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

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

2 January 2011, 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”?

3 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,

4 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,

5 Plan Analyse changing school ethnic group composition –Segregation or integration? –Differences? Does school composition matter? –Educational attainment –Values and attitudes –Identity January 2011,

6 January 2011, Change in pop’n of whites 0 Fraction of non-whites

7 January 2011, Change in pop’n of whites 0 Fraction of non-whites City

8 January 2011, 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

9 January 2011, Dynamic processes 0 Integration

10 January 2011, Dynamic processes 0 Segregation

11 January 2011,

12 January 2011, 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?

13 January 2011, 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,...

14 January 2011, 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

15 January 2011, 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)

16 June 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

17 January 2011, Short and Long Year Age ‘98‘99‘00‘01‘02‘03‘04‘05‘06‘07 11 10 9 8 7 6 Available Data Used Data Infant to Junior

18 January 2011, ‘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

19 PrimarySecondary ShortLongShortLong Cohorts2003 & 20071998 & 20072003 & 20072001 & 2007 Schools12,42110,8092,5382,514 Pupils (‘03)503,449416,687460,415452,748 Non-whites84,50175,90770,05170,180 Final Sample Balanced Panel Focussing on schools that remained open through that period. Compare opening and closing schools.

20 January 2011, Where, Dependent Variable Change in Whites:

21 January 2011, 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.

22 January 2011, Techniques for analysing the patterns 1 Graphical data exploration

23 Figure 6c: Growth vs Initial Level plot – Manchester January 2011,

24 Figure 6b: Growth vs Initial Level plot – Birmingham January 2011,

25 Figure 6a: Growth vs Initial Level plot – London January 2011,

26 Figure 6d: Growth vs Initial Level plot – Oldham January 2011,

27 Figure 6e: Growth vs Initial Level plot – Bradford January 2011,

28 Figure 6f: Growth vs Initial Level plot – Kirklees January 2011,

29 Figure 6g: Growth vs Initial Level plot – Leicester January 2011,

30 Figure 6h: Growth vs Initial Level plot – Blackburn with Darwen January 2011,

31 January 2011, Techniques for analysing the patterns 2 Summarising these graphs using different statistical procedures

32 January 2011, Initial levels plot as before

33 January 2011, Split into deciles with top and bottom 5% clipped

34 January 2011, Coefficient = 0.0521 Take the weighted differences between decile means

35 January 2011, Coefficient = 0.0521 Take the differences between decile means

36 January 2011, Coefficient = -0.0124

37 January 2011, Coefficient = 0.1013

38 Table 1: OLS and Nonparametric Coefficients LEA OLS coefficientNonparametric coefficient Oxfordshire-0.072-0.128 Solihull-0.368-0.052 Rochdale0.121-0.046 Peterborough0.175-0.039 Bolton0.107-0.038 Stoke-on-Trent0.092-0.030 Dudley0.122-0.028 Bradford0.176-0.025 Lancashire0.083-0.021 Buckinghamshire0.099-0.019 Oldham0.130-0.019 Blackburn with Darwen0.156-0.010 Walsall0.042-0.009 Hertfordshire-0.026-0.007 Liverpool0.130-0.003 Slough0.086-0.002 Derby0.4420.003 Sandwell0.1100.011 Birmingham0.2800.011 Leeds0.0960.011 Kirklees-0.0130.014 Calderdale0.0060.018 Nottingham0.2310.022 London0.2200.024 Wolverhampton0.1990.024 Reading0.1120.025 Coventry0.1780.031 Manchester0.3100.047 Middlesbrough0.0440.056 Milton Keynes0.3600.061 Sheffield0.1660.062 Thurrock1.1210.064 Tameside0.2240.067 Leicester0.1680.067 Southampton0.3940.068 Bristol0.0820.069 Luton0.3230.070 Brighton and Hove1.2390.078 Trafford0.1260.090 Wokingham-0.6680.171 Bury-0.1150.178 January 2011,

39 Table 2: 4 th Order Polynomial 4th order polynomial LEA10%15%20%25%30%p-value(F) Oxfordshire0.38-1.43-2.61- Solihull2.210.11-2.61-1.537.770.011 Brighton and Hove0.80-0.52-1.05-0.750.430.000 Thurrock3.111.06- Stoke-on-Trent0.07-0.54-0.77-0.71-0.440.509 Calderdale-0.41-0.66-0.76-0.74-0.610.000 Slough-2.68-1.51-0.67- Peterborough-0.44-0.56-0.58-0.51-0.370.004 Rochdale-0.42-0.54-0.57-0.53-0.430.000 Walsall-0.18-0.41-0.52-0.54-0.480.000 Oldham-0.91-0.65-0.44-0.28-0.140.000 Lancashire-0.21-0.37-0.44-0.43-0.360.000 Kirklees0.34-0.12-0.43-0.63-0.710.000 Bradford-0.16-0.32-0.41-0.42-0.390.000 Blackburn-0.68-0.52-0.38-0.24-0.120.000 Liverpool-0.19-0.33-0.37-0.33-0.220.000 Reading-0.22-0.39-0.36- Dudley0.19-0.07-0.23-0.29-0.260.012 Birmingham0.550.15-0.12-0.27-0.330.000 Buckinghamshire0.350.09-0.09-0.19-0.230.374 Sandwell-0.67-0.32- Sheffield0.620.23-0.06-0.26-0.360.000 Trafford-0.19-0.10-0.04-0.02 0.001 Wolverhampton-0.53-0.23- Hertfordshire0.100.170.01-0.25-0.510.573 Leeds-0.08- London-0.16- Tameside0.340. Bolton-0.64- Nottingham0. Manchester0.980.630.370.190.080.000 Middlesbrough- Leicester0.760.660.540.410.280.000 Bristol City of-0.090.360.620.710.670.027 Southampton0.520.710.670.480.230.126 Coventry-0.040.450.700.760.670.001 Milton Keynes0.210.500.730.750.430.243 Luton2.521.610.920.440.120.000 Derby0.770.880.950.960.930.000 Bury- Wokingham-2.2210.8669.38203.12441.860.023 Sorted by slope coefficient at 20% non-white January 2011,

40 January 2011, Techniques for analysing the patterns 3 Estimating transition matrices to model the dynamics of the distribution

41 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,

42 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,

43 Tables 3a-h: Transition Matrices Table 3a: London RangeMidpointInitialErgodicP(down)P(stay)P(up) 0.000- 0.0090.0040.000.420.58 0.000-0.0940.0470.1010.0510.050.500.46 0.094-0.1670.1310.0960.0790.230.390.38 0.167-0.2500.2090.1010.0910.290.360.35 0.250-0.3650.3080.0960.1220.280.420.30 0.365-0.4580.4120.0990.1060.280.330.39 0.458-0.5360.4970.0970.1050.330.300.36 0.536-0.6310.5840.1000.1450.320.390.29 0.631-0.7270.6790.1000.1300.360.390.25 0.727-0.8330.7800.0950.1120.390.470.14 0.833-1.0000.9170.0980.0490.390.540.07 1.000- 0.0090.0060.470.530.00 Min0.4060.428 Midpoint0.4550.477 Max0.5040.525 January 2011,

44 Table 3b: Birmingham RangeMidpointInitialErgodicP(down)P(stay)P(up) 0.000- 0.0200.0090.000.170.83 0.000-0.0500.0250.0940.0400.100.220.68 0.050-0.0980.0740.093 0.190.340.47 0.098-0.1330.1160.0980.0670.450.250.29 0.133-0.1820.1580.0880.0710.480.26 0.182-0.2580.2200.1020.0730.400.310.29 0.258-0.3550.3070.0830.0700.410.350.24 0.355-0.6600.5080.0970.1880.110.710.19 0.660-0.8920.7760.0930.2850.150.720.14 0.892-0.9600.9260.0910.0210.39 0.22 0.960-1.0000.9800.0930.0000.750.000.25 1.000- 0.0470.0840.570.430.00 Min0.3800.409 Midpoint0.4270.484 Max0.4730.559 January 2011,

45 Table 3c: Manchester RangeMidpointInitialErgodicP(down)P(stay)P(up) 0.000- 0.0540.0360.000.260.74 0.000-0.0380.0190.0930.0590.140.260.60 0.038-0.0650.0520.1010.0870.270.200.53 0.065-0.0830.0740.0960.0570.430.150.42 0.083-0.1140.0990.1030.0850.460.180.36 0.114-0.1900.1520.0790.1100.390.370.24 0.190-0.3100.2500.0930.1100.35 0.30 0.310-0.5250.4180.0980.2030.220.610.17 0.525-0.6670.5960.1010.1210.280.510.21 0.667-0.8700.7690.0900.1120.260.660.09 0.870-1.0000.9350.0870.0110.360.540.11 1.000- 0.0050.0100.570.430.00 Min0.269 Midpoint0.3160.326 Max0.3630.383 January 2011,

46 Table 3d: Leicester RangeMidpointInitialErgodicP(down)P(stay)P(up) 0.000- 0.0250.0050.00 1.00 0.000-0.0700.0350.0860.0590.040.440.53 0.070-0.1010.0860.1100.0450.340.150.51 0.101-0.2050.1530.0840.1730.230.540.23 0.205-0.3150.2600.1280.1040.390.340.27 0.315-0.4250.3700.0760.1460.330.480.20 0.425-0.6840.5550.0910.1440.170.800.03 0.684-0.8530.7690.1010.0930.040.670.29 0.853-0.9400.8970.0900.1400.180.560.26 0.940-0.9720.9560.1130.0450.420.360.21 0.972-1.0000.9860.0790.0130.770.150.08 1.000- 0.0170.0320.600.400.00 Min0.4510.420 Midpoint0.4980.479 Max0.5460.538 January 2011,

47 Table 3e: Oldham RangeMidpointInitialErgodicP(down)P(stay)P(up) 0.000- 0.2260.1710.000.470.53 0.000-0.0360.0180.0770.1510.280.350.37 0.036-0.0500.0430.1020.0650.520.170.31 0.050-0.0570.0540.0260.0440.520.150.33 0.057-0.0700.0640.0790.0450.640.200.16 0.070-0.0830.0770.0910.0360.560.070.37 0.083-0.1000.0920.0520.0370.570.000.43 0.100-0.1320.1160.0770.0650.570.120.31 0.132-0.1500.1410.0770.0310.710.000.29 0.150-0.7580.4540.0700.2520.220.720.07 0.758-1.0000.8790.0690.0210.150.560.30 1.000- 0.0540.0810.280.720.00 Min0.1540.159 Midpoint0.1900.243 Max0.2250.328 January 2011,

48 Table 3f: Bradford RangeMidpointInitialErgodicP(down)P(stay)P(up) 0.000- 0.0780.0480.000.440.56 0.000-0.0260.0130.0910.0290.180.300.52 0.026-0.0370.0320.0920.0260.400.180.43 0.037-0.0610.0490.0810.0480.400.240.35 0.061-0.0980.0800.0930.0550.460.230.31 0.098-0.1430.1210.0840.0430.530.190.28 0.143-0.2370.1900.0760.0570.400.410.19 0.237-0.4520.3450.1020.0800.160.640.20 0.452-0.8930.6730.0920.4740.050.850.10 0.893-0.9530.9230.0800.0150.640.090.27 0.953-1.0000.9770.0860.0000.700.000.30 1.000- 0.0450.1260.340.660.00 Min0.2930.390 Midpoint0.3390.510 Max0.3840.629 January 2011,

49 Table 3g: Kirklees RangeMidpointInitialErgodicP(down)P(stay)P(up) 0.000- 0.0760.0720.000.510.49 0.000-0.0320.0160.094 0.220.310.47 0.032-0.0470.0400.0940.0450.480.110.41 0.047-0.0880.0680.0750.1130.430.280.30 0.088-0.1270.1080.0950.0580.600.110.29 0.127-0.1520.1400.1130.0360.680.100.23 0.152-0.2440.1980.1000.1150.350.480.17 0.244-0.3730.3090.0710.0870.220.480.30 0.373-0.5200.4470.0950.0840.310.450.24 0.520-0.7950.6580.1010.2290.140.720.14 0.795-1.0000.8980.0780.0290.420.380.21 1.000- 0.0070.0390.390.610.00 Min0.2190.267 Midpoint0.2650.324 Max0.3100.382 January 2011,

50 Table 3h: Blackburn RangeMidpointInitialErgodicP(down)P(stay)P(up) 0.000- 0.2080.0780.000.50 0.000-0.0330.0170.0990.1120.210.320.47 0.033-0.0450.0390.0590.0460.460.150.38 0.045-0.0610.0530.0880.0610.380.200.43 0.061-0.0790.0700.0650.0480.550.180.27 0.079-0.1470.1130.0610.0810.700.200.10 0.147-0.4000.2740.1080.0670.320.540.15 0.400-0.5710.4860.0780.0580.230.590.18 0.571-0.7540.6630.0720.2500.050.800.15 0.754-0.9270.8410.0800.1310.210.610.18 0.927-1.0000.9640.0650.0001.000.00 1.000- 0.0160.0680.730.270.00 Min0.2400.356 Midpoint0.2820.410 Max0.3230.463 January 2011,

51 Summary Bringing together all that evidence: January 2011,

52 Table 4: Characterisation of Areas PolarisingUnclearIntegrating Blackburn with DarwenBirminghamBristol BradfordBoltonBury BuckinghamshireBrighton and HoveCoventry DudleyCalderdaleDerby LancashireHertfordshireLeeds LiverpoolKirkleesLeicester OldhamReadingLondon OxfordshireSandwellLuton PeterboroughSheffieldManchester RochdaleThurrockMiddlesbrough SloughTraffordMilton Keynes SolihullWolverhamptonNottingham Stoke-on-TrentSouthampton WalsallTameside Wokingham January 2011,

53 Table 5: Segregation Dynamics and Structural Factors NonparametricPolynomialPolarisingIntegrating Proportion Autonomous Faith Schools-0.106 * -0.6852.024 ** -0.580 (0.0545)(0.756)(0.488)(0.635) Proportion Non-Autonomous Faith Schools-0.254-3.3612.487 * -0.875 (0.200)(3.186)(1.327)(1.111) Proportion Autonomous Secular Schools-0.09190.7644.926 ** 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 Density0.08801.235-0.2441.867 ** (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) Constant0.0528 * 0.106-0.04640.0434 (0.0269)(0.437)(0.210)(0.304) Observations414041 Standard errors in parentheses * p < 0.10, ** p < 0.05 January 2011,

54 January 2011, 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.

55 How does context affect outcomes? School and neighbourhood peer groups might affect: –Educational outcomes –Values and attitudes –Identities January 2011,

56 January 2011, 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, …

57 January 2011, Black Caribbean pupils Indian pupils Pakistani pupils Test score gap and ethnic segregation School NeighbourhoodSchool Neighbourhood

58 January 2011, 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?

59 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,

60 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,

61 How does context affect outcomes? School and neighbourhood peer groups might affect: –Educational outcomes –Values and attitudes –Identities January 2011,

62 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,

63 “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,

64 “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,

65 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,

66 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,

67 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,

68 January 2011, Extras FSM ‘Opened’ FSM ‘Closed’ FSM Sample NW ‘Opened’ NW ‘Closed’ NW Sample Primary Short0.2750.352 0.2410.4390.2740.275 Primary Long0.1540.3320.2270.3490.2620.239 Secondary Short0.2050.3280.2350.4200.2360.351 Secondary Long0.1750.3080.2290.3000.2200.341 Comparing opening and closing schools

69 January 2011, 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.

70 January 2011, 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

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76 January 2011, Non-white proportion through time Bradford Primary Schools 1998-2007 By Initial Decile

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87 January 2011, Non-white proportion through time Leicester Primary Schools 1998-2007 By Initial Decile

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