Are England’s schools segregating or integrating? And does it matter? Simon Burgess and Jack Worth January 2011

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


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



Change

in pop’n

of whites

0 Fraction of non-whites


Change

in pop’n

of whites


City


Change

in pop’n

of 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


Dynamic processes

0

Integration


Dynamic processes

0

Segregation



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?


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, ...


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


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


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


‘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.


Where,

1,

1,

tj

tjjtjt N

WWCW

j schoolin pupils ofnumber

j schoolin whitesofnumber

jt

jt

N

W

Dependent Variable

• Change in Whites:


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.


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


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


Birmingham


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


London


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


Oldham


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


Bradford


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


Kirklees


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


Leicester


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


Blackburn with Darwen




• Summarising these graphs using different statistical procedures


-.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


-.6

-.4

-.2

0.2

.4C

hang

e in

num

ber

of w

hite

s 1

998-

200

7



• Split into deciles with top and bottom 5% clipped


-.6

-.4

-.2

0.2

.4C

hang

e in

num

ber

of w

hite

s 1

998-

200

7


Change in number of whites 1998-2007 m_decileChange in number of whites 1998-2007


Coefficient = 0.0521• Take the weighted differences between decile means


-.6

-.4

-.2

0.2

.4C

hang

e in

num

ber

of w

hite

s 1

998-

200

7




Coefficient = 0.0521• Take the differences between decile means


-1-.

50

.51

Cha

nge

in n

umb

er o

f whi

tes

199

8-2

007



Bradford: Primary Long 1998-2007

Coefficient = -0.0124


Coefficient = 0.1013

-.6

-.4

-.2

0.2

Cha

nge

in n

umb

er o

f whi

tes

199

8-2

007



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


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




• 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?


• 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


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


Table 3b:

Birmingham


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


Max 0.473 0.559


Table 3c:

Manchester


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


Max 0.363 0.383


Table 3d:

Leicester


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


Max 0.546 0.538


Table 3e:

Oldham


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


Max 0.225 0.328


Table 3f:

Bradford


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


Max 0.384 0.629


Table 3g:

Kirklees


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


Max 0.310 0.382


Table 3h:

Blackburn


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


Max 0.323 0.463


Summary

• Bringing together all that evidence:


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


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


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



• 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, …


Black Caribbean pupils Indian pupils Pakistani pupils

Test score gap and ethnic segregation

School

Neighbourhood


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.


• 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.


How does context affect outcomes?

• School and neighbourhood peer groups might affect:– Educational outcomes– Values and attitudes– Identities


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 …


“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)


“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)


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)


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).


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?



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


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.


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


-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)


-1-.

50

.51

Cha

nge

in n

umb

er o

f whi

tes

199

8-2

007


Birmingham: Primary Long (1998-2007)


-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: Secondary Short (2003-2007)


-1-.

50

.51

Cha

nge

in n

umb

er o

f Whi

tes

200

1-2

007


Birmingham: Secondary Long (2001-2007)


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


Non-white proportion through time

Bradford Primary Schools 1998-2007

By Initial Decile


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


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


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


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


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


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


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


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


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


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


Non-white proportion through time

Leicester Primary Schools 1998-2007

By Initial Decile


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


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


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


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


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


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


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


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


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


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

Documents

Are England’s schools segregating or integrating? And does it matter? Simon Burgess and Jack Worth January 2011