Are England’s schools segregating or integrating?And does it matter?
Simon Burgess and Jack Worth
January 2011
January 2011, UoB www.bris.ac.uk/cmpo 2
Introduction
• Dynamics of sorting, changes in school composition.
• Depending on the process:– Maybe any composition is an equilibrium, or– Maybe the only equilibrium is complete
segregation? Or integration?– Are there “tipping points”?
• Some questions from a quantitative perspective using large-scale datasets
• Different actors making decisions:– Families choosing where to live and which
schools to apply to– Schools and LAs making decisions – subject
to the Admissions code – about who goes to over-subscribed schools
January 2011, UoB www.bris.ac.uk/cmpo 3
Does it matter?
• Role of context in ethnic inequalities
• Context:– School peer groups and neighbourhoods.
– Ethnic composition of schools and neighbourhoods and ethnic segregation.
– Friendship formation.
– How, if at all, does context affect outcomes?
January 2011, UoB 4www.bris.ac.uk/cmpo
Plan
• Analyse changing school ethnic group composition – Segregation or integration?– Differences?
• Does school composition matter?– Educational attainment– Values and attitudes– Identity
January 2011, UoB www.bris.ac.uk/cmpo 5
January 2011, UoB www.bris.ac.uk/cmpo 6
Change
in pop’n
of whites
0 Fraction of non-whites
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Change
in pop’n
of whites
0 Fraction of non-whites
City
January 2011, UoB www.bris.ac.uk/cmpo 8
Change
in pop’n
of whites
0 Fraction of non-whites
Growth in white population in areas with already high white population
Decline in white population in areas with high white population
Decline in white population in areas with already low white population
Growth in white population in areas with low white population
January 2011, UoB www.bris.ac.uk/cmpo 9
Dynamic processes
0
Integration
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Dynamic processes
0
Segregation
January 2011, UoB 11www.bris.ac.uk/cmpo
January 2011, UoB www.bris.ac.uk/cmpo 12
Research questions
We aim to characterise the dynamics of sorting• Is it the same process in all cities? Or not?• To be specific
– is it the same process, but different phases?– Or different processes?
• Is the process towards segregation or integration? At what speed?
• Are any differences in the dynamics correlated with any city structural factors?
January 2011, UoB www.bris.ac.uk/cmpo 13
Data
• Administrative data set: PLASC/NPD, Pupil Level Annual Schools Census part of the National Pupil Database.
• All pupils in all state schools in England. • Data on pupil demographics including gender, age, ethnic
group, FSM eligibility, SEN status, school and residence of pupils
• Primary (Age 5 – 11) and Secondary (12 – 16) schools.• Composition from aggregation to school-cohort level.• School information: Admissions policy, VA/VC, religious
denomination, school post code etc.• Area information including population, density, location, ...
January 2011, UoB www.bris.ac.uk/cmpo 14
Sample
• Focus on areas above a threshold of ethnic minority populations (10%+ non-white)
• Study (initially at least) change in proportion white pupils as function of initial proportion of non-whites.
• Will definitely need to look at different groups:– Look at the change in their populations too– Look at change in proportion of whites relative to the
base proportion of different ethnic sub-groups
January 2011, UoB www.bris.ac.uk/cmpo 15
Space and Time
• Use two very different geographies: analysis by LEA & Travel-to-work area (TTWA):– LEA – affects educational policy and education market
boundaries– TTWA – local labour markets, where people live
• ‘Short’ datasets – 2003 through 2007 PLASCs• ‘Long’ datasets – extends the 2003 PLASC back
to entry year, using technique of a “frozen” entry cohort. – Longer time frame, but assumes changes in school
composition random (tested before; and will test again)
June 2010 www.bris.ac.uk/CMPO 16
Figure 1. Definition of Aggregate Areas
This work is based on data provided through EDINA UKBORDERS with the support of the ESRC and JISC and uses boundary material which is copyright of the Crown, the Post Office and the ED-LINE Consortium.
Local Education Authorities Travel-to-Work Areas
January 2011, UoB www.bris.ac.uk/cmpo 17
Short and LongYear
Age
‘98 ‘99 ‘00 ‘01 ‘02 ‘03 ‘04 ‘05 ‘06 ‘07
11
10
9
8
7
6
Available Data
Used Data
Infant to Junior
January 2011, UoB www.bris.ac.uk/cmpo 18
‘Frozen Entry’ Approach• Infant & Junior – some Primary schools split into
Infant (Age 5-7) & Junior (Age 5-7) schools– Match the same pupils using adjacent censuses– Match where 90% of Junior cohort came from the
same Infant and the two schools are within 1km of each other = 1,000 more schools
• Middle schools – in some LEAs schools of 11 year olds do not match schools of 6 year olds– Excluded in Primary Long dataset (mostly rural, white
areas)– Secondary Long extended back 2 years to age 14
Primary Secondary
Short Long Short Long
Cohorts 2003 & 2007 1998 & 2007 2003 & 2007 2001 & 2007
Schools 12,421 10,809 2,538 2,514
Pupils (‘03) 503,449 416,687 460,415 452,748
Non-whites 84,501 75,907 70,051 70,180
Final Sample
Balanced Panel
• Focussing on schools that remained open through that period. Compare opening and closing schools.
January 2011, UoB www.bris.ac.uk/cmpo 20
Where,
1,
1,
tj
tjjtjt N
WWCW
j schoolin pupils ofnumber
j schoolin whitesofnumber
jt
jt
N
W
Dependent Variable
• Change in Whites:
January 2011, UoB www.bris.ac.uk/cmpo 21
Results
• Graphical data exploration– Different patterns in different places
– Different dependent variables
– Data problems in some places
• Techniques for analysing these patterns:– Distribution dynamics (“twin peaks” dynamics)
– Characterise areas with a non-parametric estimate of the average relationship.
January 2011, UoB www.bris.ac.uk/cmpo 22
Techniques for analysing the patterns 1
• Graphical data exploration
Figure 6c: Growth vs Initial Level plot – Manchester
-1-.
50
.51
Cha
nge
in N
umb
er o
f Whi
te P
upi
ls 1
998
-20
09
0 .2 .4 .6 .8 1Proportion Non-White 1998
Manchester
January 2011, UoB 23www.bris.ac.uk/cmpo
Figure 6b: Growth vs Initial Level plot – Birmingham
-1-.
50
.51
Cha
nge
in N
umb
er o
f Whi
te P
upi
ls 1
998
-20
09
0 .2 .4 .6 .8 1Proportion Non-White 1998
Birmingham
January 2011, UoB 24www.bris.ac.uk/cmpo
Figure 6a: Growth vs Initial Level plot – London
-1-.
50
.51
Cha
nge
in N
umb
er o
f Whi
te P
upi
ls 1
998
-20
09
0 .2 .4 .6 .8 1Proportion Non-White 1998
London
January 2011, UoB 25www.bris.ac.uk/cmpo
Figure 6d: Growth vs Initial Level plot – Oldham
-1-.
50
.51
Cha
nge
in N
umb
er o
f Whi
te P
upi
ls 1
998
-20
09
0 .2 .4 .6 .8 1Proportion Non-White 1998
Oldham
January 2011, UoB 26www.bris.ac.uk/cmpo
Figure 6e: Growth vs Initial Level plot – Bradford
-1-.
50
.51
Cha
nge
in N
umb
er o
f Whi
te P
upi
ls 1
998
-20
09
0 .2 .4 .6 .8 1Proportion Non-White 1998
Bradford
January 2011, UoB 27www.bris.ac.uk/cmpo
Figure 6f: Growth vs Initial Level plot – Kirklees
-1-.
50
.51
Cha
nge
in N
umb
er o
f Whi
te P
upi
ls 1
998
-20
09
0 .2 .4 .6 .8 1Proportion Non-White 1998
Kirklees
January 2011, UoB 28www.bris.ac.uk/cmpo
Figure 6g: Growth vs Initial Level plot – Leicester
-1-.
50
.51
Cha
nge
in N
umb
er o
f Whi
te P
upi
ls 1
998
-20
09
0 .2 .4 .6 .8 1Proportion Non-White 1998
Leicester
January 2011, UoB 29www.bris.ac.uk/cmpo
Figure 6h: Growth vs Initial Level plot – Blackburn with Darwen
-1-.
50
.51
Cha
nge
in N
umb
er o
f Whi
te P
upi
ls 1
998
-20
09
0 .2 .4 .6 .8 1Proportion Non-White 1998
Blackburn with Darwen
January 2011, UoB 30www.bris.ac.uk/cmpo
January 2011, UoB www.bris.ac.uk/cmpo 31
Techniques for analysing the patterns 2
• Summarising these graphs using different statistical procedures
January 2011, UoB www.bris.ac.uk/cmpo 32
-.6
-.4
-.2
0.2
.4C
hang
e in
num
ber
of w
hite
s 1
998-
200
7
0 .2 .4 .6 .8 1Non-White proportion 1998
Manchester: Primary Long 1998-2007
• Initial levels plot as before
January 2011, UoB www.bris.ac.uk/cmpo 33
-.6
-.4
-.2
0.2
.4C
hang
e in
num
ber
of w
hite
s 1
998-
200
7
0 .2 .4 .6 .8 1Non-White proportion 1998
Manchester: Primary Long 1998-2007
• Split into deciles with top and bottom 5% clipped
January 2011, UoB www.bris.ac.uk/cmpo 34
-.6
-.4
-.2
0.2
.4C
hang
e in
num
ber
of w
hite
s 1
998-
200
7
0 .2 .4 .6 .8 1Non-White proportion 1998
Change in number of whites 1998-2007 m_decileChange in number of whites 1998-2007
Manchester: Primary Long 1998-2007
Coefficient = 0.0521• Take the weighted differences between decile means
January 2011, UoB www.bris.ac.uk/cmpo 35
-.6
-.4
-.2
0.2
.4C
hang
e in
num
ber
of w
hite
s 1
998-
200
7
0 .2 .4 .6 .8 1Non-White proportion 1998
Change in number of whites 1998-2007 m_decileChange in number of whites 1998-2007
Manchester: Primary Long 1998-2007
Coefficient = 0.0521• Take the differences between decile means
January 2011, UoB www.bris.ac.uk/cmpo 36
-1-.
50
.51
Cha
nge
in n
umb
er o
f whi
tes
199
8-2
007
0 .2 .4 .6 .8 1Non-White proportion 1998
Change in number of whites 1998-2007 m_decileChange in number of whites 1998-2007
Bradford: Primary Long 1998-2007
Coefficient = -0.0124
January 2011, UoB www.bris.ac.uk/cmpo 37
Coefficient = 0.1013
-.6
-.4
-.2
0.2
Cha
nge
in n
umb
er o
f whi
tes
199
8-2
007
0 .2 .4 .6 .8 1Non-White proportion 1998
Change in number of whites 1998-2007 m_decileChange in number of whites 1998-2007
Oldham: Primary Long 1998-2007
Table 1: OLS and Nonparametric Coefficients
LEA
OLS coefficient Nonparametric coefficient
Oxfordshire -0.072 -0.128
Solihull -0.368 -0.052
Rochdale 0.121 -0.046
Peterborough 0.175 -0.039
Bolton 0.107 -0.038
Stoke-on-Trent 0.092 -0.030
Dudley 0.122 -0.028
Bradford 0.176 -0.025
Lancashire 0.083 -0.021
Buckinghamshire 0.099 -0.019
Oldham 0.130 -0.019
Blackburn with Darwen 0.156 -0.010
Walsall 0.042 -0.009
Hertfordshire -0.026 -0.007
Liverpool 0.130 -0.003
Slough 0.086 -0.002
Derby 0.442 0.003
Sandwell 0.110 0.011
Birmingham 0.280 0.011
Leeds 0.096 0.011
Kirklees -0.013 0.014
Calderdale 0.006 0.018
Nottingham 0.231 0.022
London 0.220 0.024
Wolverhampton 0.199 0.024
Reading 0.112 0.025
Coventry 0.178 0.031
Manchester 0.310 0.047
Middlesbrough 0.044 0.056
Milton Keynes 0.360 0.061
Sheffield 0.166 0.062
Thurrock 1.121 0.064
Tameside 0.224 0.067
Leicester 0.168 0.067
Southampton 0.394 0.068
Bristol 0.082 0.069
Luton 0.323 0.070
Brighton and Hove 1.239 0.078
Trafford 0.126 0.090
Wokingham -0.668 0.171
Bury -0.115 0.178
January 2011, UoB 38www.bris.ac.uk/cmpo
Table 2: 4th Order Polynomial 4th order polynomial
LEA 10% 15% 20% 25% 30% p-value(F)
Oxfordshire 0.38 -1.43 -2.61 -1.09 5.24 0.361
Solihull 2.21 0.11 -2.61 -1.53 7.77 0.011
Brighton and Hove 0.80 -0.52 -1.05 -0.75 0.43 0.000
Thurrock 3.11 1.06 -1.04 0.15 8.00 0.008
Stoke-on-Trent 0.07 -0.54 -0.77 -0.71 -0.44 0.509
Calderdale -0.41 -0.66 -0.76 -0.74 -0.61 0.000
Slough -2.68 -1.51 -0.67 -0.11 0.22 0.374
Peterborough -0.44 -0.56 -0.58 -0.51 -0.37 0.004
Rochdale -0.42 -0.54 -0.57 -0.53 -0.43 0.000
Walsall -0.18 -0.41 -0.52 -0.54 -0.48 0.000
Oldham -0.91 -0.65 -0.44 -0.28 -0.14 0.000
Lancashire -0.21 -0.37 -0.44 -0.43 -0.36 0.000
Kirklees 0.34 -0.12 -0.43 -0.63 -0.71 0.000
Bradford -0.16 -0.32 -0.41 -0.42 -0.39 0.000
Blackburn -0.68 -0.52 -0.38 -0.24 -0.12 0.000
Liverpool -0.19 -0.33 -0.37 -0.33 -0.22 0.000
Reading -0.22 -0.39 -0.36 -0.19 0.06 0.370
Dudley 0.19 -0.07 -0.23 -0.29 -0.26 0.012
Birmingham 0.55 0.15 -0.12 -0.27 -0.33 0.000
Buckinghamshire 0.35 0.09 -0.09 -0.19 -0.23 0.374
Sandwell -0.67 -0.32 -0.08 0.09 0.18 0.001
Sheffield 0.62 0.23 -0.06 -0.26 -0.36 0.000
Trafford -0.19 -0.10 -0.04 -0.02 -0.02 0.001
Wolverhampton -0.53 -0.23 -0.02 0.12 0.20 0.032
Hertfordshire 0.10 0.17 0.01 -0.25 -0.51 0.573
Leeds -0.08 -0.01 0.02 0.04 0.03 0.000
London -0.16 -0.02 0.10 0.18 0.24 0.000
Tameside 0.34 0.21 0.12 0.06 0.02 0.001
Bolton -0.64 -0.17 0.16 0.38 0.50 0.000
Nottingham 0.04 0.12 0.18 0.22 0.24 0.210
Manchester 0.98 0.63 0.37 0.19 0.08 0.000
Middlesbrough -0.23 0.28 0.49 0.48 0.31 0.000
Leicester 0.76 0.66 0.54 0.41 0.28 0.000
Bristol City of -0.09 0.36 0.62 0.71 0.67 0.027
Southampton 0.52 0.71 0.67 0.48 0.23 0.126
Coventry -0.04 0.45 0.70 0.76 0.67 0.001
Milton Keynes 0.21 0.50 0.73 0.75 0.43 0.243
Luton 2.52 1.61 0.92 0.44 0.12 0.000
Derby 0.77 0.88 0.95 0.96 0.93 0.000
Bury -0.16 1.24 1.99 2.19 1.97 0.000
Wokingham -2.22 10.86 69.38 203.12 441.86 0.023
Sorted by slope coefficient at 20% non-white
January 2011, UoB 39www.bris.ac.uk/cmpo
January 2011, UoB www.bris.ac.uk/cmpo 40
Techniques for analysing the patterns 3
• Estimating transition matrices to model the dynamics of the distribution
Estimating transitions
• Take a school with low % non-white pupils; what happens to that school over the next few years?– Does it see a further fall in the number of non-
white pupils?– Or a rise?
• … school with high % non-white pupils:– Does it evolve to an all non-white school?– Does it see a more mixed pupil population?
January 2011, UoB www.bris.ac.uk/cmpo 41
• Create groups of schools within an LA based on their initial % non-white pupils.
• Estimate how schools move between those bands over the next ten years.
• Use this to compute/extrapolate a “long-run” or ergodic distribution if the same process continued indefinitely
January 2011, UoB www.bris.ac.uk/cmpo 42
Tables 3a-h: Transition Matrices
Table 3a:
London
Range Midpoint Initial Ergodic P(down) P(stay) P(up)
0.000 - 0.000 0.000 0.009 0.004 0.00 0.42 0.58
0.000 - 0.094 0.047 0.101 0.051 0.05 0.50 0.46
0.094 - 0.167 0.131 0.096 0.079 0.23 0.39 0.38
0.167 - 0.250 0.209 0.101 0.091 0.29 0.36 0.35
0.250 - 0.365 0.308 0.096 0.122 0.28 0.42 0.30
0.365 - 0.458 0.412 0.099 0.106 0.28 0.33 0.39
0.458 - 0.536 0.497 0.097 0.105 0.33 0.30 0.36
0.536 - 0.631 0.584 0.100 0.145 0.32 0.39 0.29
0.631 - 0.727 0.679 0.100 0.130 0.36 0.39 0.25
0.727 - 0.833 0.780 0.095 0.112 0.39 0.47 0.14
0.833 - 1.000 0.917 0.098 0.049 0.39 0.54 0.07
1.000 - 1.000 1.000 0.009 0.006 0.47 0.53 0.00
Min 0.406 0.428
Midpoint 0.455 0.477
Max 0.504 0.525
January 2011, UoB 43www.bris.ac.uk/cmpo
Table 3b:
Birmingham
Range Midpoint Initial Ergodic P(down) P(stay) P(up)
0.000 - 0.000 0.000 0.020 0.009 0.00 0.17 0.83
0.000 - 0.050 0.025 0.094 0.040 0.10 0.22 0.68
0.050 - 0.098 0.074 0.093 0.093 0.19 0.34 0.47
0.098 - 0.133 0.116 0.098 0.067 0.45 0.25 0.29
0.133 - 0.182 0.158 0.088 0.071 0.48 0.26 0.26
0.182 - 0.258 0.220 0.102 0.073 0.40 0.31 0.29
0.258 - 0.355 0.307 0.083 0.070 0.41 0.35 0.24
0.355 - 0.660 0.508 0.097 0.188 0.11 0.71 0.19
0.660 - 0.892 0.776 0.093 0.285 0.15 0.72 0.14
0.892 - 0.960 0.926 0.091 0.021 0.39 0.39 0.22
0.960 - 1.000 0.980 0.093 0.000 0.75 0.00 0.25
1.000 - 1.000 1.000 0.047 0.084 0.57 0.43 0.00
Min 0.380 0.409
Midpoint 0.427 0.484
Max 0.473 0.559
January 2011, UoB 44www.bris.ac.uk/cmpo
Table 3c:
Manchester
Range Midpoint Initial Ergodic P(down) P(stay) P(up)
0.000 - 0.000 0.000 0.054 0.036 0.00 0.26 0.74
0.000 - 0.038 0.019 0.093 0.059 0.14 0.26 0.60
0.038 - 0.065 0.052 0.101 0.087 0.27 0.20 0.53
0.065 - 0.083 0.074 0.096 0.057 0.43 0.15 0.42
0.083 - 0.114 0.099 0.103 0.085 0.46 0.18 0.36
0.114 - 0.190 0.152 0.079 0.110 0.39 0.37 0.24
0.190 - 0.310 0.250 0.093 0.110 0.35 0.35 0.30
0.310 - 0.525 0.418 0.098 0.203 0.22 0.61 0.17
0.525 - 0.667 0.596 0.101 0.121 0.28 0.51 0.21
0.667 - 0.870 0.769 0.090 0.112 0.26 0.66 0.09
0.870 - 1.000 0.935 0.087 0.011 0.36 0.54 0.11
1.000 - 1.000 1.000 0.005 0.010 0.57 0.43 0.00
Min 0.269 0.269
Midpoint 0.316 0.326
Max 0.363 0.383
January 2011, UoB 45www.bris.ac.uk/cmpo
Table 3d:
Leicester
Range Midpoint Initial Ergodic P(down) P(stay) P(up)
0.000 - 0.000 0.000 0.025 0.005 0.00 0.00 1.00
0.000 - 0.070 0.035 0.086 0.059 0.04 0.44 0.53
0.070 - 0.101 0.086 0.110 0.045 0.34 0.15 0.51
0.101 - 0.205 0.153 0.084 0.173 0.23 0.54 0.23
0.205 - 0.315 0.260 0.128 0.104 0.39 0.34 0.27
0.315 - 0.425 0.370 0.076 0.146 0.33 0.48 0.20
0.425 - 0.684 0.555 0.091 0.144 0.17 0.80 0.03
0.684 - 0.853 0.769 0.101 0.093 0.04 0.67 0.29
0.853 - 0.940 0.897 0.090 0.140 0.18 0.56 0.26
0.940 - 0.972 0.956 0.113 0.045 0.42 0.36 0.21
0.972 - 1.000 0.986 0.079 0.013 0.77 0.15 0.08
1.000 - 1.000 1.000 0.017 0.032 0.60 0.40 0.00
Min 0.451 0.420
Midpoint 0.498 0.479
Max 0.546 0.538
January 2011, UoB 46www.bris.ac.uk/cmpo
Table 3e:
Oldham
Range Midpoint Initial Ergodic P(down) P(stay) P(up)
0.000 - 0.000 0.000 0.226 0.171 0.00 0.47 0.53
0.000 - 0.036 0.018 0.077 0.151 0.28 0.35 0.37
0.036 - 0.050 0.043 0.102 0.065 0.52 0.17 0.31
0.050 - 0.057 0.054 0.026 0.044 0.52 0.15 0.33
0.057 - 0.070 0.064 0.079 0.045 0.64 0.20 0.16
0.070 - 0.083 0.077 0.091 0.036 0.56 0.07 0.37
0.083 - 0.100 0.092 0.052 0.037 0.57 0.00 0.43
0.100 - 0.132 0.116 0.077 0.065 0.57 0.12 0.31
0.132 - 0.150 0.141 0.077 0.031 0.71 0.00 0.29
0.150 - 0.758 0.454 0.070 0.252 0.22 0.72 0.07
0.758 - 1.000 0.879 0.069 0.021 0.15 0.56 0.30
1.000 - 1.000 1.000 0.054 0.081 0.28 0.72 0.00
Min 0.154 0.159
Midpoint 0.190 0.243
Max 0.225 0.328
January 2011, UoB 47www.bris.ac.uk/cmpo
Table 3f:
Bradford
Range Midpoint Initial Ergodic P(down) P(stay) P(up)
0.000 - 0.000 0.000 0.078 0.048 0.00 0.44 0.56
0.000 - 0.026 0.013 0.091 0.029 0.18 0.30 0.52
0.026 - 0.037 0.032 0.092 0.026 0.40 0.18 0.43
0.037 - 0.061 0.049 0.081 0.048 0.40 0.24 0.35
0.061 - 0.098 0.080 0.093 0.055 0.46 0.23 0.31
0.098 - 0.143 0.121 0.084 0.043 0.53 0.19 0.28
0.143 - 0.237 0.190 0.076 0.057 0.40 0.41 0.19
0.237 - 0.452 0.345 0.102 0.080 0.16 0.64 0.20
0.452 - 0.893 0.673 0.092 0.474 0.05 0.85 0.10
0.893 - 0.953 0.923 0.080 0.015 0.64 0.09 0.27
0.953 - 1.000 0.977 0.086 0.000 0.70 0.00 0.30
1.000 - 1.000 1.000 0.045 0.126 0.34 0.66 0.00
Min 0.293 0.390
Midpoint 0.339 0.510
Max 0.384 0.629
January 2011, UoB 48www.bris.ac.uk/cmpo
Table 3g:
Kirklees
Range Midpoint Initial Ergodic P(down) P(stay) P(up)
0.000 - 0.000 0.000 0.076 0.072 0.00 0.51 0.49
0.000 - 0.032 0.016 0.094 0.094 0.22 0.31 0.47
0.032 - 0.047 0.040 0.094 0.045 0.48 0.11 0.41
0.047 - 0.088 0.068 0.075 0.113 0.43 0.28 0.30
0.088 - 0.127 0.108 0.095 0.058 0.60 0.11 0.29
0.127 - 0.152 0.140 0.113 0.036 0.68 0.10 0.23
0.152 - 0.244 0.198 0.100 0.115 0.35 0.48 0.17
0.244 - 0.373 0.309 0.071 0.087 0.22 0.48 0.30
0.373 - 0.520 0.447 0.095 0.084 0.31 0.45 0.24
0.520 - 0.795 0.658 0.101 0.229 0.14 0.72 0.14
0.795 - 1.000 0.898 0.078 0.029 0.42 0.38 0.21
1.000 - 1.000 1.000 0.007 0.039 0.39 0.61 0.00
Min 0.219 0.267
Midpoint 0.265 0.324
Max 0.310 0.382
January 2011, UoB 49www.bris.ac.uk/cmpo
Table 3h:
Blackburn
Range Midpoint Initial Ergodic P(down) P(stay) P(up)
0.000 - 0.000 0.000 0.208 0.078 0.00 0.50 0.50
0.000 - 0.033 0.017 0.099 0.112 0.21 0.32 0.47
0.033 - 0.045 0.039 0.059 0.046 0.46 0.15 0.38
0.045 - 0.061 0.053 0.088 0.061 0.38 0.20 0.43
0.061 - 0.079 0.070 0.065 0.048 0.55 0.18 0.27
0.079 - 0.147 0.113 0.061 0.081 0.70 0.20 0.10
0.147 - 0.400 0.274 0.108 0.067 0.32 0.54 0.15
0.400 - 0.571 0.486 0.078 0.058 0.23 0.59 0.18
0.571 - 0.754 0.663 0.072 0.250 0.05 0.80 0.15
0.754 - 0.927 0.841 0.080 0.131 0.21 0.61 0.18
0.927 - 1.000 0.964 0.065 0.000 1.00 0.00 0.00
1.000 - 1.000 1.000 0.016 0.068 0.73 0.27 0.00
Min 0.240 0.356
Midpoint 0.282 0.410
Max 0.323 0.463
January 2011, UoB 50www.bris.ac.uk/cmpo
Summary
• Bringing together all that evidence:
January 2011, UoB www.bris.ac.uk/cmpo 51
Table 4: Characterisation of Areas
Polarising Unclear Integrating
Blackburn with Darwen Birmingham Bristol
Bradford Bolton Bury
Buckinghamshire Brighton and Hove Coventry
Dudley Calderdale Derby
Lancashire Hertfordshire Leeds
Liverpool Kirklees Leicester
Oldham Reading London
Oxfordshire Sandwell Luton
Peterborough Sheffield Manchester
Rochdale Thurrock Middlesbrough
Slough Trafford Milton Keynes
Solihull Wolverhampton Nottingham
Stoke-on-Trent Southampton
Walsall Tameside
Wokingham
January 2011, UoB 52www.bris.ac.uk/cmpo
Table 5: Segregation Dynamics and Structural Factors
Nonparametric Polynomial Polarising Integrating
Proportion Autonomous Faith Schools -0.106* -0.685 2.024** -0.580
(0.0545) (0.756) (0.488) (0.635)
Proportion Non-Autonomous Faith Schools -0.254 -3.361 2.487* -0.875
(0.200) (3.186) (1.327) (1.111)
Proportion Autonomous Secular Schools -0.0919 0.764 4.926** 0.446
(0.134) (3.076) (1.731) (2.459)
Proportion Non-White -0.0688 -0.359 -0.348 -0.386
(0.0546) (0.756) (0.649) (0.789)
Population Density 0.0880 1.235 -0.244 1.867**
(0.0599) (0.842) (0.676) (0.722)
Proportion Independent Schools -0.0297 -1.180 -1.603* 1.895*
(0.109) (1.854) (0.831) (1.118)
Constant 0.0528* 0.106 -0.0464 0.0434
(0.0269) (0.437) (0.210) (0.304)
Observations 41 40 41 41Standard errors in parentheses* p < 0.10, ** p < 0.05January 2011, UoB 53www.bris.ac.uk/cmpo
January 2011, UoB www.bris.ac.uk/cmpo 54
Summary
• Considerable differences between places in terms of the dynamics of sorting
• Some appear to be consistent with an integrated equilibrium; others with a segregated equilibrium.
How does context affect outcomes?
• School and neighbourhood peer groups might affect:– Educational outcomes– Values and attitudes– Identities
January 2011, UoB 55www.bris.ac.uk/cmpo
January 2011, UoB www.bris.ac.uk/cmpo 56
• Does ethnic segregation in schools have a causal effect on differential school attainment?
• Big differences in attainment between different ethnic groups in England:– Black Caribbean pupils score about 0.4 SDs lower than
White pupils in GCSEs– Indian students score 0.3 SDs higher than White pupils.
• Potential explanations: poverty, school quality and/or resources, teacher quality, teacher bias and expectations, ethnic composition of schools, …
January 2011, UoB www.bris.ac.uk/cmpo 57
Black Caribbean pupils Indian pupils Pakistani pupils
Test score gap and ethnic segregation
School
Neighbourhood
January 2011, UoB www.bris.ac.uk/cmpo 58
Conclusions• We find that segregation has no consistent and
significant impact on the minority-White British test score gap.
• Comparing the performance of a particular minority group across cities with varying levels of segregation, we find no tendency for significant negative effects of school segregation.
• This is in strong contrast to findings for the US– Card and Rothstein (2007) show that comparing a
highly segregated city to a nearly integrated city closes the Black – White test score gap by about a quarter.
• Why?
• Levels of school segregation are much lower in England than in the US.
• The nature of the academic performance of the relevant minority groups is very different.
• Much smaller variation in school quality in England. – Our approach subsumes in the segregation effect
any differences in quality between the schools differentially attended by the ethnic minority group and by White students.
January 2011, UoB www.bris.ac.uk/cmpo 59
• These differences are likely to be much larger in the US than in England because the greater centralisation of education funding in England actively attempts to equalise educational spending per head:– the great majority of school funding is
determined by a centrally-set funding formula. – the system provides significantly higher
funding per pupil to schools with more deprived intakes
– there is a smaller, specific funding stream, the Ethnic Minority Achievement Grant, which channels further additional funding to schools with high minority populations.
January 2011, UoB www.bris.ac.uk/cmpo 60
How does context affect outcomes?
• School and neighbourhood peer groups might affect:– Educational outcomes– Values and attitudes– Identities
January 2011, UoB 61www.bris.ac.uk/cmpo
Context and Attitudes
• What is the impact of school and neighbourhood ethnic composition context on students’ attitudes to other ethnic groups?
• Be great to know …
• Putnam: diverse communities associated with “hunkering down”
• One example from school twinning programmes in Bradford, Kirklees, Oldham …
January 2011, UoB 62www.bris.ac.uk/cmpo
“Some of our children could live their lives without meeting someone from another culture until they go to high school or even the workplace”
“They can grow up with such a lot of misconceptions and prejudices”
(Primary school headteacher, Huddersfield; 92% pupils of Pakistani heritage; reported in TES 27.06.08)
January 2011, UoB www.bris.ac.uk/cmpo 63
“Our pupils think its amazing that they like pizza too”
(Primary school headteacher, Huddersfield; 92% pupils of Pakistani heritage; reported in TES 27.06.08)
January 2011, UoB www.bris.ac.uk/cmpo 64
Context and Identity
• Co-evolution of identity and social network, social capital. – Who you think you are or want to be
influences the friends you (try to) make• Eg. “Acting white” dilemma (Fryer)
– The friends you have influence your view of who you are
• Eg. Evolution of important social “lines” (Putnam)
January 2011, UoB www.bris.ac.uk/cmpo 65
One way of finding out …
• Questionnaire on ethnic identity, eg. as being developed by Lucinda Platt and others for ‘Understanding Society’
• Intervention study – school twinning programme
• Re-issue questionnaire to later generations of students (in twinned and non-twinned schools).
January 2011, UoB www.bris.ac.uk/cmpo 66
Conclusions• Its hard to know whether context matters,
but we suspect it matters for some outcomes.
• But it doesn’t appear to matter for educational attainment: we find no strong, systematic relationship of ethnic segregation and educational attainment.
• Attitudes? Identity?
January 2011, UoB www.bris.ac.uk/cmpo 67
January 2011, UoB www.bris.ac.uk/cmpo 68
Extras
FSM‘Opened’
FSM ‘Closed’
FSM Sample
NW ‘Opened’
NW ‘Closed’
NW Sample
Primary Short 0.275 0.352 0.241 0.439 0.274 0.275
Primary Long 0.154 0.332 0.227 0.349 0.262 0.239
Secondary Short 0.205 0.328 0.235 0.420 0.236 0.351
Secondary Long 0.175 0.308 0.229 0.300 0.220 0.341
Comparing opening and closing schools
January 2011, UoB www.bris.ac.uk/cmpo 69
Plans 1
• Finish this “macro” characterisation of the (differing) processes of segregation
• Characterising the dynamics in a compact way, allowing for heterogeneity, and analyse differences:– Panel econometrics, autoregression with fixed effects,
heterogeneity in the slope function (but AR>1)– Distribution dynamics, create composition classes
and estimate transitions (first order markov assumption?; discretisation ad hoc)
– Relate the characterisation of the area dynamic process to measures of structural city factors that the underlying behavioural model suggests.
January 2011, UoB www.bris.ac.uk/cmpo 70
Plans 2
• Pupil analysis:– Following Burgess and Briggs (2006), we can use
variation within postcode to decompose changes in composition:
• Ethnic change in postcode population• Change in school destinations of people in each postcode.
• Different geographies:– LEA, TTWA– Data-based catchment areas (Rich Harris)
• Mathematical modelling:– Schelling– Quah
January 2011, UoB www.bris.ac.uk/cmpo 71
-1-.
50
.51
Cha
nge
in n
umb
er o
f Whi
tes
200
3-2
007
0 .2 .4 .6 .8 1Non-White proportion, 2003
Birmingham: Primary Short (2003-2007)
January 2011, UoB www.bris.ac.uk/cmpo 72
-1-.
50
.51
Cha
nge
in n
umb
er o
f whi
tes
199
8-2
007
0 .2 .4 .6 .8 1Non-White proportion 1998
Birmingham: Primary Long (1998-2007)
January 2011, UoB www.bris.ac.uk/cmpo 73
-1-.
50
.51
Cha
nge
in n
umb
er o
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tes
200
3-2
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0 .2 .4 .6 .8 1Non-White proportion, 2003
Birmingham: Secondary Short (2003-2007)
January 2011, UoB www.bris.ac.uk/cmpo 74
-1-.
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.51
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nge
in n
umb
er o
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tes
200
1-2
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0 .2 .4 .6 .8 1Non-White proportion 2001
Birmingham: Secondary Long (2001-2007)
January 2011, UoB www.bris.ac.uk/cmpo 75
01
23
Den
sity
0 .2 .4 .6 .8 1Non-White Proportion
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
kernel = gaussian, bandwidth = .05
Bradford LEA
January 2011, UoB www.bris.ac.uk/cmpo 76
Non-white proportion through time
Bradford Primary Schools 1998-2007
By Initial Decile
January 2011, UoB www.bris.ac.uk/cmpo 77
0.1
.2.3
.4.5
.6.7
.8.9
1(m
ean)
nw
h
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007(mean) year
0 20 40 60Percent
January 2011, UoB www.bris.ac.uk/cmpo 78
0.1
.2.3
.4.5
.6.7
.8.9
1(m
ean)
nw
h
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007(mean) year
0 10 20 30Percent
January 2011, UoB www.bris.ac.uk/cmpo 79
0.1
.2.3
.4.5
.6.7
.8.9
1(m
ean)
nw
h
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007(mean) year
0 10 20 30 40Percent
January 2011, UoB www.bris.ac.uk/cmpo 80
0.1
.2.3
.4.5
.6.7
.8.9
1(m
ean)
nw
h
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007(mean) year
0 5 10 15 20 25Percent
January 2011, UoB www.bris.ac.uk/cmpo 81
0.1
.2.3
.4.5
.6.7
.8.9
1(m
ean)
nw
h
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007(mean) year
0 10 20 30 40Percent
January 2011, UoB www.bris.ac.uk/cmpo 82
0.1
.2.3
.4.5
.6.7
.8.9
1(m
ean)
nw
h
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007(mean) year
0 20 40 60 80100Percent
January 2011, UoB www.bris.ac.uk/cmpo 83
0.1
.2.3
.4.5
.6.7
.8.9
1(m
ean)
nw
h
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007(mean) year
0 20 40 60 80Percent
January 2011, UoB www.bris.ac.uk/cmpo 84
0.1
.2.3
.4.5
.6.7
.8.9
1(m
ean)
nw
h
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007(mean) year
0 20 40 60 80100Percent
January 2011, UoB www.bris.ac.uk/cmpo 85
0.1
.2.3
.4.5
.6.7
.8.9
1(m
ean)
nw
h
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007(mean) year
0 20 40 60 80Percent
January 2011, UoB www.bris.ac.uk/cmpo 86
0.1
.2.3
.4.5
.6.7
.8.9
1(m
ean)
nw
h
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007(mean) year
0 20 40 60 80100Percent
January 2011, UoB www.bris.ac.uk/cmpo 87
Non-white proportion through time
Leicester Primary Schools 1998-2007
By Initial Decile
January 2011, UoB www.bris.ac.uk/cmpo 88
0.1
.2.3
.4.5
.6.7
.8.9
1(m
ean)
nw
h
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007(mean) year
0 10 20 30 40Percent
January 2011, UoB www.bris.ac.uk/cmpo 89
0.1
.2.3
.4.5
.6.7
.8.9
1(m
ean)
pnw
h
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007(mean) year
0 10 20 30 40Percent
January 2011, UoB www.bris.ac.uk/cmpo 90
0.1
.2.3
.4.5
.6.7
.8.9
1(m
ean)
pnw
h
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007(mean) year
0 20 40 60 80Percent
January 2011, UoB www.bris.ac.uk/cmpo 91
0.1
.2.3
.4.5
.6.7
.8.9
1(m
ean)
pnw
h
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007(mean) year
0 10 20 30 40Percent
January 2011, UoB www.bris.ac.uk/cmpo 92
0.1
.2.3
.4.5
.6.7
.8.9
1(m
ean)
pnw
h
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007(mean) year
0 10 20 30 40Percent
January 2011, UoB www.bris.ac.uk/cmpo 93
0.1
.2.3
.4.5
.6.7
.8.9
1(m
ean)
pnw
h
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007(mean) year
0 20 40 60 80100Percent
January 2011, UoB www.bris.ac.uk/cmpo 94
0.1
.2.3
.4.5
.6.7
.8.9
1(m
ean)
pnw
h
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007(mean) year
0 10 20 30 40 50Percent
January 2011, UoB www.bris.ac.uk/cmpo 95
0.1
.2.3
.4.5
.6.7
.8.9
1(m
ean)
pnw
h
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007(mean) year
0 20 40 60Percent
January 2011, UoB www.bris.ac.uk/cmpo 96
0.1
.2.3
.4.5
.6.7
.8.9
1(m
ean)
pnw
h
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007(mean) year
0 20 40 60Percent
January 2011, UoB www.bris.ac.uk/cmpo 97
0.1
.2.3
.4.5
.6.7
.8.9
1(m
ean)
pnw
h
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007(mean) year
0 20 40 60 80100Percent
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