • rich.harris_at_bris.ac.uk
  • http//rose.bris.ac.uk
  • KEY WORDS schools, segregation

2
Outline
  • Set the scene
  • Introduce a method for modelling the de facto,
    effective or core catchment areas of schools
  • Primaries in London (also Birmingham)
  • Use those models to look at apparent processes of
    ethnic separation

3
School choice ethnic separation?
4
Building on existing research
  • There is segregation between schools
  • But then there is segregation between
    neighbourhoods
  • Schools Vs neighbourhoods (post-residential
    sorting)
  • e.g. Johnston et al. (2006)
  • 'it has been shown herein that not only is there
    ethnic segregation in the countrys primary and
    secondary schools, but also in addition for
    both the South Asian populations and for the
    Black Caribbean and Black African populations
    that school segregation is very substantially
    (and significantly) greater than is the case with
    residential segregation.

5
Observed Vs Expected (?)
  • Census zones and their entire populations are
    taken to represent the pupils expected to attend
    the schools, not just those of school age
  • Census zones assumed congruous with the schools'
    areas of recruitment.
  • Census populations are conflated with school
    populations.
  • Develop a better counter-factual when assessing
    the (observed) ethnic profiles of schools against
    what they 'should be'.

6
A more local focus
  • The geography which matters is the local, not the
    national or regional, and the scale of analysis
    should be commensurate with the local markets
    within which schools (and parents faced with
    placement decisions) actually operate.
  • (Gibson Asthana, 2000a 304).
  • How do those local markets affect and how are
    they affected by school choices?
  • In the context of debates about segregation /
    polarization.

7
Another angle
  • A model of school allocations
  • Lijk ƒ (i, j, k, l)
  • i pupil, j school, k census neighbourhood,
    l neighbourhood group
  • Parents tend to send their child to the nearest
    school to their home
  • but only where there is a 'sufficient presence'
    of their child's ethnic group.
  • That sufficiency can vary by ethnic group and
    'neighbourhood type
  • Geographically naïve Lijk ƒ Si

8
Or, one final way
9
The Geography of Supply
  • To estimate where and by how much schools compete
    with each other within spaces of admission
  • and to consider whether the ethnic compositions
    of those spaces ('the neighbourhoods') are
    representative of the actual compositions of
    schools.
  • This is achieved by determining the core
    catchment areas of schools here, primary
    schools within Birmingham, England.

10
Mapping School Catchment Areas
  • Schools neither have de jure catchment areas
  • nor unlimited capacity
  • so parental choice is constrained, ultimately by
    admissions criteria.
  • Pupils tend to attend local primary schools
  • and so there is a clustered geography of
    attendance.
  • That geography is revealed by mapping the home
    address of each pupil attending any given school
    (from PLASC micro-data)
  • The task is to define that pattern.

11
Some Criteria
  • We dont want the modelled catchment areas to be
    over-dispersed.
  • Some pupils live far from their schools
  • But we also dont want them to be over-fitted to
    one specific set of pupils.
  • If a postcode is near to a school and contains a
    pupil attending that school then it likely
    belongs in the (potential) catchment area.
  • But so too does a neighbouring postcode even if
    it does not contain a pupil attending that school
    potentially the school could have recruited
    from there too.
  • Compact and unbroken
  • optimised to areas where attendance at any
    particular school is prevalent

12
About the data
  • PLASC
  • Pupil Level Annual Census Returns
  • Data on all pupils in primary and secondary
    schools in England
  • 2006 data
  • Information on state educated primary school
    students (5-11 years old)
  • And on secondary school pupils
  • 'Self-identified' ethnic category collected from
    parents when students enrol
  • Also records postcode unit of pupils' homes
  • Which they school they attend
  • School type (selective? Faith school?)
  • Measure of deprivation (take a free school meal)?

13
Defining core catchments
  • Imagine centring a rectangle at (mid-x, mid-y)
    based on the residential postcodes of pupils
    attending a school.

14
  • Imagine centring a rectangle at (mid-x, mid-y)
    based on the residential postcodes of pupils
    attending a school.
  • Let the rectangle grow outwards

15
  • Imagine centring a rectangle at (mid-x, mid-y)
    based on the residential postcodes of pupils
    attending a school.
  • Let the rectangle grow outwards
  • Until it encloses a certain proportion of all
    pupils who attend the school

16
  • Imagine centring a rectangle at (mid-x, mid-y)
    based on the residential postcodes of pupils
    attending a school.
  • Let the rectangle grow outwards
  • Until it encloses a certain proportion of all
    pupils who attend the school
  • Here p 0.40

17
  • Imagine centring a rectangle at (mid-x, mid-y)
    based on the residential postcodes of pupils
    attending a school.
  • Let the rectangle grow outwards
  • Until it encloses a certain proportion of all
    pupils who attend the school
  • Here p 0.50

18
Some Refinements
  • Two datasets used simultaneously one has the
    postcode grid references rotated by 45º
  • Search is now N, NE, E, SE, S, SW, W, NW, N

19
  • The direction of growth is determined as that
    which returns highest n1 / n2
  • where n1 is number of pupils in area going to the
    school
  • n2 is all pupils in the area (go to any school)

20
  • Catchment is then defined as the convex hull for
    pupils of school within the search area.

21
  • Catchment is then defined as the convex hull for
    pupils of school within the search area.
  • Continues until a certain proportion of all
    pupils who attend the school are enclosed

22
  • Imagine centring a rectangle at (mid-x, mid-y)
    based on the residential postcodes of pupils
    attending a school.
  • Let the rectangle grow outwards
  • Until it encloses a certain proportion of all
    pupils who attend the school
  • Here p 0.50

23
Other methods?
  • Why not just find the n nearest neighbours to a
    school
  • Because that does not consider prevalence
  • The at risk population
  • And it assumes the school is at the centre of
    its catchment
  • Why not use some sort of hot spot analysis
  • Could do but it would likely over-calibrate on a
    specific set of pupils rather than revealing the
    schools potential catchment areas

24
London primaries
25
Does it work?
26
Birmingham primaries
27
Processes of Segregation (?) (1)
  • Friction of distance / least cost perspective
  • identify any pupils that appear to be travelling
    further to school than they need to
  • pupils that live within the core catchment of at
    least one primary school but attend another
    school of which they are not in the core
    catchment.
  • May not be a matter of choice
  • some schools will be over-subscribed
  • catchments are defined to contain only 50 of the
    pupils at the school.
  • Is the propensity to attend any one of the near
    schools consistently lower for some ethnic
    groups?

28
Defining Near
  • Define as being near to a pupil any primary
    school that has a core catchment that includes
    the pupils residential postcode
  • Here the pupil has three near schools

29
Proportion attending any near school (target
catchment p0.50) LONDON
30
Proportion attending any near school (target
catchment p0.50) BIRMINGHAM
31
Proportion attending any near school (target
catchment p0.75) BIRMINGHAM
32
Proportion attending any near school (target
catchment p0.25) BIRMINGHAM
33
Processes of Segregation (?) (2)
  • Evidence of a migratory process
  • But also local processes of segregation?
  • when two or more schools strongly overlap in
    terms of their potential spaces of recruitment
    but attract different ethnic groups
  • polarization
  • Disentangle
  • migratory and local processes of seperation
  • residential and post-residential sorting

34
Pairwise Comparisons
  • Looking inside the catchments
  • Expected intake Vs Locally observed intake
  • Looking also at the final profile of each school
  • Expected intake Vs Fully observed intake
  • Can also compare the profiles of locally
    competing schools
  • ones that overlap (strongly) in terms of their
    core catchment areas

35
Visual Summary (LONDON)
  • Shows only those schools with highest expected
    Black Caribbean

36
Visual Summary (LONDON)
  • Shows only those schools with highest expected
    Bangladeshi

37
Visual Summary (Birmingham)
  • Shows only those schools with higher than fair
    share Black Caribbean

38
Visual Summary (Birmingham)
  • Shows only those schools with higher than fair
    share Bangladeshi

39
Segregation Index
  • Which schools are least representative of their
    catchments?
  • Compared against
  • J is a school, e is the number of ethnic groups
  • pEXPECTED is the finally observed profile of a
    school
  • pRANDOM is the ethnicity profile obtained for a
    school if half of its intake is randomly sampled
    from its core catchment and the other half
    sampled from outside of it

40
All Significantly Segregated Schools (Birmingham
)
  • Meaning schools where the final ethnic profile of
    the school is not whats expected based on the
    composition of their core catchments.

41
Significantly Segregated Schools (London)
  • 21 of all primary schools in London exhibit
    significant post-residential sorting
  • The figure rises to an average of 44 for all
    faith schools but it ranges from 18 to 78
  • When debating faith schools need to be careful
    about treating them as a homogenous group

42
Summary
  • Consistent with previous studies
  • Black Caribbean pupils appear more likely to
    travel further to school than they need do
  • Because they are more geographically dispersed?
  • Clear evidence of post-residential sorting is
    found
  • Role of faith schools?
  • Evidence of polarization where locally competing
    schools draw markedly different intakes.

43
What the study does not Show
  • That ethnic separation is a bad thing
  • That polarization would disappear without a
    system of school choice.
  • That recent school reforms have either worsened
    or improved ethnic separation.
  • That ethnic groups actively avoid each other.
  • That it is actually ethnicity that drives the
    processes of separation.
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